Ai-enabled energy edge platforms, systems, and methods including datacenter ecosystems

Abstract

In some embodiments, the techniques described herein relate to an Al-enabled platform that provides an energy edge management capability for a set of distributed energy resources (DERs). In various embodiments, the Al-enabled platform includes and / or supports one or more of: energy-related transaction orchestration and execution, embedded policy and governance, natural resource exploration and discovery, sensor and data fusion, contextual simulation and forecasting, expert systems and artificial intelligence, edge resource management, computation dependent energy supply, automated resource organization and control, adaptive networking, and high-altitude data centers.

Description

Attorney Docket No. 54250-73AI-ENABLED ENERGY EDGE PLATFORMS, SYSTEMS, AND METHODS INCLUDING DATACENTER ECOSYSTEMS CROSS-REFERENCE TO RELATED APPLICATIONS

[0001] This application claims the benefit of U.S. Prov. App. No. 63 / 751,006, filed 29 January 2025; (ii) U.S. Prov. App. No. 63 / 848,966, filed 22 July 2025. This application claims priority to International App. No. PCT / US25 / 38986, filed 23 July 2025.

[0002] The patent applications referenced above are hereby incorporated by reference as if fully set forth herein in their entirety.BACKGROUND

[0003] Energy remains a critical factor in the world economy and is undergoing an evolution and transformation, involving changes in energy generation, storage, planning, demand management, consumption and delivery systems and processes. These changes are enabled by the development and convergence of numerous diverse technologies, including more distributed, modular, mobile and / or portable energy generation and storage technologies that will make the energy market much more decentralized and localized, as well as a range of technologies that will facilitate management of energy in a more decentralized system, including edge and Internet of Things networking technologies, advanced computation and artificial intelligence technologies, transaction enablement technologies (such as blockchains, distributed ledgers and smart contracts) and others. The convergence of these more decentralized energy technologies with these networking, computation and intelligence technologies is referred to herein as the “energy edge.”

[0004] The energy market is expected to evolve and transform over the next few decades from a highly centralized model that relies on fossil fuels and a managed electrical grid to a much more distributed and decentralized model that involves many more localized generation, storage, and consumption systems. During that transition, a hybrid system will likely persist for many years in which the conventional grid becomes more intelligent, and in which distributed systems will play a growing role. A need exists for a platform that facilitates management and improvement of legacy infrastructure in coordination with distributed systems.SUMMARY

[0005] In some embodiments, the techniques described herein relate to an Al-enabled system for adaptive energy network configuration, including at least one artificial intelligence model configured to analyze distributed energy resource connectivity requirements and generate network configuration commands for establishing adaptive data networks between distributed energy resource entities based on operational linkages, and at least one adaptive network controller configured to implement the network configuration commands by establishing communication pathways that enable data exchange between the distributed energy resourceAttorney Docket No. 54250-73entities.

[0006] In some embodiments, the techniques described herein relate to an Al-enabled system, wherein the at least one adaptive network controller is configured to establish at least one intramodule communication layer enabling communication of sensor data between power operating loads and load management modules within a provider module.

[0007] In some embodiments, the techniques described herein relate to an Al-enabled system, wherein the at least one adaptive network controller is configured to establish at least one intermodule communication layer enabling communication between a provider autonomous operating layer and at least two provider modules.

[0008] In some embodiments, the techniques described herein relate to an Al-enabled system, wherein the at least one adaptive network controller is configured to establish at least one fleet communication layer enabling communication between provider autonomous operating layers and a provider services layer.

[0009] In some embodiments, the techniques described herein relate to an Al-enabled system, wherein the at least one adaptive network controller is configured to establish reconfigurable communication networks that change network members based on operational linkage changes determined by the at least one artificial intelligence model.

[0010] In some embodiments, the techniques described herein relate to an Al-enabled system, wherein the at least one adaptive network controller is configured to form an ad hoc adaptive data network linking at least two distributed energy resource providers to act as a single aggregated power supplier based on network configuration commands from the at least one artificial intelligence model.

[0011] In some embodiments, the techniques described herein relate to an Al-enabled system, wherein the at least one adaptive network controller is configured to isolate internal communications among the at least two distributed energy resource providers from communications between the single aggregated power supplier and a distributed energy resource load.

[0012] In some embodiments, the techniques described herein relate to an Al-enabled system, further including at least one sensor configured to monitor at least one of environmental data including temperature and humidity or operational data including rotational information, vibration information, and charge states, and provide sensor data to the at least one artificial intelligence model via the adaptive data networks.

[0013] In some embodiments, the techniques described herein relate to an Al-enabled system, further including at least one sensor configured to monitor flow of power between components including at least one of voltage, current, frequency, electrical noise, power fluctuations, harmonics, or transient signals, and provide power monitoring data to the at least one artificial intelligence model via the adaptive data networks.

[0014] In some embodiments, the techniques described herein relate to an Al-enabled system, wherein the at least one adaptive network controller includes at least one communication module utilizing wired communication systems including at least one of CAN Bus, wired networks, orAttorney Docket No. 54250-73specialized wiring harnesses.

[0015] In some embodiments, the techniques described herein relate to an Al-enabled system, wherein the at least one adaptive network controller includes at least one communication module utilizing wireless communication systems including at least one of Bluetooth, Bluetooth Low-Energy, ZigBee, cellular 3G, cellular 4G, cellular 5G, or cellular 6G protocols.

[0016] In some embodiments, the techniques described herein relate to an Al-enabled system, wherein the at least one adaptive network controller includes at least one communication module utilizing Power Line Communication configured to add data signals onto existing power transmission lines.

[0017] In some embodiments, the techniques described herein relate to an Al-enabled system, wherein the Power Line Communication utilizes Orthogonal Frequency Division Multiplexing to achieve high data bandwidth communication over power lines.

[0018] In some embodiments, the techniques described herein relate to an Al-enabled system, wherein the at least one artificial intelligence model is configured to determine which data from sensors within a distributed energy resource entity is communicated outside the distributed energy resource entity via the adaptive data networks based on data sharing policies.

[0019] In some embodiments, the techniques described herein relate to an Al-enabled system, further including at least one resource controller configured to send control signals to at least one power manipulation device based on analyzed sensor data received via the adaptive data networks.

[0020] In some embodiments, the techniques described herein relate to an Al-enabled system, wherein the at least one power manipulation device includes at least one of a power splitter, a power divider, a power switch, an inverter for grid connection, a grid forming inverter, a grid tied inverter, a power line conditioner, a power transformer, or a power isolator.

[0021] In some embodiments, the techniques described herein relate to an Al-enabled system, wherein the at least one artificial intelligence model is configured to analyze sensor data from at least two distributed energy resource entities and generate coordinated control commands for the at least two distributed energy resource entities based on the analysis.

[0022] In some embodiments, the techniques described herein relate to an Al-enabled system, wherein the at least one adaptive network controller is configured to establish at least one adaptive data network linking at least two top level distributed energy resource entities for local data exchange based on network configuration commands from the at least one artificial intelligence model.

[0023] In some embodiments, the techniques described herein relate to an Al-enabled system, wherein the at least one artificial intelligence model is configured to analyze sensor data in conjunction with external data including at least one of ambient environment data, presence or operation of other devices on a grid, operation of manufacturing equipment, or ambient temperature.

[0024] In some embodiments, the techniques described herein relate to an Al-enabled system, wherein the at least one adaptive network controller is configured to reconfigure the adaptiveAttorney Docket No. 54250-73data networks dynamically based on changing operational linkages and connectivity patterns identified by the at least one artificial intelligence model.

[0025] In some embodiments, the techniques described herein relate to an Al-enabled system for intelligent power flow control, including at least one artificial intelligence model configured to generate an analysis of sensor data from at least one sensor monitoring power quality parameters and generate power routing commands for directing power flow through at least one power manipulation device, and at least one resource controller configured to execute the power routing commands by sending control signals to the at least one power manipulation device based on the analysis by the at least one artificial intelligence model.

[0026] In some embodiments, the techniques described herein relate to an Al-enabled system, wherein the at least one sensor is configured to monitor at least one of voltage, current, frequency, electrical noise, power fluctuations, harmonics, or transient signals along an energy distribution path.

[0027] In some embodiments, the techniques described herein relate to an Al-enabled system, wherein the at least one artificial intelligence model is configured to analyze sensor data to identify at least one of power reliability, average power characteristics, voltage swings, or load factors.

[0028] In some embodiments, the techniques described herein relate to an Al-enabled system, wherein the at least one artificial intelligence model is configured to identify correlations between power quality at a device input and arcing on a line along a distribution path to the device input.

[0029] In some embodiments, the techniques described herein relate to an Al-enabled system, wherein the at least one resource controller is configured to send control signals to the at least one power manipulation device to route power produced by a power source between storage, an internal power load, and output power.

[0030] In some embodiments, the techniques described herein relate to an Al-enabled system, wherein the at least one resource controller is configured to send control signals to the at least one power manipulation device to determine a source of output power selected from at least two sources including a power source and an energy storage device.

[0031] In some embodiments, the techniques described herein relate to an Al-enabled system, wherein the at least one resource controller includes at least one of electronic circuits, ASICs, FPGAs, or software.

[0032] In some embodiments, the techniques described herein relate to an Al-enabled system, wherein the at least one resource controller is configured to generate the control signals based on at least one of individual sensor data, fused sensor data, or model outputs from the at least one artificial intelligence model.

[0033] In some embodiments, the techniques described herein relate to an Al-enabled system, wherein the at least one artificial intelligence model is configured to generate the power routing commands based on at least one of sensor data input, simulation output from a digital twin of a distributed energy resource and components thereof, or external information.Attorney Docket No. 54250-73

[0034] In some embodiments, the techniques described herein relate to an Al-enabled system, wherein the at least one resource controller is configured to switch load input power between at least two power providers based on power quality analysis by the at least one artificial intelligence model.

[0035] In some embodiments, the techniques described herein relate to an Al-enabled system, wherein the at least one resource controller is configured to automatically switch to a redundant power provider based on a determination by the at least one artificial intelligence model that one power source has failed.

[0036] In some embodiments, the techniques described herein relate to an Al-enabled system, wherein the at least one resource controller is configured to switch between at least two power providers based on quality of incoming power determined by the at least one artificial intelligence model from sensor data.

[0037] In some embodiments, the techniques described herein relate to an Al-enabled system, wherein the quality of incoming power is evaluated by the at least one artificial intelligence model based on at least one of voltage level, current level, volatility, instability, or power factor.

[0038] In some embodiments, the techniques described herein relate to an Al-enabled system, wherein the at least one power manipulation device includes at least one of a power splitter, a power divider, a power switch, an inverter, a grid forming inverter, a grid tied inverter, a power line conditioner, a power transformer, or a power isolator.

[0039] In some embodiments, the techniques described herein relate to an Al-enabled system, wherein the at least one resource controller is configured to send a control signal to a grid tied inverter to alter voltage being output to a power grid to improve stability of the power grid based on analysis by the at least one artificial intelligence model of existing grid power information.

[0040] In some embodiments, the techniques described herein relate to an Al-enabled system, wherein the at least one resource controller is configured to send a control signal to a power divider to change distribution of power being provided to different outputs to prioritize delivery of energy to individual devices based on analysis by the at least one artificial intelligence model.

[0041] In some embodiments, the techniques described herein relate to an Al-enabled system, wherein the at least one resource controller is configured to send a control signal to a power switch to change device power input from a first input line to a second input line based on a determination by the at least one artificial intelligence model that power available on the first input line is inadequate.

[0042] In some embodiments, the techniques described herein relate to an Al-enabled system, wherein the at least one artificial intelligence model is configured to perform time-based analysis to identify trends or patterns in sensor data including at least one of a pattern over time of day or a pattern by day of week.

[0043] In some embodiments, the techniques described herein relate to an Al-enabled system, wherein the at least one artificial intelligence model is configured to perform statistical analyses including at least one of average voltage, standard voltage deviation, average current, current deviation, transient analysis, or determination of reliability.Attorney Docket No. 54250-73

[0044] In some embodiments, the techniques described herein relate to an Al-enabled system, wherein the at least one resource controller is configured to initiate a maintenance request based on analyzed sensor data from the at least one artificial intelligence model indicating that a line in a distribution network has been sparking.

[0045] In some embodiments, the techniques described herein relate to an Al-enabled system for automated energy resource orchestration and control, including at least one artificial intelligence model configured to generate resource allocation commands for distributed energy resources based on regulatory requirements and operational data, a regulatory compliance interface configured to communicate with regulatory agencies and obtain regulatory information for use by the at least one artificial intelligence model, and at least one resource controller configured to execute the resource allocation commands by sending control signals to power manipulation devices.

[0046] In some embodiments, the techniques described herein relate to an Al-enabled system, wherein the regulatory compliance interface includes a regulatory API configured to enable communications with at least two regulatory agencies for collection of regulatory information regarding distributed energy resources and potential siting locations.

[0047] In some embodiments, the techniques described herein relate to an Al-enabled system, wherein the regulatory compliance interface includes a regulatory smart agent configured to negotiate smart contracts and manage permitting for at least one distributed energy resource based on resource allocation plans generated by the at least one artificial intelligence model.

[0048] In some embodiments, the techniques described herein relate to an Al-enabled system, further including a regulatory configuration control system configured to generate an analysis of regulatory standards and regional regulations and provide configuration information to the at least one resource controller based on the analysis.

[0049] In some embodiments, the techniques described herein relate to an Al-enabled system, wherein the configuration information includes power minimums, maximums, or ranges for various distributed energy resources that are used by the at least one resource controller to identify control signals.

[0050] In some embodiments, the techniques described herein relate to an Al-enabled system, wherein the at least one resource controller is configured to send control signals to at least one power manipulation device to route power produced by a power source between storage, an internal power load, and output power.

[0051] In some embodiments, the techniques described herein relate to an Al-enabled system, wherein the at least one resource controller is configured to send control signals to at least one power manipulation device to determine a source of output power selected from at least two sources including a power source and an energy storage device.

[0052] In some embodiments, the techniques described herein relate to an Al-enabled system, wherein the at least one resource controller includes at least one of electronic circuits, ASICs, FPGAs, software, or combinations thereof.

[0053] In some embodiments, the techniques described herein relate to an Al-enabled system,Attorney Docket No. 54250-73wherein the at least one resource controller is configured to generate the control signals based on at least one of individual sensor data, fused sensor data, or model outputs from the at least one artificial intelligence model.

[0054] In some embodiments, the techniques described herein relate to an Al-enabled system, wherein the at least one artificial intelligence model is configured to generate the resource allocation commands based on at least one of sensor data input, simulation output from a digital twin of a distributed energy resource and components thereof, or external information.

[0055] In some embodiments, the techniques described herein relate to an Al-enabled system, further including at least one sensor configured to monitor at least one of environmental data including temperature and humidity or operational data including rotational information, vibration information, and charge states.

[0056] In some embodiments, the techniques described herein relate to an Al-enabled system, further including at least one sensor configured to monitor flow of power between components including at least one of voltage, current, frequency, electrical noise, power fluctuations, harmonics, or transient signals.

[0057] In some embodiments, the techniques described herein relate to an Al-enabled system, wherein the at least one resource controller is configured to switch load input power between at least two power providers based on analysis of sensor data by the at least one artificial intelligence model.

[0058] In some embodiments, the techniques described herein relate to an Al-enabled system, wherein the at least one resource controller is configured to automatically switch to a redundant power provider based on a determination that one power source has failed.

[0059] In some embodiments, the techniques described herein relate to an Al-enabled system, wherein the at least one resource controller is configured to switch between at least two power providers based on quality of incoming power determined by the at least one artificial intelligence model from sensor data.

[0060] In some embodiments, the techniques described herein relate to an Al-enabled system, wherein the quality of incoming power is evaluated based on at least one of voltage level, current level, volatility, instability, or power factor.

[0061] In some embodiments, the techniques described herein relate to an Al-enabled system, further including a transmission orchestration interface configured to gather transmission connection information about interconnecting power infrastructure including power grids and interconnections between grids pertinent to distributed energy resource connections.

[0062] In some embodiments, the techniques described herein relate to an Al-enabled system, further including a transmission configuration control system configured to generate an analysis of a subset of the transmission connection information and provide transmission configuration information to the at least one resource controller based on the analysis.

[0063] In some embodiments, the techniques described herein relate to an Al-enabled system, wherein the transmission configuration information includes at least one of power minimums, maximums, ranges, or time bands for utilization of certain transmission infrastructure.Attorney Docket No. 54250-73

[0064] In some embodiments, the techniques described herein relate to an Al-enabled system, further including a transaction orchestration interface configured to negotiate and implement energy-related transactions to support desired power connections based on resource allocation plans generated by the at least one artificial intelligence model.

[0065] In some embodiments, the techniques described herein relate to an Al-enabled system for distributed energy resource integration and grid stabilization, including at least one artificial intelligence model configured to analyze grid conditions and generate control strategies for integrating distributed energy resources into grid infrastructure to provide grid stability, at least one sensor configured to monitor power quality parameters and provide monitoring data to the at least one artificial intelligence model, and at least one automated controller configured to execute the control strategies by adjusting power output characteristics based on the grid conditions.

[0066] In some embodiments, the techniques described herein relate to an Al-enabled system, wherein the at least one artificial intelligence model is configured to generate control strategies for mobile resources that change location and connectivity status over time.

[0067] In some embodiments, the techniques described herein relate to an Al-enabled system, wherein the at least one automated controller is configured to control connection and power supplied into existing grid infrastructure to stabilize the existing grid infrastructure based on the control strategies.

[0068] In some embodiments, the techniques described herein relate to an Al-enabled system, wherein the at least one automated controller is configured to control output of a power source including at least one of output voltage, frequency, or whether an output inverter operates in grid forming mode or grid following mode.

[0069] In some embodiments, the techniques described herein relate to an Al-enabled system, wherein the at least one automated controller is configured to adapt incoming power to meet device requirements including at least one of voltage transformation, frequency transformation, or power conditioning.

[0070] In some embodiments, the techniques described herein relate to an Al-enabled system, wherein the at least one automated controller is configured to automatically switch smoothly between at least two power sources based on grid stability requirements determined by the at least one artificial intelligence model.

[0071] In some embodiments, the techniques described herein relate to an Al-enabled system, wherein the at least one sensor is configured to monitor at least one of voltage, current, frequency, electrical noise, power fluctuations, harmonics, or transient signals.

[0072] In some embodiments, the techniques described herein relate to an Al-enabled system, wherein the power quality parameters include at least one of frequency stability, voltage stability, or transient stability.

[0073] In some embodiments, the techniques described herein relate to an Al-enabled system, further including a regulatory compliance interface configured to verify compliance with applicable regulatory standards and provide compliance data to the at least one artificialAttorney Docket No. 54250-73intelligence model for use in generating the control strategies.

[0074] In some embodiments, the techniques described herein relate to an Al-enabled system, wherein the applicable regulatory standards include at least one of voltage requirements, frequency requirements, power quality requirements, power factor requirements, islanding requirements, equipment certification requirements, verification testing requirements, design requirements, minimum protective function requirements, metering requirements, or operating requirements.

[0075] In some embodiments, the techniques described herein relate to an Al-enabled system, wherein the at least one automated controller is configured to manage at least one of load isolation, fault tolerance, over-voltage protection, or under-voltage protection based on grid integration requirements.

[0076] In some embodiments, the techniques described herein relate to an Al-enabled system, wherein the at least one artificial intelligence model is configured to process sensor data from at least two distributed energy resources to coordinate grid stabilization activities across the at least two distributed energy resources.

[0077] In some embodiments, the techniques described herein relate to an Al-enabled system, further including a digital twin simulation system configured to simulate distributed energy resource operations and provide simulation outputs to the at least one artificial intelligence model for use in generating the control strategies.

[0078] In some embodiments, the techniques described herein relate to an Al-enabled system, wherein the at least one automated controller is configured to control division of power output between at least two outputs of a power divider based on the control strategies.

[0079] In some embodiments, the techniques described herein relate to an Al-enabled system, further including an adaptive data network configured to communicate sensor data from the at least one sensor to the at least one artificial intelligence model and communicate control signals from the at least one automated controller to at least one power manipulation device.

[0080] In some embodiments, the techniques described herein relate to an Al-enabled system, wherein the at least one automated controller includes a controller associated with a power provider configured to manage power output characteristics to meet grid connection requirements.

[0081] In some embodiments, the techniques described herein relate to an Al-enabled system, wherein the at least one automated controller includes a controller associated with a power load configured to manage connection to at least one power source including at least one of a power step transformer, a frequency transformer, a power conditioner, or a transfer switch.

[0082] In some embodiments, the techniques described herein relate to an Al-enabled system, wherein the at least one automated controller is configured to smooth power signal during switching between at least two power sources based on grid stability requirements.

[0083] In some embodiments, the techniques described herein relate to an Al-enabled system, further including a transmission orchestration interface configured to gather transmission connection information about interconnecting power infrastructure and provide the transmissionAttorney Docket No. 54250-73connection information to the at least one artificial intelligence model for use in generating the control strategies.

[0084] In some embodiments, the techniques described herein relate to an Al-enabled system, wherein the at least one artificial intelligence model is configured to coordinate integration of stationary distributed energy resources and mobile distributed energy resources into the grid infrastructure using different integration protocols based on resource mobility characteristics.

[0085] In some embodiments, the techniques described herein relate to an Al-enabled system for computation-dependent energy procurement, including at least one artificial intelligence model configured to evaluate potential computational initiatives and generate resource requirement estimates including power forecasts based on projected computational demands, an initiative simulation engine configured to simulate cost, timeline, and return scenarios for the potential computational initiatives using the resource requirement estimates, and a request generation module configured to create requests for proposal to energy markets based on the power forecasts from the initiative simulation engine.

[0086] In some embodiments, the techniques described herein relate to an Al-enabled system, wherein the at least one artificial intelligence model includes an initiative identification and evaluation system configured to identify potential computational initiatives including Al model training activities, Al model pinning activities, and agentic Al self-directed activities.

[0087] In some embodiments, the techniques described herein relate to an Al-enabled system, wherein the initiative identification and evaluation system is configured to estimate potential results and impacts of the potential computational initiatives relative to specified criteria to determine whether to proceed with resource estimation.

[0088] In some embodiments, the techniques described herein relate to an Al-enabled system, wherein the at least one artificial intelligence model is configured to estimate resource requirements based on at least one of known algorithms for computational power estimation, historical data regarding similar initiatives, or digital twin simulations of components of the potential computational initiatives.

[0089] In some embodiments, the techniques described herein relate to an Al-enabled system, wherein the initiative simulation engine is configured to generate at least two operational scenarios that account for different timelines and profiles of anticipated resource requirements over time.

[0090] In some embodiments, the techniques described herein relate to an Al-enabled system, wherein the initiative simulation engine is configured to generate simulations including cash flow analysis and return analysis with total output to complete an initiative and timing of when returns for the initiative would begin to accrue.

[0091] In some embodiments, the techniques described herein relate to an Al-enabled system, further including a scenario identification system configured to identify at least one promising initiative scenario based on simulation outputs from the initiative simulation engine and prioritize the at least one promising initiative scenario based on potential return relative to specified criteria.Attorney Docket No. 54250-73

[0092] In some embodiments, the techniques described herein relate to an Al-enabled system, further including a contractually available power evaluation system configured to evaluate existing power contracts to determine whether contractually available power can meet power forecasts for the at least one promising initiative scenario.

[0093] In some embodiments, the techniques described herein relate to an Al-enabled system, wherein the contractually available power evaluation system is configured to generate alternative scenarios based on using a portion of contractually available power and identify alternate power forecast timelines indicating additional power needed from new suppliers.

[0094] In some embodiments, the techniques described herein relate to an Al-enabled system, further including a proposal evaluation system configured to evaluate proposals received from energy markets and rerun simulations for the at least one promising initiative scenario using pricing and timing provided in the proposals.

[0095] In some embodiments, the techniques described herein relate to an Al-enabled system, wherein the proposal evaluation system is configured to rate combinations of scenarios and corresponding proposals relative to specified criteria and identify combinations that no longer meet interest thresholds based on the proposals.

[0096] In some embodiments, the techniques described herein relate to an Al-enabled system, wherein the at least one artificial intelligence model includes an agentic Al system configured to identify computational goals or actions requiring additional energy power consumption for a defined period of time.

[0097] In some embodiments, the techniques described herein relate to an Al-enabled system, wherein the agentic Al system is configured to evaluate potential benefits of additional computational activities including model training improvements, data digestion for internal understanding, and internal forecasting operations.

[0098] In some embodiments, the techniques described herein relate to an Al-enabled system, further including a client power evaluation system configured to generate real-time estimations of power needs based on utilization forecasts and estimated power requirements from digital twin simulations of client environments.

[0099] In some embodiments, the techniques described herein relate to an Al-enabled system, further including a client configured intelligence system configured to evaluate power metrics received from the client power evaluation system and determine whether to send a request for additional power to an energy market.

[0100] In some embodiments, the techniques described herein relate to an Al-enabled system, further including a computation-dependent energy supply monitor configured to monitor current availability of power based on data from existing energy contracts and poll at least one energy market to determine available power including metadata regarding price, quality, power metrics, and availability over time.

[0101] In some embodiments, the techniques described herein relate to an Al-enabled system, wherein the request generation module is configured to generate at least two requests for proposal corresponding to different promising initiative scenarios and alternative scenarios, eachAttorney Docket No. 54250-73request including a power forecast timeline and requisite conditions and criteria.

[0102] In some embodiments, the techniques described herein relate to an Al-enabled system, further including a smart contract interface configured to negotiate smart contracts for power supply based on accepted proposals and provide negotiated terms to a transaction orchestration execution system.

[0103] In some embodiments, the techniques described herein relate to an Al-enabled system, wherein the at least one artificial intelligence model is configured to evaluate time sensitivity of proposed initiatives by determining whether a timeline exists after which an initiative is not worth pursuing and whether returns significantly increase based on expedited completion.

[0104] In some embodiments, the techniques described herein relate to an Al-enabled system, further including an action evaluation system configured to generate estimates of cost of implementation, estimates of impact of actions once completed, time required for action implementation, lost opportunity costs, and impact on existing processes during implementation for the potential computational initiatives.

[0105] In some embodiments, the techniques described herein relate to an Al-enabled system for energy market proposal matching, including at least one artificial intelligence model configured to identify potential matches between requests for proposal from energy clients and available power forecasts from energy providers based on compatibility of power forecast timelines and compliance with governance policies, a market simulation engine configured to simulate compatibility scenarios for the potential matches identified by the at least one artificial intelligence model, and a proposal routing system configured to communicate matched requests to corresponding energy providers based on results from the market simulation engine.

[0106] In some embodiments, the techniques described herein relate to an Al-enabled system, wherein the at least one artificial intelligence model is configured to evaluate governance and regulatory policies to determine whether a specific provider and a potential client are compatible in terms of governance and regulatory requirements.

[0107] In some embodiments, the techniques described herein relate to an Al-enabled system, wherein the governance and regulatory policies include characteristics of power including carbon neutral requirements, limits on transients, uptime requirements, and legality of power in a region of the potential client.

[0108] In some embodiments, the techniques described herein relate to an Al-enabled system, further including a market twin system configured to create potential client digital twins for each request for proposal and at least one provider digital twin for energy providers.

[0109] In some embodiments, the techniques described herein relate to an Al-enabled system, wherein the market simulation engine is configured to use the potential client digital twins and the at least one provider digital twin to simulate fulfillment scenarios and identify permissible matches.

[0110] In some embodiments, the techniques described herein relate to an Al-enabled system, wherein the market simulation engine is configured to simulate fulfillment of at least two requests by a single provider based on the requests being for compatible time periods or smallAttorney Docket No. 54250-73amounts of power.

[0111] In some embodiments, the techniques described herein relate to an Al-enabled system, further including a scenario ranking system configured to rank simulation results against specified criteria and identify possible matches including combinations of requests for proposal for a single provider.

[0112] In some embodiments, the techniques described herein relate to an Al-enabled system, wherein the at least one artificial intelligence model is configured to receive requests for proposal including requested power forecast timelines and requisite conditions and criteria.

[0113] In some embodiments, the techniques described herein relate to an Al-enabled system, wherein the at least one artificial intelligence model is configured to receive available power forecasts including available power forecast timelines and conditions and criteria associated with an availability of power.

[0114] In some embodiments, the techniques described herein relate to an Al-enabled system, wherein the available power forecasts include at least one of prices, price points, or time frames during which power will be available on existing infrastructure.

[0115] In some embodiments, the techniques described herein relate to an Al-enabled system, further including a spot market module configured to enable short-term contracts for power by allocating available power that a provider has made available according to specified criteria including price.

[0116] In some embodiments, the techniques described herein relate to an Al-enabled system, wherein the spot market module is configured to generate proposal responses based on spot market available power without further consultation of providers prior to contract finalization.

[0117] In some embodiments, the techniques described herein relate to an Al-enabled system, further including a market assessment system configured to determine permissible matches between requests for proposal and available power forecasts by evaluating governance and regulatory policies from requesting entities and selling entities.

[0118] In some embodiments, the techniques described herein relate to an Al-enabled system, wherein the at least one artificial intelligence model is configured to evaluate whether available power forecasts can fulfill incremental power requests based on at least one of power availability, timeline compatibility, or compliance with conditions and criteria.

[0119] In some embodiments, the techniques described herein relate to an Al-enabled system, wherein the proposal routing system is configured to communicate at least one of individual requests for proposal or combinations of requests for proposal to provider transaction orchestration systems.

[0120] In some embodiments, the techniques described herein relate to an Al-enabled system, further including a provider value analysis system configured to run simulations depicting configuration options for meeting requests for proposal at different price points.

[0121] In some embodiments, the techniques described herein relate to an Al-enabled system, wherein the provider value analysis system is configured to simulate fulfillment of at least two potential client requests using excess power in current provider configurations.Attorney Docket No. 54250-73

[0122] In some embodiments, the techniques described herein relate to an Al-enabled system, wherein the provider value analysis system is configured to simulate meeting power needs for existing clients and potential clients with different configurations of provider systems.

[0123] In some embodiments, the techniques described herein relate to an Al-enabled system, further including a scenario prioritization system configured to evaluate different fulfillment scenarios based on business metrics including return on investment, timeframe for return, required investment, revenue at given price points, and risk assessment of potential clients.

[0124] In some embodiments, the techniques described herein relate to an Al-enabled system, wherein the at least one artificial intelligence model is configured to evaluate impact on energy providers including whether a potential client would introduce large fluctuations into associated power networks, wear on equipment, and whether reconfiguration of equipment for existing clients would be required.

[0125] In some embodiments, the techniques described herein relate to an Al-enabled system for distributed energy resource management, including at least one artificial intelligence model configured to analyze discovered energy resources and generate optimized resource allocation strategies based on generation capacity, storage capacity, and operational circumstances, a dynamic resource discovery engine configured to automatically identify mobile and stationary distributed energy resources and provide resource data to the at least one artificial intelligence model, and a transaction orchestration interface configured to execute energy transactions based on the optimized resource allocation strategies.

[0126] In some embodiments, the techniques described herein relate to an Al-enabled system, wherein the dynamic resource discovery engine is configured to determine real-time availability status, connectivity parameters, and operational circumstances for the discovered energy resources.

[0127] In some embodiments, the techniques described herein relate to an Al-enabled system, further including an energy edge resource management network architecture configured to enable dynamic connectivity management that handles changing availability status and connectivity patterns across distributed energy resources.

[0128] In some embodiments, the techniques described herein relate to an Al-enabled system, further including a mobile versus stationary resource classification module configured to differentiate between grid-connected stationary distributed energy resources and dynamic transitory distributed energy resources that move between locations with varying states of charge.

[0129] In some embodiments, the techniques described herein relate to an Al-enabled system, wherein the at least one artificial intelligence model is configured to apply different management protocols to resources based on classifications determined by the mobile versus stationary resource classification module.

[0130] In some embodiments, the techniques described herein relate to an Al-enabled system, further including a status monitoring and analysis component configured to provide real-time understanding of distributed energy resource status including state of charge metrics, reliabilityAttorney Docket No. 54250-73parameters, and projected operational life.

[0131] In some embodiments, the techniques described herein relate to an Al-enabled system, wherein the at least one artificial intelligence model includes a pattern recognition module configured to identify energy demand patterns, supply patterns, storage capacity patterns, and market price patterns.

[0132] In some embodiments, the techniques described herein relate to an Al-enabled system, further including an adaptive data pipeline integration interface configured to handle data processing, filtering, compression, storage, routing, and transport for energy edge resource management operations and provide processed data to the at least one artificial intelligence model.

[0133] In some embodiments, the techniques described herein relate to an Al-enabled system, further including an edge and loT networking integration module configured to integrate with edge and loT networking systems for real-time data collection from energy-related entities and distributed energy resource status monitoring.

[0134] In some embodiments, the techniques described herein relate to an Al-enabled system, further including an intelligent data layer architecture configured to process information through at least two stages including an ingestion stage, an analysis stage, a derived intelligence stage, and a consumer visualization portal.

[0135] In some embodiments, the techniques described herein relate to an Al-enabled system, further including vector-based communication protocols configured to enable efficient data exchange between energy edge resource management components and distributed resources.

[0136] In some embodiments, the techniques described herein relate to an Al-enabled system, wherein the at least one artificial intelligence model includes generative artificial intelligence capabilities configured to create energy operations proposals and transaction offerings through dynamic feedback loops.

[0137] In some embodiments, the techniques described herein relate to an Al-enabled system, further including a cross-service resource orchestration agent configured to manage, configure, deploy, provision, and optimize subsystems operating within linked systems including energy allocation across platforms based on recommendations from the at least one artificial intelligence model.

[0138] In some embodiments, the techniques described herein relate to an Al-enabled system, further including a market orchestration integration module configured to connect with energy marketplaces based on energy type and location including market forming capabilities, market demand processing, and market response coordination.

[0139] In some embodiments, the techniques described herein relate to an Al-enabled system, further including a grid and off-grid resource management controller configured to optimize and deploy generation and energy storage resources for both grid-connected applications and islanded off-grid applications based on the optimized resource allocation strategies.

[0140] In some embodiments, the techniques described herein relate to an Al-enabled system, further including a smart contract integration interface configured to connect with smart contractAttorney Docket No. 54250-73systems configured to negotiate smart contracts to meet distributed energy resource client energy needs based on the optimized resource allocation strategies.

[0141] In some embodiments, the techniques described herein relate to an Al-enabled system, further including a simulation and forecasting integration interface configured to connect with simulation and forecasting systems that utilize resource data from the dynamic resource discovery engine for energy planning and optimization.

[0142] In some embodiments, the techniques described herein relate to an Al-enabled system, further including a digital twin integration interface configured to connect with stakeholder energy digital twins that provide virtual representations of energy assets for monitoring and management purposes.

[0143] In some embodiments, the techniques described herein relate to an Al-enabled system, further including a client API and GUI integration interface configured to provide client API and GUI interfaces for energy edge resource management interaction with client systems based on outputs from the at least one artificial intelligence model.

[0144] In some embodiments, the techniques described herein relate to an Al-enabled system, further including an adaptive networking integration component configured to integrate with adaptive networking capabilities that employ loT devices to communicate among smart grid assets including smart metering and transmission and distribution monitoring.

[0145] In some embodiments, the techniques described herein relate to an Al-enabled system for mobile energy resource tracking and forecasting, including at least one artificial intelligence model configured to track and forecast where mobile distributed energy resources are positioned and determine operational status across different times and locations, a status monitoring component configured to collect real-time data including state of charge metrics and reliability parameters from the mobile distributed energy resources and provide the real-time data to the at least one artificial intelligence model, and a resource control orchestration module configured to generate automated resource control commands based on forecasts produced by the at least one artificial intelligence model.

[0146] In some embodiments, the techniques described herein relate to an Al-enabled system, wherein the at least one artificial intelligence model is configured to process historical movement patterns and operational data to generate predictive models that anticipate resource availability and positioning.

[0147] In some embodiments, the techniques described herein relate to an Al-enabled system, further including a dynamic resource discovery engine configured to automatically identify the mobile distributed energy resources and determine real-time availability status, connectivity parameters, and operational circumstances.

[0148] In some embodiments, the techniques described herein relate to an Al-enabled system, further including a mobile versus stationary resource classification module configured to differentiate between grid-connected stationary distributed energy resources and dynamic transitory distributed energy resources including electric vehicles.

[0149] In some embodiments, the techniques described herein relate to an Al-enabled system,Attorney Docket No. 54250-73wherein the at least one artificial intelligence model is configured to generate different forecasting models for mobile distributed energy resources and stationary distributed energy resources based on classifications from the mobile versus stationary resource classification module.

[0150] In some embodiments, the techniques described herein relate to an Al-enabled system, further including a resource capability assessment engine configured to analyze the mobile distributed energy resources and determine generation capacity, storage capacity, transmission capability, and operational circumstances based on data from the status monitoring component.

[0151] In some embodiments, the techniques described herein relate to an Al-enabled system, wherein the at least one artificial intelligence model includes a resource optimization intelligence engine configured to synthesize collected data and create energy operations proposals and transaction offerings through dynamic feedback loops.

[0152] In some embodiments, the techniques described herein relate to an Al-enabled system, further including a pattern recognition module configured to identify energy demand patterns, supply patterns, storage capacity patterns, and market price patterns and provide pattern data to the at least one artificial intelligence model to enhance forecasting accuracy.

[0153] In some embodiments, the techniques described herein relate to an Al-enabled system, further including an energy edge resource management network architecture configured to enable dynamic connectivity management that handles changing availability status and connectivity patterns across the mobile distributed energy resources.

[0154] In some embodiments, the techniques described herein relate to an Al-enabled system, further including a market orchestration integration module configured to connect with energy marketplaces based on energy type and location and execute market transactions based on the forecasts produced by the at least one artificial intelligence model.

[0155] In some embodiments, the techniques described herein relate to an Al-enabled system, further including a transaction orchestration and execution interface module configured to integrate with energy transaction processing for automated orchestration of energy-related transactions based on predicted positioning and operational status from the at least one artificial intelligence model.

[0156] In some embodiments, the techniques described herein relate to an Al-enabled system, further including a smart contract integration interface configured to connect with smart contract systems configured to negotiate smart contracts based on forecasted availability of the mobile distributed energy resources.

[0157] In some embodiments, the techniques described herein relate to an Al-enabled system, further including an adaptive data pipeline integration interface configured to handle data processing, filtering, compression, storage, routing, and transport for mobile resource tracking data and provide processed data to the at least one artificial intelligence model.

[0158] In some embodiments, the techniques described herein relate to an Al-enabled system, further including an edge and loT networking integration module configured to integrate with edge and loT networking systems for real-time data collection from the mobile distributedAttorney Docket No. 54250-73energy resources.

[0159] In some embodiments, the techniques described herein relate to an Al-enabled system, further including an intelligent data layer architecture configured to process information from the status monitoring component through at least two stages including an ingestion stage, an analysis stage, and a derived intelligence stage that provides inputs to the at least one artificial intelligence model.

[0160] In some embodiments, the techniques described herein relate to an Al-enabled system, further including vector-based communication protocols configured to enable efficient data exchange between the at least one artificial intelligence model and the mobile distributed energy resources.

[0161] In some embodiments, the techniques described herein relate to an Al-enabled system, further including a digital twin integration interface configured to connect with stakeholder energy digital twins that provide virtual representations of the mobile distributed energy resources for monitoring and management purposes.

[0162] In some embodiments, the techniques described herein relate to an Al-enabled system, further including a client API and GUI integration interface configured to provide client API and GUI interfaces that display forecasted positioning and operational status generated by the at least one artificial intelligence model.

[0163] In some embodiments, the techniques described herein relate to an Al-enabled system, further including a broadcast and poll interface configured to receive proposals from distributed energy resource marketplace orchestration layers regarding the mobile distributed energy resources and provide proposal data to the at least one artificial intelligence model.

[0164] In some embodiments, the techniques described herein relate to an Al-enabled system, further including a cross-service resource orchestration agent configured to manage, configure, deploy, provision, and optimize subsystems operating within linked systems based on mobile resource forecasts generated by the at least one artificial intelligence model.

[0165] In some embodiments, the techniques described herein relate to an Al-enabled system for multi-variable energy optimization, including at least one artificial intelligence model configured to analyze energy generation, storage, and consumption data to optimize for reliability, cost-effectiveness, and system uptime concurrently across distributed energy resources, and a digital twin system configured to maintain virtual representations of physical energy assets synchronized with real-time operational data processed by the at least one artificial intelligence model.

[0166] In some embodiments, the techniques described herein relate to an Al-enabled system, further including an expert system module configured to apply rule-based analysis of energy profiles to generate decision frameworks based on predefined operational knowledge for energy management operations.

[0167] In some embodiments, the techniques described herein relate to an Al-enabled system, wherein the at least one artificial intelligence model includes generative Al capabilities configured to synthesize energy operations proposals and automated optimizationAttorney Docket No. 54250-73recommendations based on expert assessment.

[0168] In some embodiments, the techniques described herein relate to an Al-enabled system, wherein the at least one artificial intelligence model includes neural network architectures including convolutional neural networks for energy data processing and recurrent neural networks for time-series energy predictions.

[0169] In some embodiments, the techniques described herein relate to an Al-enabled system, further including an energy routing and control optimization system configured to provide intelligent path optimization for energy transmission based on grid conditions and demand patterns generated by the at least one artificial intelligence model.

[0170] In some embodiments, the techniques described herein relate to an Al-enabled system, further including a distributed energy resource management system configured to coordinate fleet-level optimization for mobile and stationary energy assets including electric vehicles based on analysis performed by the at least one artificial intelligence model.

[0171] In some embodiments, the techniques described herein relate to an Al-enabled system, wherein the at least one artificial intelligence model includes demand forecasting algorithms configured to utilize weather-based energy forecasting and historical consumption patterns to predict energy requirements.

[0172] In some embodiments, the techniques described herein relate to an Al-enabled system, further including a transaction management system configured to automate energy transaction processing and provide enterprise decision support based on market data and optimization recommendations from the at least one artificial intelligence model.

[0173] In some embodiments, the techniques described herein relate to an Al-enabled system, further including an energy orchestration support system configured to coordinate intelligent orchestration across at least two energy types including wind, solar, nuclear, and fossil fuel generation based on optimization directives from the at least one artificial intelligence model.

[0174] In some embodiments, the techniques described herein relate to an Al-enabled system, further including a self-optimizing controller configured to implement adaptive learning algorithms that automatically adjust operational parameters based on performance feedback from energy management operations.

[0175] In some embodiments, the techniques described herein relate to an Al-enabled system, wherein the digital twin system includes at least one executive energy digital twin with rolebased data presentation and at least one stakeholder-specific digital twin with intelligent agent alerts.

[0176] In some embodiments, the techniques described herein relate to an Al-enabled system, further including an Al-based data processing and integration system configured to perform automated energy-related data extraction and cleansing with pattern detection in energy data streams that are provided to the at least one artificial intelligence model.

[0177] In some embodiments, the techniques described herein relate to an Al-enabled system, further including a context-aware sensor fusion system configured to integrate disparate marketplace and operational data sources through Al classification and prediction capabilitiesAttorney Docket No. 54250-73that provide inputs to the at least one artificial intelligence model.

[0178] In some embodiments, the techniques described herein relate to an Al-enabled system, further including at least one edge-deployed Al processing unit configured to provide local energy management through distributed decision-making at points of energy consumption based on processing performed by the at least one artificial intelligence model.

[0179] In some embodiments, the techniques described herein relate to an Al-enabled system, wherein the at least one edge-deployed Al processing unit includes semi-sentient capabilities providing distributed decision-making with local processing capabilities for energy optimization without centralized control.

[0180] In some embodiments, the techniques described herein relate to an Al-enabled system, wherein the at least one artificial intelligence model includes graph neural networks configured to model distributed energy resources through nodes representing power generation, storage, transmission, and consumption capabilities connected by edges representing energy-related relationships.

[0181] In some embodiments, the techniques described herein relate to an Al-enabled system, further including natural language processing systems configured to automate processing of transaction documents and energy-related communications for intelligent extraction of trading opportunities and risk factors.

[0182] In some embodiments, the techniques described herein relate to an Al-enabled system, further including a hybrid human-AI coordination interface configured to enable collaborative decision-making between human experts and Al systems with configurable autonomy levels based on trust and reliability factors.

[0183] In some embodiments, the techniques described herein relate to an Al-enabled system, further including an environmental governance system configured to provide carbon emission monitoring and compliance reporting capabilities that integrate with the at least one artificial intelligence model for regulatory adherence.

[0184] In some embodiments, the techniques described herein relate to an Al-enabled system, wherein the at least one artificial intelligence model includes transformer neural network architectures configured for energy data compression and analysis through constrained transformer networks for data embedding paired with decoding neural networks for data reproduction.

[0185] In some embodiments, the techniques described herein relate to an Al-enabled system for autonomous energy management and control, including at least one artificial intelligence model configured to generate automated control commands for energy generation assets and load balancing decisions across distributed energy networks based on real-time operational data, and at least one edge computing unit configured to execute the automated control commands at local energy consumption points.

[0186] In some embodiments, the techniques described herein relate to an Al-enabled system, wherein the at least one artificial intelligence model includes machine learning algorithms configured to provide pattern recognition and prediction capabilities for energy consumptionAttorney Docket No. 54250-73analysis.

[0187] In some embodiments, the techniques described herein relate to an Al-enabled system, further including an energy optimization engine configured to process supply and demand data from distributed sources and apply mathematical analysis algorithms to reconcile generation, storage, and load requirements based on outputs from the at least one artificial intelligence model.

[0188] In some embodiments, the techniques described herein relate to an Al-enabled system, further including a self-optimizing controller configured to implement adaptive learning algorithms that receive performance feedback from energy management operations and automatically adjust operational parameters based on the performance feedback.

[0189] In some embodiments, the techniques described herein relate to an Al-enabled system, further including a distributed energy resource coordination system configured to manage electric vehicle charging schedules, energy storage deployment, and distributed generation coordination based on optimization strategies generated by the at least one artificial intelligence model.

[0190] In some embodiments, the techniques described herein relate to an Al-enabled system, wherein the at least one edge computing unit is configured to process local sensor readings and equipment status information to generate control command recommendations for energy equipment at local energy consumption points.

[0191] In some embodiments, the techniques described herein relate to an Al-enabled system, further including an Al orchestration controller configured to synthesize intelligence outputs from at least two Al systems to provide coordinated decision-making frameworks for energy optimization scenarios.

[0192] In some embodiments, the techniques described herein relate to an Al-enabled system, further including an Al training system configured to provide continuous model improvement through automated parameter adjustment based on operational feedback from energy management operations.

[0193] In some embodiments, the techniques described herein relate to an Al-enabled system, further including a transaction automation system configured to execute automated transaction processing for energy exchanges based on Al-driven recommendations from the at least one artificial intelligence model.

[0194] In some embodiments, the techniques described herein relate to an Al-enabled system, further including an energy-aware workflow integration system configured to coordinate building management and load-side optimization applications based on consumption characteristics including peak power requirements and continuity needs.

[0195] In some embodiments, the techniques described herein relate to an Al-enabled system, wherein the at least one artificial intelligence model includes deep learning systems configured to enable energy demand forecasting through supervised and unsupervised learning approaches.

[0196] In some embodiments, the techniques described herein relate to an Al-enabled system, further including a digital twin simulation system configured to perform scenario simulation andAttorney Docket No. 54250-73predictive modeling by processing integrated data from at least two energy management sources.

[0197] In some embodiments, the techniques described herein relate to an Al-enabled system, further including graph neural networks configured to analyze complex interconnected energy system relationships and provide optimization recommendations based on graph-based algorithms.

[0198] In some embodiments, the techniques described herein relate to an Al-enabled system, further including a dynamic routing system configured to implement automated control interfaces that adjust energy distribution parameters in real-time based on intelligent path optimization decisions from the at least one artificial intelligence model.

[0199] In some embodiments, the techniques described herein relate to an Al-enabled system, wherein the at least one artificial intelligence model is configured to coordinate at least two energy types including wind, solar, nuclear, and fossil fuel generation through intelligent orchestration capabilities.

[0200] In some embodiments, the techniques described herein relate to an Al-enabled system, further including an Al system management controller configured to coordinate computational resource allocation across at least two Al processing nodes based on real-time demand forecasting requirements.

[0201] In some embodiments, the techniques described herein relate to an Al-enabled system, further including reinforcement learning algorithms configured to enable continuous strategy optimization through automated adjustment of operational parameters based on market conditions and performance feedback.

[0202] In some embodiments, the techniques described herein relate to an Al-enabled system, further including a quality-of-service optimization system configured to implement adaptive routing protocols for dynamic path optimization based on energy availability and performance requirements determined by the at least one artificial intelligence model.

[0203] In some embodiments, the techniques described herein relate to an Al-enabled system, further including an Al system generation module configured to create specialized Al systems including primary content Al systems for core energy operations and supplemental content Al systems for supporting functions.

[0204] In some embodiments, the techniques described herein relate to an Al-enabled system, further including natural language processing capabilities configured to analyze transaction documents and energy-related communications for intelligent extraction of trading opportunities and risk factors that are provided to the at least one artificial intelligence model.

[0205] In some embodiments, the techniques described herein relate to an Al-enabled system for orchestrating energy transmission pathways, including at least one artificial intelligence model configured to analyze distributed energy resource connection plans and identify optimal transmission pathways through transmission infrastructure, and a smart contract negotiation module configured to establish transmission agreements with transmission facility operators.

[0206] In some embodiments, the techniques described herein relate to an Al-enabled system, further including a transmission communication system configured to communicate with publicAttorney Docket No. 54250-73utilities and private distributed energy resource entities to collect transmission infrastructure data.

[0207] In some embodiments, the techniques described herein relate to an Al-enabled system, wherein the transmission infrastructure data includes at least one of power quality data, pricing data, peak pricing information, step pricing information, or power limit data.

[0208] In some embodiments, the techniques described herein relate to an Al-enabled system, wherein the at least one artificial intelligence model is configured to process the distributed energy resource connection plans to identify planned interconnections between energy providers and energy consumers.

[0209] In some embodiments, the techniques described herein relate to an Al-enabled system, wherein the smart contract negotiation module is configured to negotiate smart connection contracts and smart transmission contracts with transmission infrastructure entities.

[0210] In some embodiments, the techniques described herein relate to an Al-enabled system, wherein the smart contract negotiation module is further configured to negotiate at least one of scheduling information, pricing information, or connection requirements.

[0211] In some embodiments, the techniques described herein relate to an Al-enabled system, wherein the at least one artificial intelligence model is configured to evaluate two or more potential transmission pathways and select optimal pathways based on cost, availability, and power quality criteria.

[0212] In some embodiments, the techniques described herein relate to an Al-enabled system, further including a transmission execution agent configured to receive transmission connection information for the optimal transmission pathways from the smart contract negotiation module.

[0213] In some embodiments, the techniques described herein relate to an Al-enabled system, wherein the transmission connection information includes time frames for accessing certain distributed energy resource entities and maximum power limits at certain times of day.

[0214] In some embodiments, the techniques described herein relate to an Al-enabled system, wherein the transmission infrastructure includes power grids and interconnections between power grids.

[0215] In some embodiments, the techniques described herein relate to an Al-enabled system, wherein the at least one artificial intelligence model is configured to identify a plurality of transmission pathways for a plurality of interconnections between distributed energy resource entities.

[0216] In some embodiments, the techniques described herein relate to an Al-enabled system, further including an edge device resource management system configured to develop the distributed energy resource connection plans based on transmission infrastructure data.

[0217] In some embodiments, the techniques described herein relate to an Al-enabled system, wherein the smart contract negotiation module includes a transmission orchestration agent configured to communicate with a subset of transmission facilities corresponding to the optimal transmission pathways.

[0218] In some embodiments, the techniques described herein relate to an Al-enabled system,Attorney Docket No. 54250-73further including a connection analysis system configured to analyze the distributed energy resource connection plans and provide input data to the at least one artificial intelligence model.

[0219] In some embodiments, the techniques described herein relate to an Al-enabled system, further including a plurality of resource controllers configured to receive the transmission connection information from the transmission execution agent.

[0220] In some embodiments, the techniques described herein relate to an Al-enabled system, wherein each resource controller is configured to send control signals to one or more power manipulation devices to control energy flow through a corresponding transmission pathway.

[0221] In some embodiments, the techniques described herein relate to an Al-enabled system, wherein each resource controller is in communication with a power provider connection, a power load connection, and an intervening transmission infrastructure connection.

[0222] In some embodiments, the techniques described herein relate to an Al-enabled system, further including a plurality of adaptive data networks, wherein each adaptive data network connects entities relevant for a corresponding interconnection.

[0223] In some embodiments, the techniques described herein relate to an Al-enabled system, wherein the at least one artificial intelligence model is configured to continuously update transmission pathway selections based on real-time changes in transmission infrastructure availability and pricing.

[0224] In some embodiments, the techniques described herein relate to an Al-enabled system, wherein the smart contract negotiation module is configured to automatically execute smart contracts when transmission pathway conditions satisfy predetermined criteria.

[0225] In some embodiments, the techniques described herein relate to an Al-enabled system for controlling distributed energy transmission, including at least one artificial intelligence model configured to generate transmission execution commands based on negotiated connection terms, and a plurality of resource controllers configured to control energy flow through transmission pathways based on the transmission execution commands.

[0226] In some embodiments, the techniques described herein relate to an Al-enabled system, further including a transmission orchestration system configured to negotiate the connection terns with transmission infrastructure entities and provide transmission connection information to the at least one artificial intelligence model.

[0227] In some embodiments, the techniques described herein relate to an Al-enabled system, wherein the transmission orchestration system includes a transmission communication system configured to communicate with public utilities and private distributed energy resource entities.

[0228] In some embodiments, the techniques described herein relate to an Al-enabled system, wherein the at least one artificial intelligence model is configured to process transmission connection information including scheduling information, pricing information, and connection requirements.

[0229] In some embodiments, the techniques described herein relate to an Al-enabled system, wherein each resource controller of the plurality of resource controllers is configured to send control signals to one or more power manipulation devices.Attorney Docket No. 54250-73

[0230] In some embodiments, the techniques described herein relate to an Al-enabled system, wherein the control signals regulate at least one of energy flow rate, voltage level, or power quality through a corresponding transmission pathway.

[0231] In some embodiments, the techniques described herein relate to an Al-enabled system, wherein each resource controller is in communication with a power provider connection, a power load connection, and an intervening transmission infrastructure connection.

[0232] In some embodiments, the techniques described herein relate to an Al-enabled system, wherein the transmission execution commands include time-based control parameters specifying when energy transmission is permitted through specific transmission pathways.

[0233] In some embodiments, the techniques described herein relate to an Al-enabled system, wherein the transmission execution commands include power limit parameters specifying maximum power levels at certain times of day.

[0234] In some embodiments, the techniques described herein relate to an Al-enabled system, further including a transmission execution agent configured to distribute the transmission execution commands to the plurality of resource controllers.

[0235] In some embodiments, the techniques described herein relate to an Al-enabled system, wherein the transmission execution agent is configured to receive transmission connection information from a transmission orchestration system and provide the transmission connection information to the at least one artificial intelligence model.

[0236] In some embodiments, the techniques described herein relate to an Al-enabled system, wherein the at least one artificial intelligence model is configured to generate individual transmission execution commands for each of a plurality of interconnections between distributed energy resource entities.

[0237] In some embodiments, the techniques described herein relate to an Al-enabled system, wherein the transmission execution commands identify connection requirements for transmission execution control for each of the plurality of interconnections.

[0238] In some embodiments, the techniques described herein relate to an Al-enabled system, further including a plurality of adaptive data networks, wherein each adaptive data network connects entities relevant for a corresponding transmission pathway.

[0239] In some embodiments, the techniques described herein relate to an Al-enabled system, wherein the negotiated connection terms are embodied in smart contracts negotiated with transmission infrastructure entities.

[0240] In some embodiments, the techniques described herein relate to an Al-enabled system, wherein the smart contracts include smart connection contracts and smart transmission contracts.

[0241] In some embodiments, the techniques described herein relate to an Al-enabled system, wherein the at least one artificial intelligence model is configured to monitor actual energy flow through the transmission pathways and adjust the transmission execution commands based on deviations from expected performance.

[0242] In some embodiments, the techniques described herein relate to an Al-enabled system, wherein the at least one artificial intelligence model is configured to process real-timeAttorney Docket No. 54250-73transmission infrastructure conditions and dynamically modify the transmission execution commands to maintain optimal energy flow.

[0243] In some embodiments, the techniques described herein relate to an Al-enabled system, wherein the transmission orchestration system includes a connection analysis system configured to identify transmission pathways based on a distributed energy resource entity connection plan.

[0244] In some embodiments, the techniques described herein relate to an Al-enabled system, wherein the plurality of resource controllers is configured to control energy transmission for a plurality of interconnections between distributed energy resource entities based on individualized transmission execution commands for each interconnection.

[0245] In some embodiments, the techniques described herein relate to an Al-enabled system for governance policy enforcement over energy-related transactions, including at least one artificial intelligence model configured to extract policy rules from regulatory documents and convert the policy rules into executable governance logic, and a smart contract generation module configured to automatically generate smart contracts that enforce the governance policies on energy-related transactions.

[0246] In some embodiments, the techniques described herein relate to an Al-enabled system, wherein the at least one artificial intelligence model includes a natural language processing engine configured to parse regulatory text using transformer-based language models to identify compliance requirements.

[0247] In some embodiments, the techniques described herein relate to an Al-enabled system, further including a governance security module configured to generate digital signatures for policy documents using cryptographic hash functions and private keys stored in tamper-resistant storage.

[0248] In some embodiments, the techniques described herein relate to an Al-enabled system, further including a policy synchronization module configured to propagate policy updates between edge devices and conduct governance consensus rounds to achieve agreement on policy states across a plurality of edge devices.

[0249] In some embodiments, the techniques described herein relate to an Al-enabled system, wherein the policy synchronization module is configured to implement fault tolerant consensus protocols that commit to a consensus state when receiving identical policy state confirmations from at least a threshold number of participating edge devices.

[0250] In some embodiments, the techniques described herein relate to an Al-enabled system, wherein the smart contract generation module is configured to monitor transaction environments for transaction initiation events, retrieve applicable governance policies, and deploy generated governance smart contracts to blockchain infrastructure.

[0251] In some embodiments, the techniques described herein relate to an Al-enabled system, wherein the smart contract generation module is configured to automatically aggregate carbon offset credits by querying distributed ledger repositories for available offset certificates, calculating total carbon generation from energy consumption data, determining offset quantities needed to achieve carbon neutrality, and executing smart contract transactions to acquireAttorney Docket No. 54250-73required offset certificates.

[0252] In some embodiments, the techniques described herein relate to an Al-enabled system, further including a policy memory subsystem that includes a distributed hash table storage system configured to partition policy data across two or more edge devices using consistent hashing algorithms.

[0253] In some embodiments, the techniques described herein relate to an Al-enabled system, further including a dynamic policy adaptation module configured to monitor regulatory framework integration interfaces for regulatory update notifications and automatically parse updated regulatory documents to extract modified policy requirements.

[0254] In some embodiments, the techniques described herein relate to an Al-enabled system, wherein the dynamic policy adaptation module is configured to generate proposed governance policy modifications reflecting regulatory changes, submit the proposed governance policy modifications for consensus approval across a plurality of edge devices, and automatically update governance smart contract logic upon achieving consensus.

[0255] In some embodiments, the techniques described herein relate to an Al-enabled system, wherein the dynamic policy adaptation module is configured to simulate governance adaptations using digital twin models before deployment by maintaining virtual replicas of physical energy infrastructure, applying proposed policy adaptations to the digital twin models, executing simulated transactions through the digital twin models, and deploying adapted policies to physical edge devices only after simulation demonstrates successful policy operation.

[0256] In some embodiments, the techniques described herein relate to an Al-enabled system, further including a vector embedding algorithm configured to represent each governance policy as a numerical vector in high-dimensional embedding space and compute semantic similarity between policies by calculating cosine similarity scores between their respective embedding vectors.

[0257] In some embodiments, the techniques described herein relate to an Al-enabled system, further including a policy stream processing framework configured to detect and analyze governance decision patterns by subscribing to governance event message queues produced by distributed edge devices, maintaining sliding time windows of recent events, and applying streaming aggregation operations to compute real-time metrics including policy enforcement rates and compliance rates.

[0258] In some embodiments, the techniques described herein relate to an Al-enabled system, further including a decision traceability framework configured to link governance outcomes to specific policy applications through audit trail mechanisms that maintain complete records of governance decision-making processes including decision context and reasoning patterns.

[0259] In some embodiments, the techniques described herein relate to an Al-enabled system, further including a knowledge graph database configured to represent complex regulatory relationships through interconnected node structures capturing policy interdependencies, wherein nodes represent regulatory entities and edges represent relationships between entities including hierarchical relationships, dependency relationships, and conflict relationships.Attorney Docket No. 54250-73

[0260] In some embodiments, the techniques described herein relate to an Al-enabled system, wherein the at least one artificial intelligence model is configured to enable automated policy conflict detection by executing graph query algorithms that identify policy nodes with conflict relationship edges connecting them and automatically resolve conflicts by applying resolution rules through graph pattern matching algorithms.

[0261] In some embodiments, the techniques described herein relate to an Al-enabled system, wherein the smart contract generation module is configured to implement multi-party governance consensus by deploying multi-signature smart contracts that store public key identifiers for authorized governance authorities, collect approval responses including digital signatures from each authority, verify each received signature against stored public keys, and execute proposed governance policy changes when a count of valid approvals reaches or exceeds a configured threshold.

[0262] In some embodiments, the techniques described herein relate to an Al-enabled system, further including a compliance verification system configured to automate detection of deviations from regulatory standards by continuously monitoring edge device configurations, comparing monitored configurations against required baseline configurations, calculating configuration deviation scores, and triggering automated violation alerts when deviation scores exceed threshold levels.

[0263] In some embodiments, the techniques described herein relate to an Al-enabled system, further including a blockchain-compatible storage architecture configured to record governance decisions, policy enforcement actions, and compliance verification results as immutable audit trail entries with cryptographic verification preventing unauthorized modification.

[0264] In some embodiments, the techniques described herein relate to an Al-enabled system, wherein the at least one artificial intelligence model is configured to convert structured policy templates into executable policy logic that can be deployed to a plurality of edge devices throughout an energy distribution network.

[0265] In some embodiments, the techniques described herein relate to an Al-enabled system for regulatory compliance monitoring over energy-related transactions, including at least one artificial intelligence model configured to analyze operational data from distributed energy infrastructure against regulatory compliance requirements and detect compliance violations through pattern recognition, a plurality of policy adherence monitoring sensors positioned throughout an energy distribution network and configured to collect the operational data, and an immutable audit trail system configured to record compliance events and governance decisions.

[0266] In some embodiments, the techniques described herein relate to an Al-enabled system, wherein the plurality of policy adherence monitoring sensors include environmental monitoring sensors configured to track air quality, water contamination, and soil conditions within resource extraction areas.

[0267] In some embodiments, the techniques described herein relate to an Al-enabled system, wherein the plurality of policy adherence monitoring sensors includes safety monitoring sensors configured to monitor hazard conditions through gas sensors, structural integrity monitors, andAttorney Docket No. 54250-73worker proximity tracking systems.

[0268] In some embodiments, the techniques described herein relate to an Al-enabled system, wherein the plurality of policy adherence monitoring sensors includes resource quality assessment sensors positioned at material extraction points and configured to continuously sample extracted materials to ensure compliance with specifications and regulatory standards.

[0269] In some embodiments, the techniques described herein relate to an Al-enabled system, wherein the at least one artificial intelligence model includes a policy inference engine configured to process energy consumption pattern data through neural network architectures optimized for edge deployment scenarios.

[0270] In some embodiments, the techniques described herein relate to an Al-enabled system, wherein the at least one artificial intelligence model includes a violation pattern recognition algorithm configured to identify compliance violations by monitoring time-series energy transaction data, extracting feature vectors representing transaction characteristics, comparing the feature vectors against learned violation signatures using trained neural network classifiers, and calculating anomaly scores by measuring distance from normal transaction patterns.

[0271] In some embodiments, the techniques described herein relate to an Al-enabled system, wherein the violation pattern recognition algorithm is configured to trigger violation alerts when anomaly scores exceed threshold levels configured based on regulatory severity classifications.

[0272] In some embodiments, the techniques described herein relate to an Al-enabled system, wherein the at least one artificial intelligence model includes a computer vision policy violation detection system configured to monitor energy operations through image and video analysis by processing video frames through convolutional neural network architectures trained to recognize equipment states and personnel activities, comparing detected equipment configurations against regulatory-compliant reference configurations, and identifying deviations.

[0273] In some embodiments, the techniques described herein relate to an Al-enabled system, further including a smart contract enforcement system configured to implement automated policy enforcement by embedding governance policies directly into transaction-enabling smart contracts such that policy checks are performed atomically with transaction processing.

[0274] In some embodiments, the techniques described herein relate to an Al-enabled system, further including a carbon footprint governance system configured to monitor energy consumption, calculate carbon dioxide equivalent emissions from the monitored energy consumption, compare calculated carbon emissions against threshold levels specified in governance policies, and initiate preventative actions when projected carbon generation indicates probable threshold violations.

[0275] In some embodiments, the techniques described herein relate to an Al-enabled system, wherein the carbon footprint governance system is configured to execute device shutdown operations when calculated carbon emissions reach or exceed critical thresholds by maintaining priority rankings of energy-consuming devices based on operational criticality, calculating carbon generation contributions of each device, identifying lowest-priority devices whose shutdown would bring projected carbon totals below threshold levels, and issuing shutdownAttorney Docket No. 54250-73commands to selected devices.

[0276] In some embodiments, the techniques described herein relate to an Al-enabled system, further including a regulatory compliance verification system configured to automate compliance verification by maintaining repositories of regulatory requirements, parsing the regulatory requirements using natural language processing to extract specific technical requirements, automatically collecting evidence of compliance by querying edge devices for configuration data, and comparing collected evidence against verification criteria.

[0277] In some embodiments, the techniques described herein relate to an Al-enabled system, wherein the regulatory compliance verification system is configured to automate detection of deviations from critical infrastructure protection standards by continuously monitoring edge device configurations, comparing monitored configurations against required baseline configurations, calculating configuration deviation scores, and triggering automated violation alerts when deviation scores exceed threshold levels.

[0278] In some embodiments, the techniques described herein relate to an Al-enabled system, wherein the immutable audit trail system is configured to record governance-relevant events including policy enforcement actions, compliance verifications, regulatory violation detections, and remediation initiations by formatting governance data into blockchain-compatible ledger structures and recording using append -only log structures.

[0279] In some embodiments, the techniques described herein relate to an Al-enabled system, further including a governance-constrained transaction orchestration system configured to orchestrate energy transactions by automatically evaluating proposed transactions against governance policies before initiating execution and verifying that transactions satisfy governance policies including carbon generation limits, renewable energy requirements, and financial control policies.

[0280] In some embodiments, the techniques described herein relate to an Al-enabled system, further including a policy-compliant transaction configuration system configured to automatically configure transaction terms by retrieving applicable governance policies for contemplated transactions, analyzing retrieved governance policies to extract enforceable constraints on transaction parameters, and configuring transaction parameters to satisfy extracted governance constraints through a constraint satisfaction process.

[0281] In some embodiments, the techniques described herein relate to an Al-enabled system, wherein the at least one artificial intelligence model is configured to apply real-time policy constraint satisfaction solvers that apply mathematical programming techniques to identify transaction parameter combinations satisfying all constraints simultaneously.

[0282] In some embodiments, the techniques described herein relate to an Al-enabled system, further including a distributed governance state synchronization module configured to enable each edge device to maintain a local replica of governance policy state, propagate detected governance policy update events to peer devices through mesh networking protocols, and conduct governance consensus rounds including exchanging proposed governance policy states, comparing received governance states against locally computed states, and voting on acceptingAttorney Docket No. 54250-73or rejecting proposed governance states based on validation criteria.

[0283] In some embodiments, the techniques described herein relate to an Al-enabled system, wherein the at least one artificial intelligence model includes a sensor fusion processor configured to integrate multi-sensor data streams from loT devices, operational monitoring systems, and market data feeds to provide comprehensive governance evaluation inputs through real-time data correlation and pattern recognition.

[0284] In some embodiments, the techniques described herein relate to an Al-enabled system, wherein the smart contract enforcement system is configured to implement multi-party governance consensus by deploying multi-signature smart contracts that collect cryptographic approvals from a threshold number of governance authorities and execute proposed governance policy changes upon achieving the threshold number of valid approvals.

[0285] In some embodiments, the techniques described herein relate to an Al-enabled system for energy resource exploration and discovery, including at least one artificial intelligence model configured to analyze geological data and identify resource-rich formations for petroleum deposits, natural gas formations, and rare earth metals, and a digital twin simulation system configured to model underground energy resource formations with real-time synchronization based on exploration data.

[0286] In some embodiments, the techniques described herein relate to an Al-enabled system, further including a multi -resource detection framework configured to enable simultaneous exploration and discovery of petroleum deposits, natural gas formations, and rare earth metals through differentiated processing capabilities within unified detection protocols.

[0287] In some embodiments, the techniques described herein relate to an Al-enabled system, wherein the multi-resource detection framework includes an underground petroleum deposit discovery exploration module configured to implement targeted algorithms addressing petroleum exploration requirements through petroleum-specific detection protocols.

[0288] In some embodiments, the techniques described herein relate to an Al-enabled system, wherein the multi-resource detection framework includes a natural gas formation identification module configured to implement formation-specific analysis techniques that optimize natural gas resource discovery processes through contextual simulation and forecasting processes.

[0289] In some embodiments, the techniques described herein relate to an Al-enabled system, wherein the multi-resource detection framework includes an energy infrastructure materials rare earth metal detection system configured to provide targeted exploration capabilities for rare earth metals essential for renewable energy technologies including materials required for magnets, batteries, and electrical systems.

[0290] In some embodiments, the techniques described herein relate to an Al-enabled system, wherein the at least one artificial intelligence model is configured to utilize neural network architectures enabling automated pattern identification from complex geological datasets.

[0291] In some embodiments, the techniques described herein relate to an Al-enabled system, wherein the digital twin simulation system is configured to simulate different scenarios in discovery of formations and underground deposits and predict resource availability throughAttorney Docket No. 54250-73formation analysis methods.

[0292] In some embodiments, the techniques described herein relate to an Al-enabled system, further including a resource exploration scenario reconciliation system configured to deploy expert systems and artificial intelligence for reconciliation of two or more exploration scenarios using simulation models that optimize resource discovery strategies based on geological data analysis and predictive modeling outcomes.

[0293] In some embodiments, the techniques described herein relate to an Al-enabled system, further including an adaptive data processing pipeline configured to handle geological, seismic, and resource exploration data through intelligent processing systems with automated data extraction, transformation, and loading capabilities that adapt processing workflows based on geological data characteristics.

[0294] In some embodiments, the techniques described herein relate to an Al-enabled system, further including a satellite imagery processing system configured to utilize computer vision systems for processing satellite imagery and geological mapping data for resource identification that analyze remote sensing data to identify geological features correlating with underground resource deposits.

[0295] In some embodiments, the techniques described herein relate to an Al-enabled system, further including a predictive analytics system configured to provide forecasting models for predicting resource quantities and availability that integrate market data to optimize exploration priorities.

[0296] In some embodiments, the techniques described herein relate to an Al-enabled system, further including an exploration site local data processing edge computing system configured to process exploration data at remote drilling and survey sites through distributed computing capabilities enabling real-time data analysis at exploration locations.

[0297] In some embodiments, the techniques described herein relate to an Al-enabled system, further including an exploration equipment monitoring loT sensor network configured to provide real-time monitoring of exploration equipment and environmental conditions during resource discovery operations.

[0298] In some embodiments, the techniques described herein relate to an Al-enabled system, wherein the exploration equipment monitoring loT sensor network is configured to transmit data through a secure data communication network that implements encrypted data transmission systems and adaptive networking protocols for protecting sensitive geological information.

[0299] In some embodiments, the techniques described herein relate to an Al-enabled system, further including a multi-variable exploration scenario analysis system configured to combine two or more data sources for comprehensive resource assessment through intelligent systems that evaluate exploration scenarios and discovery potential by processing geological, environmental, and market variables simultaneously.

[0300] In some embodiments, the techniques described herein relate to an Al-enabled system, wherein the at least one artificial intelligence model is configured to process geological survey data, seismic measurements, and historical exploration results to produce pattern recognitionAttorney Docket No. 54250-73reports and resource probability assessments.

[0301] In some embodiments, the techniques described herein relate to an Al-enabled system, wherein the digital twin simulation system is configured to provide visualization interfaces that enable interactive access to formation models.

[0302] In some embodiments, the techniques described herein relate to an Al-enabled system, wherein the at least one artificial intelligence model is configured to process seismic data, geological surveys, and satellite imagery to produce resource probability maps and exploration target recommendations.

[0303] In some embodiments, the techniques described herein relate to an Al-enabled system, further including an environmental impact assessment monitoring system configured to evaluate environmental side-effects of resource extraction and utilization through Al systems integrated with regulatory compliance systems for environmental monitoring.

[0304] In some embodiments, the techniques described herein relate to an Al-enabled system, further including a coordinated resource management integration energy edge platform interface configured to integrate resource discovery capabilities within broader Al-based energy edge platforms for coordinated resource management between resource discovery systems and distributed energy resource management capabilities.

[0305] In some embodiments, the techniques described herein relate to an Al-enabled system for integrating discovered energy resources into supply chains, including at least one artificial intelligence model configured to automatically coordinate discovered petroleum deposits, natural gas formations, and rare earth metals into energy supply chains based on resource characteristics and market demands, and a cross-platform information exchange system configured to connect resource exploration data to energy transaction orchestration systems.

[0306] In some embodiments, the techniques described herein relate to an Al-enabled system, wherein the at least one artificial intelligence model is configured to optimize allocation decisions by processing resource discovery reports, supply chain capacity data, and market pricing information to produce resource allocation plans and integration performance metrics.

[0307] In some embodiments, the techniques described herein relate to an Al-enabled system, wherein the at least one artificial intelligence model is configured to automatically integrate discovered resources into raw material supply chains for energy generation, natural material supply chains for energy infrastructure, and materials transaction marketplace supply chains.

[0308] In some embodiments, the techniques described herein relate to an Al-enabled system, further including a resource exploration active operations real-time data feed configured to deliver continuous information flow from resource exploration to energy market orchestration layers based on real-time data transmission algorithms.

[0309] In some embodiments, the techniques described herein relate to an Al-enabled system, further including a multi -resource detection framework configured to generate resource discovery data for petroleum deposits, natural gas formations, and rare earth metals that is provided to the at least one artificial intelligence model for supply chain coordination.

[0310] In some embodiments, the techniques described herein relate to an Al-enabled system,Attorney Docket No. 54250-73further including a resource exploration priorities optimization market data integration system configured to integrate market data to predict resource demand and optimize exploration priorities through analytics systems that steer research funding toward projects based on discovered resource availability.

[0311] In some embodiments, the techniques described herein relate to an Al-enabled system, further including a supply chain downstream process adaptation system configured to connect upstream exploration and discovery processes with downstream processing and utilization operations through dynamic adjustment of processing capabilities based on discovered resource characteristics and market requirements.

[0312] In some embodiments, the techniques described herein relate to an Al-enabled system, wherein the cross-platform information exchange system is configured to provide APIs and data integration layers connecting resource discoveries to energy transaction orchestration systems while maintaining data integrity across different system architectures.

[0313] In some embodiments, the techniques described herein relate to an Al-enabled system, further including a coordinated resource management integration energy edge platform interface configured to integrate resource discovery capabilities within broader Al-based energy edge platforms for coordinated resource management.

[0314] In some embodiments, the techniques described herein relate to an Al-enabled system, further including a geological exploration adaptive data processing pipeline configured to handle geological, seismic, and resource exploration data through intelligent processing systems with automated data extraction, transformation, and loading capabilities.

[0315] In some embodiments, the techniques described herein relate to an Al-enabled system, further including a predictive analytics system configured to provide forecasting models for predicting resource quantities and availability that integrate market data to optimize exploration priorities.

[0316] In some embodiments, the techniques described herein relate to an Al-enabled system, further including an exploration site local data processing edge computing system configured to process exploration data at remote drilling and survey sites through distributed computing capabilities enabling real-time data analysis at exploration locations.

[0317] In some embodiments, the techniques described herein relate to an Al-enabled system, further including an exploration equipment monitoring loT sensor network configured to provide real-time monitoring of exploration equipment and environmental conditions during resource discovery operations and transmit data to the exploration site local data processing edge computing system.

[0318] In some embodiments, the techniques described herein relate to an Al-enabled system, further including a satellite imagery processing system configured to utilize computer vision systems for processing satellite imagery and geological mapping data for resource identification that analyze remote sensing data to identify geological features correlating with underground energy resource deposits.

[0319] In some embodiments, the techniques described herein relate to an Al-enabled system,Attorney Docket No. 54250-73further including a resource exploration pattern recognition system configured to implement AI-powered geological pattern recognition through machine learning models trained on geological survey data for identifying energy resource-rich formations.

[0320] In some embodiments, the techniques described herein relate to an Al-enabled system, further including a digital twin simulation system configured to model underground energy resource formations and provide three-dimensional modeling capabilities that simulate underground deposit characteristics and predict resource availability.

[0321] In some embodiments, the techniques described herein relate to an Al-enabled system, further including an environmental impact assessment monitoring system configured to evaluate environmental side-effects of resource extraction and utilization through Al systems integrated with regulatory compliance systems for environmental monitoring that assess exploration activities for compliance with environmental regulations.

[0322] In some embodiments, the techniques described herein relate to an Al-enabled system, further including a multi-variable exploration scenario analysis system configured to combine two or more data sources for comprehensive resource assessment through intelligent systems that evaluate exploration scenarios and discovery potential by processing geological, environmental, and market variables simultaneously.

[0323] In some embodiments, the techniques described herein relate to an Al-enabled system, wherein the at least one artificial intelligence model is configured to coordinate resource allocation decisions based on real-time exploration data and market demand requirements for raw material supply chains for energy generation and natural material supply chains for energy infrastructure.

[0324] In some embodiments, the techniques described herein relate to an Al-enabled system, further including a resource discovery information secure data communication network configured to transmit sensitive geological and resource discovery information through encrypted data transmission systems and adaptive networking protocols.

[0325] In some embodiments, the techniques described herein relate to an Al-enabled system for sensor fusion in energy infrastructure, including a multi-sensor fusion system configured to generate fused sensor data by combining readings from at least two sensors measuring operational parameters of distributed energy resources and detect anomalies through pattern recognition, and at least one artificial intelligence model configured to analyze fused sensor data from the at least two sensors.

[0326] In some embodiments, the techniques described herein relate to an Al-enabled system, wherein the multi-sensor fusion system includes a classical sensor fusion architecture configured to combine readings from at least two sensors that measure a variable through mathematical fusion algorithms and weighted averaging processors.

[0327] In some embodiments, the techniques described herein relate to an Al-enabled system, wherein the multi-sensor fusion system includes a multivariable analysis module configured to fuse sensor data across different variables including vibration, temperature, and pressure through cross-correlation processors and multivariate statistical engines.Attorney Docket No. 54250-73

[0328] In some embodiments, the techniques described herein relate to an Al-enabled system, wherein the at least one artificial intelligence model includes neural networks configured to leverage the fused sensor data for equipment monitoring and predictive maintenance of distributed energy resources.

[0329] In some embodiments, the techniques described herein relate to an Al-enabled system, wherein the at least one artificial intelligence model includes graph neural networks configured to model relationships between sensors for coordinated sensor data analysis across distributed energy resources.

[0330] In some embodiments, the techniques described herein relate to an Al-enabled system, further including a multi-sensor redundancy system including hardware sensor arrays with at least two physical sensors measuring energy edge participant parameters, software-based sensor validation algorithms, and interpolation processors configured to calculate estimated values based on individual sensor failures.

[0331] In some embodiments, the techniques described herein relate to an Al-enabled system, further including automated interpolation algorithms configured to perform linear, polynomial, or spline interpolation between functioning sensors to maintain continuous data flow to downstream systems.

[0332] In some embodiments, the techniques described herein relate to an Al-enabled system, further including controlled sensor data processing pipelines incorporating sensor data ingestion engines, intelligence service modules, and pattern recognition processors specifically designed for processing the fused sensor data.

[0333] In some embodiments, the techniques described herein relate to an Al-enabled system, further including self-organizing sensor data storage systems including adaptive storage algorithms, content-based indexing systems, and context-aware data organization engines configured to automatically organize the fused sensor data based on patterns, attributes, content, and context.

[0334] In some embodiments, the techniques described herein relate to an Al-enabled system, further including intelligent sensor data layers including extraction processors, transformation engines, loading systems, normalization algorithms, cleansing filters, compression modules, and encoding systems specifically designed for sensor fusion data streams.

[0335] In some embodiments, the techniques described herein relate to an Al-enabled system, further including real-time sensor data processing systems incorporating sensor data stream processing engines, immediate sensor data analysis algorithms, and low-latency sensor data response systems configured to provide instantaneous analysis of sensor inputs from energy grid assets using sensor fusion techniques.

[0336] In some embodiments, the techniques described herein relate to an Al-enabled system, wherein the at least one artificial intelligence model includes anomaly detection engines and multi-sensor fusion processors configured to continuously monitor sensor data streams to identify anomalies through fusion of at least two sensor inputs.

[0337] In some embodiments, the techniques described herein relate to an Al-enabled system,Attorney Docket No. 54250-73further including a context-aware sensor fusion system configured to integrate marketplace data with operational data to provide inputs to the at least one artificial intelligence model for classification, prediction, and optimization for computation-intensive energy industries.

[0338] In some embodiments, the techniques described herein relate to an Al-enabled system, wherein the at least one artificial intelligence model includes computer vision networks for equipment monitoring and predictive analytics models that leverage the fused sensor data for maintenance optimization.

[0339] In some embodiments, the techniques described herein relate to an Al-enabled system, wherein the at least one artificial intelligence model includes deep reinforcement learning models configured to provide real-time control and performance optimization of individual distributed energy resources using the fused sensor data from at least two sources.

[0340] In some embodiments, the techniques described herein relate to an Al-enabled system, further including federated learning capabilities configured to enable sharing of operational insights across sensor networks while maintaining data privacy and supporting distributed sensor fusion learning across at least two nodes.

[0341] In some embodiments, the techniques described herein relate to an Al-enabled system, further including unusual sensor data-related event detection algorithms configured to identify malfunctions, faults, and natural phenomena impacting energy systems through multivariable sensor fusion analysis performed by the at least one artificial intelligence model.

[0342] In some embodiments, the techniques described herein relate to an Al-enabled system, wherein the at least one artificial intelligence model is configured to operate on infrastructure asset sensor data using sensor fusion to produce optimized operating parameters for energy generation, storage, and consumption.

[0343] In some embodiments, the techniques described herein relate to an Al-enabled system, wherein the at least one artificial intelligence model includes self-organizing neural networks configured to provide visualization capabilities for identifying structures in unlabeled sensor data from transactional environments by combining at least two sensor inputs through sensor fusion.

[0344] In some embodiments, the techniques described herein relate to an Al-enabled system, further including a sensor digital twin integration module configured to create accurate virtual representations of physical energy systems by integrating real-time operational data through sensor fusion techniques and providing the virtual representations to the at least one artificial intelligence model.

[0345] In some embodiments, the techniques described herein relate to an Al-enabled system for adaptive energy data routing and communication, including at least one artificial intelligence model configured to optimize routing of sensor data from distributed energy resources based on network conditions and dynamically select communication protocols for sensor networks, and an adaptive sensor data pipeline configured to transmit the sensor data based on the routing optimized by the at least one artificial intelligence model.

[0346] In some embodiments, the techniques described herein relate to an Al-enabled system,Attorney Docket No. 54250-73wherein the adaptive sensor data pipeline includes intelligent transmission managers, network condition analyzers, and dynamic routing algorithms configured to manage sensor data transmission across network nodes with filtering, compression, and routing capabilities based on current network conditions.

[0347] In some embodiments, the techniques described herein relate to an Al-enabled system, further including vector-based sensor data communication protocols incorporating vectorization engines, data structure optimization algorithms, and efficient transmission systems configured to combine at least two sensor inputs into optimized vectorized data structures based on instructions from the at least one artificial intelligence model.

[0348] In some embodiments, the techniques described herein relate to an Al-enabled system, further including a distributed sensor resources integration module configured to gather a data set from solar panels, wind turbines, battery storage systems, and consumption monitoring devices simultaneously using sensor fusion techniques and provide the data set to the at least one artificial intelligence model.

[0349] In some embodiments, the techniques described herein relate to an Al-enabled system, further including APIs and service-oriented architecture interfaces including service orchestration engines, data format translation processors, and integration protocol handlers configured to handle diverse sensor data formats, communication protocols, and service interfaces from various energy system components and sensors.

[0350] In some embodiments, the techniques described herein relate to an Al-enabled system, further including a cross-service sensor optimization framework including computational sensor allocation algorithms, distributed sensor asset coordination engines, and system-wide sensor load balancing processors configured to coordinate at least two sensor and data fusion services based on resource requirements determined by the at least one artificial intelligence model.

[0351] In some embodiments, the techniques described herein relate to an Al-enabled system, further including location-based sensing capabilities incorporating geographic positioning systems, event detection algorithms, and location data fusion processors configured to combine geographic information with operational sensor data to identify location-specific events and conditions.

[0352] In some embodiments, the techniques described herein relate to an Al-enabled system, further including an edge device integration module including local processors, on-device sensor fusion algorithms, and edge networking interfaces configured to perform sensor fusion processing directly at energy asset locations to reduce latency and bandwidth requirements.

[0353] In some embodiments, the techniques described herein relate to an Al-enabled system, further including loT sensor network interfaces including distributed sensing device connectors, network-level sensor fusion processors, and loT communication protocol handlers configured to manage at least two distributed sensors while performing coordinated sensor fusion across the sensor network.

[0354] In some embodiments, the techniques described herein relate to an Al-enabled system, further including grid sensor monitoring systems incorporating interconnected device trackers,Attorney Docket No. 54250-73intelligent equipment analyzers, and multi-point data combination processors configured to track loT devices and intelligent equipment using sensor fusion to combine data from at least two monitoring points and provide the data combined data from the at least two monitoring points to the at least one artificial intelligence model.

[0355] In some embodiments, the techniques described herein relate to an Al-enabled system, further including redundant sensor networks including parallel data collection systems, sensor fusion combination algorithms, and reliability improvement processors configured to provide parallel data collection from energy infrastructure with sensor fusion algorithms to combine redundant measurements.

[0356] In some embodiments, the techniques described herein relate to an Al-enabled system, further including an energy operations data fusion module configured to generate a data set by combining sensor readings with market and environmental data through multivariable sensor fusion techniques and provide the data set to the at least one artificial intelligence model for analysis.

[0357] In some embodiments, the techniques described herein relate to an Al-enabled system, further including smart distributed energy resource sensor integration systems incorporating distributed energy resource performance monitors, performance metric analyzers, and sensor fusion combination processors configured to analyze efficiency, output, reliability, and other performance indicators simultaneously across distributed energy resources.

[0358] In some embodiments, the techniques described herein relate to an Al-enabled system, further including automated sensor discovery systems including computer vision processors, satellite image analyzers, web image content processors, natural language processing engines, and artificial intelligence data processing systems configured to identify energy generation or storage resources using sensor fusion techniques.

[0359] In some embodiments, the techniques described herein relate to an Al-enabled system, further including a sensor digital twin integration module including virtual system model processors, operational data integration engines, and comprehensive system representation generators configured to create accurate virtual representations of physical energy systems by integrating real-time operational data through sensor fusion.

[0360] In some embodiments, the techniques described herein relate to an Al-enabled system, further including an energy ecosystem terminology adaptation interface incorporating terminology translation engines, data format converters, and measurement unit standardization processors configured to enable sensor fusion across systems with different data formats and measurement units for various energy management concepts and standards.

[0361] In some embodiments, the techniques described herein relate to an Al-enabled system, further including a standardized data formats module including data representation processors, compatibility assurance engines, and format standardization systems configured to provide consistent sensor data representation across different devices supporting sensor fusion by ensuring compatibility between different sensor types and manufacturers.

[0362] In some embodiments, the techniques described herein relate to an Al-enabled system,Attorney Docket No. 54250-73further including interoperability protocols including communication standardization engines, diverse sensor type interfaces, and heterogeneous network coordination systems configured to enable seamless communication between diverse sensor types and manufacturers facilitating sensor fusion across heterogeneous sensor networks.

[0363] In some embodiments, the techniques described herein relate to an Al-enabled system, further including user interface systems incorporating data layer configuration processors, algorithm portal managers, data retention rule controllers, resource usage prioritization engines, and data security maintenance systems configured to facilitate user-centric data processing requirements for sensor fusion systems.

[0364] In some embodiments, the techniques described herein relate to an Al-enabled system, wherein the at least one artificial intelligence model is configured to dynamically adjust routing paths based on current network conditions, select optimal protocols for sensor communications, and adapt routing strategies based on radio frequency conditions for wireless sensor networks.

[0365] In some embodiments, the techniques described herein relate to an Al-enabled system for energy system simulation and forecasting, including at least one artificial intelligence model configured to simulate generation, consumption, and storage operations across distributed energy resources and generate predictive forecasts based on historical patterns and real-time operational data, and a digital twin integration interface configured to synchronize the predictive forecasts with digital representations of physical energy assets.

[0366] In some embodiments, the techniques described herein relate to an Al-enabled system, further including a weather-correlated forecasting engine configured to integrate meteorological data with energy generation information to provide day-ahead generation predictions for renewable energy sources.

[0367] In some embodiments, the techniques described herein relate to an Al-enabled system, further including a DER behavioral modeling system configured to model behaviors of mobile and stationary distributed energy resources including electric vehicles, solar photovoltaic systems, wind generation, and fuel cells.

[0368] In some embodiments, the techniques described herein relate to an Al-enabled system, wherein the DER behavioral modeling system is configured to model energy prosumers that function as both energy producers and energy consumers.

[0369] In some embodiments, the techniques described herein relate to an Al-enabled system, further including a real-time data fusion interface configured to process information from loT devices, edge devices, public data resources, and energy-relevant event streams to provide comprehensive operational data to the at least one artificial intelligence model.

[0370] In some embodiments, the techniques described herein relate to an Al-enabled system, further including an edge computing orchestration module configured to coordinate simulation operations across cloud, colocation, on-premises, and edge infrastructure environments based on energy availability and processing requirements.

[0371] In some embodiments, the techniques described herein relate to an Al-enabled system, wherein the at least one artificial intelligence model includes a multi-variable integrationAttorney Docket No. 54250-73framework configured to coordinate integration of generation simulations, consumption simulations, and storage simulations.

[0372] In some embodiments, the techniques described herein relate to an Al-enabled system, wherein the at least one artificial intelligence model includes a generation algorithm simulation module configured to simulate generation capacity forecasts that account for weather dependencies, equipment performance characteristics, and asset operational constraints.

[0373] In some embodiments, the techniques described herein relate to an Al-enabled system, wherein the at least one artificial intelligence model includes a consumption pattern simulation engine configured to process consumption patterns from industrial facilities, data centers, residential prosumers, and mobile energy consumers.

[0374] In some embodiments, the techniques described herein relate to an Al-enabled system, wherein the at least one artificial intelligence model includes a storage operations modeling system configured to simulate charging and discharging cycles, state-of-charge management, and storage system degradation patterns.

[0375] In some embodiments, the techniques described herein relate to an Al-enabled system, further including a scenario analysis and planning engine configured to generate at least two operational scenarios that account for weather variations, demand fluctuations, equipment failures, and market conditions.

[0376] In some embodiments, the techniques described herein relate to an Al-enabled system, further including a predictive decision-making interface configured to generate actionable insights and recommendations based on simulation results from the at least one artificial intelligence model.

[0377] In some embodiments, the techniques described herein relate to an Al-enabled system, further including a contextual simulation environment module configured to analyze energy-related behaviors based on historical patterns, current operational states including market conditions, and anticipated states of entities involved in energy generation, storage, delivery, and consumption.

[0378] In some embodiments, the techniques described herein relate to an Al-enabled system, further including a composite model employment system configured to coordinate Al technologies, digital twin systems, and probabilistic modeling approaches to leverage at least two modeling methodologies concurrently.

[0379] In some embodiments, the techniques described herein relate to an Al-enabled system, further including a 3D visualization and simulation interface configured to present simulation outputs from the at least one artificial intelligence model in three-dimensional digital twin environments.

[0380] In some embodiments, the techniques described herein relate to an Al-enabled system, wherein the at least one artificial intelligence model includes GPU-accelerated computing clusters configured to perform parallel processing of multi-variable energy calculations and realtime optimization scenarios.

[0381] In some embodiments, the techniques described herein relate to an Al-enabled system,Attorney Docket No. 54250-73wherein the at least one artificial intelligence model is configured to incorporate distributed energy resource variability and prosumer behavioral patterns to enhance prediction accuracy.

[0382] In some embodiments, the techniques described herein relate to an Al-enabled system, wherein the edge computing orchestration module is configured to optimize computational resource allocation while maintaining energy efficiency through coordination with the at least one artificial intelligence model for workload prediction.

[0383] In some embodiments, the techniques described herein relate to an Al-enabled system, wherein the weather-correlated forecasting engine is configured to employ solar irradiance calculation algorithms, wind resource assessment tools, and statistical correlation methods that establish relationships between meteorological condition forecasts and renewable energy generation capacity modeling.

[0384] In some embodiments, the techniques described herein relate to an Al-enabled system, further including a continuous learning system configured to compare model-based predictions from the at least one artificial intelligence model to actual operational outcomes and incorporate expert feedback to improve simulation accuracy over time.

[0385] In some embodiments, the techniques described herein relate to an Al-enabled system for probabilistic energy scenario generation, including at least one artificial intelligence model configured to generate at least two operational scenarios that account for uncertainty in energy system operations using probabilistic techniques and random walk algorithms, and a continuous learning system configured to improve accuracy of the at least two operational scenarios based on comparison of predicted outcomes to actual operational results.

[0386] In some embodiments, the techniques described herein relate to an Al-enabled system, further including a probabilistic modeling framework configured to employ Monte Carlo simulations, Bayesian inference, and stochastic processes to generate probability distributions for energy generation capacity, demand patterns, and operational parameters.

[0387] In some embodiments, the techniques described herein relate to an Al-enabled system, wherein the at least one artificial intelligence model includes a random walk algorithm engine configured to implement random walk and random forest algorithms to simulate stochastic energy system behaviors including energy price volatility, demand fluctuations, and generation variability.

[0388] In some embodiments, the techniques described herein relate to an Al-enabled system, further including a trend projection and analysis module configured to analyze historical data patterns and project trends to identify seasonal patterns, growth trends, and cyclical behaviors in energy generation and consumption data.

[0389] In some embodiments, the techniques described herein relate to an Al-enabled system, wherein the at least one artificial intelligence model includes a scenario model generation system configured to create diverse operational scenario models that span different weather conditions, demand patterns, equipment availability, and market conditions.

[0390] In some embodiments, the techniques described herein relate to an Al-enabled system, wherein the scenario model generation system is configured to generate scenario model familiesAttorney Docket No. 54250-73that explore best-case, worst-case, and most-likely operational conditions for comprehensive energy system planning.

[0391] In some embodiments, the techniques described herein relate to an Al-enabled system, further including a financial outcome simulation interface configured to create simulated financial outcomes for different operational scenarios including revenue projections, cost analysis, and economic optimization.

[0392] In some embodiments, the techniques described herein relate to an Al-enabled system, further including an operational outcome modeling system configured to simulate operational consequences of different scenarios including system reliability, asset utilization, and performance metrics.

[0393] In some embodiments, the techniques described herein relate to an Al-enabled system, further including a decision-making support framework configured to provide decision trees, risk assessment matrices, and optimization recommendations based on probabilistic scenario analysis results from the at least one artificial intelligence model.

[0394] In some embodiments, the techniques described herein relate to an Al-enabled system, wherein the continuous learning system includes a prediction-outcome comparison engine configured to measure prediction accuracy across energy generation forecasts, consumption pattern predictions, and storage operation estimates.

[0395] In some embodiments, the techniques described herein relate to an Al-enabled system, wherein the continuous learning system includes an expert feedback integration module configured to incorporate input from energy system operators, grid engineers, and domain specialists regarding observed system behaviors and operational constraints.

[0396] In some embodiments, the techniques described herein relate to an Al-enabled system, wherein the continuous learning system includes a continuous learning algorithm system configured to employ online learning algorithms, reinforcement learning, and adaptive neural networks that update model parameters based on streaming operational data.

[0397] In some embodiments, the techniques described herein relate to an Al-enabled system, wherein the continuous learning system includes a closed-loop optimization framework configured to coordinate feedback collection, model assessment, and parameter adjustment processes to create self-improving energy simulation systems.

[0398] In some embodiments, the techniques described herein relate to an Al-enabled system, wherein the continuous learning system includes a performance feedback interface configured to establish standardized feedback loops that collect performance data from operational systems, sensor networks, and control interfaces.

[0399] In some embodiments, the techniques described herein relate to an Al-enabled system, wherein the continuous learning system includes a model adaptation and refinement engine configured to implement model versioning, parameter adjustment, and architecture modification capabilities based on changing energy system characteristics.

[0400] In some embodiments, the techniques described herein relate to an Al-enabled system, wherein the continuous learning system includes an accuracy maintenance and validation systemAttorney Docket No. 54250-73configured to monitor prediction accuracy trends, identify degradation patterns, and implement corrective measures to preserve long-term forecasting reliability.

[0401] In some embodiments, the techniques described herein relate to an Al-enabled system, further including a real-time data fusion interface configured to process a data set collected from loT devices, edge devices, public data resources, and energy-relevant event streams to generate a result, and provide the result of processing the data set to the at least one artificial intelligence model.

[0402] In some embodiments, the techniques described herein relate to an Al-enabled system, further including a contextual simulation environment module configured to analyze energy-related behaviors based on historical patterns, current operational states including market conditions, and anticipated states of entities involved in energy generation, storage, delivery, and consumption.

[0403] In some embodiments, the techniques described herein relate to an Al-enabled system, further including a composite model employment system configured to coordinate Al technologies, digital twin systems, and probabilistic modeling approaches and provide consolidated analysis results to the at least one artificial intelligence model.

[0404] In some embodiments, the techniques described herein relate to an Al-enabled system, further including an edge computing orchestration module configured to coordinate simulation operations across cloud, colocation, on-premises, and edge infrastructure environments based on energy availability and computational workload requirements determined by the at least one artificial intelligence model.

[0405] In some embodiments, the techniques described herein relate to an Al-enabled system for high-altitude computational processing, including at least one artificial intelligence model configured to analyze computational workload demands and generate task scheduling strategies for executing computational operations at a high-altitude location above a planetary body, at least one computational processing unit positioned at the high-altitude location and configured to execute computational workloads based on the task scheduling strategies, and at least one power collection system configured to collect energy at the high-altitude location and provide power to the at least one computational processing unit.

[0406] In some embodiments, the techniques described herein relate to an Al-enabled system, wherein the high-altitude location includes at least one of a low atmospheric orbit altitude, a high atmospheric altitude, a geosynchronous orbit altitude, or a space altitude outside of an atmosphere of the planetary body.

[0407] In some embodiments, the techniques described herein relate to an Al-enabled system, wherein the at least one power collection system includes solar panels configured to collect solar energy at the high-altitude location.

[0408] In some embodiments, the techniques described herein relate to an Al-enabled system, wherein the at least one power collection system includes a power receiver configured to receive laser-transmitted power from a laser transmitter.

[0409] In some embodiments, the techniques described herein relate to an Al-enabled system,Attorney Docket No. 54250-73wherein the at least one power collection system includes a micronuclear reactor configured to generate power at the high-altitude location.

[0410] In some embodiments, the techniques described herein relate to an Al-enabled system, wherein the at least one computational processing unit includes at least one of compute servers, high-performance computing clusters, or artificial intelligence architectures configured to perform training, testing, or validation on training data.

[0411] In some embodiments, the techniques described herein relate to an Al-enabled system, further including a thermal management system configured to dissipate heat generated by the at least one computational processing unit through radiative cooling into space based on thermal load data analyzed by the at least one artificial intelligence model.

[0412] In some embodiments, the techniques described herein relate to an Al-enabled system, wherein the thermal management system includes at least one of deployable radiator panels, heat pipes, or phase-change thermal management systems configured to operate in vacuum or nearvacuum conditions.

[0413] In some embodiments, the techniques described herein relate to an Al-enabled system, further including a communication system configured to transmit and receive data with clients located on or below a surface of the planetary body based on communication scheduling determined by the at least one artificial intelligence model.

[0414] In some embodiments, the techniques described herein relate to an Al-enabled system, wherein the communication system includes at least one of optical communication systems including laser transceivers or radio frequency communication systems operating across at least two frequency bands.

[0415] In some embodiments, the techniques described herein relate to an Al-enabled system, further including a position control system configured to maintain or adjust an orbital position of the Al-enabled system based on positioning commands generated by the at least one artificial intelligence model.

[0416] In some embodiments, the techniques described herein relate to an Al-enabled system, wherein the position control system includes at least one of rockets, solar sails, or attachment interfaces for connecting to propulsion sources.

[0417] In some embodiments, the techniques described herein relate to an Al-enabled system, further including radiation shielding configured to protect the at least one computational processing unit from ionizing radiation, cosmic rays, or solar particle events.

[0418] In some embodiments, the techniques described herein relate to an Al-enabled system, further including debris protection systems configured to protect the Al-enabled system from micrometeorites or orbital debris.

[0419] In some embodiments, the techniques described herein relate to an Al-enabled system, further including fault-tolerant architectures including at least one of redundant computing nodes, distributed data storage with error correction, or autonomous damage detection and isolation systems configured to enable continued operation based on failure detection by the at least one artificial intelligence model.Attorney Docket No. 54250-73

[0420] In some embodiments, the techniques described herein relate to an Al-enabled system, wherein the at least one artificial intelligence model is configured to schedule computational tasks based on anticipated solar energy availability at different orbital positions.

[0421] In some embodiments, the techniques described herein relate to an Al-enabled system, further including a weather monitoring system configured to monitor high-altitude atmospheric conditions and provide monitoring data to the at least one computational processing unit for processing into weather forecasting or climate change monitoring outputs.

[0422] In some embodiments, the techniques described herein relate to an Al-enabled system, further including modular components including at least one of replaceable computing modules or swappable storage units configured for in-situ servicing at the high-altitude location.

[0423] In some embodiments, the techniques described herein relate to an Al-enabled system, further including at least one compartment configured to accommodate at least one of passengers or robots at the high-altitude location.

[0424] In some embodiments, the techniques described herein relate to an Al-enabled system, wherein the at least one artificial intelligence model is configured to select the high-altitude location based on at least one of security considerations, jurisdictional requirements, or latency requirements for data services.

[0425] In some embodiments, the techniques described herein relate to an Al-enabled system for distributed high-altitude data center coordination, including at least one artificial intelligence model configured to coordinate computational workloads and communication routing between at least two high-altitude data centers positioned at different orbital altitudes and generate workload distribution commands based on computational capacity and orbital geometry, at least one communication relay system associated with each of the at least two high-altitude data centers and configured to transmit data between the at least two high-altitude data centers based on routing decisions from the at least one artificial intelligence model, and at least one computational processing unit associated with each of the at least two high-altitude data centers and configured to execute assigned computational workloads based on the workload distribution commands.

[0426] In some embodiments, the techniques described herein relate to an Al-enabled system, wherein the at least one artificial intelligence model is configured to analyze orbital positions of the at least two high-altitude data centers and generate communication schedules based on changing distances and line-of-sight availability.

[0427] In some embodiments, the techniques described herein relate to an Al-enabled system, wherein the at least one communication relay system includes optical communication systems including laser transceivers configured to transmit data between the at least two high-altitude data centers.

[0428] In some embodiments, the techniques described herein relate to an Al-enabled system, wherein the at least one communication relay system is configured to serve as a relay node receiving data from a first client and retransmitting the data to a second client based on routing decisions from the at least one artificial intelligence model.Attorney Docket No. 54250-73

[0429] In some embodiments, the techniques described herein relate to an Al-enabled system, wherein the at least one artificial intelligence model is configured to predict time -varying latency to different clients based on changing orbital positions of the at least two high-altitude data centers and schedule data transfers based on anticipated orbital geometry.

[0430] In some embodiments, the techniques described herein relate to an Al-enabled system, further including at least one power collection system associated with each of the at least two high-altitude data centers configured to collect energy and provide power availability data to the at least one artificial intelligence model.

[0431] In some embodiments, the techniques described herein relate to an Al-enabled system, wherein the at least one power collection system includes solar panel arrays configured to collect solar energy at a high-altitude location.

[0432] In some embodiments, the techniques described herein relate to an Al-enabled system, wherein at least one of the at least two high-altitude data centers is positioned in geosynchronous orbit to maintain stable distance with respect to a location on a surface of a planetary body.

[0433] In some embodiments, the techniques described herein relate to an Al-enabled system, wherein the at least two high-altitude data centers are positioned at different altitudes to provide tiered latency services based on altitude -dependent signal propagation distances.

[0434] In some embodiments, the techniques described herein relate to an Al-enabled system, wherein the at least one artificial intelligence model is configured to route lower-latency service requests to data centers at lower altitudes and route higher-capacity or longer-term storage requests to data centers at higher altitudes.

[0435] In some embodiments, the techniques described herein relate to an Al-enabled system, further including thermal management systems associated with each of the at least two high-altitude data centers configured to dissipate heat through radiative cooling based on thermal coordination commands from the at least one artificial intelligence model.

[0436] In some embodiments, the techniques described herein relate to an Al-enabled system, wherein the at least one artificial intelligence model is configured to balance computational workloads across the at least two high-altitude data centers based on computational capacity and thermal dissipation capacity at each data center.

[0437] In some embodiments, the techniques described herein relate to an Al-enabled system, further including position control systems associated with each of the at least two high-altitude data centers configured to adjust orbital positions based on positioning commands from the at least one artificial intelligence model.

[0438] In some embodiments, the techniques described herein relate to an Al-enabled system, wherein the at least one artificial intelligence model is configured to generate the positioning commands to maintain desired spacing between the at least two high-altitude data centers for communication efficiency.

[0439] In some embodiments, the techniques described herein relate to an Al-enabled system, wherein the at least one artificial intelligence model is configured to monitor radiation events and redistribute computational workloads among the at least two high-altitude data centers basedAttorney Docket No. 54250-73on radiation exposure levels.

[0440] In some embodiments, the techniques described herein relate to an Al-enabled system, further including fault-tolerant architectures associated with each of the at least two high-altitude data centers including redundant computing nodes and distributed data storage configured to enable continued operation based on autonomous damage detection by the at least one artificial intelligence model.

[0441] In some embodiments, the techniques described herein relate to an Al-enabled system, further including weather monitoring systems associated with at least one of the at least two high-altitude data centers configured to monitor atmospheric conditions and provide weather data for processing by computational resources coordinated by the at least one artificial intelligence model.

[0442] In some embodiments, the techniques described herein relate to an Al-enabled system, further including at least one energy transmission interface configured to transmit energy between the at least two high-altitude data centers based on energy routing commands generated by the at least one artificial intelligence model.

[0443] In some embodiments, the techniques described herein relate to an Al-enabled system, wherein the at least one artificial intelligence model is configured to schedule computational tasks across the at least two high-altitude data centers based on anticipated power availability at different orbital positions of each data center.

[0444] In some embodiments, the techniques described herein relate to an Al-enabled system, further including modular expansion interfaces associated with each of the at least two high-altitude data centers configured to enable attachment of additional computing capacity, storage capacity, or power generation capacity modules based on expansion recommendations from the at least one artificial intelligence model.BRIEF DESCRIPTION OF THE DRAWINGS

[0445] The present disclosure will become more fully understood from the detailed description and the accompanying drawings.

[0446] FIG. 1 is a schematic diagram that presents examples of platforms and main elements according to some embodiments.

[0447] FIGS. 2A and 2B are schematic diagrams that present an introduction of main subsystems of a major ecosystem, according to some embodiments.

[0448] FIG. 3 is a schematic diagram that presents more detail on distributed energy generation systems, according to some embodiments.

[0449] FIG. 4 is a schematic diagram that presents more detail on data resources, according to some embodiments.

[0450] FIG. 5 is a schematic diagram that presents more detail on configured energy edge stakeholders, according to some embodiments.

[0451] FIG. 6 is a schematic diagram that presents more detail on intelligence enablement systems, according to some embodiments.Attorney Docket No. 54250-73

[0452] FIG. 7 is a schematic diagram that presents more detail on Al-based energy orchestration, according to some embodiments.

[0453] FIG. 8 is a schematic diagram that presents more detail on configurable data and intelligence, according to some embodiments.

[0454] FIG. 9 is a schematic diagram that presents a dual-process learning function of a dualprocess artificial neural network, according to some embodiments.

[0455] FIG. 10 is a schematic view of an exemplary embodiment of a quantum computing service according to some embodiments of the present disclosure.

[0456] FIG. 11 illustrates quantum computing service request handling according to some embodiments of the present disclosure.

[0457] FIG. 12 is a diagrammatic view of a thalamus service and how it coordinates within the modules in accordance with the present disclosure.

[0458] FIG. 13 is another diagrammatic view of a thalamus service and how it coordinates within the modules in accordance with the present disclosure.

[0459] FIG. 14 is a diagrammatic view of an energy edge converging technology stack in accordance with the present disclosure.

[0460] FIG. 15 is a diagrammatic view of a set of capabilities of an energy edge convergence technology stack in accordance with the present disclosure.

[0461] FIG. 16 depicts a schematic of a Distributed Energy Resource (DER) platform.

[0462] FIG. 17 depicts a schematic of a configured DER provider.

[0463] FIG. 18 depicts a schematic of DER generator module.

[0464] FIG. 19 depicts a schematic of DER generator system.

[0465] FIG. 20 depicts a schematic of provider service layer.

[0466] FIG. 21 depicts a schematic of a generic DER assist layer.

[0467] FIG. 22 depicts a schematic of a client load.

[0468] FIG. 23 depicts a schematic of a client orchestration layer.

[0469] FIG. 24 depicts a schematic of details of the client assist library of the client orchestration layer.

[0470] FIG. 25 depicts a schematic of DER market orchestration layer.

[0471] FIG. 26 depicts a schematic of a market value analysis system and a DER market assist layer.

[0472] FIG. 27 depicts a schematic of an automated resource orchestration and control system.

[0473] FIG. 28 depicts a schematic of a power evaluation system.

[0474] FIG. 29 depicts a schematic of components and interactions of a data collection architecture involving application of cognitive and machine learning systems to data collection and processing in accordance with the present disclosure.

[0475] FIG. 30 is a schematic view of an example Al convergence system of systems.

[0476] FIG. 31 is a schematic view of an example offering layer.

[0477] FIG. 32 is a schematic view of an example transactions layer.

[0478] FIG. 33 is a schematic view of an example operations layer.Attorney Docket No. 54250-73

[0479] FIG. 34 is a schematic view of an example network layer.

[0480] FIG. 35 is a schematic view of an example data layer.

[0481] FIG. 36 is a schematic view of an example data layer.

[0482] FIG. 37 is a schematic view of an example intelligent data layer architecture.

[0483] FIG. 38 is a schematic view of an example network layer.

[0484] FIG. 39 is a schematic view of an example Al subsystem integrator system.

[0485] FIG. 40 is a schematic view of an example multiplatform attention management system.

[0486] FIG. 41 is a schematic view of an example configured artificial intelligence system.

[0487] FIG. 42 is a schematic view of a KYX system.

[0488] FIG. 43 is a schematic view of a system of models architecture within the intelligence system of the configured artificial intelligence system.

[0489] FIG. 44 is a schematic view of a chipset architectures for systems of models.

[0490] FIG. 45 is an illustration of a matrix for organizing and interconnecting various features of Al agent understanding.

[0491] FIG. 46 is a schematic diagram detailing an example artificial neural network with multiple layers.

[0492] FIG. 47 is a schematic diagram detailing an example of training and inference of an example artificial neural network.

[0493] FIG. 48 is a schematic diagram detailing an example of a determination of attention by a machine learning model.

[0494] FIG. 49 is a schematic diagram of a first transformer model.

[0495] FIG. 50 is a schematic diagram of a second transformer model.

[0496] FIG. 51 is a schematic diagram detailing an example system in which a large language model including includes a retrieval component that provides a RAG capability.

[0497] FIG. 52 is a schematic diagram detailing an example of tool use by an example Al agent

[0498] FIG. 53 is a schematic diagram detailing an example Al agent featuring an agent loop.

[0499] FIG. 54 is a schematic diagram detailing a development of an artificial neural network by reinforcement learning.

[0500] FIG. 55 depicts a schematic of various adaptive data networks.

[0501] FIGS. 56A-56D depict schematics of various configurations of energy suppliers, energy consumers, and underlying connectivity infrastructure.

[0502] FIGS. 57A-57C depict schematics of various power manipulation devices.

[0503] FIG. 58 depicts a schematic of an example power operating load and interconnecting adaptive networks.

[0504] FIG. 59 depicts a simplified schematic of a configured DER provider and interconnecting adaptive networks.

[0505] FIG. 60 depicts a simplified schematic of a DER client and interconnecting adaptive networks.

[0506] FIG. 61 depicts a simplified schematic of an energy edge platform including a DER market orchestration layer, DER clients, and DER providers and interconnecting adaptiveAttorney Docket No. 54250-73networks.

[0507] FIG. 62 depicts a schematic of connections to and automated resource and control orchestration system.

[0508] FIG. 63 depicts an automated resource control system in a power provider.

[0509] FIG. 64 depicts automated resource control systems in an energy edge environment.

[0510] FIG. 65 depicts schematic details of an automated resource orchestration system and communications with regulatory agencies.

[0511] FIG. 66 depicts schematic details of an automated resource orchestration system and interactions with transmission infrastructure.

[0512] FIG. 67 depicts schematic details of an energy edge environment.

[0513] FIG. 68 depicts a workflow for evaluation of a potential new initiative in accordance with embodiments of the present disclosure.

[0514] FIG. 69 depicts a schematic of a market value analysis system for evaluating requests for proposal for additional power according to some embodiments.

[0515] FIG. 70 depicts a schematic of DER provider service layer for evaluating and responding to requests for proposal.

[0516] FIG. 71 depicts a schematic of potions of a client orchestration layer.

[0517] FIG. 72 illustrates an exemplary foundational architecture of an energy edge resource management solution.

[0518] FIG. 73 illustrates a comprehensive data processing and intelligence architecture.

[0519] FIG. 74 illustrates a comprehensive market integration and transaction orchestration architecture.

[0520] FIG. 75 illustrates an example method for managing distributed energy resources.

[0521] FIG. 76 depicts a block diagram of a foundational architecture of core expert and artificial intelligence (ESAI) systems.

[0522] FIG. 77 depicts a block diagram of energy management, orchestration, or control systems.

[0523] FIG. 78 depicts a block diagram of context-focused elements, methods, or systems of the ESAI architecture.

[0524] FIG. 79 depicts a flow diagram of a method of expert system and artificial intelligence actions within a distributed energy network management framework.

[0525] FIG. 80 is a schematic example of an energy transmission orchestration and execution system in accordance with embodiments of the present disclosure.

[0526] FIG. 81 is a schematic example of an energy transmission orchestration and execution system with additional detail regarding transmission orchestration in accordance with embodiments of the present disclosure.

[0527] FIG. 82 is a schematic example of an energy transmission orchestration and execution system with an example of a transmission execution system interaction with multiple interconnections in accordance with embodiments of the present disclosure.

[0528] FIG. 83 depicts a block diagram depicts structural and functional elements of energyAttorney Docket No. 54250-73edge device governance and control systems.

[0529] FIG. 84 depicts components working cooperatively for vector-based edge communications, advanced protocol implementations, and vector processing enhancements to enhance a foundational communication infrastructure.

[0530] FIG. 85 depicts a block diagram depicts components, resources, and services of edgebased systems for decision monitoring.

[0531] FIG. 86 depicts automated regulatory compliance monitoring and comprehensive oversight frameworks for energy edge network participant operations.

[0532] FIG. 87 depicts method elements for transaction governance in energy edge environments.

[0533] FIG. 88 depicts decision consistency enforcement that may ensure uniform governance policy application across distributed energy resources.

[0534] FIG. 89 depicts a block diagram of governance frameworks.

[0535] FIG. 90 depicts a block diagram of a resource extraction governance platform.

[0536] FIG. 91 depicts a block diagram of operations layer asset management compliance.

[0537] FIG. 92 depicts a block diagram featuring governance for an energy edge network across a plurality of affiliated activities.

[0538] FIG. 93 depicts a block diagram featuring a carbon footprint governance system for establishing comprehensive environmental policy enforcement capabilities within an energy edge network.

[0539] FIG. 94 depicts an automated carbon footprint governance method that provides continuous monitoring, projection, and enforcement of carbon emission limits across distributed energy infrastructure operations.

[0540] FIG. 95 depicts a block diagram of a core architecture and digital twin system for resource formation modeling, along with the Al-based resource discovery platform and multiresource detection framework.

[0541] FIG. 96 depicts a block diagram of intelligent detection and analysis capabilities, including Al -powered natural resource pattern recognition, predictive analytics, and the adaptive data pipeline architecture for processing natural resource and exploration data.

[0542] FIG. 97 depicts a block diagram of an energy edge platform integration architecture, cross-platform data exchange systems, and system interfaces and standards, including market-driven resource prioritization and environmental impact assessment integration.

[0543] FIG. 98 depicts a flowchart of a method for natural resource exploration and discovery.

[0544] FIG. 99 depicts a block diagram of a core sensor fusion and data processing architecture.

[0545] FIG. 100 depicts a block diagram of Al and machine learning systems for sensor fusion.

[0546] FIG. 101 depicts a block diagram of sensor system integration and network infrastructure.

[0547] FIG. 102 depicts a flow diagram for a method of sensor and data fusion.

[0548] FIG. 103 depicts a block diagram of a core architecture of contextual simulation and forecasting capabilities of an energy edge network.Attorney Docket No. 54250-73

[0549] FIG. 104 depicts a block diagram of components of a multi-variable energy simulation engine associated with an energy edge network.

[0550] FIG. 105 depicts a block diagram of components of a probabilistic scenario generation engine associated with an energy edge network.

[0551] FIG. 106 depicts a block diagram of components of an iterative model enhancement framework associated with an energy edge network.

[0552] FIG. 107 depicts a flow diagram of a method of contextual simulation and forecasting associated with an energy edge network.DETAILED DESCRIPTION FIG. 1: INTRODUCTION OF PLATFORM AND MAIN ELEMENTS

[0553] In some embodiments, provided herein is an Al-based energy edge platform, referred to herein for convenience in some cases as simply the platform 102, including a set of systems, subsystems, applications, processes, methods, modules, services, layers, devices, components, machines, products, sub-systems, interfaces, connections, and other elements working in coordination to enable intelligent, and in some cases autonomous or semi-autonomous, orchestration and management of power and energy in a variety of ecosystems and environments that include distributed entities (referred to herein in some cases as “distributed energy resources” or “DERs”) and other energy resources and systems that generate, store, consume, and / or transport energy and that include loT, edge and other devices and systems that process data in connection with the DERs and other energy resources and that can be used to inform, analyze, control, optimize, forecast, and otherwise assist in the orchestration of the distributed energy resources and other energy resources.

[0554] By way of example, distributed energy resources (“DERs”) may include (without limitation): wind turbines (including wind turbine farms), solar photovoltaics (PV). flexible and / or floating solar energy systems (including solar energy farms), fuel cells (including natural-gas-fired fuel cells and biomass-fired fuel cells), coal mines, petroleum wells, natural gas wells, modular nuclear reactors, nuclear batteries, modular hydropower systems, microturbines and turbine arrays, reciprocating engines, combustion turbines, cogeneration plants, biomass generators, municipal solid waste incinerators, battery storage energy (including chemical batteries and others), capacitive energy storage, geothermal energy systems, molten salt energy storage, electro-thermal energy storage (ETES), gravity-based storage, compressed fluid energy storage, pumped hydroelectric energy storage (PHES), liquid air energy storage (LAES), coal storage facilities, petroleum storage tanks, natural gas storage tanks, liquefied natural gas (LNG) storage tanks, physical energy storage systems such as flywheels, gravity batteries (e.g., mass suspended in a gravity well), fuel transport vehicles, fuel transport pipelines, wired power transmission systems, wireless power transmission systems, or the like.

[0555] In some embodiments, the platform 102 enables a set of configured stakeholder energy edge solutions 108, with a wide range of functions, applications, capabilities, and uses that may be accomplished, without limitation, by using or orchestrating a set of advanced energyAttorney Docket No. 54250-73resources and systems 104, including DERs and others. The set of configured stakeholder energy edge solutions 108 may integrate, for example, domain-specific stakeholder data, such as proprietary data sets that are generated in connection with enterprise operations, analysis and / or strategy, real-time data from stakeholder assets (such as collected by loT and edge devices located in proximity to the assets and operations of the stakeholder), stakeholder-specific energy resources and systems 104 (such as available energy generation, storage, or distribution systems that may be positioned at stakeholder locations to augment or substitute for an electrical grid), and the like into a solution that meets the energy needs and capabilities of the stakeholder, including baseline, period, and peak energy needs to conduct operations such as large-scale data processing, transportation, production of goods and materials, resource extraction and processing, heating and cooling, and many others.

[0556] In some embodiments, the platform 102 (and / or elements thereof) and / or the set of configured stakeholder energy edge solutions 108 may take data from, provide data to and / or exchange data with a set of data resources for energy edge orchestration 110. The platform 102 obtains information from the set of data resources for the energy edge orchestration 110. These data resources may include datasets, ranging from real-time energy consumption metrics to predictive analytics on future energy demands. By using these resources, the platform 102 is able to make decisions that are both timely and informed. The platform 102 is also equipped to provide data back to the set of data resources for the energy edge orchestration 110. Such data may include feedback on energy optimization strategies, insights derived from Al analyses, and / or even raw data collected from various sensors and nodes within the energy infrastructure. This feedback loop ensures that the data resources remain updated, facilitating more accurate and dynamic energy management. Further, the set of configured stakeholder energy edge solutions 108, tailored to meet the unique needs of various stakeholders, can contribute data to and derive insights from the platform 102. By way of example, a stakeholder solution designed for a solar energy farm may provide real-time data on solar panel efficiency, which the platform 102 can then use to optimize energy distribution. Such data exchange between the platform 102, the set of configured stakeholder energy edge solutions 108, and the set of data resources for energy edge orchestration 110 ensures that optimizations are based on the most updated available data.

[0557] The platform 102 may include, integrate with, exchange data with and / or otherwise link to a set of intelligence enablement systems 112, a set of Al-based energy orchestration, optimization, and automation systems 114 and a set of configurable data and intelligence modules and services 118. The set of intelligence enablement systems 112 serves as the cognitive backbone of the platform 102. The set of intelligence enablement systems 112, utilizing advanced algorithms and computational tools, enable the platform 102 with the requisite intelligence to parse vast datasets, recognize patterns, and make informed decisions. The set of Al -based energy orchestration, optimization, and automation systems 114 ensures that the platform 102 achieves efficiency and adaptability. By orchestrating energy sources, optimizing energy flows, and automating processes, the set of Al-based energy orchestration,Attorney Docket No. 54250-73optimization, and automation systems 114 transform the platform 102 into a dynamic entity, responsive to real-time changes and proactive in its strategies. The set of configurable data and intelligence modules and services 118 provides the platform 102 with flexibility of modularity and customization. Depending on specific use-cases, stakeholders can configure these modules to cater to their unique requirements.

[0558] The set of intelligence enablement systems 112 may include a set of intelligent data layers 130 that manage and process information, a set of distributed ledger and smart contract systems 132 that ensure secure and transparent transactions and data management, a set of adaptive energy digital twin systems 134 that create virtual replicas of physical energy assets for better monitoring and optimization, and / or a set of energy simulation systems 136 that model potential energy scenarios to aid in decision-making. These integrated systems work collectively within the set of intelligence enablement systems 112 to provide a comprehensive solution for advanced energy management.

[0559] The set of Al -based energy orchestration, optimization, and automation systems 114 may include a set of energy generation orchestration systems 138 that manage and coordinate energy production sources, a set of energy consumption orchestration systems 140 that oversee and optimize how energy is used, a set of energy marketplace orchestration systems 146 that facilitate energy trading and transactions, a set of energy delivery orchestration systems 147 that ensure efficient and reliable energy distribution, and a set of energy storage orchestration systems 142 that manage the storage of energy. Together, these systems provide a holistic approach to orchestrating the entire energy lifecycle.

[0560] The set of configurable data and intelligence modules and services 118 may include a set of energy transaction enablement systems 144 that facilitate and streamline energy-related transactions, a set of stakeholder energy digital twins 148 that provide virtual representations of stakeholder-specific energy assets for better monitoring and management, and a set of data integrated microservices 150 that may enable or contribute to enablement of the set of configured stakeholder energy edge solutions 108, ensuring an integrated approach to energy management.

[0561] The platform 102 may include, integrate with, link to, exchange data with, be governed by, take inputs from, and / or provide outputs to one or more artificial intelligence (Al) systems, which may include models, rule -based systems, expert systems, neural networks, deep learning systems, supervised learning systems, robotic process automation systems, natural language processing systems, intelligent agent systems, self-optimizing and self-organizing systems, and others as described throughout this disclosure and in the documents incorporated by reference herein. Except where context specifically indicates otherwise, references to Al, or to one or more examples of Al, should be understood to encompass these various alternative methods and systems; for example, without limitation, an Al system described for enabling any of a wide variety of functions, capabilities and solutions described herein (such as optimization, autonomous operation, prediction, control, orchestration, or the like) should be understood to be capable of implementation by operation on a model or rule set; by training on a training data setAttorney Docket No. 54250-73of human tag, labels, or the like; by training on a training data set of human interactions (e.g., human interactions with software interfaces or hardware systems); by training on a training data set of outcomes; by training on an Al-generated training data set (e.g., where a full training data set is generated by Al from a seed training data set); by supervised learning; by semi-supervised learning; by deep learning; or the like. For any given function or capability that is described herein, neural networks of various types may be used, including any of the types described herein or in the documents incorporated by reference, and, In some embodiments, a hybrid set of neural networks may be selected such that within the set a neural network type that is more favorable for performing each element of a multi-function or multi-capability system or method is implemented. As one example among many, a deep learning, or black box, system may use a gated recurrent neural network for a function like language translation for an intelligent agent, where the underlying mechanisms of Al operation need not be understood as long as outcomes are favorably perceived by users, while a more transparent model or system and a simpler neural network may be used for a system for automated governance, where a greater understanding of how inputs are translated to outputs may be needed to comply with regulations or policies. SUBSYSTEMS AND MODULES OF AI-BASED ENERGY ORCHESTRATION, OPTIMIZATION, AND AUTOMATION SYSTEMS

[0562] The set of Al-based energy orchestration, optimization, and automation systems 114 may include the set of energy generation orchestration systems 138, the set of energy consumption orchestration systems 140, the set of energy storage orchestration systems 142, the set of energy marketplace orchestration systems 146 and the set of energy delivery orchestration systems 147, among others. For example, the set of energy delivery orchestration systems 147 may enable orchestration of the delivery of energy to a point of consumption, such as by fixed transmission lines, wireless energy transmission, delivery of fuel, delivery of stored energy (e.g., chemical or nuclear batteries), or the like, and may involve autonomously optimizing the mix of energy types among the foregoing available resources based on various factors, such as location (e.g., based on distance from the grid), purpose or type of consumption (e.g., whether there is a need for very high peak energy delivery, such as for power-intensive production processes), and the like. Consider a remote industrial unit located far from the main grid, requiring power for its production processes. The set of energy generation orchestration systems 138 may analyze the location and determine that connecting such unit to the main grid may not be feasible. Instead, the set of energy generation orchestration systems 138 may suggest that a combination of wireless energy transmission and delivery of chemical batteries may be most suitable in this case.

[0563] In some embodiments, the platform 102 may employ demand forecasting, including automated forecasting by artificial intelligence or by taking a data stream of forecast information from a third party. Among other things, forecasting demand helps inform site selection and intelligently planned network expansion. In some embodiments, machine learning algorithms may generate multiple forecasts, such as about weather, prices, solar generation, energy demand, and other factors, and analyze how energy assets can best capture or generate value at differentAttorney Docket No. 54250-73times and / or locations.

[0564] In some embodiments, the Al-based energy orchestration, optimization, and automation systems 114 may enable energy pattern optimization, such as by analyzing building or other operational energy usage and seeking to reshape patterns for optimization (e.g., by modeling demand response to various stimuli). By analyzing energy consumption trends, the Al-based energy orchestration, optimization, and automation systems 114 can identify areas of wastage or inefficiency. By way of example, they can evaluate how energy consumption of a building varies during different times of the day or in different seasons. Using this knowledge, the automation systems 114 can then reshape these patterns to achieve optimal energy usage. This may be applied in a commercial office building where the Al-based energy orchestration, optimization, and automation systems 114 may notice that energy consumption spikes during the early afternoon due to the simultaneous use of lighting, heating, and cooling systems. By modeling how the building may respond to certain stimuli, such as optimizing Heating, Ventilation, and Air Conditioning (HVAC) system based on real-time occupancy data, the AI-based energy orchestration, optimization, and automation systems 114 can suggest measures to distribute energy consumption more evenly throughout the day, thereby reducing peak demand and associated costs.

[0565] The Al-based energy orchestration, optimization, and automation systems 114 may be enabled by the set of intelligence enablement systems 112 that provide functions and capabilities that support a range of applications and use cases.

[0566] In some embodiments, the platform 102 may be configured to integrate data from an at least one internal edge device located within an environment e.g. sensors within a building, vehicle, machine, utility) and an at least one external edge device located outside the environment (e.g. sensors on weather monitoring stations broadcasting real-time data, vehicles, etc.). The platform 102 may collect real-time energy intelligence data and provide the real-time energy intelligence data to an intelligence circuit that is trained on the data and outcomes and automatically executes an action to optimize energy management. For example, an edge device connected to a DER may be taken in combination with an edge device from a local weather monitoring station. Local weather data (e.g. cloud cover, temperature, wind, precipitation, etc.) may be correlated with energy output from the DER, and a machine learning model may be trained to utilize variables from the second edge device to anticipate actions related to the environment of the first edge device. By way of further example, a radar signature output by the weather station edge device may be used to action a ramping up or down of energy from the DER.

[0567] In some embodiments, data output from one or more edge devices may be vectorized and / or stored in a distributed database. Capturing energy data from devices may be optimized further through use of vector-based updating of the data in which only changes that impact a model of the consumption information are communicated. The vector may be developed based on the analysis of data from consuming devices described above. A vector for a composite energy consuming system may be a multi-dimensional vector that represents consumption type,Attorney Docket No. 54250-73purpose, device, and the like to form a highly efficient way of communicating complex energy usage environments. By way of example, consider a smart grid system where thousands of home appliances, HVAC systems, and lighting solutions are continuously sending energy consumption data. Instead of sending every minute detail, the system analyzes this data, and based on the consumption patterns, develops a vector. This vector, especially for a composite energy consuming system, may include various parameters like consumption type, the purpose of consumption, the specific device consuming energy, among others.

[0568] In some embodiments, patterns of energy usage may include localized patterns, such as based on work-a-day schedules of consumers. However, patterns of energy usage may be based on a wider range of data, including weather forecast data; energy consumption in areas being currently affected by a weather system for preparing an area predicted to receive the weather system; and the like. Pattern analysis may include not only raw usage, but may include information about consumers (e.g., devices being operated that consume energy) that may impact learnings. By way of example, work-a-day schedules of consumers, which may involve turning off all home appliances during working hours and increasing energy consumption in the evenings, may be a localized pattern which may be recognized and adapted to by the system.

[0569] Demographics and other human-based activity may play a role in energy pattern analysis. In an example, demographics of an area that suggest consumers replace older vehicles with new vehicles more frequently than in other areas may suggest that local energy demand for electric vehicle charging might increase sooner in such areas. When demographics and / or consumer behaviors suggest that consumers in a region tend to replace vehicles with used vehicles, then maintenance of legacy energy sourcing may be indicated as preferred for those areas.Energy Simulation Systems

[0570] In some embodiments, a set of energy simulation systems 136 is provided, such as to develop and evaluate detailed simulations of energy generation, demand response and charge management, including a simulation environment that simulates the outcomes of use of various algorithms that may govern generation across various generations assets, consumption by devices and systems that demand energy, and storage of energy. Data can be used to simulate the interaction of non-controllable loads and optimized charging processes, among other use cases. The simulation environment may provide output to, integrate with, or share data with the set of adaptive energy digital twin systems 134. By way of example, if a city plans to transition to renewable energy sources, the city can use the set of energy simulation systems 136 to simulate various outcomes. This simulation can predict how solar panels may respond to varying weather conditions, how wind turbines may operate during different seasons, or how energy storage solutions may need to be managed during peak demand periods.

[0571] In some embodiments, as more enterprises embrace hybrid infrastmcture, uptime is becoming more complex, requiring backup and failover strategies that span cloud, colocation, on-premises facilities, and edge infrastructure. This may include Al-based algorithms for automatically managing energy for devices and systems in such devices. For example, artificial intelligence may enable autonomous data center cooling and industrial control. In someAttorney Docket No. 54250-73embodiments, distributed energy resources, or DERs 128, may be integrated into or with, for example, Al-driven computing infrastructure, smart Power Distribution Units (PDUs), Uninterrupted Power Supply (UPS) systems, energy-enabled air flow management systems, and HVAC systems, among others. By simulating energy scenarios, the set of energy simulation systems 136 ensures that enterprises, irrespective of their infrastructure model, operate seamlessly and sustainably.SUBSYSTEMS AND MODULES OF INTELLIGENCE ENABLEMENT SYSTEMSIntelligent Data Layers

[0572] The set of intelligence enablement systems 112 may include a set of intelligent data layers 130, such as a set of services (including microservices), APIs, interfaces, modules, applications, programs, and the like which may consume any of the data entities and types described throughout this disclosure and undertake a wide range of processing functions, such as extraction, cleansing, normalization, calculation, transformation, loading, batch processing, streaming, filtering, routing, parsing, converting, pattern recognition, content recognition, object recognition, and others. Through a set of interfaces, a user of the platform 102 may configure the set of intelligent data layers 130 or outputs thereof to meet internal platform needs and / or to enable further configuration, such as for the set of configured stakeholder energy edge solutions 108. The set of intelligent data layers 130, the set of intelligence enablement systems 112 more generally, and / or the configurable data and intelligence modules and services 118 may access data from various sources throughout the platform 102 and, In some embodiments, may operate from the set of shared data resources, which may be contained in a centralized database and / or in a set of distributed databases, or which may consist of a set of distributed or decentralized data sources, such as loT or edge devices that produce energy-relevant event logs or streams. The set of intelligent data layers 130 may be configured for a wide range of energy-relevant tasks, such as prediction / forecasting of energy consumption, generation, storage or distribution parameters (e.g., at the level of individual devices, subsystems, systems, machines, or fleets); optimization of energy generation, storage, distribution or consumption (also at various levels of optimization); automated discovery, configuration and / or execution of energy transactions (including microtransactions and / or larger transactions in spot and futures markets as well as in peer-to-peer groups or single counterparty transactions); monitoring and tracking of parameters and attributes of energy consumption, generation, distribution and / or storage (e.g., baseline levels, volatility, periodic patterns, episodic events, peak levels, and the like); monitoring and tracking of energy-related parameters and attributes (e.g., pollution, carbon production, renewable energy credits, production of waste heat, and others); automated generation of energy-related alerts, recommendations and other content (e.g., messaging to prompt or promote favorable user behavior); and many others.

[0573] In some embodiments, the platform 102 may be configured to analyze a monitored energy data set and generate configuration recommendations for a distributed system to produce and consume energy. The platform 102 may be configured to analyze streams from one or more local power consumption entities and generate recommendations. For example, a manufacturingAttorney Docket No. 54250-73plant may have a set of needs that differ greatly from a hospital campus. As such, the Al-based platform may perform analysis of each of a plurality of energy consumption scenarios and related devices and demands, and recommend types of DERs for providing energy and conditioning energy corresponding to the needs and demands of the local power consumption entities. A hospital may have an ER that has a specific set of demands, such as times when an operating theater is open, or contingent demands based on emergencies. Examples of a monitored energy data set may include one or more of grid-based energy resources and mobile energy resources. Grid-based energy resources may include, for example, fossil fuel-based energy production facilities (coal, oil, natural gas, etc.), renewable energy-based production facilities (solar farms, wind farms, geothermal generators, tidal generators, hydroelectric power facilities, etc.) Mobile energy resources may include, for example, mobile battery installations, mobile fossil fuel-based generators, mobile renewable energy producers, mobile transformers and power conditioning systems, drone-based power delivery / storage systems, vehicle-based power delivery / storage systems, etc.Distributed Ledger and Smart Contract Systems

[0574] The set of intelligence enablement systems 112 may include a smart contract system 132 for handling a set of smart contracts, each of which may optionally operate on a set of blockchain-based distributed ledgers. Each of the smart contracts may operate on data stored in the set of distributed ledgers or blockchains, such as to record energy-related transactional events, such as energy purchases and sales (in spot, forward and peer-to-peer markets, as well as direct counterparty transactions), relevant service charges and the like; transaction relevant energy events, such as consumption, generation, distribution and / or storage events, and other transaction-relevant events often associated with energy, such as carbon production or abatement events, renewable energy credit events, pollution production or abatement events, and the like. The set of smart contracts handled by the smart contract system 132 may consume as a set of inputs any of the data types and entities described throughout this disclosure, undertake a set of calculations (optionally configured in a flow that takes inputs from disparate systems in a multi-step transaction), and provide a set of outputs that enable completion of a transaction, reporting (optionally recorded on a set of distributed ledgers), and the like. The set of energy transaction enablement systems 144 may be enabled or augmented by artificial intelligence, including to autonomously discover, configure, and execute transactions according to a strategy and / or to provide automation or semi-automation of transactions based on training and / or supervision by a set of transaction experts.

[0575] In some embodiments, the smart contract systems 132 may be used by the set of energy transaction enablement systems 144 (described elsewhere in this disclosure) to configure transactional solutions. Each smart contract within the smart contract systems 132 is intricately designed to process data stored within these distributed ledgers or blockchains. The functionality of the smart contracts extends to documenting a variety of energy-associated transactional events. This includes, but is not limited to, recording peer-to-peer energy transactions and even direct transactions between parties. Furthermore, they capture data related to service charges andAttorney Docket No. 54250-73other transaction-relevant energy events, including information on energy consumption, generation, distribution, and storage. For example, an energy grid of a city having integrated renewable energy sources, such as solar and wind, the smart contract systems 132 can autonomously execute contracts that purchase solar energy during peak sunlight hours and wind energy during windy periods. Simultaneously, it records each transaction, the associated service charges, and even the carbon offset achieved by using renewable sources.Adaptive Energy Digital Twin Systems

[0576] Any entity, analytic results, output of artificial intelligence, state, operating condition, or other feature noted throughout this disclosure may, In some embodiments, be presented in a digital twin, such as the set of adaptive energy digital twin systems 134, which is widely applicable, and / or the set of stakeholder energy digital twins 148, which is configured for the needs of a particular stakeholder or stakeholder solution. The set of adaptive energy digital twin systems 134 may, for example, provide a visual or analytic indicator of energy consumption by a set of machines, a group of factories, a fleet of vehicles, or the like; a subset of the same (e.g., to compare energy parameters by each of a set of similar machines to identify out-of-range behavior); and many other aspects. A digital twin may be adaptive, such as to filter, highlight, or otherwise adjust data presented based on real-time conditions, such as changes in energy costs, changes in operating behavior, or the like.

[0577] In some embodiments, the platform 102 may be configured to create, manage, and / or otherwise provide a dynamic digital twin of historical, current, and forecast distributed energy demand for both mobile and fixed entities within a domain based. For example, relatively large companies or organization settings may be modeled via digital twins, such as industrial environments, factory environments, distribution centers, hospital settings, university / college environments, office building settings, mining operations, etc. In a specific example, for a manufacturing facility with numerous machines, assembly lines, and automated systems, the platform 102 can create a digital twin of this environment, capturing every detail of its energy consumption patterns. Such digital twin can provide real-time information about the energy demands of the facility, from the historical energy usage data of each machine to the present consumption rates, and even predictions about future energy needs based on forecasted production schedules. Larger environments may be modeled where the costs can be shifted significantly based on energy adjustments across entire environment. By way of example, in larger environments, where energy consumption is high, even minor adjustments can lead to substantial financial implications. By having a dynamic digital twin, stakeholders can simulate various energy adjustments and analyze their impact. By way of example, in an office building setting, adjusting the operation of the HVAC system based on real-time occupancy data or optimizing lighting based on natural daylight availability can shift the energy costs considerably.

[0578] In some embodiments, the platform 102 may be configured to model government entities via one or more digital twins, such as states, counties, cities, towns, developmental areas, communities, and the like. In an example, for a city, having thousands or hundreds of thousands of residents, businesses, public transport systems, and numerous amenities, the platform 102 canAttorney Docket No. 54250-73create a digital twin of such city, capturing every aspect of its energy consumption. This digital representation may include everything from the lighting in public parks, the HVAC systems in government buildings, to the energy demands of public transport systems. By doing so, the platform 102 offers city administrators a holistic view of the energy footprint of the city, facilitating informed decisions on energy management. The platform 102 can even model larger entities like states or counties, capturing the diverse energy demands of various regions, from urban hubs to rural areas. On the other end, the platform 102 can also represent smaller entities, like towns. By way of example, in a new town which is being developed for industrial use, the platform 102 can model the expected energy demands based on planned industries, ensuring that the energy infrastructure is adequately prepared to meet the demand. In another example, a county planning to transition to renewable energy sources can utilize its digital twin to simulate the impact of integrating solar farms or wind turbines. This simulation can provide insights into potential energy savings, grid stability, and even the environmental benefits of such a transition.

[0579] In some embodiments, the platform 102 may include an Al -based system for updating a digital twin based on set of energy parameters which may include adapting energy consumption data from a physical device for the digital twin based on the set of energy parameters, such as by adjusting a cost incurred for energy consumed based on a dynamic energy marketplace from which the device sources energy. By way of example, consider a device that sources its energy from a dynamic energy marketplace, where the cost of energy fluctuates based on demand, supply, and other market factors. If the device consumes energy at a time when costs are high, the Al-based system can adjust the digital twin to reflect this, ensuring that the virtual representation accurately mirrors the financial implications of real-world energy consumption. The Al-based system may also incorporate energy sourcing preferences of user(s) of the device (optionally as expressed in the device digital twin) when updating the device. By way of example, if a user, through the digital twin of their device, has expressed a preference for green energy, the Al system ensures that this preference is factored into the energy consumption data updates. For a shared device (e.g., e-bike), energy consumed during and / or associated with a user share of the device (while the e-bike is checked out in the account of the user) may be assigned to / across specific energy source(s) based on the user profile. For example, when a user checks out the e-bike on their user account, the energy consumed during their usage can be specifically sourced from their preferred energy source, as detailed in their user profile associated with the user account. Additionally or alternatively, an owner of the device and / or digital twin may identify an allocation of consumed energy to be assigned to each of a plurality of energy sources. By way of example, there may be scenarios where the owner of the device has specific allocations for consumed energy across multiple energy sources. In such cases, the Al system ensures that the digital twin reflects this allocation accurately. For example, an owner may specify that 50% of the energy consumed by a device should be sourced from wind energy and the remaining 50% from hydro energy. The Al system, when updating the digital twin, may ensure that this allocation is accurately represented. Thus, the platform 102, with its Al-based system, provides digital twins which are not just static representations but are dynamic,Attorney Docket No. 54250-73responsive, and tailored to individual preferences and real-world scenarios.

[0580] In some embodiments, the Al-based system for updating a digital twin based on a set of energy parameters may include adapting energy production and / or allocation control for an upcoming time period (e.g., during an upcoming high-demand event and the like) based on the set of energy parameters. This may include relying on an Al-based forecast of energy demand for a future period of time to adjust how an energy sourcing system operates, such as energy parameters that determine how much energy to store versus generate and deliver, for example. By way of example, in a scenario where there is an anticipated high-demand event, perhaps due to a festival, the Al-based system, by analyzing the energy parameters, can predict this surge in demand and adapt the energy production and / or allocation controls accordingly. In another example, based on past data and current trends, the Al-based system may anticipate increased energy demand during the summer months. In addition to Al-based energy demand forecasts, an Al-based system may evaluate macro trends / activity based on the energy parameters. In an example, an Al-based system that updates an energy consumption system may detect pricing patterns that suggest energy costs may sharply increase (e.g., due to a major weather event, or the like), the set of energy parameters may guide the Al-based system to adapt energy consumption and / or storage guidance for at least select consumers (e.g., public systems (e.g., tax -based systems) so as to avoid unnecessary burden on taxpayers). By way of example, if the Al-based system detects patterns suggesting that energy costs may increase due to an upcoming major weather event, it can take preemptive measures. By analyzing the set of energy parameters, the Al-based system may guide certain consumers to adapt their energy consumption or storage patterns, or guide public systems to reduce consumption or increase storage. Thus, the platform 102, with its Al-based system, ensures that energy management is proactive and efficient.

[0581] In some embodiments, the platform 102 may be configured to provide and / or facilitate digital twins of common device types (e.g., same model of e-bike). The digital twins may exchange consumption data across a range of instances of use to develop an understanding of how this common device type consumes energy in different environments, during different times of day, different geographies, demographics of users (including demographics local to a point of use). For example, an e-bike used predominantly in a hilly terrain may exhibit different energy consumption patterns compared to one used in a flat urban setting. The platform 102, by aggregating this data from various digital twins, can identify these patterns and make informed predictions. This can allow digital twins of specific devices (a specific e-bike) to better forecast energy demand leading to, among other things, dynamic recharging profiles. Some devices may be located in an area of high demand that suggests a need for more frequent charging, whereas others may be permitted to sustain a lower average energy charge due to, for example, shorter and less frequent utilization. For example, an e-bike stationed in a busy urban center may be identified to require frequent recharging due to high demand: on the other hand, another e-bike, perhaps stationed in a less frequented area, may operate optimally even without frequent recharging. This can also allow aggregation of demand profiles for a range of geographic areasAttorney Docket No. 54250-73to identify demand, such as recharging needs, available energy and the like. By way of example, in a locality with a high concentration of e-bikes (for example), the platform 102 may suggest staggered recharging schedules to balance the demand and prevent grid overloads. This can lead to management of charging activities for e-bikes, including demand balance of other rechargeable devices in an area.

[0582] In some embodiments, the platform 102 may be configured such that not every physical instance of a device (e.g., a specific model e-bike) needs to have its own permanent digital twin. Most of these types of devices are dormant for significantly longer durations than they are in use (duty cycle is very sparse), so even energy demand for processing to support digital twins of these types of devices can be managed based on a demand profile. An instance of a physical device (or a configured genetic instance) can be activated (can be allocated energy resources) based on predictions of demand. Consider the scenario of a specific model of an e-bike. While these e-bikes may be scattered across various locations and be available for use all the time, their actual usage or “duty cycle” may be infrequent, with the devices lying dormant for extended periods. Understanding this unique characteristic, the platform 102 is configured in a way that instead of maintaining a continuous digital twin for each e-bike, the platform 102 can activate digital twins for these devices based on predicted demand. By way of example, in an urban setting, if the platform 102 predicts a surge in demand for e-bikes during, say, the morning rush hours, it can activate the digital twins for the e-bikes during such time. These digital twins can then facilitate energy management, ensuring that the e-bikes are charged and ready for use. Post the rush hour, these digital twins can be deactivated to conserve processing energy. This demand-driven approach ensures that energy resources for processing the digital twins are optimally utilized.

[0583] In some embodiments, the platform 102 may provide and / or facilitate sharing, exchange, and / or aggregation of energy consumption data provided to digital twins by physical device instances that can be harvested to establish a set of energy demand parameters for predictive energy demand models, and the like. For example, the platform 102 is designed to facilitate the exchange and aggregation of energy consumption data from various physical device instances and channeled to their respective digital twins. By way of example, consider a neighborhood with multiple smart homes, each equipped with multiple smart devices. While each home may have its unique energy consumption paterns, the collective data from all these homes can reveal broader trends. The platform 102, by aggregating this data, may identify patterns like increased energy consumption during holiday seasons or reduced demand during vacation periods. These insights can then inform predictive models, ensuring that energy providers are well-prepared to meet the anticipated demands.

[0584] In some embodiments, the platform 102 may be configured such that energy consumption data provided to digital twins can also facilitate prediction of energy-related demands, such as maintenance of energy providing infrastructure, and the like. For example, a need for addressing waste from energy production can be better predicted based on not only consumption, but supply sourcing that can be available to digital twins. In other words, not onlyAttorney Docket No. 54250-73does a physical device consume energy, but it must also be supplied with (or must generate its own) energy. Energy supply and / or sourcing can be used by digital twins to indicate times / regions / specific sources of energy production for support (waste removal, refurbishment, etc.). By way of example, if a local energy production facility predominantly relies on nonrenewable sources, the associated waste generation would be higher. The digital twin, by predicting this, can ensure that adequate waste management measures are in place. Further, a digital twin of a local energy production facility can utilize predicted demand from energy consumption digital twins to address not only production, but up-the-chain sourcing. For example, if a predicted demand for (again using e-bikes as the example) e-bike utilization for upcoming event(s) (graduation, new student day, etc.) can be forecasted along with, for example, availability of solar produced energy expectations, local energy supply depots can source up-chain energy only if needed and / or as needed. By way of example, if the solar energy predictions are favorable, the depots can rely predominantly on solar energy, otherwise the depots can source energy from up-the-chain energy providers to meet the demand.

[0585] In some embodiments, the platform 102 may include a set of configurable data and intelligence modules and services 118. These may include a set of energy transaction enablement systems 144, a set of stakeholder energy digital twins 148, a set of data integrated microservices 150, and others. Each module or service (optionally configured in a microservices architecture) may exchange data with the various data resources in order to provide a relevant output, such as to support a set of internal functions or capabilities of the platform 102 and / or to support a set of functions or capabilities of one or more of the set of configured stakeholder energy edge solutions 108. As one example among many, a service may be configured to take event data from an loT device that has cameras or sensors that monitor a generator and integrate it with weather data from public data resources 162 to provide a weather-correlated timeline of energy generation data for the generator, which in turn may be consumed by a set of configured stakeholder energy edge solutions 108, such as to assist with forecasting day-ahead energy generation by the generator based on a day-ahead weather forecast. A wide range of such configured data and intelligence modules and services 118 may be enabled by the platform 102, representing, for example, various outputs that consist of the fusion or combination of the wide range of energy edge data sources handled by the platform, higher-level analytic outputs resulting from expert analysis of data, forecasts and predictions based on patterns of data, automation and control outputs, and many others.

[0586] In some embodiments, the platform 102 may be configured such that energy consumption devices and / or systems (e.g., a set of energy consuming devices in a household) may arbitrate locally for access to energy sources, such as main line energy, first level stored energy (e.g., at a device), local stored energy (e.g., a local battery that can source energy to a plurality of devices), and the like. Also, devices may consume energy for a range of purposes, consumption, storage, balancing sourcing, acting as a proxy for other devices, and the like. Yet further, energy consuming devices may be configured / configurable to use a plurality of energy types, such as electric grid, solar, geothermal, fossil fuel (combustion engine), hydrogen, and theAttorney Docket No. 54250-73like. Also, within an energy consumption system (set of devices as noted above) energy consumption may span a range of energy sources (e.g., hydrogen for cooking, solar for energy storage, waste energy recovery, and the like). By way of example, consider a household equipped with multiple energy-consuming devices, each with its unique energy demands and preferences. The platform 102 can facilitate a dynamic environment where these devices can locally arbitrate for access to various energy sources based on their immediate needs and available resources. By way of example, on a sunny day, solar panels in a house may be generating excess energy, in such case, the platform 102 may utilize energy primarily from the solar panels, reducing energy consumption from the grid.

[0587] In some embodiments, the platform 102 may capture the energy consumption information from / via the edge devices and develop a data set that represents a plurality of perspectives regarding consumed energy. Edge devices that may communicate (e.g., locally or in close proximity) with a range of energy consuming devices and device types may collect data about the devices, including, for example, what sources can the devices consume, what source have the devices consumed, purpose / use of the consumed energy, and the like. Further examples may include whether it appear as if the devices performing any sort of optimization, such as utilizing local storage during high energy cost periods (including high transmission costs which might be measured based on efficiencies of the delivery and the like), consuming energy for replenishing storage during off-peak times, and / or utilizing low cost sources (e.g., solar) when readily available. A wide range of analytics may be generated, captured, used in an energy management system, and the like. By way of example, consider a smart plug connected to a refrigerator which can provide insights into energy consumption patterns thereof, revealing details like its preference for utilizing local storage during high energy cost periods. By aggregating this data from various edge devices, the platform 102 can identify patterns, predict future energy demands, and optimize energy consumption across devices.Energy Transaction Enablement Systems

[0588] Configurable data and intelligence modules and services 118 may include a set of energy transaction enablement systems 144. The set of energy transaction enablement systems 144 may include a set of smart contracts, which may operate on data stored in a set of distributed ledgers or blockchains, such as to record energy-related transactional events, such as energy purchases and sales (in spot, forward and peer-to-peer markets, as well as direct counterparty transactions) and relevant service charges; transaction relevant energy events, such as consumption, generation, distribution and / or storage events, and other transaction-relevant events often associated with energy, such as carbon production or abatement events, renewable energy credit events, pollution production or abatement events, and the like. The set of smart contracts may consume as a set of inputs any of the data types and entities described throughout this disclosure, undertake a set of calculations (optionally configured in a flow that takes inputs from disparate systems in a multi-step transaction), and provide a set of outputs that enable completion of a transaction, reporting (optionally recorded on a set of distributed ledgers), and the like. The set of energy transaction enablement systems 144 may be enabled or augmented by artificialAttorney Docket No. 54250-73intelligence, including to autonomously discover, configure, and execute transactions according to a strategy and / or to provide automation or semi-automation of transactions based on training and / or supervision by a set of transaction experts. Autonomy and / or automation (supervised or semi-supervised) may be enabled by robotic process automation, such as by training a set of intelligent agents on transactional discovery, configuration, or execution interactions of a set of transactional experts with transaction-enabling systems (such as software systems used to configure and execute energy trading activities).

[0589] As energy is increasingly produced and consumed in local, decentralized markets, the energy market is likely to follow patterns of other peer-to-peer or shared economy markets, such as ride sharing, apartment sharing and used goods markets. Technology enables the bypassing of top-down or centralized energy supply and enables operators to create platforms that can manage and monetize spare capacity, such as through the leasing and trading of assets and outputs.

[0590] As more distributed or peer-to-peer transactive energy markets develop, the platform 102 may include systems or link to, integrate with, or enable other platforms that facilitate P2P trading, wholesale contracts, renewable energy certificate (REC) tracking, and broader distributed energy provisioning, payment management and other transaction elements. In some embodiments, the foregoing may use blockchain, distributed ledger and / or smart contract systems 132. By way of example, a homeowner with excess solar energy may decide to sell this surplus energy. This transaction gets securely recorded on the blockchain.

[0591] In some embodiments, with increased transparency, choice, and flexibility, consumers will be able to participate actively in energy markets, by generating, storing, and selling, as well as consuming electricity. By way of example, a local community may decide to capitalize on its collective solar energy generation. The platform 102 enables homes with solar panels to trade their excess energy with those without, ensuring that the entire community benefits.

[0592] In some embodiments, transactional elements may be configured by a set of energy transaction enablement systems 144 to optimize energy generation, storage, or consumption, such as utility time of use charges. Shifting energy demand away from high-priced time periods with loT-based platforms that can identify periods where energy costs are the least expensive. By way of example, in regions where utility charges vary based on the time of use, the platform 102 can shift energy demand to periods when energy is cheaper. In an example, smart home devices, linked to the platform 102, can identify periods when energy costs are lowest and adjust operations, ensuring efficient and cost-effective energy consumption.Stakeholder Energy Digital Twins

[0593] The configurable data and intelligence modules and services 118 may include a set of stakeholder energy digital twins 148, which may, In some embodiments, include set of digital twins that are configured to represent a set of stakeholder entities that are relevant to energy, including stakeholder-owned and stakeholder-operated energy generation resources, energy distribution resources, and / or energy distribution resources (including representing them by type, such as indicating renewable energy systems, carbon-producing systems, and others):Attorney Docket No. 54250-73stakeholder information technology and networking infrastructure entities (e.g., edge and loT devices and systems, networking systems, data centers, cloud data systems, on premises information technology systems, and the like); energy-intensive stakeholder production facilities, such as machines and systems used in manufacturing; stakeholder transportation systems; market conditions (e.g., relating to current and forward market pricing for energy, for the supply chain of the stakeholder, for the stakeholders product and services, and the like), and others. The set of stakeholder energy digital twins 148 may provide real-time information, such as provided sensor data from loT and edge devices, event logs, and other information streams, about status, operating conditions, and the like, particularly relating to energy consumption, generation, storage, and or distribution.

[0594] The set of stakeholder energy digital twins 148 may provide a visual, real-time view of the impact of energy on all aspects of an enterprise. A digital twin may be role-based, such as providing visual and analytic indicators that are suitable for the role of the user, such as financial reporting information for a Chief Financial Officer (CFO); operating parameter information for a power plant manager; and energy market information for an energy trader. A CFO, by way of example, may need a visual representation highlighting the financial cost of energy consumption, like how shifting operations to off-peak hours impacts the energy cost. In contrast, a power plant manager may be more interested in operational parameters, like the efficiency of the energy generation resources. An energy trader, on the other hand, may want insights into the energy market, like tracking prices. Thus, by offering insights tailored to individual roles, the set of stakeholder energy digital twins 148 ensures that different stakeholders have the relevant information they need to make informed decisions.Data Integrated Microservices

[0595] The configurable data and intelligence modules and services 118 may include a set of data integrated microservices 150, such as organized in a service-oriented architecture, such that various microservices can be grouped in series, in parallel, or in more complex flows to create higher-level, more complex services that each provide a defined set of outputs by processing a defined set of outputs, such as to enable a set of configured stakeholder energy edge solutions 108 or to facilitate Al -based orchestration, optimization and / or automation systems 114. The configurable data and intelligence modules and services 118 may, without limitation, be configured from various functions and capabilities of the set of intelligent data layers 130, which in turn operate on various data resources for energy edge orchestration 110 and / or internal event logs, outputs, data streams and the like of the platform 102.FIGS.2A-8: ENERGY EDGE ECOSYSTEMDATA RESOURCES FOR ENERGY EDGE ORCHESTRATION

[0596] Referring to FIG. 2A, the data resources for energy edge orchestration 110 may include a set of edge and loT networking systems 160, public data resources 162, and / or a set of enterprise data resources 168, which in embodiments may use or be enabled by an adaptive energy data pipeline 164 that automatically handles data processing, filtering, compression, storage, routing, transport, error correction, security, extraction, transformation, loading, normalization, cleansingAttorney Docket No. 54250-73and / or other data handling capabilities involved in the transport of data over a network or communication system. This may include adapting one or more of these aspects of data handling based on data content (e.g., by packet inspection or other mechanisms for understanding the same), based on network conditions (e.g., congestion, delays / latency, packet loss, error rates, cost of transport, quality of service (QoS), or the like), based on context of usage (e.g., based on user, system, use case, application, or the like, including based on prioritization of the same), based on market factors (e.g., price or cost factors), based on user configuration, or other factors, as well as based on various combinations of the same. For example, among many others, a leastcost route may be automatically selected for data that relates to management of a low-priority use of energy, such as heating a swimming pool, while a fastest or highest-QoS route may be selected for data that supports a prioritized use or energy, such as support of critical healthcare infrastructure.

[0597] Referring to FIG. 2B. the platform 102 and orchestration may include, integrate, link to, integrate with, use, create, or otherwise handle, a wide range of data resources for the advanced energy resources and systems 104, the set of configured stakeholder energy edge solutions 108, and / or the energy edge orchestration 110. In some embodiments, elements of the advanced energy resources and systems 104, the set of configured stakeholder energy edge solutions 108, and / or the energy edge orchestration 110 may be the same as, similar to, or different from corresponding elements shown in Figure 1. The data resources may include separate databases, distributed databases, and / or federated data resources, among many others.Edge and loT Networking Systems

[0598] A wide range of energy-related data may be collected and processed (including by artificial intelligence services and other capabilities), and control instructions may be handled, by a set of edge and loT networking systems 160, such as ones integrated into devices, components or systems, ones located in loT devices and systems, ones located in edge devices and systems, or the like, such as where the foregoing are located in or around energy-related entities, such as ones used by consumers or enterprises, such as ones involved in energy generation, storage, delivery or use. These include any of the wide range of software, data and networking systems described herein.Public Data Resources

[0599] In some embodiments, the platform 102 may track public data resources 162, such as weather data. Weather conditions can impact energy use, particularly as they relate to HVAC systems. Collecting, compiling, and analyzing weather data in connection with other building information allows building managers to be proactive about HVAC energy consumption. The public data resources 162 may include satellite data, demographic and psychographic data, population data, census data, market data, website data, ecommerce data, and many other types.Enterprise Data Resources

[0600] A set of enterprise data resources 168 may include a wide range of enterprise resources, such as enterprise resource planning data, sales and marketing data, financial planning data, accounting data, tax data, customer relationship management data, demand planning data,Attorney Docket No. 54250-73supply chain data, procurement data, pricing data, customer data, product data, operating data, and many others.ADVANCED ENERGY RESOURCES AND SYSTEMS

[0601] In some embodiments, the advanced energy resources and systems 104 may include distributed energy resources, or DERs 128. More decentralized energy resources will mean that more individuals, networked groups, and energy communities will be capable of generating and sharing their own energy and coordinating systems to achieve ultimate efficacy. The DER 128 may be a small- or medium-scale unit of power generation and / or storage that operates locally and may be connected to a larger power grid at the distribution level. For example, the DERs 128 may be either connected to the local electric power grid or isolated from the grid in standalone applications.Transformed Energy Infrastructure

[0602] The advanced energy resources and systems 104 orchestrated by the platform 102 may include a set of transformed energy infrastructure systems 120. The energy edge will involve increasing digitalization of generation, transmission, substation, and distribution assets, which in turn will shape the operations, maintenance, and expansion of legacy grid infrastructure. In some embodiments, a set of transformed energy infrastructure systems 120 may be integrated with or linked to the platform 102. The transition to improved infrastructure may include moving from SCADA systems and other existing control, automation, and monitoring systems to loT platforms with advanced capabilities.

[0603] In some embodiments, new assets added to or coordinated with the grid (e.g., DERs 128) may be compatible with existing infrastructure to maintain voltage, frequency, and phase synchronization. By way of example, consider a city that is incorporating renewable energy sources like wind turbines and solar panels (DERs 128) into its existing power grid. These new assets need to integrate with the older infrastructure to ensure consistent power delivery. This compatibility ensures that even as the city transitions to greener energy sources, residents experience no fluctuations in voltage, frequency, or phase synchronization, ensuring a stable power supply.

[0604] Any improvements to legacy grid assets, new grid-connected equipment, and supporting systems may, In some embodiments, comply with regulatory standards from NERC, FERC, NIST, and other relevant authorities; positively impact the reliability of the grid; reduce the susceptibility of the grid to cyberattacks and other security threats; increase the ability of the grid to adapt to extensive bi-directional flow of energy (i.e., DER proliferation); and offer interoperability with technologies that improve the efficiency of the grid (i.e., by providing and promoting demand response, reducing grid congestion, etc.).

[0605] Digitalization of legacy grid assets may relate to assets used for generation, transmission, storage, distribution or the like, including power stations, substations, transmission wires, and others.

[0606] In some embodiments, in order to maintain and improve existing energy infrastructure, the platform 102 may include various capabilities, including fully integrated predictiveAttorney Docket No. 54250-73maintenance across utility -owned assets (i.e., generation, transmission, substations, and distribution); smart (Al I ML-based) outage detection and response; and / or smart (Al / ML-based) load forecasting, including optional integration of the DERs 128 with the existing grid. By way of example, consider a scenario where a utility company has a network of power generation and distribution assets, some of which are decades old. To ensure the longevity and efficiency of these assets, the platform 102 can offer predictive maintenance, alerting the utility company about potential issues before they become critical.

[0607] In some embodiments, power grid maintenance may be provided. With proactive maintenance, utilities can accurately detect defects and reduce unplanned outages to better serve customers. Al systems, deployed with loT and / or edge computing, can help monitor energy assets and reduce maintenance costs. By way of example, if a transmission line shows signs of wear and tear, the platform 102 can alert the utility company for timely repair. This proactive approach not only reduces unplanned outages but also reduce maintenance costs, leading to a more efficient and cost-effective power grid.Digitized Resources

[0608] In some embodiments, the platform 102 may take advantage of the digital transformation of a wide range of digitized resources. Machines are becoming smarter, and software intelligence is being embedded into every aspect of a business, helping drive new levels of operational efficiency and innovation. Also, digital transformation is ongoing, involving increasing presence of smart devices and systems that are capable of data processing and communication, nearly ubiquitous sensors in edge, loT and other devices, and generation of large, dense streams of data, all of which provide opportunities for increased intelligence, automation, optimization, and agility, as information flows continuously between the physical and digital world. Such devices and systems demand large amounts of energy. Data centers, for example, consume massive amounts of energy, and edge and loT devices may be deployed in off-grid environments that require alternative forms of generation, storage, or mobility of energy. In some embodiments, a set of digitized resources may be integrated, accessed, or used for optimization of energy for compute, storage, and other resources in data centers and at the edge, among other places. In some embodiments, as more and more devices are embedded with sensors and controls, information can flow continuously between the physical and digital worlds as machines ‘talk’ to each other. Products can be tracked from source to customer, or while they are in use, enabling fast responses to internal and external changes. Those tasked with managing or regulating such systems can gain detailed data from these devices to optimize the operation of the entire process. This trend turns big data into smart data, enabling significant cost- and process efficiencies.

[0609] In some embodiments, advances in digital technologies enable a level of monitoring and operational performance that was not previously possible. Thanks to sensors and other smart assets, a service provider can collect a wide range of data across multiple parameters, monitoring in real-time, 24 hours a day.

[0610] In some embodiments, the DERs 128 will be integrated into computational networks andAttorney Docket No. 54250-73infrastructure devices and systems, augmenting the existing power grid and serving to decrease costs and improve reliability. For example, the platform 102 by integrating DERs 128, such as localized solar farms or wind turbines, into a city infrastructure can significantly augment the existing power grid. By way of example, during peak demand times, rather than solely relying on traditional power plants, the platform 102 can enable energy management system of the city to utilize localized energy sources, which may, in turn, reduce the strain on the main grid and can also lead to substantial cost savings.Mobile Energy Resources

[0611] In some embodiments, DERs may be integrated into mobile energy resources 124, such as electric vehicles (EVs) and their charging networks / infrastructure, thereby augmenting the existing power grid and serving to decrease costs and improve reliability. Given the rise of EVs (of all types) charging infrastructure and vehicle charging plans will need to be optimized to match supply and demand. Also, growing electricity demand and development of EV infrastructure will require optimization using edge and other related technologies such as loT. Electric vehicle charging may be integrated into decentralized infrastructure and may even be used as the DER 128 by adding to the grid, such as through two-way charging stations, or by powering another system locally. Vehicle power electronic systems and batteries can benefit the power grid by providing system and grid services. Excess energy can be stored in the vehicles as needed and discharged when required. This flexibility option not only avoids expensive load peaks during times of short-term, high-energy demand but also increases the share of renewable energy use.

[0612] In some embodiments, in order to universally integrate electric vehicles and charging infrastructure into a distribution network, coordination with various other standardized communication protocols is needed. The platform 102 may include, integrate and / or link to a set of communication protocols that enable management, provisioning, governance, control or the like of energy edge devices and systems using such protocols. Herein, the platform 102 can serve as a central hub, integrating various protocols, ensuring that when an EV docks at a charging station, the communication between the vehicle, the station, and the grid is smooth, efficient, and coordinated.DISTRIBUTED ENERGY GENERATION SYSTEMS

[0613] Referring to FIG. 3, a distributed energy generation systems 302 may include wind turbines, solar photovoltaics (PV), flexible and / or floating solar systems, fuel cells, modular nuclear reactors, nuclear batteries, modular hydropower systems, microturbines and turbine arrays, reciprocating engines, combustion turbines, and cogeneration plants, among others. The distributed energy storage systems 304 may include battery storage energy (including chemical batteries and others), molten salt energy storage, electro-thermal energy storage (ETES), gravity-based storage, compressed fluid energy storage, pumped hydroelectric energy storage (PHES), and liquid air energy storage (LAES), among others. The distributed energy storage systems 304 may be managed by the platform 102. In some embodiments, the distributed energy storage systems 304 may be portable, such that units of energy may be transported to points ofAttorney Docket No. 54250-73use, including points of use that are not connected to the conventional grid or ones where the conventional grid does not fully satisfy demand (e.g., where greater peak power, more reliable continuous power, or other capabilities are needed). Management may include the integration, coordination, and maximizing of return-on-investment (ROI) on distributed energy resources (DERs), while providing reliability and flexibility for energy needs.

[0614] In some embodiments, the DERs 128 may use various distributed energy delivery methods and systems 308 having various energy delivery capabilities, including transmission lines (e.g., conventional grid and building infrastructure), wireless energy transmission (including by coupled, resonant transfer between high-Q resonators, near-field energy transfer and other methods), transportation of fluids, batteries, fuel cells, small nuclear systems, and the like), and others.

[0615] The mobile energy resources 124 include a wide range of resources for generation, storage, or delivery of energy at various scales; accordingly, the mobile energy resources 124 may comprise a subcategory of the DERs 128 that have attributes of mobility, such as where the mobile energy resources 124 are integrated into a vehicle 310 (e.g., an electric vehicle, hybrid electric vehicle, hydrogen fuel cell vehicle, or the like, and in embodiments including a set of autonomous vehicles, which may be unmanned autonomous vehicles (UAVs), drones, or the like); where resources are integrated into or used by a mobile electronic device 312, or other mobile system; where the mobile energy resources 124 are portable resources 314 (including where they are removable and replaceable from a vehicle or other system), and the like. As the mobile energy resources 124 and supporting infrastructure (e.g., charging stations) scale in capacity and availability, orchestration of the mobile energy resources 124 and other DERs 128, optionally in coordination with available grid resources, takes on increased importance.

[0616] Resources involved in generation, storage, and transmission of energy are increasingly undergoing digital transformation. These digitized resources 122 may include smart resources 318 (such as smart devices (e.g., thermostats), smart home devices (e.g., speakers), smart buildings, smart wearable devices and many others that are enabled with processors, network connectivity, intelligent agents, and other onboard intelligence features) where intelligence features of the smart resources 318 can be used for energy orchestration, optimization, autonomy, control or the like and / or used to supply data for artificial intelligence and analytics in connection with the foregoing. The digitized resources 122 may also include loT- and edge-digitized resources 320, where sensors or other data collectors (such as data collectors that monitor event logs, network packets, network traffic patterns, networked device location patterns, or other available data) provide additional energy-related intelligence, such as in connection with energy generation, storage, transmission or consumption by legacy infrastmcture systems and devices ranging from large scale generators and transformers to consumer or business devices, appliances, and other systems that are in proximity to a set of loT or edge devices that can monitor the same. Thus, loT and edge device can provide digital information about energy states and flows for such devices and systems whether or not the devices and systems have onboard intelligence features; for example, among many others, anAttorney Docket No. 54250-73loT device can deploy a current sensor on a power line to an appliance to detect utilization patterns, or an edge networking device can detect whether another device or system connected to the device is in use (and in what state) by monitoring network traffic from the other device. The digitized resources 122 may also include cloud-aggregated resources 322 about energy generation, storage, transmission, or use, such as by aggregating data across a fleet of similar resources that are owned or operated by an enterprise, that are used in connection with a defined workflow or activity, or the like. The cloud-aggregated resources 322 may consume data from the various data resources, from crowdsourcing, from sensor data collection, from edge device data collection, and many other sources.

[0617] In some embodiments, the digitized resources 122 may be used for a wide range of uses that involve or benefit from real time information about the attributes, states, or flows of energy generation, storage, transmission, or consumption, including to enable digital twins, such as a set of adaptive energy digital twin systems 134 and / or the set of stakeholder energy digital twins 148 and for the set of configured stakeholder energy edge solutions 108. By way of example, a digital twin of public transport system in a city can predict energy needs based on commuter patterns, adjusting the operation of electric buses accordingly. Similarly, digital twins can be employed in various sectors, such as manufacturing units monitoring machinery energy consumption. Integration of the platform 102 with these digital twins ensures that energy is always used optimally, adjusting to the real-time needs of the corresponding system.

[0618] Energy generation, storage, and consumption, particularly involving green or renewable energy, have been the subject of intensive research and development in recent decades, yielding higher peak power generation capacity, increases in storage capacity, reductions in size and weight, improvements in intelligence and autonomy, and many others. The advanced energy resources and systems 104 may include a wide range of advanced energy infrastructure systems and devices that result from combinations of features and capabilities. In some embodiments, flexible hybrid energy systems 324 may be provided that is adaptable to meet varying energy consumption requirements, such as ones that can provide more than one kind of energy (e.g., solar or wind power) to meet baseline requirements of an off-grid operation, along with a nuclear battery to satisfy much higher peak power requirements, such as for temporary, resource intensive activities, such as operating a drill in a mine or running a large factory machine on a periodic basis. A wide variety of flexible hybrid energy systems 324 are contemplated herein, including ones that are configured for modular interconnection with various types of localized production infrastructure as described elsewhere herein. In some embodiments, the advanced energy resources and systems 104 may include advanced energy generation systems that draw power from fluid flows, such as portable turbine arrays 328 that can be transported to points of consumption that are in proximity to wind or water flows to substitute for or augment grid resources. The advanced energy resources and systems 104 may also include modular nuclear systems 330, including ones that are configured to use a nuclear battery and ones that are configured with mechanical, electrical and data interfaces to work with various consumption systems, including vehicles, localized production systems (as described elsewhere herein), smartAttorney Docket No. 54250-73buildings, and many others. The modular nuclear systems 330 may include SMRs and other reactor types. The advanced energy resources and systems 104 may include advanced storage systems 332, including advanced batteries and fuel cells, including batteries with onboard intelligence for autonomous management, batteries with network connectivity for remote management, batteries with alternative chemistry (including green chemistry, such as nickel zinc), batteries made from alternative materials or structures (e.g., diamond batteries), batteries that incorporate generation capacity (e.g., nuclear batteries), advanced fuel cells (e.g., cathode layer fuels cells, alkaline fuel cells, polymer electrolyte fuel cells, solid oxide fuel cells, and many others).ENERGY EDGE DATA RESOURCES

[0619] Referring to FIG. 4, the data resources for energy edge orchestration 110 may include a wide range of public data sets, as well as private or proprietary data sets of an enterprise or individual. This may include data sets generated by or passed through the edge and loT networking systems 160, such as sensor data 402 (e.g., from sensors integrated into or placed on machines or devices, sensors in wearable devices, and others); network data 404 (such as data on network traffic volume, latency, congestion, quality of service (QoS), packet loss, error rate, and the like); event data 408 (such as data from event logs of edge and loT devices, data from event logs of operating assets of an enterprise, event logs of wearable devices, event data detected by inspection of traffic on application programming interfaces, event streams published by devices and systems, user interface interaction events (such as captured by tracking clicks, eye tracking and the like), user behavioral events, transaction events (including financial transaction, database transactions and others), events within workflows (including directed, acyclic flows, iterative and / or looping flows, and the like), and others); state data 410 (such as data indicating historical, current or predicted / anticipated states of entities (such as machines, systems, devices, users, objects, individuals, and many others) and including a wide range of attributes and parameters relevant to energy generation, storage, delivery or utilization of such entities); and / or combinations of the foregoing (e.g., data indicating the state of an entity and of a workflow involving the entity).

[0620] In some embodiments, data resources may include, among many others, public data resources 162 that are relevant to energy, such as energy grid data 422 (such as historical, current and anticipated / predicted maintenance status, operating status, energy production status, capacity, efficiency, or other attribute of energy grid assets involved in generation, storage or transmission of energy); energy market data 424 (such as historical, current and anticipated / predicted pricing data for energy or energy-related entities, including spot market prices of energy based on location, type of consumption, type of generation and the like, day-ahead or other futures market pricing for the same, costs of fuel, cost of raw materials involved (e.g., costs of materials used in battery production), costs of energy-related activities, such as mineral extraction, and many others); location and mobility data 428 (such as data indicating historical, current and / or anticipated / predicted locations or movements of groups of individuals (e.g., crowds attending large events, such as concerts, festivals, sporting events, conventions.Attorney Docket No. 54250-73and the like), data indicating historical, current and / or anticipated / predicted locations or movements of vehicles (such as used in transportation of people, goods, fuel, materials, and the like), data indicating historical, current and / or anticipated / predicted locations or movements of points of production and / or demand for resources, and others); and weather and climate data 430 (such as indicating historical, current and / or anticipated / predicted energy-relevant weather patterns, including temperature data, precipitation data, cloud cover data, humidity data, wind velocity data, wind direction data, storm data, barometric pressure data, and others).

[0621] In some embodiments, the data resources for energy edge orchestration 110 may include a set of enterprise data resources 168, which may include, among many others, energy-relevant financial and transactional data 432 (such as indicating historical, current and / or anticipated / predicted state, event, or workflow data involving financial entities, assets, and the like, such as data relating to prices and / or costs of energy and / or of goods and services, data related to transactions, data relating to valuation of assets, balance sheet data, accounting data, data relating to profits or losses, data relating to investments, interest rate data, data relating to debt and equity financing, capitalization data, and many others); operational data 434 (such as indicating historical, current and / or anticipated / predicted states or flows of operating entities, such as relating to operation of assets and systems used in production of goods and performance of services, relating to movement of individuals, devices, vehicles, machines and systems, relating to maintenance and repair operations, and many others); human resources data 438 (such as indicating historical, current and / or anticipated / predicted states, activities, locations or movements of enterprise personnel); and sales and marketing data 440 (such as indicating historical, current and / or anticipated / predicted states or activities of customers, advertising data, promotional data, loyalty program data, customer behavioral data, demand planning data, pricing data, and many others); and others.

[0622] In some embodiments, the data resources for energy edge orchestration 110 may be handled by an adaptive energy data pipeline 164, which may leverage artificial intelligence capabilities of the platform 102 in order to optimize the handling of the various data resources. Increases in processing power and storage capacity of devices are combining with wider deployment of edge and loT devices to produce massive increases in the scale and granularity of data of available data of the many types described herein. Accordingly, even more powerful networks like 5G, and anticipated 6G, are likely to have difficulty transmitting available volumes of data without problems of congestion, latency, errors, and reduced QoS. The adaptive energy data pipeline 164 can include a set of artificial intelligence capabilities for adapting the pipeline of the data resources to enable more effective orchestration of energy-related activities, such as by optimizing various elements of data transmission in coordination with energy orchestration needs. In some embodiments, the adaptive energy data pipeline 164 may include self-organizing data storage 412 (such as storing data on a device or system (e.g., an edge, loT, or other networking device, cloud or data center system, on-premises system, or the like) based on the patterns or attributes of the data (e.g., patterns in volume of data over time, or other metrics), the content of the data, the context of the data e.g., whether the data relates high-Attorney Docket No. 54250-73stakes enterprise activities), and the like). In some embodiments, the adaptive energy data pipeline 164 may include automated, adaptive networking 414 (such as adaptive routing based on network route conditions (including packet loss, error rates, QoS, congestion, cost / pricing and the like)), adaptive protocol selection (such as selecting among transport layer protocols (e.g., TCP or UDP) and others), adaptive routing based on RF conditions (e.g., adaptive selection among available RF networks (e.g., Bluetooth, Zigbee, NFC, and others)), adaptive filtering of data (e.g., DSP-based filtering of data based on recognition of whether a device is permitted to use RF capability), adaptive slicing of network bandwidth, adaptive use of cognitive and / or peer-to-peer network capacity, and others. In some embodiments, the adaptive energy data pipeline 164 may include enterprise contextual adaptation 418, such as where data is automatically processed based on context (such as operating context of an enterprise (e.g., distinguishing between mission-critical and less critical operations, distinguishing between timesensitive and other operations, distinguishing between context required for compliance with policy or law, and the like), transactional or financial context (e.g., based on whether the data is required based on contractual requirements, based on whether the data is useful or necessary for real-time transactional or financial benefits (e.g., time-sensitive arbitrage opportunities or damage-mitigation needs)), and many others). In some embodiments, the adaptive energy data pipeline 164 may include market-based adaptation 420, such as where storage, networking, or other adaptation is based on historical, current and / or anticipated / predicted market factors (such as based on the cost of storage, transmission and / or processing of the data (including the cost of energy used for the same), the price, cost, and / or marginal profit of goods or services that are produced based on the data, and many others).

[0623] In some embodiments, the adaptive energy data pipeline 164 may adapt any and all aspects of data handling, including storage, routing, transmission, error correction, timing, security, extraction, transformation, loading, cleansing, normalization, filtering, compression, protocol selection (including physical layer, media access control layer and application layer protocol selection), encoding, decoding, and others.CONFIGURED ENERGY EDGE STAKEHOLDER SOLUTIONS

[0624] The set of configured stakeholder energy edge solutions 108 may include a set of mobility demand solutions 152, a set of enterprise optimization solutions 154, a set of energy provisioning and governance solutions 156, and / or a set of localized production solutions 158, among others, that use various advanced energy resources and systems 104 and / or various configurable data and intelligence modules and services 118 to enable benefits to particular stakeholders, such as private enterprises, non-governmental organizations, independent service organizations, governmental organizations, and others. All such solutions may leverage edge intelligence, such as using data collected from onboard or integrated sensors, loT systems, and edge devices that are located in proximity to entities that generate, store, deliver and / or use energy to feed models, expert systems, analytic systems, data services, intelligent agents, robotic process automation systems, and other artificial intelligence systems into order to facilitate a solution for a particular stakeholder needs. By way of example, in the case of a city, the set ofAttorney Docket No. 54250-73mobility demand solutions 152 can be utilized to predict peak travel times and adjust public transport schedules accordingly. Similarly, in case of a large corporate campus, the set of enterprise optimization solutions 154 can be utilized to manage its energy consumption, ensuring that office buildings are adequately powered during work hours while conserving energy during off-hours.Localized Production Solutions

[0625] In some embodiments, a set of localized production solutions 158 may be integrated with, linked to, or managed by the platform 102, such that localized production demand can be met, particularly for goods that are very costly to transport (e.g., food) or services where the cost of energy distribution has a large adverse impact on product or service margins e.g., where there is a need for intensive computation in places where the electrical grid is absent, lacks capacity, is unreliable, or is too expensive). The platform 102 can manage the energy consumption of the set of localized production solutions 158, optimizing usage based on available resources, especially in places where the conventional electrical grid may be absent or unreliable.

[0626] In some embodiments, power management systems may converge with other systems, such as building management systems, operational management systems, production systems, services systems, data centers, and others to allow for enterprise-wide energy management. The platform 102 by converging power management with the building management systems, the operational management systems, the production systems, the services systems, the data centers, and the like, can ensure that energy is used optimally across the board in the enterprise. For example, during off-hours, while the building management system reduces lighting, the data center can shift its heavy computations, balancing the overall energy load.

[0627] Referring to FIG. 5, the platform 102 may orchestrate the various services and capabilities described in order to configure the set of configured stakeholder energy edge solutions 108, including the set of mobility demand solutions 152, the set of enterprise optimization solutions 154, energy provisioning and governance solutions 156, and a set of localized production solutions 158.

[0628] The set of localized production solutions 158 may include a set of computation intensive solutions 522 where the demand for energy involved in computation activities in a location is operationally significant, either in terms of overall energy usage or peak demand (particularly ones where location is a relevant factor in operations, but energy availability may not be assured in adequate capacity, at acceptable prices), such as data center operations (e.g., to support high-frequency trading operations that require low-latency and benefit from close proximity to the computational systems of marketplaces and exchanges), operations using quantum computation, operations using very large neural networks or computation-intensive artificial intelligence solutions (e.g., encoding and decoding systems used in cryptography), operations involving complex optimization solutions (e.g., high-dimensionality database operations, analytics and the like, such as route optimization in computer networks, behavioral targeting in marketing, route optimization in transportation), operations supporting cryptocurrencies (such as miningAttorney Docket No. 54250-73operations in cryptocurrencies that use proof-of-work or other computationally intensive approaches), operations where energy is sourced from local energy sources (e.g., hydropower dams, wind farms, and the like), and many others.

[0629] The set of localized production solutions 158 may include a set of transport cost mitigation solutions 524, such as ones where the cost of energy required to transport raw materials or finished goods to a point of sale or to a point of use is a significant component in overall cost of goods. The set of transport cost mitigation solutions 524 may configure a set of DERs 128 or other advanced energy resources to provide energy that either supplements or substitutes for conventional grid energy in order to allow localized production of goods that are conventionally produced remotely and transported by transportation and logistics networks (e.g., long-haul trucking) to points of sale or use. For example, crops that have high water content can be produced locally, such as in containers that are equipped with lighting systems, hydration systems, and the like in order to shift the energy mix toward production of the crops, rather than transportation of the finished goods. The platform 102 may be used to optimize, at a fleet level, the mix of a set of localized, modular energy generation systems or storage systems to support a set of localized production systems for heavy goods, such as by rotating the energy generation or storage systems among the localized production systems to meet demand (e.g., seasonal demand, demand based on crop cycles, demand based on market cycles and the like).

[0630] The set of localized production solutions 158 may include a set of remote production operation solutions 528, such as to orchestrate DERs 128 or other advanced energy resources to provide energy in a more optimal way to remote operations, such as mineral mining operations, energy exploration operations, drilling operations, military operations, firefighting and other disaster response operations, forestry operations, and others where localized energy demand at given points of time periodically exceeds what can be provided by the energy grid, or where the energy grid is not available. This may include orchestration of the routing and provisioning of a fleet of portable energy storage systems (e.g., vehicles, batteries, and others), the routing and provisioning of a fleet of portable renewable energy generation systems (wind, solar, nuclear, hydropower and others), and the routing and provisioning of fuels (e.g.. fuel cells).

[0631] The set of localized production solutions 158 may include a set of flexible and variable production solutions 530, such as where a set of production assets (e.g., 3D printers, CNC machines, reactors, fabrication systems, conveyors and other components) are configured to interface with a set of modular energy production systems, such as to accept a combination of energy from the grid and from a localized energy generation or storage source, and where the energy storage and generation systems are configured to be modular, removable, and portable among the production assets in order to provide grid augmentation or substitution at a fleet level, without requiring a dedicated energy asset for each production asset. The platform 102 may be used to configure and orchestrate the set of energy assets and the set of production assets in order to optimize localized production, including based on various factors noted herein, such as marketplace conditions in the energy market and in the market for the goods and services of an enterprise.Attorney Docket No. 54250-73Enterprise Optimization Solutions

[0632] The set of configured stakeholder energy edge solutions 108 may also include a set of enterprise optimization solutions 154, such as to provide an enterprise with greater visibility into the role that energy plays in enterprise operations (such as to enable targeted, strategic investment in energy-relevant assets); greater agility in configuring operations and transactions to meet operational and financial objectives that are driven at least in part by energy availability energy market prices or the like; improved governance and control over energy-related factors, such as carbon production, waste heat and pollution emissions; and improved efficiency in use of energy at any and all scales of use, ranging from electronic devices and smart buildings to factories and energy extraction activities. The term “enterprise,” as used herein, may, except where context requires otherwise, include private and public enterprises, including corporations, limited liability companies, partnerships, proprietorships and the like, non-governmental organizations, for-profit organizations, non-profit organizations, public-private partnerships, military organizations, first responder organizations (police, fire departments, emergency medical services and the like), private and public educational entities (schools, colleges, universities and others), governmental entities (municipal, county, state, provincial, regional, federal, national and international), agencies (local, state, federal, national and international, cooperative (e.g., treaty -based agencies), regulatory, environmental, energy, defense, civil rights, educational, and many others), and others. Examples provided in connection with a for-profit business should be understood to apply to other enterprises, and vice versa, except where context precludes such applicability.

[0633] The set of enterprise optimization solutions 154 may include a set of smart building solutions 512, where the platform 102 may be used to orchestrate energy generation, transmission, storage and / or consumption across a set of buildings owned or operated by the enterprise, such as by aggregating energy purchasing transactions across a fleet of smart buildings, providing a set of shared mobile or portable energy units across a fleet of smart buildings that are provisioned based on contextual factors, such as utilization requirements, weather, market prices and the like at each of the buildings, and many others.

[0634] The set of enterprise optimization solutions 154 may include a set of smart energy delivery solutions 514, where the platform 102 may be used to orchestrate delivery or energy at a favorable cost and at a favorable time to a point of operational use. In some embodiments, the platform 102 may, for example, be used to time the routing of liquid fuel through elements of a pipeline by automatically controlling switching points of the pipeline based on contextual factors, such as operational utilization requirements, regulatory requirements, market prices, and the like. In other embodiments, the platform 102 may be used to orchestrate routing of portable energy storage units or portable energy generation units in order to deliver energy to augment or substitute for grid energy capacity at a point and time of operational use. In some embodiments, the platform 102 may be used to orchestrate routing and delivery of wireless power to deliver energy to a point and time of use. Energy delivery optimization may be based on market prices (historical, cunent, futures market, and / or predicted), based on operational conditions (currentAttorney Docket No. 54250-73and predicted), based on policies (e.g., dictating priority for certain uses) and many other factors.

[0635] The set of enterprise optimization solutions 154 may include a set of smart energy transaction solutions 518, where the platform 102 may be used to orchestrate transactions in energy or energy-related entities (e.g., renewable energy credits (RECs), pollution abatement credits, carbon-reduction credits, or the like) across a fleet of enterprise assets and / or operations, such as to optimize energy purchases and sales in coordination with energy-relevant operations at any and all scales of energy usage. This may include, In some embodiments, aggregating and timing current and futures market energy purchases across assets and operations, automatically configuring purchases of shared generation, storage or delivery capacity for enterprise operational usage and the like. The platform 102 may leverage blockchain, smart contract, and artificial intelligence capabilities, trained as described throughout this disclosure, to undertake such activities based on the operational needs, strategic objectives, and contextual factors of an enterprise, as well as external contextual factors, such as market needs. For example, an anticipated need for energy by an enterprise machine may be provided as an event stream to a smart contract, which may automatically secure a future energy delivery contract to meet the need, either by purchasing grid-based energy from a provider or by ordering a portable energy storage unit, among other possibilities. The smart contract may be configured with intelligence, such as to time the purchase based on a predicted market price, which may be predicated, such as by an intelligent agent, based on historical market prices and current contextual factors.

[0636] The set of enterprise optimization solutions 154 may include a set of enterprise energy digital twin solutions 520, where the platform 102 may be used to collect, monitor, store, process and represent in a digital twin a wide range of data representing states, conditions, operating parameters, events, workflows and other attributes of energy-relevant entities, such as assets of the enterprise involved in operations, assets of external entities that are relevant to the energy utilization or transactions of the enterprise (e.g., energy grid entities, pipelines, charging locations, and the like), energy market entities (e.g., counterparties, smart contracts, blockchains, prices and the like). A user of the set of enterprise energy digital twin solutions 520 may, for example, view a set of factories that are consuming energy and be presented with a view that indicates the relative efficiency of each factory, of individual machines within the factory, or of components of the machines, such as to identify inefficient assets or components that should be replaced because the cost of replacement would be rapidly recouped by reduced energy usage. The digital twin, in such example, may provide a visual indicator of inefficient assets, such as a red flag, may provide an ordered list of the assets most benefiting from replacement, may provide a recommendation that can be accepted by the user (e.g., triggering an order for replacement), or the like. Digital twins may be role-based, adaptive based on context or market conditions, personalized, augmented by artificial intelligence, and the like, in the many ways described herein and in the documents incorporated by reference herein.

[0637] In some embodiments, the DERs 128 will be integrated with or into enterprises and shared resources, augmenting the existing power grid and serving to decrease costs and improve reliability. Increasing levels of digitalization will help integrate activities and facilitate new waysAttorney Docket No. 54250-73of optimizing energy in buildings / operations, and across campuses and enterprises. By way of example, by integrating the DERs 128, the campus can supplement its power needs with renewable sources. Digitalization of energy management can help the campus monitor and adjust its energy consumption in real-time. In some embodiments, this may enable increasing the operational bottom line of a for-profit enterprise by leveraging big data and plug load analytics to efficiently manage buildings. For example, the campus can manage its buildings efficiently, ensuring that energy is used where needed, optimizing operational costs.

[0638] In some embodiments, loT sensors and building automation control systems may be configured to assist in optimizing floor space, identifying unused equipment, automating efficient energy consumption, improving safety, and reducing environmental impact of buildings. By way of example, in a multi-storied office building equipped with loT sensors and building automation control systems, these systems can monitor energy consumption of each floor, ensuring that lighting and HVAC systems are optimized for the number of occupants. In an example, unused conference rooms can automatically switch off lights and adjust temperatures, reducing energy wastage.

[0639] In some embodiments, the platform 102 may manage total energy consumption of systems and equipment connected to the electrical network or to a set of DERs 128. Some systems are almost always operational, while other pieces of equipment and machinery may be connected only occasionally. By maintaining an understanding of both the total daily electrical consumption of a building and the role individual devices play in the overall energy use of a specific system, the platform 102 may forecast, provision, manage and control, optionally by Al or algorithm, the total consumption. For example, the platform 102, through Al and algorithms, can monitor and adjust energy consumption based on the specific needs of each building, optimizing energy use.

[0640] In some embodiments, the platform 102 may track and leverage an understanding of occupants’ behavior. Activity levels, behavior patterns, and comfort preferences of occupants may be a consideration for energy efficiency measures. This may include tracking various cyclical or seasonal factors. Over time, energy generation, storage and / or consumption of a building may follow predictable patterns that an loT-based analytics platform can take into consideration when generating proposed solutions. By way of example, during winter, if the platform notices residents tend to stay in during evenings, it can adjust heating accordingly. Over time, the system learns from these patterns, ensuring energy is used efficiently.

[0641] In some embodiments, the platform 102 may enable or integrate with systems or platforms for autonomous operations. For example, industrial sites, such as oil rigs and power plants, require extensive monitoring for efficiency and safety because liquid, steam, or oil leakages can be catastrophic, costly, and wasteful. Al and machine learning may provide autonomous capabilities for power plants, such as those served by edge devices, loT devices, and onsite cameras and sensors. Models may be deployed at the edge in power plants or on DERs 128, such as to use real-time inferencing and pattern detection to identify faults, such as leaks, shaking, stress, or the like. Operators may use computer vision, deep learning, andAttorney Docket No. 54250-73intelligent video analytics (IVA) to monitor heavy machinery, detect potential hazards, and alert workers in real-time to protect their health and safety, prevent accidents, and assign repair technicians for maintenance. By way of example, in a factory with multiple machines, the platform 102, through Al and machine learning, can monitor the health of the machines in realtime, predicting potential weak points, and suggesting timely maintenance and repair.

[0642] In some embodiments, the platform 102 may enable or integrate with systems or platforms for pipeline optimization. For example, oil and gas enterprises may rely on finding the best-fit routes to transfer oil to refineries and eventually to fuel stations. Edge Al can calculate the optimal flow of oil to ensure reliability of production and protect long-term pipeline health. In some embodiments, enterprises can inspect pipelines for defects that can lead to dangerous failures and automatically alert pipeline operators.Mobility Demand Solutions

[0643] Referring still to FIG. 5, the set of configured stakeholder energy edge solutions 108 may include a set of mobility demand solutions 152, such as where the platform 102 may be used to orchestrate energy generation, storage, delivery and or consumption by or for a set of mobile entities, such as a fleet of vehicles, a set of individuals, a set of mobile event production units, or a set of mobile factory units, among many others.

[0644] The set of mobility demand solutions 510 may include a set of transportation solutions 502, such as where the platform 102 may be used to orchestrate energy generation, storage, delivery and or consumption by or for a set of vehicles, such as used to transport goods, passengers, or the like. The platform 102 may handle relevant operational and contextual data, such as indicating needs, priorities, and the like for transportation, as well as relevant energy data, such as the cost of energy used to transport entities using different modes of transportation at different points in time, and may provide a set of recommendations, or automated provisioning, of transportation in order to optimize transportation operations while accounting fully for energy costs and prices. For example, among many others, an electric or hybrid passenger tour bus may be automatically routed to a scenic location that is in proximity to a low cost, renewable energy charging station, so that the bus can be recharged while the tourists experience the location, thus satisfying an energy-related objective (cost reduction) and an operational objective (customer satisfaction). An intelligent agent may be trained, using techniques described herein and in the documents incorporated by reference (such as by training robotic process automation on a training set of expert interactions), to provide a set of recommendations for optimizing energy-related objectives and other operational objectives.

[0645] The set of mobility demand solutions 510 may include a set of mobile user solutions 504, such as where the platform 102 may be used to orchestrate energy generation, storage, delivery and or consumption by or for a set of mobile users, such as users of mobile devices. For example, in anticipation of a large, temporary increase in the number of people at a location (such as in a small city hosting a major sporting event), the platform 102 may provide a set of recommendations for, or automatically configure a set of orders for a set of portable recharging units to support charging of consumer devices.Attorney Docket No. 54250-73

[0646] The set of mobility demand solutions 510 may include a set of mobile event production solutions 508, such as where the platform 102 may be used to orchestrate energy generation, storage, delivery and or consumption by or for a set of mobile entities involved in production of an event, such as a concert, sporting event, convention, circus, fair, revival, graduation ceremony, college reunion, festival, or the like. This may include automatically configuring a set of energy generation, storage or delivery units based on the operational configuration of the event (e.g., to meet needs for lighting, food service, transportation, loudspeakers and other audio-visual elements, machines (e.g., 3D printers, video gaming machines, and the like), rides and others), automatically configuring such operational configuration based on energy capabilities, configuring one or more of energy or operational factors based on contextual factors (e.g., market prices, demographic factors of attendees, or the like), and the like.

[0647] The set of mobility demand solutions 510 may include a set of mobile factory solutions, such as where the platform 102 may be used to orchestrate energy generation, storage, delivery and or consumption by or for a set of mobile factory entities. These may include container-based factories, such as where a 3D printer, CNC machine, closed-environment agriculture system, semiconductor fabricator, gene editing machine, biological or chemical reactor, furnace, or other factory machine is integrated into or otherwise contained in a shipping container or other mobile factory housing, wherein the platform 102 may, based on a set of operational needs of the set of factory machines, configure a set of recommendations or instructions to provision energy generation, storage, or delivery to meet the operational needs of the set of factory machine at a set of times and places. The configuration may be based on energy factors, operational factors, and / or contextual factors, such as market prices of goods and energy, needs of a population (such as disaster recovery needs), and many other factors.Energy Provisioning and Governance Solutions

[0648] The energy provisioning and governance solutions 156 may include solutions for governance of mining operations. Cobalt, nickel, and other metals are fundamental components of the batteries that will be needed for the green EV revolution. Amounts required to support the growing market will create economic pressure on mining operations, many of which take place in regions like the DRC where there is a long history of corruption, child labor, and violence. Companies are exploring areas like Greenland for cobalt, in part on the basis that it can offer reliable labor law enforcement, taxation compliance, and the like. Such promises can be made there and in other jurisdictions with greater reliability through a set of mining governance solutions 542. The set of mining governance solutions 542 may include mine-level loT sensing of the mine environment, ground-penetrating sensing of unmined portions, mass spectrometry and computer vision-based sensing of mined materials, asset tagging of smart containers (e.g., detecting and recording opening and closing events to ensure that the material placed in a container is the same material delivered at the end point), wearable devices for detecting physiological status of miners, secure (e.g., blockchain- and DLT-based) recording and resolution of transactions and transaction-related events, smart contracts for automatically allocating proceeds (e.g., to tax authorities, to workers, and the like), and an automated systemAttorney Docket No. 54250-73for recording, reporting, and assessing compliance with contractual, regulatory, and legal policy requirements. All of the above, from base sensors to compliance reports can be optionally represented in a digital twin that represents each mine owner or operated by an enterprise.

[0649] The energy provisioning and governance solutions 156 may also include a set of carbon-aware energy solutions, where controls for operating entities that generate (or capture) carbon are managed by data collection through edge and loT devices about current carbon generation or emission status and by automated generation of a set of recommendations and or control instructions to govern the operating entities to satisfy policies, such as by keeping operations within a range that is offset by available carbon offset credits, or the like.

[0650] Referring still to FIG. 5, the set of configured stakeholder energy edge solutions 108 may include a set of energy provisioning and governance solutions 156, such as where the platform 102 may be used to orchestrate energy generation, storage, delivery and or consumption by or for a set of entities based on a set of policies, regulations, laws, or the like, such as to facilitate compliance with company financial control policies, government or company policies on carbon reduction, and many others.

[0651] The set of energy provisioning and governance solutions 156 may include a set of carbon-aware energy edge solutions 532, such as where a set of policies regarding carbon generation may be explored, configured, and implemented in the platform 102, such as to require energy production by one or more assets or operations to be monitored in order to track carbon generation or emissions, to require offsetting of such generation or emissions, or the like. In some embodiments, energy generation control instructions (such as for a machine or set of machines) may be configured with embedded policy instructions, such as required confirmation of available offsets before a machine is permitted to generate energy (and carbon), or before a machine can exceed a given amount of production in a given period. In some embodiments, the embedded policy instructions may include a set of override provisions that enable the policy to be overridden (such as by a user, or based on contextual factors, such as a declared state of emergency) for mission critical or emergency operations. Carbon generation, reduction and offsets may be optimized across operations and assets of an enterprise, such as by an intelligent agent trained in various ways as described elsewhere in this disclosure.

[0652] The set of energy provisioning and governance solutions 156 may include a set of automated energy policy deployment solutions 534, such as where a user may interact with a user interface to design, develop or configure (such as by entering rules or parameters) a set of policies relating to energy generation, storage, delivery and / or utilization, which may be handled by the platform, such as by presenting the policies to users who interact with entities that are subject to the policies (such as interfaces of such entities and / or digital twins of such entities, such as to provide alerts as to actions that risk noncompliance, to log noncompliant events, to recommend alternative, compliance options, and the like), by embedding the policies in control systems of entities that generate, store, deliver or use energy (such that operations of such entities are controlled in a manner that is compliant with the policies), by embedding the policies in smart contracts that enable energy-related transactions (such that transactions areAttorney Docket No. 54250-73automatically executed in compliance with the policies, such that warnings or alerts are provided in the case of non-compliance, or the like), by setting policies that are automatically reconfigured based on contextual factors (such as operational and / or market factors) and others. In some embodiments, an intelligent agent may be trained, such as on a training data set of historical data, on feedback from outcomes, and / or on a training data set of human policy-setting interactions, to generate policies, to configure or modify policies, and / or to undertake actions based on policies. A wide range of policies and configurations may be implemented, such as setting maximum energy usage for an entity for a time period, setting maximum energy cost for an entity for a time period, setting maximum carbon production for an entity for a time period, setting maximum pollution emissions for an entity for a time period, setting carbon offset requirements, setting renewable energy credit requirements, setting energy mix requirements (e.g., requiring a minimum fraction of renewable energy), setting profit margin minimums based on energy and other marginal costs for a production entity, setting minimum storage baselines for energy storage entities (such as to provide a margin of safety for disaster recovery), and many others.

[0653] The set of energy provisioning and governance solutions 156 may include a set of energy governance smart contract solutions 538, such as to allow a user of the platform 102 to design, generate, configure and / or deploy a smart contract that automatically provides a degree of governance of a set of energy transactions, such as where the smart contract takes a set of operational, market or other contextual inputs (such as energy utilization information collected by edge devices about operating assets) as inputs and automatically configures a set of contracts that are compliance with a set of policies for the purchase, sale, reservation, sharing, or other transaction for energy, energy-related credits, and the like. For example, a smart contract may automatically aggregate carbon offset credits needed to balance carbon generation detected across a set of machines used in enterprise operations.

[0654] The set of energy provisioning and governance solutions 156 may include a set of automated energy financial control solutions 540, such as to allow a user of the platform 102 and / or an intelligent agent to design, generate, configure, or deploy a policy related to control of financial factors related to energy generation, storage, delivery and / or utilization. For example, a user may set a policy requiring minimum marginal profit for a machine to continue operation, and the policy may be presented to an operator of the machine, to a manager, or the like. As another example, the policy may be embedded in a control system for the machine that takes a set of inputs needed to determine marginal profitability (e.g., cost of inputs and other non-energy resources used in production, cost of energy, predicted energy required to produce outputs, and market price of outputs) and automatically determines whether to continue production, and at what level, in order to maintain marginal profitability. Such a policy may take further inputs, such as relating to anticipated market and customer behavior, such as based on elasticity of demand for relevant outputs.

[0655] In some embodiments, an automated energy and governance policy may refer to a policy that to which an underlying system must adhere. In other words, the automated energy andAttorney Docket No. 54250-73governance policy is like a rulebook that a system strictly follows. In some embodiments, a set of edge devices may enforce the energy policies for a set of “downstream devices” (which is any device that uses power in the edge devices covered area). By way of example, in a smart city grid, the automated energy and governance policy may be utilized to ensure that streetlights operate within certain energy constraints. Edge devices, as part of the platform 102, which may include energy-efficient controllers, may be tasked with ensuring these energy policies for other devices connected to them. In an example, during festive seasons when there are additional decorative lights in use, these edge devices can enforce energy policies, ensuring that the overall energy consumption of all lights (including the additional decorative lights, i.e., the "downstream devices") does not cross a predefined limit.

[0656] In some embodiments, an energy policy may define an upper limit of “carbon creation", meaning that individual devices or the collection of downstream devices may not exceed a total carbon footprint over a given time. By way of example, for a corporation aiming for carbon neutrality, an energy policy may be set to ensure that their buildings or factories don't exceed a certain carbon footprint. In an example, a company may have a policy stating that its operations do not create more than a specific tonnage of carbon emissions in a year. This ensures that the activities of the company remain environmentally sustainable, even as it scales up its operations.

[0657] In some embodiments, energy delivery mechanisms may include “energy source” metadata indicating how the energy being delivered was generated and a measure of carbon output per “unit-of-usage”. Thereby, if energy was generated by wind, solar, nuclear, etc., the carbon footprint per unit of usage would be zero or close to zero, but if it was coal, natural gas, gas, etc., it would have a non-zero factor. Consider an industrial plant powered by a mix of renewable and non-renewable energy sources. The energy delivered to the plant may come with metadata indicating its origin. If the energy was predominantly generated through green sources like wind or solar, the associated carbon footprint would be low. However, if a significant portion was from coal or natural gas, the footprint would be higher. In these cases, the power may be delivered in portable storage or wired storage. If wired storage with mixed grid, the energy source metadata may indicate the overall percentage of energy from each power source feeding into the grid (e.g., 20% renewable, 50% nuclear, 10% coal), such that the carbon output per unit of usage parameter may be derived from the respective percentages. Overall, this metadata can be especially useful for businesses operating in regions with mixed energy grids, helping them calculate their actual carbon impact.

[0658] In some embodiments, the edge device may monitor the amount of power being used by the set of downstream devices and may determine the carbon output based on the energy source metadata and carbon output rate associated with the energy source metadata. When the edge device determines that the set of downstream devices is approaching the policy limit, the energy and governance engine may take a set of preventative actions to avoid hitting the upper limit. Examples of preventative actions may include switching to a different energy delivery mechanism (which may be more expensive or less optimal in other ways), shutting down certain devices to reduce the energy spend, toggling energy usage between different devices, sendingAttorney Docket No. 54250-73alerts to human users, or the like. When the edge device determines the set of downstream devices exceeded the upper limit, the energy and governance engine may take a set of corrective actions to avoid hitting the upper limit. The corrective actions may include one or more of buying carbon offset credits, turning off the system, and switching to a carbon neutral operating mode. By way of example, in a residential community powered by multiple energy sources, an edge device may monitor energy consumption of households and determine their carbon output. If the residential community is approaching its carbon limit due to excessive use of nonrenewable energy, the platform 102 may shift more households in the residential community to solar power, despite potential added costs.

[0659] In some embodiments, management of the reliability and uptime from energy edge components may be critical parts of overall operation of a distributed edge environment. Like for any business, ensuring that its operations are uninterrupted is crucial. This is especially true for sectors like healthcare or data centers, where energy reliability directly impacts human lives or vital data. Therefore, maintaining the reliability and uptime of energy edge components becomes a non-negotiable aspect of their operations. The platform 102 is configured to ensure to identify such operations, and ensure that their operations are uninterrupted, such as, by diverting energy from other sources if needed.

[0660] In some embodiments, the platform 102 may be configured to provide and / or facilitate artificial general intelligence (AGI)-based governance of energy resources. The platform 102 may include one or more AGI agents configured to make decisions and interact with one or more of humans, other AGI agents, and components of the platform 102. The one or more AGI agents may be configured to make decisions based on an internal state of the one or more AGI agents. The platform 102 may be configured to create snapshots of the internal state of the one or more AGI agents, the snapshot being associated with decisions made by the one or more AGI agents. The platform 102 may be configured to analyze and / or monitor the snapshots to improve management and / or governance of energy resources.

[0661] In some embodiments, the platform 102 may be configured to monitor decisions of components of the platform 102 to perform, provide, and / or facilitate continuous and / or near-continuous correction and / or micro-adjustment of the components to align with strategic goals of the platform 102. By way of example, in large-scale energy projects, it is essential to ensure that all components work towards the strategic goals of the project. By continuously monitoring decisions of these components, the platform 102 can realign any deviations, ensuring that the entire system works in harmony.

[0662] In some embodiments, the platform 102 may be configured to detect bad actors. With increasing cyber threats, the ability of the platform 102 to detect bad actors becomes important. The platform 102 may be configured to perform one or more actions in response to detection of a bad actor. By way of example, if someone tries to manipulate the energy consumption data of a smart grid to gain undue advantages, the platform 102 can detect such anomalies and take corrective actions, like blocking of such manipulating agents, raising flags, etc.

[0663] In some embodiments, the platform 102 may be configured to track and monitor humanAttorney Docket No. 54250-73interaction with components of the platform 102 and related edge devices and / or energy devices. The platform 102 may track and monitor human interaction to evaluate consistency of decisions of distributed agents, thereby encouraging that decisions made by the platform 102 and components thereof are consistent across a plurality of distributed energy resources. The platform 102 may be configured to additionally, or alternatively, track and monitor one or more of decision-making about resource allocation by components of the platform 102, management of supply and demand...

Claims

Attorney Docket No. 54250-73CLAIMS ADAPTIVE NETWORKING (ADNW)1. An Al-enabled system for adaptive energy network configuration, comprising: at least one artificial intelligence model configured to analyze distributed energy resource connectivity requirements and generate network configuration commands for establishing adaptive data networks between distributed energy resource entities based on operational linkages, and at least one adaptive network controller configured to implement the network configuration commands by establishing communication pathways that enable data exchange between the distributed energy resource entities.

2. The Al-enabled system of claim 1, wherein the at least one adaptive network controller is configured to establish at least one intra-module communication layer enabling communication of sensor data between power operating loads and load management modules within a provider module.

3. The Al-enabled system of claim 1, wherein the at least one adaptive network controller is configured to establish at least one inter-module communication layer enabling communication between a provider autonomous operating layer and at least two provider modules.

4. The Al-enabled system of claim 1, wherein the at least one adaptive network controller is configured to establish at least one fleet communication layer enabling communication between provider autonomous operating layers and a provider services layer.

5. The Al-enabled system of claim 1, wherein the at least one adaptive network controller is configured to establish reconfigurable communication networks that change network members based on operational linkage changes determined by the at least one artificial intelligence model.

6. The Al-enabled system of claim 1, wherein the at least one adaptive network controller is configured to form an ad hoc adaptive data network linking at least two distributed energy resource providers to act as a single aggregated power supplier based on network configuration commands from the at least one artificial intelligence model.

7. The Al-enabled system of claim 6, wherein the at least one adaptive network controller is configured to isolate internal communications among the at least two distributed energy resource providers from communications between the single aggregated power supplier and a distributed energy resource load.

8. The Al-enabled system of claim 1, further comprising at least one sensor configured to monitor at least one of environmental data including temperature and humidity or operational data including rotational information, vibration information, and charge states, and provide sensor data to the at least one artificial intelligence model via the adaptive data networks.

9. The Al-enabled system of claim 1, further comprising at least one sensor configured to monitor flow of power between components including at least one of voltage, current, frequency, electrical noise, power fluctuations, harmonics, or transient signals, and provide power monitoring data to the at least one artificial intelligence model via the adaptive data networks.

10. The Al-enabled system of claim 1 , wherein the at least one adaptive network controllerAttorney Docket No. 54250-73comprises at least one communication module utilizing wired communication systems including at least one of CAN Bus, wired networks, or specialized wiring harnesses.

11. The Al-enabled system of claim 1 , wherein the at least one adaptive network controller comprises at least one communication module utilizing wireless communication systems including at least one of Bluetooth, Bluetooth Low -Energy, ZigBee, cellular 3G, cellular 4G, cellular 5G, or cellular 6G protocols.

12. The Al-enabled system of claim 1, wherein the at least one adaptive network controller comprises at least one communication module utilizing Power Line Communication configured to add data signals onto existing power transmission lines.

13. The Al-enabled system of claim 12, wherein the Power Line Communication utilizes Orthogonal Frequency Division Multiplexing to achieve high data bandwidth communication over power lines.

14. The Al-enabled system of claim 1, wherein the at least one artificial intelligence model is configured to determine which data from sensors within a distributed energy resource entity is communicated outside the distributed energy resource entity via the adaptive data networks based on data sharing policies.

15. The Al-enabled system of claim 1, further comprising at least one resource controller configured to send control signals to at least one power manipulation device based on analyzed sensor data received via the adaptive data networks.

16. The Al-enabled system of claim 15, wherein the at least one power manipulation device comprises at least one of a power splitter, a power divider, a power switch, an inverter for grid connection, a grid forming inverter, a grid tied inverter, a power line conditioner, a power transformer, or a power isolator.

17. The Al-enabled system of claim 1, wherein the at least one artificial intelligence model is configured to analyze sensor data from at least two distributed energy resource entities and generate coordinated control commands for the at least two distributed energy resource entities based on the analysis.

18. The Al-enabled system of claim 1, wherein the at least one adaptive network controller is configured to establish at least one adaptive data network linking at least two top level distributed energy resource entities for local data exchange based on network configuration commands from the at least one artificial intelligence model.

19. The Al-enabled system of claim 1, wherein the at least one artificial intelligence model is configured to analyze sensor data in conjunction with external data including at least one of ambient environment data, presence or operation of other devices on a grid, operation of manufacturing equipment, or ambient temperature.

20. The Al-enabled system of claim 1, wherein the at least one adaptive network controller is configured to reconfigure the adaptive data networks dynamically based on changing operational linkages and connectivity patterns identified by the at least one artificial intelligence model.

21. An Al-enabled system for intelligent power flow control, comprising: at least one artificial intelligence model configured to generate an analysis of sensor data from at least one sensorAttorney Docket No. 54250-73monitoring power quality parameters and generate power routing commands for directing power flow through at least one power manipulation device, and at least one resource controller configured to execute the power routing commands by sending control signals to the at least one power manipulation device based on the analysis by the at least one artificial intelligence model.

22. The Al-enabled system of claim 21, wherein the at least one sensor is configured to monitor at least one of voltage, current, frequency, electrical noise, power fluctuations, harmonics, or transient signals along an energy distribution path.

23. The Al-enabled system of claim 21, wherein the at least one artificial intelligence model is configured to analyze sensor data to identify at least one of power reliability, average power characteristics, voltage swings, or load factors.

24. The Al-enabled system of claim 21, wherein the at least one artificial intelligence model is configured to identify correlations between power quality at a device input and arcing on a line along a distribution path to the device input.

25. The Al-enabled system of claim 21, wherein the at least one resource controller is configured to send control signals to the at least one power manipulation device to route power produced by a power source between storage, an internal power load, and output power.

26. The Al-enabled system of claim 21, wherein the at least one resource controller is configured to send control signals to the at least one power manipulation device to determine a source of output power selected from at least two sources including a power source and an energy storage device.

27. The Al-enabled system of claim 21, wherein the at least one resource controller comprises at least one of electronic circuits, ASICs, FPGAs, or software.

28. The Al-enabled system of claim 21, wherein the at least one resource controller is configured to generate the control signals based on at least one of individual sensor data, fused sensor data, or model outputs from the at least one artificial intelligence model.

29. The Al-enabled system of claim 21, wherein the at least one artificial intelligence model is configured to generate the power routing commands based on at least one of sensor data input, simulation output from a digital twin of a distributed energy resource and components thereof, or external information.

30. The Al-enabled system of claim 21, wherein the at least one resource controller is configured to switch load input power between at least two power providers based on power quality analysis by the at least one artificial intelligence model.

31. The Al-enabled system of claim 30, wherein the at least one resource controller is configured to automatically switch to a redundant power provider based on a determination by the at least one artificial intelligence model that one power source has failed.

32. The Al-enabled system of claim 21, wherein the at least one resource controller is configured to switch between at least two power providers based on quality of incoming power determined by the at least one artificial intelligence model from sensor data.

33. The Al-enabled system of claim 32, wherein the quality of incoming power is evaluated by the at least one artificial intelligence model based on at least one of voltage level, current level,Attorney Docket No. 54250-73volatility, instability, or power factor.

34. The Al-enabled system of claim 21, wherein the at least one power manipulation device comprises at least one of a power splitter, a power divider, a power switch, an inverter, a grid forming inverter, a grid tied inverter, a power line conditioner, a power transformer, or a power isolator.

35. The Al-enabled system of claim 21, wherein the at least one resource controller is configured to send a control signal to a grid tied inverter to alter voltage being output to a power grid to improve stability of the power grid based on analysis by the at least one artificial intelligence model of existing grid power information.

36. The Al-enabled system of claim 21, wherein the at least one resource controller is configured to send a control signal to a power divider to change distribution of power being provided to different outputs to prioritize delivery of energy to individual devices based on analysis by the at least one artificial intelligence model.

37. The Al-enabled system of claim 21, wherein the at least one resource controller is configured to send a control signal to a power switch to change device power input from a first input line to a second input line based on a determination by the at least one artificial intelligence model that power available on the first input line is inadequate.

38. The Al-enabled system of claim 21, wherein the at least one artificial intelligence model is configured to perform time-based analysis to identify trends or patterns in sensor data including at least one of a pattern over time of day or a pattern by day of week.

39. The Al-enabled system of claim 21, wherein the at least one artificial intelligence model is configured to perform statistical analyses including at least one of average voltage, standard voltage deviation, average current, current deviation, transient analysis, or determination of reliability.

40. The Al-enabled system of claim 21, wherein the at least one resource controller is configured to initiate a maintenance request based on analyzed sensor data from the at least one artificial intelligence model indicating that a line in a distribution network has been sparking.AUTOMATED RESOURCE ORGANIZATION AND CONTROL (AROC) 41. An Al-enabled system for automated energy resource orchestration and control, comprising: at least one artificial intelligence model configured to generate resource allocation commands for distributed energy resources based on regulatory requirements and operational data, a regulatory compliance interface configured to communicate with regulatory agencies and obtain regulatory information for use by the at least one artificial intelligence model, and at least one resource controller configured to execute the resource allocation commands by sending control signals to power manipulation devices.

42. The Al-enabled system of claim 41, wherein the regulatory compliance interface comprises a regulatory API configured to enable communications with at least two regulatory agencies for collection of regulatory information regarding distributed energy resources and potential siting locations.

43. The Al-enabled system of claim 41, wherein the regulatory compliance interface comprises aAttorney Docket No. 54250-73regulatory smart agent configured to negotiate smart contracts and manage permitting for at least one distributed energy resource based on resource allocation plans generated by the at least one artificial intelligence model.

44. The Al-enabled system of claim 41, further comprising a regulatory configuration control system configured to generate an analysis of regulatory standards and regional regulations and provide configuration information to the at least one resource controller based on the analysis.

45. The Al-enabled system of claim 44, wherein the configuration information includes power minimums, maximums, or ranges for various distributed energy resources that are used by the at least one resource controller to identify control signals.

46. The Al-enabled system of claim 41, wherein the at least one resource controller is configured to send control signals to at least one power manipulation device to route power produced by a power source between storage, an internal power load, and output power.

47. The Al-enabled system of claim 41, wherein the at least one resource controller is configured to send control signals to at least one power manipulation device to determine a source of output power selected from at least two sources including a power source and an energy storage device.

48. The Al-enabled system of claim 41, wherein the at least one resource controller comprises at least one of electronic circuits, ASICs, FPGAs, software, or combinations thereof.

49. The Al-enabled system of claim 41, wherein the at least one resource controller is configured to generate the control signals based on at least one of individual sensor data, fused sensor data, or model outputs from the at least one artificial intelligence model.

50. The Al-enabled system of claim 41, wherein the at least one artificial intelligence model is configured to generate the resource allocation commands based on at least one of sensor data input, simulation output from a digital twin of a distributed energy resource and components thereof, or external information.

51. The Al-enabled system of claim 41, further comprising at least one sensor configured to monitor at least one of environmental data including temperature and humidity or operational data including rotational information, vibration information, and charge states.

52. The Al-enabled system of claim 41, further comprising at least one sensor configured to monitor flow of power between components including at least one of voltage, current, frequency, electrical noise, power fluctuations, harmonics, or transient signals.

53. The Al-enabled system of claim 41, wherein the at least one resource controller is configured to switch load input power between at least two power providers based on analysis of sensor data by the at least one artificial intelligence model.

54. The Al-enabled system of claim 53, wherein the at least one resource controller is configured to automatically switch to a redundant power provider based on a determination that one power source has failed.

55. The Al-enabled system of claim 41, wherein the at least one resource controller is configured to switch between at least two power providers based on quality of incoming power determined by the at least one artificial intelligence model from sensor data.Attorney Docket No. 54250-7356. The Al-enabled system of claim 55, wherein the quality of incoming power is evaluated based on at least one of voltage level, current level, volatility, instability, or power factor.

57. The Al-enabled system of claim 41, further comprising a transmission orchestration interface configured to gather transmission connection infoimation about interconnecting power infrastmcture including power grids and interconnections between grids pertinent to distributed energy resource connections.

58. The Al-enabled system of claim 57, further comprising a transmission configuration control system configured to generate an analysis of a subset of the transmission connection information and provide transmission configuration information to the at least one resource controller based on the analysis.

59. The Al-enabled system of claim 58, wherein the transmission configuration information includes at least one of power minimums, maximums, ranges, or time bands for utilization of certain transmission infrastmcture.

60. The Al-enabled system of claim 41, further comprising a transaction orchestration interface configured to negotiate and implement energy-related transactions to support desired power connections based on resource allocation plans generated by the at least one artificial intelligence model.

61. An Al-enabled system for distributed energy resource integration and grid stabilization, comprising: at least one artificial intelligence model configured to analyze grid conditions and generate control strategies for integrating distributed energy resources into grid infrastmcture to provide grid stability, at least one sensor configured to monitor power quality parameters and provide monitoring data to the at least one artificial intelligence model, and at least one automated controller configured to execute the control strategies by adjusting power output characteristics based on the grid conditions.

62. The Al-enabled system of claim 61, wherein the at least one artificial intelligence model is configured to generate control strategies for mobile resources that change location and connectivity status over time.

63. The Al-enabled system of claim 61, wherein the at least one automated controller is configured to control connection and power supplied into existing grid infrastmcture to stabilize the existing grid infrastmcture based on the control strategies.

64. The Al-enabled system of claim 61, wherein the at least one automated controller is configured to control output of a power source including at least one of output voltage, frequency, or whether an output inverter operates in grid forming mode or grid following mode.

65. The Al-enabled system of claim 61, wherein the at least one automated controller is configured to adapt incoming power to meet device requirements including at least one of voltage transformation, frequency transformation, or power conditioning.

66. The Al-enabled system of claim 61, wherein the at least one automated controller is configured to automatically switch smoothly between at least two power sources based on grid stability requirements determined by the at least one artificial intelligence model.

67. The Al-enabled system of claim 61, wherein the at least one sensor is configured to monitorAttorney Docket No. 54250-73at least one of voltage, current, frequency, electrical noise, power fluctuations, harmonics, or transient signals.

68. The Al-enabled system of claim 61, wherein the power quality parameters include at least one of frequency stability, voltage stability, or transient stability.

69. The Al-enabled system of claim 61, further comprising a regulatory compliance interface configured to verify compliance with applicable regulatory standards and provide compliance data to the at least one artificial intelligence model for use in generating the control strategies.

70. The Al-enabled system of claim 69, wherein the applicable regulatory standards include at least one of voltage requirements, frequency requirements, power quality requirements, power factor requirements, islanding requirements, equipment certification requirements, verification testing requirements, design requirements, minimum protective function requirements, metering requirements, or operating requirements.

71. The Al-enabled system of claim 61, wherein the at least one automated controller is configured to manage at least one of load isolation, fault tolerance, over-voltage protection, or under-voltage protection based on grid integration requirements.

72. The Al-enabled system of claim 61, wherein the at least one artificial intelligence model is configured to process sensor data from at least two distributed energy resources to coordinate grid stabilization activities across the at least two distributed energy resources.

73. The Al-enabled system of claim 61, further comprising a digital twin simulation system configured to simulate distributed energy resource operations and provide simulation outputs to the at least one artificial intelligence model for use in generating the control strategies.

74. The Al-enabled system of claim 61, wherein the at least one automated controller is configured to control division of power output between at least two outputs of a power divider based on the control strategies.

75. The Al-enabled system of claim 61, further comprising an adaptive data network configured to communicate sensor data from the at least one sensor to the at least one artificial intelligence model and communicate control signals from the at least one automated controller to at least one power manipulation device.

76. The Al-enabled system of claim 61, wherein the at least one automated controller comprises a controller associated with a power provider configured to manage power output characteristics to meet grid connection requirements.

77. The Al-enabled system of claim 61, wherein the at least one automated controller comprises a controller associated with a power load configured to manage connection to at least one power source including at least one of a power step transformer, a frequency transformer, a power conditioner, or a transfer switch.

78. The Al-enabled system of claim 61, wherein the at least one automated controller is configured to smooth power signal during switching between at least two power sources based on grid stability requirements.

79. The Al-enabled system of claim 61, further comprising a transmission orchestration interface configured to gather transmission connection information about interconnecting powerAttorney Docket No. 54250-73infrastructure and provide the transmission connection information to the at least one artificial intelligence model for use in generating the control strategies.

80. The Al-enabled system of claim 61, wherein the at least one artificial intelligence model is configured to coordinate integration of stationary distributed energy resources and mobile distributed energy resources into the grid infrastructure using different integration protocols based on resource mobility characteristics.COMPUTATION DEPENDENT ENERGY SUPPLY (CDES) 81. An Al-enabled system for computation-dependent energy procurement, comprising: at least one artificial intelligence model configured to evaluate potential computational initiatives and generate resource requirement estimates including power forecasts based on projected computational demands, an initiative simulation engine configured to simulate cost, timeline, and return scenarios for the potential computational initiatives using the resource requirement estimates, and a request generation module configured to create requests for proposal to energy markets based on the power forecasts from the initiative simulation engine.

82. The Al-enabled system of claim 81, wherein the at least one artificial intelligence model comprises an initiative identification and evaluation system configured to identify potential computational initiatives, including Al model training activities, Al model pruning activities, and agentic Al self-directed activities.

83. The Al-enabled system of claim 82, wherein the initiative identification and evaluation system is configured to estimate potential results and impacts of the potential computational initiatives relative to specified criteria to determine whether to proceed with resource estimation.

84. The Al-enabled system of claim 81, wherein the at least one artificial intelligence model is configured to estimate resource requirements based on at least one of known algorithms for computational power estimation, historical data regarding similar initiatives, or digital twin simulations of components of the potential computational initiatives.

85. The Al-enabled system of claim 81, wherein the initiative simulation engine is configured to generate at least two operational scenarios that account for different timelines and profiles of anticipated resource requirements over time.

86. The Al-enabled system of claim 81, wherein the initiative simulation engine is configured to generate simulations including cash flow analysis and return analysis with total output to complete an initiative and timing of when returns for the initiative would begin to accrue.

87. The Al-enabled system of claim 81, further comprising a scenario identification system configured to identify at least one promising initiative scenario based on simulation outputs from the initiative simulation engine and prioritize the at least one promising initiative scenario based on potential return relative to specified criteria.

88. The Al-enabled system of claim 87, further comprising a contractually available power evaluation system configured to evaluate existing power contracts to determine whether contractually available power can meet power forecasts for the at least one promising initiative scenario.

89. The Al-enabled system of claim 88, wherein the contractually available power evaluationAttorney Docket No. 54250-73system is configured to generate alternative scenarios based on using a portion of contractually available power and identify alternate power forecast timelines indicating additional power needed from new suppliers.

90. The Al-enabled system of claim 81, further comprising a proposal evaluation system configured to evaluate proposals received from energy markets and rerun simulations for the at least one promising initiative scenario using pricing and timing provided in the proposals.

91. The Al-enabled system of claim 90, wherein the proposal evaluation system is configured to rate combinations of scenarios and corresponding proposals relative to specified criteria and identify combinations that no longer meet interest thresholds based on the proposals.

92. The Al-enabled system of claim 81, wherein the at least one artificial intelligence model comprises an agentic Al system configured to identify computational goals or actions requiring additional energy power consumption for a defined period of time.

93. The Al-enabled system of claim 92, wherein the agentic Al system is configured to evaluate potential benefits of additional computational activities including model training improvements, data digestion for internal understanding, and internal forecasting operations.

94. The Al-enabled system of claim 81, further comprising a client power evaluation system configured to generate real-time estimations of power needs based on utilization forecasts and estimated power requirements from digital twin simulations of client environments.

95. The Al-enabled system of claim 94, further comprising a client configured intelligence system configured to evaluate power metrics received from the client power evaluation system and determine whether to send a request for additional power to an energy market.

96. The Al-enabled system of claim 81, further comprising a computation-dependent energy supply monitor configured to monitor current availability of power based on data from existing energy contracts and poll at least one energy market to determine available power including metadata regarding price, quality, power metrics, and availability over time.

97. The Al-enabled system of claim 81, wherein the request generation module is configured to generate at least two requests for proposal corresponding to different promising initiative scenarios and alternative scenarios, each request including a power forecast timeline and requisite conditions and criteria.

98. The Al-enabled system of claim 81, further comprising a smart contract interface configured to negotiate smart contracts for power supply based on accepted proposals and provide negotiated terms to a transaction orchestration execution system.

99. The Al-enabled system of claim 81, wherein the at least one artificial intelligence model is configured to evaluate time sensitivity of proposed initiatives by determining whether a timeline exists after which an initiative is not worth pursuing and whether returns significantly increase based on expedited completion.

100. The Al-enabled system of claim 81, further comprising an action evaluation system configured to generate estimates of cost of implementation, estimates of impact of actions once completed, time required for action implementation, lost opportunity costs, and impact on existing processes during implementation for the potential computational initiatives.Attorney Docket No. 54250-73101. An Al-enabled system for energy market proposal matching, comprising: at least one artificial intelligence model configured to identify potential matches between requests for proposal from energy clients and available power forecasts from energy providers based on compatibility of power forecast timelines and compliance with governance policies, a market simulation engine configured to simulate compatibility scenarios for the potential matches identified by the at least one artificial intelligence model, and a proposal routing system configured to communicate matched requests to corresponding energy providers based on results from the market simulation engine.

102. The Al-enabled system of claim 101, wherein the at least one artificial intelligence model is configured to evaluate governance and regulatory policies to determine whether a specific provider and a potential client are compatible in terms of governance and regulatory requirements.

103. The Al-enabled system of claim 102, wherein the governance and regulatory policies include characteristics of power including carbon neutral requirements, limits on transients, uptime requirements, and legality of power in a region of the potential client.

104. The Al-enabled system of claim 101, further comprising a market twin system configured to create potential client digital twins for each request for proposal and at least one provider digital twin for energy providers.

105. The Al-enabled system of claim 104, wherein the market simulation engine is configured to use the potential client digital twins and the at least one provider digital twin to simulate fulfillment scenarios and identify permissible matches.

106. The Al-enabled system of claim 101, wherein the market simulation engine is configured to simulate fulfillment of at least two requests by a single provider based on the requests being for compatible time periods or small amounts of power.

107. The Al-enabled system of claim 101, further comprising a scenario ranking system configured to rank simulation results against specified criteria and identify possible matches, including combinations of requests for proposal for a single provider.

108. The Al-enabled system of claim 101, wherein the at least one artificial intelligence model is configured to receive requests for proposal including requested power forecast timelines and requisite conditions and criteria.

109. The Al-enabled system of claim 101, wherein the at least one artificial intelligence model is configured to receive available power forecasts, including available power forecast timelines and conditions and criteria associated with an availability of power.

110. The Al-enabled system of claim 109, wherein the available power forecasts include at least one of prices, price points, or time frames during which power will be available on existing infrastmcture.

111. The Al-enabled system of claim 101, further comprising a spot market module configured to enable short-term contracts for power by allocating available power that a provider has made available according to specified criteria including price.

112. The Al-enabled system of claim 111, wherein the spot market module is configured toAttorney Docket No. 54250-73generate proposal responses based on spot market available power without further consultation of providers prior to contract finalization.

113. The Al-enabled system of claim 101, further comprising a market assessment system configured to determine permissible matches between requests for proposal and available power forecasts by evaluating governance and regulatory policies from requesting entities and selling entities.

114. The Al-enabled system of claim 101, wherein the at least one artificial intelligence model is configured to evaluate whether available power forecasts can fulfill incremental power requests based on at least one of power availability, timeline compatibility, or compliance with conditions and criteria.

115. The Al-enabled system of claim 101, wherein the proposal routing system is configured to communicate at least one of individual requests for proposal or combinations of requests for proposal to provider transaction orchestration systems.

116. The Al-enabled system of claim 101, further comprising a provider value analysis system configured to run simulations depicting configuration options for meeting requests for proposal at different price points.

117. The Al-enabled system of claim 116, wherein the provider value analysis system is configured to simulate fulfillment of at least two potential client requests using excess power in current provider configurations.

118. The Al-enabled system of claim 116, wherein the provider value analysis system is configured to simulate meeting power needs for existing clients and potential clients with different configurations of provider systems.

119. The Al-enabled system of claim 101, further comprising a scenario prioritization system configured to evaluate different fulfillment scenarios based on business metrics including return on investment, timeframe for return, required investment, revenue at given price points, and risk assessment of potential clients.

120. The Al-enabled system of claim 101, wherein the at least one artificial intelligence model is configured to evaluate impact on energy providers including whether a potential client would introduce large fluctuations into associated power networks, wear on equipment, and whether reconfiguration of equipment for existing clients would be required.EDGE RESOURCE MANAGEMENT (EDRM)121. An Al-enabled system for distributed energy resource management, comprising: at least one artificial intelligence model configured to analyze discovered energy resources and generate optimized resource allocation strategies based on generation capacity, storage capacity, and operational circumstances, a dynamic resource discovery engine configured to automatically identify mobile and stationary distributed energy resources and provide resource data to the at least one artificial intelligence model, and a transaction orchestration interface configured to execute energy transactions based on the optimized resource allocation strategies.

122. The Al-enabled system of claim 121, wherein the dynamic resource discovery engine is configured to determine real-time availability status, connectivity parameters, and operationalAttorney Docket No. 54250-73circumstances for the discovered energy resources.

123. The Al-enabled system of claim 121, further comprising an energy edge resource management network architecture configured to enable dynamic connectivity management that handles changing availability status and connectivity patterns across distributed energy resources.

124. The Al-enabled system of claim 121, further comprising a mobile versus stationary resource classification module configured to differentiate between grid-connected stationary distributed energy resources and dynamic transitory distributed energy resources that move between locations with varying states of charge.

125. The Al-enabled system of claim 124, wherein the at least one artificial intelligence model is configured to apply different management protocols to resources based on classifications determined by the mobile versus stationary resource classification module.

126. The Al-enabled system of claim 121, further comprising a status monitoring and analysis component configured to provide real-time understanding of distributed energy resource status, including state of charge metrics, reliability parameters, and projected operational life.

127. The Al-enabled system of claim 121, wherein the at least one artificial intelligence model comprises a pattern recognition module configured to identify energy demand patterns, supply patterns, storage capacity patterns, and market price patterns.

128. The Al-enabled system of claim 121, further comprising an adaptive data pipeline integration interface configured to handle data processing, filtering, compression, storage, routing, and transport for energy edge resource management operations and provide processed data to the at least one artificial intelligence model.

129. The Al-enabled system of claim 121, further comprising an edge and loT networking integration module configured to integrate with edge and loT networking systems for real-time data collection from energy-related entities and distributed energy resource status monitoring.

130. The Al-enabled system of claim 121, further comprising an intelligent data layer architecture configured to process information through at least two stages including an ingestion stage, an analysis stage, a derived intelligence stage, and a consumer visualization portal.

131. The Al-enabled system of claim 121, further comprising vector-based communication protocols configured to enable efficient data exchange between energy edge resource management components and distributed resources.

132. The Al-enabled system of claim 121, wherein the at least one artificial intelligence model comprises generative artificial intelligence capabilities configured to create energy operations proposals and transaction offerings through dynamic feedback loops.

133. The Al-enabled system of claim 121, further comprising a cross-service resource orchestration agent configured to manage, configure, deploy, provision, and optimize subsystems operating within linked systems including energy allocation across platforms based on recommendations from the at least one artificial intelligence model.

134. The Al-enabled system of claim 121, further comprising a market orchestration integration module configured to connect with energy marketplaces based on energy type and locationAttorney Docket No. 54250-73including market forming capabilities, market demand processing, and market response coordination.

135. The Al-enabled system of claim 121, further comprising a grid and off-grid resource management controller configured to optimize and deploy generation and energy storage resources for both grid-connected applications and islanded off-grid applications based on the optimized resource allocation strategies.

136. The Al-enabled system of claim 121, further comprising a smart contract integration interface configured to connect with smart contract systems configured to negotiate smart contracts to meet distributed energy resource client energy needs based on the optimized resource allocation strategies.

137. The Al-enabled system of claim 121, further comprising a simulation and forecasting integration interface configured to connect with simulation and forecasting systems that utilize resource data from the dynamic resource discovery engine for energy planning and optimization.

138. The Al-enabled system of claim 121, further comprising a digital twin integration interface configured to connect with stakeholder energy digital twins that provide virtual representations of energy assets for monitoring and management purposes.

139. The Al-enabled system of claim 121, further comprising a client API and GUI integration interface configured to provide client API and GUI interfaces for energy edge resource management interaction with client systems based on outputs from the at least one artificial intelligence model.

140. The Al-enabled system of claim 121, further comprising an adaptive networking integration component configured to integrate with adaptive networking capabilities that employ loT devices to communicate among smart grid assets including smart metering and transmission and distribution monitoring.

141. An Al-enabled system for mobile energy resource tracking and forecasting, comprising: at least one artificial intelligence model configured to track and forecast where mobile distributed energy resources are positioned and determine operational status across different times and locations, a status monitoring component configured to collect real-time data including state of charge metrics and reliability parameters from the mobile distributed energy resources and provide the real-time data to the at least one artificial intelligence model, and a resource control orchestration module configured to generate automated resource control commands based on forecasts produced by the at least one artificial intelligence model.

142. The Al-enabled system of claim 141, wherein the at least one artificial intelligence model is configured to process historical movement patterns and operational data to generate predictive models that anticipate resource availability and positioning.

143. The Al-enabled system of claim 141, further comprising a dynamic resource discovery engine configured to automatically identify the mobile distributed energy resources and determine real-time availability status, connectivity parameters, and operational circumstances.

144. The Al-enabled system of claim 141, further comprising a mobile versus stationary resource classification module configured to differentiate between grid-connected stationaryAttorney Docket No. 54250-73distributed energy resources and dynamic transitory distributed energy resources including electric vehicles.

145. The Al-enabled system of claim 144, wherein the at least one artificial intelligence model is configured to generate different forecasting models for mobile distributed energy resources and stationary distributed energy resources based on classifications from the mobile versus stationary resource classification module.

146. The Al-enabled system of claim 141, further comprising a resource capability assessment engine configured to analyze the mobile distributed energy resources and determine generation capacity, storage capacity, transmission capability, and operational circumstances based on data from the status monitoring component.

147. The Al-enabled system of claim 141, wherein the at least one artificial intelligence model comprises a resource optimization intelligence engine configured to synthesize collected data and create energy operations proposals and transaction offerings through dynamic feedback loops.

148. The Al-enabled system of claim 141, further comprising a pattern recognition module configured to identify energy demand patterns, supply patterns, storage capacity patterns, and market price patterns and provide pattern data to the at least one artificial intelligence model to enhance forecasting accuracy.

149. The Al-enabled system of claim 141, further comprising an energy edge resource management network architecture configured to enable dynamic connectivity management that handles changing availability status and connectivity patterns across the mobile distributed energy resources.

150. The Al-enabled system of claim 141, further comprising a market orchestration integration module configured to connect with energy marketplaces based on energy type and location and execute market transactions based on the forecasts produced by the at least one artificial intelligence model.

151. The Al-enabled system of claim 141, further comprising a transaction orchestration and execution interface module configured to integrate with energy transaction processing for automated orchestration of energy-related transactions based on predicted positioning and operational status from the at least one artificial intelligence model.

152. The Al-enabled system of claim 141, further comprising a smart contract integration interface configured to connect with smart contract systems configured to negotiate smart contracts based on forecasted availability of the mobile distributed energy resources.

153. The Al-enabled system of claim 141, further comprising an adaptive data pipeline integration interface configured to handle data processing, filtering, compression, storage, routing, and transport for mobile resource tracking data and provide processed data to the at least one artificial intelligence model.

154. The Al-enabled system of claim 141, further comprising an edge and loT networking integration module configured to integrate with edge and loT networking systems for real-time data collection from the mobile distributed energy resources.Attorney Docket No. 54250-73155. The Al-enabled system of claim 141, further comprising an intelligent data layer architecture configured to process information from the status monitoring component through at least two stages, including an ingestion stage, an analysis stage, and a derived intelligence stage that provides inputs to the at least one artificial intelligence model.

156. The Al-enabled system of claim 141, further comprising vector-based communication protocols configured to enable efficient data exchange between the at least one artificial intelligence model and the mobile distributed energy resources.

157. The Al-enabled system of claim 141, further comprising a digital twin integration interface configured to connect with stakeholder energy digital twins that provide virtual representations of the mobile distributed energy resources for monitoring and management purposes.

158. The Al-enabled system of claim 141, further comprising a client API and GUI integration interface configured to provide client API and GUI interfaces that display forecasted positioning and operational status generated by the at least one artificial intelligence model.

159. The Al-enabled system of claim 141, further comprising a broadcast and poll interface configured to receive proposals from distributed energy resource marketplace orchestration layers regarding the mobile distributed energy resources and provide proposal data to the at least one artificial intelligence model.

160. The Al-enabled system of claim 141, further comprising a cross-service resource orchestration agent configured to manage, configure, deploy, provision, and optimize subsystems operating within linked systems based on mobile resource forecasts generated by the at least one artificial intelligence model.EXPERT SYSTEMS AND ARTIFICIAL INTELLIGENCE (ESAI) 161. An Al-enabled system for multi-variable energy optimization, comprising: at least one artificial intelligence model configured to analyze energy generation, storage, and consumption data to optimize for reliability, cost-effectiveness, and system uptime concurrently across distributed energy resources, and a digital twin system configured to maintain virtual representations of physical energy assets synchronized with real-time operational data processed by the at least one artificial intelligence model.

162. The Al-enabled system of claim 161, further comprising an expert system module configured to apply rule-based analysis of energy profiles to generate decision frameworks based on predefined operational knowledge for energy management operations.

163. The Al-enabled system of claim 161, wherein the at least one artificial intelligence model comprises generative Al capabilities configured to synthesize energy operations proposals and automated optimization recommendations based on expert assessment.

164. The Al-enabled system of claim 161, wherein the at least one artificial intelligence model comprises neural network architectures including convolutional neural networks for energy data processing and recurrent neural networks for time-series energy predictions.

165. The Al-enabled system of claim 161, further comprising an energy routing and control optimization system configured to provide intelligent path optimization for energy transmission based on grid conditions and demand patterns generated by the at least one artificial intelligenceAttorney Docket No. 54250-73model.

166. The Al-enabled system of claim 161, further comprising a distributed energy resource management system configured to coordinate fleet-level optimization for mobile and stationary energy assets including electric vehicles based on analysis performed by the at least one artificial intelligence model.

167. The Al-enabled system of claim 161, wherein the at least one artificial intelligence model comprises demand forecasting algorithms configured to utilize weather-based energy forecasting and historical consumption patterns to predict energy requirements.

168. The Al-enabled system of claim 161, further comprising a transaction management system configured to automate energy transaction processing and provide enterprise decision support based on market data and optimization recommendations from the at least one artificial intelligence model.

169. The Al-enabled system of claim 161, further comprising an energy orchestration support system configured to coordinate intelligent orchestration across at least two energy types including wind, solar, nuclear, and fossil fuel generation based on optimization directives from the at least one artificial intelligence model.

170. The Al-enabled system of claim 161, further comprising a self-optimizing controller configured to implement adaptive learning algorithms that automatically adjust operational parameters based on performance feedback from energy management operations.

171. The Al-enabled system of claim 161, wherein the digital twin system comprises at least one executive energy digital twin with role-based data presentation and at least one stakeholderspecific digital twin with intelligent agent alerts.

172. The Al-enabled system of claim 161, further comprising an Al-based data processing and integration system configured to perform automated energy-related data extraction and cleansing with pattern detection in energy data streams that are provided to the at least one artificial intelligence model.

173. The Al-enabled system of claim 161, further comprising a context-aware sensor fusion system configured to integrate disparate marketplace and operational data sources through Al classification and prediction capabilities that provide inputs to the at least one artificial intelligence model.

174. The Al-enabled system of claim 161, further comprising at least one edge-deployed Al processing unit configured to provide local energy management through distributed decisionmaking at points of energy consumption based on processing performed by the at least one artificial intelligence model.

175. The Al-enabled system of claim 174, wherein the at least one edge-deployed Al processing unit comprises semi-sentient capabilities providing distributed decision-making with local processing capabilities for energy optimization without centralized control.

176. The Al-enabled system of claim 161, wherein the at least one artificial intelligence model comprises graph neural networks configured to model distributed energy resources through nodes representing power generation, storage, transmission, and consumption capabilitiesAttorney Docket No. 54250-73connected by edges representing energy-related relationships.

177. The Al-enabled system of claim 161, further comprising natural language processing systems configured to automate processing of transaction documents and energy-related communications for intelligent extraction of trading opportunities and risk factors.

178. The Al-enabled system of claim 161, further comprising a hybrid human- Al coordination interface configured to enable collaborative decision-making between human experts and Al systems with configurable autonomy levels based on trust and reliability factors.

179. The Al-enabled system of claim 161, further comprising an environmental governance system configured to provide carbon emission monitoring and compliance reporting capabilities that integrate with the at least one artificial intelligence model for regulatory adherence.

180. The Al-enabled system of claim 161, wherein the at least one artificial intelligence model comprises transformer neural network architectures configured for energy data compression and analysis through constrained transformer networks for data embedding paired with decoding neural networks for data reproduction.

181. An Al-enabled system for autonomous energy management and control, comprising: at least one artificial intelligence model configured to generate automated control commands for energy generation assets and load balancing decisions across distributed energy networks based on real-time operational data, and at least one edge computing unit configured to execute the automated control commands at local energy consumption points.

182. The Al-enabled system of claim 181, wherein the at least one artificial intelligence model comprises machine learning algorithms configured to provide pattern recognition and prediction capabilities for energy consumption analysis.

183. The Al-enabled system of claim 181, further comprising an energy optimization engine configured to process supply and demand data from distributed sources and apply mathematical analysis algorithms to reconcile generation, storage, and load requirements based on outputs from the at least one artificial intelligence model.

184. The Al-enabled system of claim 181, further comprising a self-optimizing controller configured to implement adaptive learning algorithms that receive performance feedback from energy management operations and automatically adjust operational parameters based on the performance feedback.

185. The Al-enabled system of claim 181, further comprising a distributed energy resource coordination system configured to manage electric vehicle charging schedules, energy storage deployment, and distributed generation coordination based on optimization strategies generated by the at least one artificial intelligence model.

186. The Al-enabled system of claim 181, wherein the at least one edge computing unit is configured to process local sensor readings and equipment status information to generate control command recommendations for energy equipment at local energy consumption points.

187. The Al-enabled system of claim 181, further comprising an Al orchestration controller configured to synthesize intelligence outputs from at least two Al systems to provide coordinated decision-making frameworks for energy optimization scenarios.Attorney Docket No. 54250-73188. The Al-enabled system of claim 181, further comprising an Al training system configured to provide continuous model improvement through automated parameter adjustment based on operational feedback from energy management operations.

189. The Al-enabled system of claim 181, further comprising a transaction automation system configured to execute automated transaction processing for energy exchanges based on AI-driven recommendations from the at least one artificial intelligence model.

190. The Al-enabled system of claim 181, further comprising an energy-aware workflow integration system configured to coordinate building management and load-side optimization applications based on consumption characteristics, including peak power requirements and continuity needs.

191. The Al-enabled system of claim 181, wherein the at least one artificial intelligence model comprises deep learning systems configured to enable energy demand forecasting through supervised and unsupervised learning approaches.

192. The Al-enabled system of claim 181, further comprising a digital twin simulation system configured to perform scenario simulation and predictive modeling by processing integrated data from at least two energy management sources.

193. The Al-enabled system of claim 181, further comprising graph neural networks configured to analyze complex interconnected energy system relationships and provide optimization recommendations based on graph-based algorithms.

194. The Al-enabled system of claim 181, further comprising a dynamic routing system configured to implement automated control interfaces that adjust energy distribution parameters in real-time based on intelligent path optimization decisions from the at least one artificial intelligence model.

195. The Al-enabled system of claim 181, wherein the at least one artificial intelligence model is configured to coordinate at least two energy types including wind, solar, nuclear, and fossil fuel generation through intelligent orchestration capabilities.

196. The Al-enabled system of claim 181, further comprising an Al system management controller configured to coordinate computational resource allocation across at least two Al processing nodes based on real-time demand forecasting requirements.

197. The Al-enabled system of claim 181, further comprising reinforcement learning algorithms configured to enable continuous strategy optimization through automated adjustment of operational parameters based on market conditions and performance feedback.

198. The Al-enabled system of claim 181, further comprising a quality -of-service optimization system configured to implement adaptive routing protocols for dynamic path optimization based on energy availability and performance requirements determined by the at least one artificial intelligence model.

199. The Al-enabled system of claim 181, further comprising an Al system generation module configured to create specialized Al systems, including primary content Al systems for core energy operations and supplemental content Al systems for supporting functions.

200. The Al-enabled system of claim 181, further comprising natural language processingAttorney Docket No. 54250-73capabilities configured to analyze transaction documents and energy-related communications for intelligent extraction of trading opportunities and risk factors that are provided to the at least one artificial intelligence model.ENERGY-RELATED TRANSACTION ORCHESTRATION AND EXECUTION (ETOE) 201. An Al-enabled system for orchestrating energy transmission pathways, comprising: at least one artificial intelligence model configured to analyze distributed energy resource connection plans and identify optimal transmission pathways through transmission infrastructure, and a smart contract negotiation module configured to establish transmission agreements with transmission facility operators.

202. The Al-enabled system of claim 201, further comprising a transmission communication system configured to communicate with public utilities and private distributed energy resource entities to collect transmission infrastructure data.

203. The Al-enabled system of claim 202, wherein the transmission infrastructure data includes at least one of power quality data, pricing data, peak pricing information, step pricing information, or power limit data.

204. The Al-enabled system of claim 201, wherein the at least one artificial intelligence model is configured to process the distributed energy resource connection plans to identify planned interconnections between energy providers and energy consumers.

205. The Al-enabled system of claim 201, wherein the smart contract negotiation module is configured to negotiate smart connection contracts and smart transmission contracts with transmission infrastructure entities.

206. The Al-enabled system of claim 205, wherein the smart contract negotiation module is further configured to negotiate at least one of scheduling information, pricing information, or connection requirements.

207. The Al-enabled system of claim 201, wherein the at least one artificial intelligence model is configured to evaluate two or more potential transmission pathways and select optimal pathways based on cost, availability, and power quality criteria.

208. The Al-enabled system of claim 201, further comprising a transmission execution agent configured to receive transmission connection information for the optimal transmission pathways from the smart contract negotiation module.

209. The Al-enabled system of claim 208, wherein the transmission connection information includes time frames for accessing certain distributed energy resource entities and maximum power limits at certain times of day.

210. The Al-enabled system of claim 201, wherein the transmission infrastructure includes power grids and interconnections between power grids.

211. The Al-enabled system of claim 201, wherein the at least one artificial intelligence model is configured to identify a plurality of transmission pathways for a plurality of interconnections between distributed energy resource entities.

212. The Al-enabled system of claim 201, further comprising an edge device resource management system configured to develop the distributed energy resource connection plansAttorney Docket No. 54250-73based on transmission infrastructure data.

213. The Al-enabled system of claim 201, wherein the smart contract negotiation module includes a transmission orchestration agent configured to communicate with a subset of transmission facilities corresponding to the optimal transmission pathways.

214. The Al-enabled system of claim 201, further comprising a connection analysis system configured to analyze the distributed energy resource connection plans and provide input data to the at least one artificial intelligence model.

215. The Al-enabled system of claim 208, further comprising a plurality of resource controllers configured to receive the transmission connection information from the transmission execution agent.

216. The Al-enabled system of claim 215, wherein each resource controller is configured to send control signals to one or more power manipulation devices to control energy flow through a corresponding transmission pathway.

217. The Al-enabled system of claim 216, wherein each resource controller is in communication with a power provider connection, a power load connection, and an intervening transmission infrastructure connection.

218. The Al-enabled system of claim 217, further comprising a plurality of adaptive data networks, wherein each adaptive data network connects entities relevant for a corresponding interconnection.

219. The Al-enabled system of claim 201, wherein the at least one artificial intelligence model is configured to continuously update transmission pathway selections based on real-time changes in transmission infrastructure availability and pricing.

220. The Al-enabled system of claim 205, wherein the smart contract negotiation module is configured to automatically execute smart contracts when transmission pathway conditions satisfy predetermined criteria.

221. An Al-enabled system for controlling distributed energy transmission, comprising: at least one artificial intelligence model configured to generate transmission execution commands based on negotiated connection terms, and a plurality of resource controllers configured to control energy flow through transmission pathways based on the transmission execution commands.

222. The Al-enabled system of claim 221, further comprising a transmission orchestration system configured to negotiate the connection terms with transmission infrastructure entities and provide transmission connection information to the at least one artificial intelligence model.

223. The Al-enabled system of claim 222, wherein the transmission orchestration system includes a transmission communication system configured to communicate with public utilities and private distributed energy resource entities.

224. The Al-enabled system of claim 221, wherein the at least one artificial intelligence model is configured to process transmission connection information including scheduling information, pricing information, and connection requirements.

225. The Al-enabled system of claim 221, wherein each resource controller of the plurality of resource controllers is configured to send control signals to one or more power manipulationAttorney Docket No. 54250-73devices.

226. The Al-enabled system of claim 225, wherein the control signals regulate at least one of energy flow rate, voltage level, or power quality through a corresponding transmission pathway.

227. The Al-enabled system of claim 221, wherein each resource controller is in communication with a power provider connection, a power load connection, and an intervening transmission infrastructure connection.

228. The Al-enabled system of claim 221, wherein the transmission execution commands include time-based control parameters specifying when energy transmission is permitted through specific transmission pathways.

229. The Al-enabled system of claim 221, wherein the transmission execution commands include power limit parameters specifying maximum power levels at certain times of day.

230. The Al-enabled system of claim 221, further comprising a transmission execution agent configured to distribute the transmission execution commands to the plurality of resource controllers.

231. The Al-enabled system of claim 230, wherein the transmission execution agent is configured to receive transmission connection information from a transmission orchestration system and provide the transmission connection information to the at least one artificial intelligence model.

232. The Al-enabled system of claim 221, wherein the at least one artificial intelligence model is configured to generate individual transmission execution commands for each of a plurality of interconnections between distributed energy resource entities.

233. The Al-enabled system of claim 232, wherein the transmission execution commands identify connection requirements for transmission execution control for each of the plurality of interconnections.

234. The Al-enabled system of claim 221, further comprising a plurality of adaptive data networks, wherein each adaptive data network connects entities relevant for a corresponding transmission pathway.

235. The Al-enabled system of claim 221, wherein the negotiated connection terns are embodied in smart contracts negotiated with transmission infrastructure entities.

236. The Al-enabled system of claim 235, wherein the smart contracts comprise smart connection contracts and smart transmission contracts.

237. The Al-enabled system of claim 221, wherein the at least one artificial intelligence model is configured to monitor actual energy flow through the transmission pathways and adjust the transmission execution commands based on deviations from expected performance.

238. The Al-enabled system of claim 221, wherein the at least one artificial intelligence model is configured to process real-time transmission infrastructure conditions and dynamically modify the transmission execution commands to maintain optimal energy flow.

239. The Al-enabled system of claim 222, wherein the transmission orchestration system includes a connection analysis system configured to identify transmission pathways based on a distributed energy resource entity connection plan.Attorney Docket No. 54250-73240. The Al-enabled system of claim 221, wherein the plurality of resource controllers is configured to control energy transmission for a plurality of interconnections between distributed energy resource entities based on individualized transmission execution commands for each interconnection.EMBEDDED POLICY AND GOVERNANCE (GVNC) 241. An Al-enabled system for governance policy enforcement over energy-related transactions, comprising: at least one artificial intelligence model configured to extract policy rules from regulatory documents and convert the policy rules into executable governance logic, and a smart contract generation module configured to automatically generate smart contracts that enforce the governance policies on energy-related transactions.

242. The Al-enabled system of claim 241, wherein the at least one artificial intelligence model comprises a natural language processing engine configured to parse regulatory text using transformer-based language models to identify compliance requirements.

243. The Al-enabled system of claim 241, further comprising a governance security module configured to generate digital signatures for policy documents using cryptographic hash functions and private keys stored in tamper-resistant storage.

244. The Al-enabled system of claim 241, further comprising a policy synchronization module configured to propagate policy updates between edge devices and conduct governance consensus rounds to achieve agreement on policy states across a plurality of edge devices.

245. The Al-enabled system of claim 244, wherein the policy synchronization module is configured to implement fault tolerant consensus protocols that commit to a consensus state when receiving identical policy state confirmations from at least a threshold number of participating edge devices.

246. The Al-enabled system of claim 241, wherein the smart contract generation module is configured to monitor transaction environments for transaction initiation events, retrieve applicable governance policies, and deploy generated governance smart contracts to blockchain infrastructure.

247. The Al-enabled system of claim 246, wherein the smart contract generation module is configured to automatically aggregate carbon offset credits by querying distributed ledger repositories for available offset certificates, calculating total carbon generation from energy consumption data, determining offset quantities needed to achieve carbon neutrality, and executing smart contract transactions to acquire required offset certificates.

248. The Al-enabled system of claim 241, further comprising a policy memory subsystem that comprises a distributed hash table storage system configured to partition policy data across two or more edge devices using consistent hashing algorithms.

249. The Al-enabled system of claim 241, further comprising a dynamic policy adaptation module configured to monitor regulatory framework integration interfaces for regulatory update notifications and automatically parse updated regulatory documents to extract modified policy requirements.

250. The Al-enabled system of claim 249, wherein the dynamic policy adaptation module isAttorney Docket No. 54250-73configured to generate proposed governance policy modifications reflecting regulatory changes, submit the proposed governance policy modifications for consensus approval across a plurality of edge devices, and automatically update governance smart contract logic upon achieving consensus.

251. The Al-enabled system of claim 250, wherein the dynamic policy adaptation module is configured to simulate governance adaptations using digital twin models before deployment by maintaining virtual replicas of physical energy infrastructure, applying proposed policy adaptations to the digital twin models, executing simulated transactions through the digital twin models, and deploying adapted policies to physical edge devices only after simulation demonstrates successful policy operation.

252. The Al-enabled system of claim 241, further comprising a vector embedding algorithm configured to represent each governance policy as a numerical vector in a high-dimensional embedding space and compute semantic similarity between policies by calculating cosine similarity scores between their respective embedding vectors.

253. The Al-enabled system of claim 241, further comprising a policy stream processing framework configured to detect and analyze governance decision patterns by subscribing to governance event message queues produced by distributed edge devices, maintaining sliding time windows of recent events, and applying streaming aggregation operations to compute realtime metrics, including policy enforcement rates and compliance rates.

254. The Al-enabled system of claim 241, further comprising a decision traceability framework configured to link governance outcomes to specific policy applications through audit trail mechanisms that maintain complete records of governance decision-making processes, including decision context and reasoning patterns.

255. The Al-enabled system of claim 241, further comprising a knowledge graph database configured to represent complex regulatory relationships through interconnected node structures capturing policy interdependencies, wherein nodes represent regulatory entities and edges represent relationships between entities, including hierarchical relationships, dependency relationships, and conflict relationships.

256. The Al-enabled system of claim 255, wherein the at least one artificial intelligence model is configured to enable automated policy conflict detection by executing graph query algorithms that identify policy nodes with conflict relationship edges connecting them and automatically resolve conflicts by applying resolution rales through graph pattern matching algorithms.

257. The Al-enabled system of claim 241, wherein the smart contract generation module is configured to implement multi-party governance consensus by deploying multi-signature smart contracts that store public key identifiers for authorized governance authorities, collect approval responses including digital signatures from each authority, verify each received signature against stored public keys, and execute proposed governance policy changes when a count of valid approvals reaches or exceeds a configured threshold.

258. The Al-enabled system of claim 241, further comprising a compliance verification system configured to automate detection of deviations from regulatory standards by continuouslyAttorney Docket No. 54250-73monitoring edge device configurations, comparing monitored configurations against required baseline configurations, calculating configuration deviation scores, and triggering automated violation alerts when deviation scores exceed threshold levels.

259. The Al-enabled system of claim 241, further comprising a blockchain-compatible storage architecture configured to record governance decisions, policy enforcement actions, and compliance verification results as immutable audit trail entries with cryptographic verification preventing unauthorized modification.

260. The Al-enabled system of claim 241, wherein the at least one artificial intelligence model is configured to convert structured policy templates into executable policy logic that can be deployed to a plurality of edge devices throughout an energy distribution network.

261. An Al-enabled system for regulatory compliance monitoring over energy-related transactions, comprising: at least one artificial intelligence model configured to analyze operational data from distributed energy infrastructure against regulatory compliance requirements and detect compliance violations through pattern recognition, a plurality of policy adherence monitoring sensors positioned throughout an energy distribution network and configured to collect the operational data, and an immutable audit trail system configured to record compliance events and governance decisions.

262. The Al-enabled system of claim 261, wherein the plurality of policy adherence monitoring sensors comprises environmental monitoring sensors configured to track air quality, water contamination, and soil conditions within resource extraction areas.

263. The Al-enabled system of claim 261, wherein the plurality of policy adherence monitoring sensors comprises safety monitoring sensors configured to monitor hazard conditions through gas sensors, structural integrity monitors, and worker proximity tracking systems.

264. The Al-enabled system of claim 261, wherein the plurality of policy adherence monitoring sensors comprises resource quality assessment sensors positioned at material extraction points and configured to continuously sample extracted materials to ensure compliance with specifications and regulatory standards.

265. The Al-enabled system of claim 261, wherein the at least one artificial intelligence model comprises a policy inference engine configured to process energy consumption pattern data through neural network architectures optimized for edge deployment scenarios.

266. The Al-enabled system of claim 261, wherein the at least one artificial intelligence model comprises a violation pattern recognition algorithm configured to identify compliance violations by monitoring time-series energy transaction data, extracting feature vectors representing transaction characteristics, comparing the feature vectors against learned violation signatures using trained neural network classifiers, and calculating anomaly scores by measuring distance from normal transaction patterns.

267. The Al-enabled system of claim 266, wherein the violation pattern recognition algorithm is configured to trigger violation alerts when anomaly scores exceed threshold levels configured based on regulatory severity classifications.

268. The Al-enabled system of claim 261, wherein the at least one artificial intelligence modelAttorney Docket No. 54250-73comprises a computer vision policy violation detection system configured to monitor energy operations through image and video analysis by processing video frames through convolutional neural network architectures trained to recognize equipment states and personnel activities, comparing detected equipment configurations against regulatory -compliant reference configurations, and identifying deviations.

269. The Al-enabled system of claim 261, further comprising a smart contract enforcement system configured to implement automated policy enforcement by embedding governance policies directly into transaction-enabling smart contracts such that policy checks are performed atomically with transaction processing.

270. The Al-enabled system of claim 261, further comprising a carbon footprint governance system configured to monitor energy consumption, calculate carbon dioxide equivalent emissions from the monitored energy consumption, compare calculated carbon emissions against threshold levels specified in governance policies, and initiate preventative actions when projected carbon generation indicates probable threshold violations.

271. The Al-enabled system of claim 270, wherein the carbon footprint governance system is configured to execute device shutdown operations when calculated carbon emissions reach or exceed critical thresholds by maintaining priority rankings of energy-consuming devices based on operational criticality, calculating carbon generation contributions of each device, identifying lowest-priority devices whose shutdown would bring projected carbon totals below threshold levels, and issuing shutdown commands to selected devices.

272. The Al-enabled system of claim 261, further comprising a regulatory compliance verification system configured to automate compliance verification by maintaining repositories of regulatory requirements, parsing the regulatory requirements using natural language processing to extract specific technical requirements, automatically collecting evidence of compliance by querying edge devices for configuration data, and comparing collected evidence against verification criteria.

273. The Al-enabled system of claim 272, wherein the regulatory compliance verification system is configured to automate detection of deviations from critical infrastructure protection standards by continuously monitoring edge device configurations, comparing monitored configurations against required baseline configurations, calculating configuration deviation scores, and triggering automated violation alerts when deviation scores exceed threshold levels.

274. The Al-enabled system of claim 261, wherein the immutable audit trail system is configured to record governance-relevant events, including policy enforcement actions, compliance verifications, regulatory violation detections, and remediation initiations by formatting governance data into blockchain-compatible ledger structures and recording using append-only log structures.

275. The Al-enabled system of claim 261, further comprising a governance-constrained transaction orchestration system configured to orchestrate energy transactions by automatically evaluating proposed transactions against governance policies before initiating execution and verifying that transactions satisfy governance policies, including carbon generation limits,Attorney Docket No. 54250-73renewable energy requirements, and financial control policies.

276. The Al-enabled system of claim 261, further comprising a policy-compliant transaction configuration system configured to automatically configure transaction terms by retrieving applicable governance policies for contemplated transactions, analyzing retrieved governance policies to extract enforceable constraints on transaction parameters, and configuring transaction parameters to satisfy extracted governance constraints through a constraint satisfaction process.

277. The Al-enabled system of claim 276, wherein the at least one artificial intelligence model is configured to apply real-time policy constraint satisfaction solvers that apply mathematical programming techniques to identify transaction parameter combinations satisfying all constraints simultaneously.

278. The Al-enabled system of claim 261, further comprising a distributed governance state synchronization module configured to enable each edge device to maintain a local replica of governance policy state, propagate detected governance policy update events to peer devices through mesh networking protocols, and conduct governance consensus rounds comprising exchanging proposed governance policy states, comparing received governance states against locally computed states, and voting on accepting or rejecting proposed governance states based on validation criteria.

279. The Al-enabled system of claim 261, wherein the at least one artificial intelligence model comprises a sensor fusion processor configured to integrate multi-sensor data streams from loT devices, operational monitoring systems, and market data feeds to provide comprehensive governance evaluation inputs through real-time data correlation and pattern recognition.

280. The Al-enabled system of claim 269, wherein the smart contract enforcement system is configured to implement multi-party governance consensus by deploying multi-signature smart contracts that collect cryptographic approvals from a threshold number of governance authorities and execute proposed governance policy changes upon achieving the threshold number of valid approvals.NATURAL RESOURCE EXPLORATION AND DISCOVERY (NRED) 281. An Al-enabled system for energy resource exploration and discovery, comprising: at least one artificial intelligence model configured to analyze geological data and identify resource-rich formations for petroleum deposits, natural gas formations, and rare earth metals, and a digital twin simulation system configured to model underground energy resource formations with realtime synchronization based on exploration data.

282. The Al-enabled system of claim 281, further comprising a multi -resource detection framework configured to enable simultaneous exploration and discovery of petroleum deposits, natural gas formations, and rare earth metals through differentiated processing capabilities within unified detection protocols.

283. The Al-enabled system of claim 282, wherein the multi-resource detection framework includes an underground petroleum deposit discovery exploration module configured to implement targeted algorithms addressing petroleum exploration requirements through petroleum-specific detection protocols.Attorney Docket No. 54250-73284. The Al-enabled system of claim 282, wherein the multi-resource detection framework includes a natural gas formation identification module configured to implement formationspecific analysis techniques that optimize natural gas resource discovery processes through contextual simulation and forecasting processes.

285. The Al-enabled system of claim 282, wherein the multi-resource detection framework includes an energy infrastructure materials rare earth metal detection system configured to provide targeted exploration capabilities for rare earth metals essential for renewable energy technologies including materials required for magnets, batteries, and electrical systems.

286. The Al-enabled system of claim 281, wherein the at least one artificial intelligence model is configured to utilize neural network architectures enabling automated pattern identification from complex geological datasets.

287. The Al-enabled system of claim 281, wherein the digital twin simulation system is configured to simulate different scenarios in discovery of formations and underground deposits and predict resource availability through formation analysis methods.

288. The Al-enabled system of claim 281, further comprising a resource exploration scenario reconciliation system configured to deploy expert systems and artificial intelligence for reconciliation of two or more exploration scenarios using simulation models that optimize resource discovery strategies based on geological data analysis and predictive modeling outcomes.

289. The Al-enabled system of claim 281, further comprising an adaptive data processing pipeline configured to handle geological, seismic, and resource exploration data through intelligent processing systems with automated data extraction, transformation, and loading capabilities that adapt processing workflows based on geological data characteristics.

290. The Al-enabled system of claim 281, further comprising a satellite imagery processing system configured to utilize computer vision systems for processing satellite imagery and geological mapping data for resource identification that analyze remote sensing data to identify geological features correlating with underground resource deposits.

291. The Al-enabled system of claim 281, further comprising a predictive analytics system configured to provide forecasting models for predicting resource quantities and availability that integrate market data to optimize exploration priorities.

292. The Al-enabled system of claim 281, further comprising an exploration site local data processing edge computing system configured to process exploration data at remote drilling and survey sites through distributed computing capabilities enabling real-time data analysis at exploration locations.

293. The Al-enabled system of claim 281, further comprising an exploration equipment monitoring loT sensor network configured to provide real-time monitoring of exploration equipment and environmental conditions during resource discovery operations.

294. The Al-enabled system of claim 293, wherein the exploration equipment monitoring loT sensor network is configured to transmit data through a secure data communication network that implements encrypted data transmission systems and adaptive networking protocols forAttorney Docket No. 54250-73protecting sensitive geological information.

295. The Al-enabled system of claim 281, further comprising a multi-variable exploration scenario analysis system configured to combine two or more data sources for comprehensive resource assessment through intelligent systems that evaluate exploration scenarios and discovery potential by processing geological, environmental, and market variables simultaneously.

296. The Al-enabled system of claim 281, wherein the at least one artificial intelligence model is configured to process geological survey data, seismic measurements, and historical exploration results to produce pattern recognition reports and resource probability assessments.

297. The Al-enabled system of claim 281, wherein the digital twin simulation system is configured to provide visualization interfaces that enable interactive access to formation models.

298. The Al-enabled system of claim 281, wherein the at least one artificial intelligence model is configured to process seismic data, geological surveys, and satellite imagery to produce resource probability maps and exploration target recommendations.

299. The Al-enabled system of claim 281, further comprising an environmental impact assessment monitoring system configured to evaluate environmental side-effects of resource extraction and utilization through Al systems integrated with regulatory compliance systems for environmental monitoring.

300. The Al-enabled system of claim 281, further comprising a coordinated resource management integration energy edge platform interface configured to integrate resource discovery capabilities within broader Al-based energy edge platforms for coordinated resource management between resource discovery systems and distributed energy resource management capabilities.

301. An Al-enabled system for integrating discovered energy resources into supply chains, comprising: at least one artificial intelligence model configured to automatically coordinate discovered petroleum deposits, natural gas formations, and rare earth metals into energy supply chains based on resource characteristics and market demands, and a cross-platform information exchange system configured to connect resource exploration data to energy transaction orchestration systems.

302. The Al-enabled system of claim 301, wherein the at least one artificial intelligence model is configured to optimize allocation decisions by processing resource discovery reports, supply chain capacity data, and market pricing information to produce resource allocation plans and integration performance metrics.

303. The Al-enabled system of claim 301, wherein the at least one artificial intelligence model is configured to automatically integrate discovered resources into raw material supply chains for energy generation, natural material supply chains for energy infrastructure, and materials transaction marketplace supply chains.

304. The Al-enabled system of claim 301, further comprising a resource exploration active operations real-time data feed configured to deliver continuous information flow from resource exploration to energy market orchestration layers based on real-time data transmissionAttorney Docket No. 54250-73algorithms.

305. The Al-enabled system of claim 301, further comprising a multi-resource detection framework configured to generate resource discovery data for petroleum deposits, natural gas formations, and rare earth metals that is provided to the at least one artificial intelligence model for supply chain coordination.

306. The Al-enabled system of claim 301, further comprising a resource exploration priorities optimization market data integration system configured to integrate market data to predict resource demand and optimize exploration priorities through analytics systems that steer research funding toward projects based on discovered resource availability.

307. The Al-enabled system of claim 301, further comprising a supply chain downstream process adaptation system configured to connect upstream exploration and discovery processes with downstream processing and utilization operations through dynamic adjustment of processing capabilities based on discovered resource characteristics and market requirements.

308. The Al-enabled system of claim 301, wherein the cross-platform information exchange system is configured to provide APIs and data integration layers connecting resource discoveries to energy transaction orchestration systems while maintaining data integrity across different system architectures.

309. The Al-enabled system of claim 301, further comprising a coordinated resource management integration energy edge platform interface configured to integrate resource discovery capabilities within broader Al-based energy edge platforms for coordinated resource management.

310. The Al-enabled system of claim 301, further comprising a geological exploration adaptive data processing pipeline configured to handle geological, seismic, and resource exploration data through intelligent processing systems with automated data extraction, transformation, and loading capabilities.

311. The Al-enabled system of claim 301, further comprising a predictive analytics system configured to provide forecasting models for predicting resource quantities and availability that integrate market data to optimize exploration priorities.

312. The Al-enabled system of claim 301, further comprising an exploration site local data processing edge computing system configured to process exploration data at remote drilling and survey sites through distributed computing capabilities enabling real-time data analysis at exploration locations.

313. The Al-enabled system of claim 312, further comprising an exploration equipment monitoring loT sensor network configured to provide real-time monitoring of exploration equipment and environmental conditions during resource discovery operations and transmit data to the exploration site local data processing edge computing system.

314. The Al-enabled system of claim 301, further comprising a satellite imagery processing system configured to utilize computer vision systems for processing satellite imagery and geological mapping data for resource identification that analyze remote sensing data to identify geological features correlating with underground energy resource deposits.Attorney Docket No. 54250-73315. The Al-enabled system of claim 301, further comprising a resource exploration partem recognition system configured to implement Al-powered geological pattern recognition through machine learning models trained on geological survey data for identifying energy resource -rich formations.

316. The Al-enabled system of claim 301, further comprising a digital twin simulation system configured to model underground energy resource formations and provide three-dimensional modeling capabilities that simulate underground deposit characteristics and predict resource availability.

317. The Al-enabled system of claim 301, further comprising an environmental impact assessment monitoring system configured to evaluate environmental side-effects of resource extraction and utilization through Al systems integrated with regulatory compliance systems for environmental monitoring that assess exploration activities for compliance with environmental regulations.

318. The Al-enabled system of claim 301, further comprising a multi-variable exploration scenario analysis system configured to combine two or more data sources for comprehensive resource assessment through intelligent systems that evaluate exploration scenarios and discovery potential by processing geological, environmental, and market variables simultaneously.

319. The Al-enabled system of claim 301, wherein the at least one artificial intelligence model is configured to coordinate resource allocation decisions based on real-time exploration data and market demand requirements for raw material supply chains for energy generation and natural material supply chains for energy infrastructure.

320. The Al-enabled system of claim 301, further comprising a resource discovery information secure data communication network configured to transmit sensitive geological and resource discovery information through encrypted data transmission systems and adaptive networking protocols.SENSOR AND DATA FUSION (SFUS)321. An Al-enabled system for sensor fusion in energy infrastructure, comprising: a multisensor fusion system configured to generate fused sensor data by combining readings from at least two sensors measuring operational parameters of distributed energy resources and detect anomalies through pattern recognition, and at least one artificial intelligence model configured to analyze fused sensor data from the at least two sensors.

322. The Al-enabled system of claim 321, wherein the multi-sensor fusion system comprises a classical sensor fusion architecture configured to combine readings from at least two sensors that measure a variable through mathematical fusion algorithms and weighted averaging processors.

323. The Al-enabled system of claim 321, wherein the multi-sensor fusion system comprises a multivariable analysis module configured to fuse sensor data across different variables including vibration, temperature, and pressure through cross-correlation processors and multivariate statistical engines.

324. The Al-enabled system of claim 321, wherein the at least one artificial intelligence modelAttorney Docket No. 54250-73comprises neural networks configured to leverage the fused sensor data for equipment monitoring and predictive maintenance of distributed energy resources.

325. The Al-enabled system of claim 321, wherein the at least one artificial intelligence model comprises graph neural networks configured to model relationships between sensors for coordinated sensor data analysis across distributed energy resources.

326. The Al-enabled system of claim 321, further comprising a multi-sensor redundancy system including hardware sensor arrays with at least two physical sensors measuring energy edge participant parameters, software-based sensor validation algorithms, and interpolation processors configured to calculate estimated values based on individual sensor failures.

327. The Al-enabled system of claim 321, further comprising automated interpolation algorithms configured to perform linear, polynomial, or spline interpolation between functioning sensors to maintain continuous data flow to downstream systems.

328. The Al-enabled system of claim 321, further comprising controlled sensor data processing pipelines incorporating sensor data ingestion engines, intelligence service modules, and pattern recognition processors specifically designed for processing the fused sensor data.

329. The Al-enabled system of claim 321, further comprising self-organizing sensor data storage systems including adaptive storage algorithms, content-based indexing systems, and context-aware data organization engines configured to automatically organize the fused sensor data based on patterns, attributes, content, and context.

330. The Al-enabled system of claim 321, further comprising intelligent sensor data layers comprising extraction processors, transformation engines, loading systems, normalization algorithms, cleansing filters, compression modules, and encoding systems specifically designed for sensor fusion data streams.

331. The Al-enabled system of claim 321, further comprising real-time sensor data processing systems incorporating sensor data stream processing engines, immediate sensor data analysis algorithms, and low-latency sensor data response systems configured to provide instantaneous analysis of sensor inputs from energy grid assets using sensor fusion techniques.

332. The Al-enabled system of claim 321, wherein the at least one artificial intelligence model comprises anomaly detection engines and multi-sensor fusion processors configured to continuously monitor sensor data streams to identify anomalies through fusion of at least two sensor inputs.

333. The Al-enabled system of claim 321, further comprising a context-aware sensor fusion system configured to integrate marketplace data with operational data to provide inputs to the at least one artificial intelligence model for classification, prediction, and optimization for computation-intensive energy industries.

334. The Al-enabled system of claim 321, wherein the at least one artificial intelligence model comprises computer vision networks for equipment monitoring and predictive analytics models that leverage the fused sensor data for maintenance optimization.

335. The Al-enabled system of claim 321, wherein the at least one artificial intelligence model comprises deep reinforcement learning models configured to provide real-time control andAttorney Docket No. 54250-73performance optimization of individual distributed energy resources using the fused sensor data from at least two sources.

336. The Al-enabled system of claim 321, further comprising federated learning capabilities configured to enable sharing of operational insights across sensor networks while maintaining data privacy and supporting distributed sensor fusion learning across at least two nodes.

337. The Al-enabled system of claim 321, further comprising unusual sensor data-related event detection algorithms configured to identify malfunctions, faults, and natural phenomena impacting energy systems through multivariable sensor fusion analysis performed by the at least one artificial intelligence model.

338. The Al-enabled system of claim 321, wherein the at least one artificial intelligence model is configured to operate on infrastructure asset sensor data using sensor fusion to produce optimized operating parameters for energy generation, storage, and consumption.

339. The Al-enabled system of claim 321, wherein the at least one artificial intelligence model comprises self-organizing neural networks configured to provide visualization capabilities for identifying structures in unlabeled sensor data from transactional environments by combining at least two sensor inputs through sensor fusion.

340. The Al-enabled system of claim 321, further comprising a sensor digital twin integration module configured to create accurate virtual representations of physical energy systems by integrating real-time operational data through sensor fusion techniques and providing the virtual representations to the at least one artificial intelligence model.

341. An Al-enabled system for adaptive energy data routing and communication, comprising at least one artificial intelligence model configured to optimize routing of sensor data from distributed energy resources based on network conditions and dynamically select communication protocols for sensor networks, and an adaptive sensor data pipeline configured to transmit the sensor data based on the routing optimized by the at least one artificial intelligence model.

342. The Al-enabled system of claim 341, wherein the adaptive sensor data pipeline comprises intelligent transmission managers, network condition analyzers, and dynamic routing algorithms configured to manage sensor data transmission across network nodes with filtering, compression, and routing capabilities based on current network conditions.

343. The Al-enabled system of claim 341, further comprising vector-based sensor data communication protocols incorporating vectorization engines, data structure optimization algorithms, and efficient transmission systems configured to combine at least two sensor inputs into optimized vectorized data structures based on instructions from the at least one artificial intelligence model.

344. The Al-enabled system of claim 341, further comprising a distributed sensor resources integration module configured to gather a data set from solar panels, wind turbines, battery storage systems, and consumption monitoring devices simultaneously using sensor fusion techniques and provide the data set to the at least one artificial intelligence model.

345. The Al-enabled system of claim 341, further comprising APIs and service-orientedAttorney Docket No. 54250-73architecture interfaces, including service orchestration engines, data format translation processors, and integration protocol handlers configured to handle diverse sensor data formats, communication protocols, and service interfaces from various energy system components and sensors.

346. The Al-enabled system of claim 341, further comprising a cross-service sensor optimization framework comprising computational sensor allocation algorithms, distributed sensor asset coordination engines, and system-wide sensor load balancing processors configured to coordinate at least two sensor and data fusion services based on resource requirements determined by the at least one artificial intelligence model.

347. The Al-enabled system of claim 341, further comprising location-based sensing capabilities incorporating geographic positioning systems, event detection algorithms, and location data fusion processors configured to combine geographic information with operational sensor data to identify location-specific events and conditions.

348. The Al-enabled system of claim 341, further comprising an edge device integration module including local processors, on-device sensor fusion algorithms, and edge networking interfaces configured to perform sensor fusion processing directly at energy asset locations to reduce latency and bandwidth requirements.

349. The Al-enabled system of claim 341, further comprising loT sensor network interfaces comprising distributed sensing device connectors, network-level sensor fusion processors, and loT communication protocol handlers configured to manage at least two distributed sensors while performing coordinated sensor fusion across the sensor network.

350. The Al-enabled system of claim 341, further comprising grid sensor monitoring systems incorporating interconnected device trackers, intelligent equipment analyzers, and multi-point data combination processors configured to track loT devices and intelligent equipment using sensor fusion to combine data from at least two monitoring points and provide the data combined data from the at least two monitoring points to the at least one artificial intelligence model.

351. The Al-enabled system of claim 341, further comprising redundant sensor networks including parallel data collection systems, sensor fusion combination algorithms, and reliability improvement processors configured to provide parallel data collection from energy infrastructure with sensor fusion algorithms to combine redundant measurements.

352. The Al-enabled system of claim 341, further comprising an energy operations data fusion module configured to generate a data set by combining sensor readings with market and environmental data through multivariable sensor fusion techniques and provide the data set to the at least one artificial intelligence model for analysis.

353. The Al-enabled system of claim 341, further comprising smart distributed energy resource sensor integration systems incorporating distributed energy resource performance monitors, performance metric analyzers, and sensor fusion combination processors configured to analyze efficiency, output, reliability, and other performance indicators simultaneously across distributed energy resources.Attorney Docket No. 54250-73354. The Al-enabled system of claim 341, further comprising automated sensor discovery systems including computer vision processors, satellite image analyzers, web image content processors, natural language processing engines, and artificial intelligence data processing systems configured to identify energy generation or storage resources using sensor fusion techniques.

355. The Al-enabled system of claim 341, further comprising a sensor digital twin integration module comprising virtual system model processors, operational data integration engines, and comprehensive system representation generators configured to create accurate virtual representations of physical energy systems by integrating real-time operational data through sensor fusion.

356. The Al-enabled system of claim 341, further comprising an energy ecosystem terminology adaptation interface incorporating terminology translation engines, data format converters, and measurement unit standardization processors configured to enable sensor fusion across systems with different data formats and measurement units for various energy management concepts and standards.

357. The Al-enabled system of claim 341, further comprising a standardized data formats module including data representation processors, compatibility assurance engines, and format standardization systems configured to provide consistent sensor data representation across different devices supporting sensor fusion by ensuring compatibility between different sensor types and manufacturers.

358. The Al-enabled system of claim 341, further comprising interoperability protocols comprising communication standardization engines, diverse sensor type interfaces, and heterogeneous network coordination systems configured to enable seamless communication between diverse sensor types and manufacturers facilitating sensor fusion across heterogeneous sensor networks.

359. The Al-enabled system of claim 341, further comprising user interface systems incorporating data layer configuration processors, algorithm portal managers, data retention rule controllers, resource usage prioritization engines, and data security maintenance systems configured to facilitate user-centric data processing requirements for sensor fusion systems.

360. The Al-enabled system of claim 341, wherein the at least one artificial intelligence model is configured to dynamically adjust routing paths based on current network conditions, select optimal protocols for sensor communications, and adapt routing strategies based on radio frequency conditions for wireless sensor networks.CONTEXTUAL SIMULATION AND FORECASTING (SIMU) 361. An Al-enabled system for energy system simulation and forecasting, comprising: at least one artificial intelligence model configured to simulate generation, consumption, and storage operations across distributed energy resources and generate predictive forecasts based on historical patterns and real-time operational data, and a digital twin integration interface configured to synchronize the predictive forecasts with digital representations of physical energy assets.Attorney Docket No. 54250-73362. The Al-enabled system of claim 361, further comprising a weather-correlated forecasting engine configured to integrate meteorological data with energy generation information to provide day-ahead generation predictions for renewable energy sources.

363. The Al-enabled system of claim 361, further comprising a DER behavioral modeling system configured to model behaviors of mobile and stationary distributed energy resources, including electric vehicles, solar photovoltaic systems, wind generation, and fuel cells.

364. The Al-enabled system of claim 363, wherein the DER behavioral modeling system is configured to model energy prosumers that function as both energy producers and energy consumers.

365. The Al-enabled system of claim 361, further comprising a real-time data fusion interface configured to process information from loT devices, edge devices, public data resources, and energy-relevant event streams to provide comprehensive operational data to the at least one artificial intelligence model.

366. The Al-enabled system of claim 361, further comprising an edge computing orchestration module configured to coordinate simulation operations across cloud, colocation, on-premises, and edge infrastructure environments based on energy availability and processing requirements.

367. The Al-enabled system of claim 361, wherein the at least one artificial intelligence model comprises a multi-variable integration framework configured to coordinate integration of generation simulations, consumption simulations, and storage simulations.

368. The Al-enabled system of claim 361, wherein the at least one artificial intelligence model comprises a generation algorithm simulation module configured to simulate generation capacity forecasts that account for weather dependencies, equipment performance characteristics, and asset operational constraints.

369. The Al-enabled system of claim 361, wherein the at least one artificial intelligence model comprises a consumption pattern simulation engine configured to process consumption patterns from industrial facilities, data centers, residential prosumers, and mobile energy consumers.

370. The Al-enabled system of claim 361, wherein the at least one artificial intelligence model comprises a storage operations modeling system configured to simulate charging and discharging cycles, state-of-charge management, and storage system degradation patterns.

371. The Al-enabled system of claim 361, further comprising a scenario analysis and planning engine configured to generate at least two operational scenarios that account for weather variations, demand fluctuations, equipment failures, and market conditions.

372. The Al-enabled system of claim 361, further comprising a predictive decision-making interface configured to generate actionable insights and recommendations based on simulation results from the at least one artificial intelligence model.

373. The Al-enabled system of claim 361, further comprising a contextual simulation environment module configured to analyze energy-related behaviors based on historical patterns, current operational states, including market conditions, and anticipated states of entities involved in energy generation, storage, delivery, and consumption.

374. The Al-enabled system of claim 361, further comprising a composite model employmentAttorney Docket No. 54250-73system configured to coordinate Al technologies, digital twin systems, and probabilistic modeling approaches to leverage at least two modeling methodologies concurrently.

375. The Al-enabled system of claim 361, further comprising a 3D visualization and simulation interface configured to present simulation outputs from the at least one artificial intelligence model in three-dimensional digital twin environments.

376. The Al-enabled system of claim 361, wherein the at least one artificial intelligence model comprises GPU-accelerated computing clusters configured to perform parallel processing of multi-variable energy calculations and real-time optimization scenarios.

377. The Al-enabled system of claim 361, wherein the at least one artificial intelligence model is configured to incorporate distributed energy resource variability and prosumer behavioral patterns to enhance prediction accuracy.

378. The Al-enabled system of claim 366, wherein the edge computing orchestration module is configured to optimize computational resource allocation while maintaining energy efficiency through coordination with the at least one artificial intelligence model for workload prediction.

379. The Al-enabled system of claim 362, wherein the weather-correlated forecasting engine is configured to employ solar irradiance calculation algorithms, wind resource assessment tools, and statistical correlation methods that establish relationships between meteorological condition forecasts and renewable energy generation capacity modeling.

380. The Al-enabled system of claim 361, further comprising a continuous learning system configured to compare model-based predictions from the at least one artificial intelligence model to actual operational outcomes and incorporate expert feedback to improve simulation accuracy over time.

381. An Al-enabled system for probabilistic energy scenario generation, comprising: at least one artificial intelligence model configured to generate at least two operational scenarios that account for uncertainty in energy system operations using probabilistic techniques and random walk algorithms, and a continuous learning system configured to improve accuracy of the at least two operational scenarios based on comparison of predicted outcomes to actual operational results.

382. The Al-enabled system of claim 381, further comprising a probabilistic modeling framework configured to employ Monte Carlo simulations, Bayesian inference, and stochastic processes to generate probability distributions for energy generation capacity, demand patterns, and operational parameters.

383. The Al-enabled system of claim 381, wherein the at least one artificial intelligence model comprises a random walk algorithm engine configured to implement random walk and random forest algorithms to simulate stochastic energy system behaviors including energy price volatility, demand fluctuations, and generation variability.

384. The Al-enabled system of claim 381, further comprising a trend projection and analysis module configured to analyze historical data patterns and project trends to identify seasonal patterns, growth trends, and cyclical behaviors in energy generation and consumption data.

385. The Al-enabled system of claim 381, wherein the at least one artificial intelligence modelAttorney Docket No. 54250-73comprises a scenario model generation system configured to create diverse operational scenario models that span different weather conditions, demand patterns, equipment availability, and market conditions.

386. The Al-enabled system of claim 385, wherein the scenario model generation system is configured to generate scenario model families that explore best-case, worst-case, and most-likely operational conditions for comprehensive energy system planning.

387. The Al-enabled system of claim 381, further comprising a financial outcome simulation interface configured to create simulated financial outcomes for different operational scenarios including revenue projections, cost analysis, and economic optimization.

388. The Al-enabled system of claim 381, further comprising an operational outcome modeling system configured to simulate operational consequences of different scenarios including system reliability, asset utilization, and performance metrics.

389. The Al-enabled system of claim 381, further comprising a decision-making support framework configured to provide decision trees, risk assessment matrices, and optimization recommendations based on probabilistic scenario analysis results from the at least one artificial intelligence model.

390. The Al-enabled system of claim 381, wherein the continuous learning system comprises a prediction-outcome comparison engine configured to measure prediction accuracy across energy generation forecasts, consumption pattern predictions, and storage operation estimates.

391. The Al-enabled system of claim 381, wherein the continuous learning system comprises an expert feedback integration module configured to incorporate input from energy system operators, grid engineers, and domain specialists regarding observed system behaviors and operational constraints.

392. The Al-enabled system of claim 381, wherein the continuous learning system comprises a continuous learning algorithm system configured to employ online learning algorithms, reinforcement learning, and adaptive neural networks that update model parameters based on streaming operational data.

393. The Al-enabled system of claim 381, wherein the continuous learning system comprises a closed-loop optimization framework configured to coordinate feedback collection, model assessment, and parameter adjustment processes to create self-improving energy simulation systems.

394. The Al-enabled system of claim 381, wherein the continuous learning system comprises a performance feedback interface configured to establish standardized feedback loops that collect performance data from operational systems, sensor networks, and control interfaces.

395. The Al-enabled system of claim 381, wherein the continuous learning system comprises a model adaptation and refinement engine configured to implement model versioning, parameter adjustment, and architecture modification capabilities based on changing energy system characteristics.

396. The Al-enabled system of claim 381, wherein the continuous learning system comprises an accuracy maintenance and validation system configured to monitor prediction accuracy trends,Attorney Docket No. 54250-73identify degradation patterns, and implement corrective measures to preserve long-term forecasting reliability.

397. The Al-enabled system of claim 381, further comprising a real-time data fusion interface configured to process a data set collected from loT devices, edge devices, public data resources, and energy-relevant event streams to generate a result, and provide the result of processing the data set to the at least one artificial intelligence model.

398. The Al-enabled system of claim 381, further comprising a contextual simulation environment module configured to analyze energy-related behaviors based on historical patterns, current operational states including market conditions, and anticipated states of entities involved in energy generation, storage, delivery, and consumption.

399. The Al-enabled system of claim 381, further comprising a composite model employment system configured to coordinate Al technologies, digital twin systems, and probabilistic modeling approaches and provide consolidated analysis results to the at least one artificial intelligence model.

400. The Al-enabled system of claim 381, further comprising an edge computing orchestration module configured to coordinate simulation operations across cloud, colocation, on-premises, and edge infrastructure environments based on energy availability and computational workload requirements determined by the at least one artificial intelligence model.HIGH- ALTITUDE DATA CENTERS401. An Al-enabled system for high-altitude computational processing, comprising: at least one artificial intelligence model configured to analyze computational workload demands and generate task scheduling strategies for executing computational operations at a high-altitude location above a planetary body, at least one computational processing unit positioned at the high-altitude location and configured to execute computational workloads based on the task scheduling strategies, and at least one power collection system configured to collect energy at the high-altitude location and provide power to the at least one computational processing unit.

402. The Al-enabled system of claim 401, wherein the high-altitude location comprises at least one of a low atmospheric orbit altitude, a high atmospheric altitude, a geosynchronous orbit altitude, or a space altitude outside of an atmosphere of the planetary body.

403. The Al-enabled system of claim 401, wherein the at least one power collection system comprises solar panels configured to collect solar energy at the high-altitude location.

404. The Al-enabled system of claim 401, wherein the at least one power collection system comprises a power receiver configured to receive laser-transmitted power from a laser transmitter.

405. The Al-enabled system of claim 401, wherein the at least one power collection system comprises a micronuclear reactor configured to generate power at the high-altitude location.

406. The Al-enabled system of claim 401, wherein the at least one computational processing unit comprises at least one of compute servers, high-performance computing clusters, or artificial intelligence architectures configured to perform training, testing, or validation on training data.Attorney Docket No. 54250-73407. The Al-enabled system of claim 401, further comprising a thermal management system configured to dissipate heat generated by the at least one computational processing unit through radiative cooling into space based on thermal load data analyzed by the at least one artificial intelligence model.

408. The Al-enabled system of claim 407, wherein the thermal management system comprises at least one of deployable radiator panels, heat pipes, or phase-change thermal management systems configured to operate in vacuum or near-vacuum conditions.

409. The Al-enabled system of claim 401, further comprising a communication system configured to transmit and receive data with clients located on or below a surface of the planetary body based on communication scheduling determined by the at least one artificial intelligence model.

410. The Al-enabled system of claim 409, wherein the communication system comprises at least one of optical communication systems including laser transceivers or radio frequency communication systems operating across at least two frequency bands.

411. The Al-enabled system of claim 401, further comprising a position control system configured to maintain or adjust an orbital position of the Al-enabled system based on positioning commands generated by the at least one artificial intelligence model.

412. The Al-enabled system of claim 411, wherein the position control system comprises at least one of rockets, solar sails, or attachment interfaces for connecting to propulsion sources.

413. The Al-enabled system of claim 401, further comprising radiation shielding configured to protect the at least one computational processing unit from ionizing radiation, cosmic rays, or solar particle events.

414. The Al-enabled system of claim 401, further comprising debris protection systems configured to protect the Al-enabled system from micrometeorites or orbital debris.

415. The Al-enabled system of claim 401, further comprising fault-tolerant architectures including at least one of redundant computing nodes, distributed data storage with error correction, or autonomous damage detection and isolation systems configured to enable continued operation based on failure detection by the at least one artificial intelligence model.

416. The Al-enabled system of claim 401, wherein the at least one artificial intelligence model is configured to schedule computational tasks based on anticipated solar energy availability at different orbital positions.

417. The Al-enabled system of claim 401, further comprising a weather monitoring system configured to monitor high-altitude atmospheric conditions and provide monitoring data to the at least one computational processing unit for processing into weather forecasting or climate change monitoring outputs.

418. The Al-enabled system of claim 401, further comprising modular components including at least one of replaceable computing modules or swappable storage units configured for in-situ servicing at the high-altitude location.

419. The Al-enabled system of claim 401, further comprising at least one compartment configured to accommodate at least one of passengers or robots at the high-altitude location.Attorney Docket No. 54250-73420. The Al-enabled system of claim 401, wherein the at least one artificial intelligence model is configured to select the high-altitude location based on at least one of security considerations, jurisdictional requirements, or latency requirements for data services.

421. An Al-enabled system for distributed high-altitude data center coordination, comprising: at least one artificial intelligence model configured to coordinate computational workloads and communication routing between at least two high-altitude data centers positioned at different orbital altitudes and generate workload distribution commands based on computational capacity and orbital geometry, at least one communication relay system associated with each of the at least two high-altitude data centers and configured to transmit data between the at least two high-altitude data centers based on routing decisions from the at least one artificial intelligence model, and at least one computational processing unit associated with each of the at least two high-altitude data centers and configured to execute assigned computational workloads based on the workload distribution commands.

422. The Al-enabled system of claim 421, wherein the at least one artificial intelligence model is configured to analyze orbital positions of the at least two high-altitude data centers and generate communication schedules based on changing distances and line-of-sight availability.

423. The Al-enabled system of claim 421, wherein the at least one communication relay system comprises optical communication systems including laser transceivers configured to transmit data between the at least two high-altitude data centers.

424. The Al-enabled system of claim 421, wherein the at least one communication relay system is configured to serve as a relay node receiving data from a first client and retransmitting the data to a second client based on routing decisions from the at least one artificial intelligence model.

425. The Al-enabled system of claim 421, wherein the at least one artificial intelligence model is configured to predict time- varying latency to different clients based on changing orbital positions of the at least two high-altitude data centers and schedule data transfers based on anticipated orbital geometry.

426. The Al-enabled system of claim 421, further comprising at least one power collection system associated with each of the at least two high-altitude data centers configured to collect energy and provide power availability data to the at least one artificial intelligence model.

427. The Al-enabled system of claim 426, wherein the at least one power collection system comprises solar panel arrays configured to collect solar energy at a high-altitude location.

428. The Al-enabled system of claim 421, wherein at least one of the at least two high-altitude data centers is positioned in geosynchronous orbit to maintain stable distance with respect to a location on a surface of a planetary body.

429. The Al-enabled system of claim 421, wherein the at least two high-altitude data centers are positioned at different altitudes to provide tiered latency services based on altitude-dependent signal propagation distances.

430. The Al-enabled system of claim 429, wherein the at least one artificial intelligence model is configured to route lower-latency service requests to data centers at lower altitudes and routeAttorney Docket No. 54250-73higher-capacity or longer-term storage requests to data centers at higher altitudes.

431. The Al-enabled system of claim 421, further comprising thermal management systems associated with each of the at least two high-altitude data centers configured to dissipate heat through radiative cooling based on thermal coordination commands from the at least one artificial intelligence model.

432. The Al-enabled system of claim 421, wherein the at least one artificial intelligence model is configured to balance computational workloads across the at least two high-altitude data centers based on computational capacity and thermal dissipation capacity at each data center.

433. The Al-enabled system of claim 421, further comprising position control systems associated with each of the at least two high-altitude data centers configured to adjust orbital positions based on positioning commands from the at least one artificial intelligence model.

434. The Al-enabled system of claim 433, wherein the at least one artificial intelligence model is configured to generate the positioning commands to maintain desired spacing between the at least two high-altitude data centers for communication efficiency.

435. The Al-enabled system of claim 421, wherein the at least one artificial intelligence model is configured to monitor radiation events and redistribute computational workloads among the at least two high-altitude data centers based on radiation exposure levels.

436. The Al-enabled system of claim 421, further comprising fault-tolerant architectures associated with each of the at least two high-altitude data centers including redundant computing nodes and distributed data storage configured to enable continued operation based on autonomous damage detection by the at least one artificial intelligence model.

437. The Al-enabled system of claim 421, further comprising weather monitoring systems associated with at least one of the at least two high-altitude data centers configured to monitor atmospheric conditions and provide weather data for processing by computational resources coordinated by the at least one artificial intelligence model.

438. The Al-enabled system of claim 421, further comprising at least one energy transmission interface configured to transmit energy between the at least two high-altitude data centers based on energy routing commands generated by the at least one artificial intelligence model.

439. The Al-enabled system of claim 421, wherein the at least one artificial intelligence model is configured to schedule computational tasks across the at least two high-altitude data centers based on anticipated power availability at different orbital positions of each data center.

440. The Al-enabled system of claim 421, further comprising modular expansion interfaces associated with each of the at least two high-altitude data centers configured to enable attachment of additional computing capacity, storage capacity, or power generation capacity modules based on expansion recommendations from the at least one artificial intelligence model.

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