Ai-based energy edge platforms, systems, and methods

Abstract

An Al -based energy edge platform is provided herein with a wide range of features, components and capabilities for management and improvement of legacy infrastructure, coordination, and orchestration with distributed systems to support important use cases for a range of enterprises. An Al -based energy edge platform may include a graph neural network including a set of nodes respectively representing at least one distributed energy resource (DER) and a set of edges respectively interconnecting the set of nodes, wherein each edge represents at least one energy - related feature among at least two nodes of the set of nodes. The platform may incorporate emerging technologies to enable ecosystem and individual energy edge node efficiencies, agility, engagement, and profitability. Embodiments may forecast, plan for, and manage the demand and utilization of energy in greater distributed environments. Embodiments may employ intelligent provisioning, data aggregation, and analytics to leverage energy market connection, communication, and transaction enablement platforms.

Description

AI-BASED ENERGY EDGE PLATFORMS, SYSTEMS, AND METHODSCROSS-REFERENCE TO RELATED APPLICATIONS

[0001] This application claims the benefit ofU.S. provisional patent application 63 / 625,613 filed 26 January 2024, U.S. provisional patent application 63 / 638,601 filed 25 April 2024, and U.S. provisional patent application 63 / 639,907 filed 29 April 2024. Each patent application referenced above is hereby incorporated by reference as if fully set forth herein in its entirety.BACKGROUND

[0002] 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.’'

[0003] The energy7market 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

[0004] An Al-based energy7edge platform is provided herein with a wide range of features, components and capabilities for management and improvement of legacy infrastructure and coordination with distributed systems to support important use cases for a range of enterprises. The platform may incorporate emerging technologies to enable ecosystem and individual energy7edge node efficiencies, agility, engagement, and profitability. Embodiments may7be guided by, and in some cases integrated with, methodologies and systems that are used to forecast, plan for, and manage the demand and utilization of energy in greater distributed environments. Embodiments may use Al, and Al enablers such as loT, which may be deployed in vastly denserdata environments (reflecting the proliferation of smart energy systems and of sensors in the loT), as well as technologies that filter, process, and move data more effectively across communication networks. Embodiments of the platform may leverage energy market connection, communication, and transaction enablement platforms. Embodiments may employ intelligent provisioning, data aggregation, and analytics. Among many use cases the platform may enable improvements in the optimization of energy generation, storage, delivery and / or enterprise consumption in operations (e.g., buildings, data centers, and factories, among many others), the integration and use of new power generation and energy' storage technologies and assets (distributed energy resources, or “DERs”), the optimization of energy utilization across existing networks and the digitalization of existing infrastructure and supporting systems.

[0005] In some aspects, the techniques described herein relate to an Al-based platform for enabling intelligent orchestration and management of distributed energy resources (DERs), including: a DER client interface configured to provide client demand forecasts to one or more DER marketplace orchestration layers; a client energy' transaction orchestration and execution system including a client smart contract system configured to negotiate smart contracts to meet DER client energy needs; and a client assist layer including a configured intelligence system and a client API / GUI.

[0006] In some aspects, the techniques described herein relate to a platform, wherein the DER client interface includes a client broadcast / poll system configured to receive proposals from the DER marketplace orchestration layers.

[0007] In some aspects, the techniques described herein relate to a platform, wherein the proposals include: specific contract proposals to meet a client demand forecast: or information regarding available energy solutions.

[0008] In some aspects, the techniques described herein relate to a platform, further including a client needs analysis system configured to analyze energy requirements.

[0009] In some aspects, the techniques described herein relate to a platform, further including a market demand system including transaction aggregation systems configured to automatically orchestrate energy-related transactions.

[0010] In some aspects, the techniques described herein relate to a platform, wherein the energy-related transactions include: purchases; sales; orders; futures contracts; hedging contracts; limit orders; or stop loss orders.

[0011] In some aspects, the techniques described herein relate to a platform, further including a DER market orchestration layer including one or more energy' marketplaces.

[0012] In some aspects, the techniques described herein relate to a platform, wherein the energy marketplaces are based on: type of energy; or location of energy.

[0013] In some aspects, the techniques described herein relate to a platform, wherein the DER market orchestration layer includes: a market forming system; a market demand system; a market response system; and a market value analysis system.

[0014] In some aspects, the techniques described herein relate to a platform, wherein the DER market orchestration layer includes a DER market interface configured to broadcast information regarding current and forecasted energy capacity and pricing.

[0015] In some aspects, the techniques described herein relate to a platform, wherein the DER market interface is configured to receive information from DER clients including: requests for power to meet immediate needs; or requests for bids on projected future needs.

[0016] In some aspects, the techniques described herein relate to a platform, further including a digital platform for DER management module that leverages expert systems and generative artificial intelligence.

[0017] In some aspects, the techniques described herein relate to a platform, wherein the expert systems utilize a rule-based approach to analyze energy7profiles and transaction histories.

[0018] In some aspects, the techniques described herein relate to a platform, wherein the generative Al algorithms synthesize data to create and propose energy operations and transaction offerings.

[0019] In some aspects, the techniques described herein relate to a platform, wherein the module implements targeting and recommendation use cases through a dynamic feedback loop.

[0020] In some aspects, the techniques described herein relate to a platform, wherein interactions with offerings and proposals are continuously fed back into the system to refine Al models.

[0021] In some aspects, the techniques described herein relate to a platform, further including a set of futures market optimization systems configured to automatically orchestrate aggregation of futures markets contracts based on a forecast of future energy needs.

[0022] In some aspects, the techniques described herein relate to a platform, wherein the forecast is based on: historical usage patterns; current operating conditions; current market conditions; or anticipated operational needs.

[0023] In some aspects, the techniques described herein relate to an Al-based platform for enabling intelligent orchestration and management of distributed energy resources (DERs), including: a DER client interface providing client demand forecasts; a client energy transaction system with smart contract capabilities; a DER market orchestration layer including energy marketplaces; a market demand system with transaction aggregation capabilities; and a digital platform for DER management leveraging expert systems and generative Al.

[0024] In some aspects, the techniques described herein relate to a system for managing distributed energy resources (DERs), including: a market forming system; a market demand system; a market response system; a market value analysis system; and a market orchestration assist layer.

[0025] In some aspects, the techniques described herein relate to a system, wherein the market demand system includes transaction aggregation systems configured to automatically orchestrate energy-related transactions for: energy generation; energy storage; energy delivery; energy consumption; renewable energy credits; carbon abatement credits; or pollution abatement credits.

[0026] In some aspects, the techniques described herein relate to a system, further including a set of intelligent data layers configured to: produce energy generation data layers; produce energy storage data layers; produce energy delivery data layers; and produce energy consumption data layers.

[0027] In some aspects, the techniques described herein relate to a system, wherein the intelligent data layers are configured to perform: extraction: transformation; loading; normalization; cleansing; compression; route selection; protocol selection; self-organization of storage; filtering; timing of transmission; encoding; or decoding.

[0028] In some aspects, the techniques described herein relate to a distributed energy resource (DER) management platform, including: a market transaction and orchestration system; a set of transaction aggregation systems; a set of futures market optimization systems; and a predictive model configured to generate forecasts using machine learning on outcomes, human output, or human-labeled data.

[0029] In some aspects, the techniques described herein relate to a platform, wherein the predictive model is configured to design, configure, and execute a series of futures market transactions across various jurisdictions to meet anticipated timing, location, and type of needs.

[0030] In some aspects, the techniques described herein relate to a system for distributed energy resource (DER) market orchestration, including: a market broadcast / poll system for providing information regarding current and forecasted energy capacity and pricing; a market value analysis system; and a market orchestration assist layer configured to coordinate multiple DER providers and DER clients.

[0031] In some aspects, the techniques described herein relate to a system, further including distributed ledger and smart contract systems configured to enable: energy-related transactions; purchases; sales; leases; futures contracts; renewable energy credits; carbon abatement credits; pollution abatement credits; leasing of assets; shared economy transactions; shared consumption contracts; bulk purchases; or provisioning of mobile resources.

[0032] In some aspects, the techniques described herein relate to a system, further including energy transaction intelligent agents configured to: design smart contracts; generate smart contracts; deploy smart contracts; optimize transaction parameters; discover counterparties; discover arbitrage opportunities; recommend contract execution steps; or resolve contracts upon completion based on blockchain data.

[0033] In some aspects, the techniques described herein relate to a system for Al convergence in distributed energy resource management, including: an Al system generation module configured to generate one or more generative Al systems; an Al system orchestration module configured to manage allocation of computational resources; and operations modules configured to generate and invoke Al systems for energy-related operations.

[0034] In some aspects, the techniques described herein relate to a system, wherein the Al system generation module is configured to: generate primary content Al systems; generate supplemental content Al systems; generate metadata Al systems; and generate content review Al systems.

[0035] In some aspects, the techniques described herein relate to a system, wherein the Al system orchestration module is configured to: adjust allocations of computational resources; provision resources to enable Al systems to fulfill requests; manage acquisition and decommissioning of resources; and reorganize resources based on changes in processing demands.

[0036] In some aspects, the techniques described herein relate to a system, wherein the operations modules are configured to add executive "smart" features to energy-related operations, including autonomously executing transactions to acquire energy resources in anticipation of shortages.

[0037] In some aspects, the techniques described herein relate to a platform for Al-enabled distributed energy resource management, including: a data layer system of systems including: transaction / market-oriented capabilities; market platforms; transaction flows; user interfaces; and content providers.

[0038] In some aspects, the techniques described herein relate to a platform, wherein the data layer system includes an intelligent data layer architecture including: an ingestion stage; an analysis stage; a derived intelligence stage; and a consumer visualization portal.

[0039] In some aspects, the techniques described herein relate to a platform, wherein the ingestion stage is configured to: receive data from multiple sources; parse content; determine structure: determine relationships among data elements; and determine intended meaning of data elements.

[0040] In some aspects, the techniques described herein relate to a platform, further including a cross-service resource optimization system including Al agents trained to: manage subsystems; configure subsystems; deploy subsystems: provision subsystems; and optimize subsystems operating within a linked system.

[0041] In some aspects, the techniques described herein relate to a platform, wherein the crossservice resource optimization system is configured to measure and allocate energy7used across platforms, including: battery7storage by devices; energy7use by GPUs in cloud computing; and energy use by data centers for generative Al workloads.

[0042] In some aspects, the techniques described herein relate to a system for Al-based distributed energy resource orchestration, including: a digital twin platform configured to provide an environment for decision making; an adaptive energy digital twin representing energy stakeholder entities; and a decision making framework for distributing authority among human beings, human-AI systems, and autonomous Al systems.

[0043] In some aspects, the techniques described herein relate to a system, wherein the decision making framework is selected from: a hierarchical framework; a rules-based framework; a simulation framework; an enterprise planning framework; an algorithmic framework; a principles-based framework; a collaborative framework; a peer-to-peer framework; or a competitive framework.

[0044] In some aspects, the techniques described herein relate to a system, wherein the digital twin platform includes: an interface system for designating trainers for Al system creation; asystem for displaying training metrics; and an embedded intelligent agent system for discovering available systems.

[0045] In some aspects, the techniques described herein relate to a distributed energy resource management platform with Al convergence capabilities, including: a mobile distributed energy resources intelligence framework implementing Al systems to: optimize localized demand response through mobile energy assets; predict local energy demand patterns; and optimize positioning and dispatch of mobile DERs.

[0046] In some aspects, the techniques described herein relate to a platform, wherein the framework employs: deep learning models analyzing grid conditions; weather data analysis; historical usage pattern analysis; reinforcement learning for dynamic routing; predictive maintenance models; and federated learning techniques.

[0047] In some aspects, the techniques described herein relate to a platform, further including an energy' trading systems intelligence platform including: neural networks for automated energy' trading; deep learning models for price prediction; natural language processing systems for market analysis; and reinforcement learning algorithms for trading optimization.

[0048] In some aspects, the techniques described herein relate to a smart distributed energy resources intelligence system, including: neural networks for autonomous DER operation; deep reinforcement learning models for real-time control; computer vision networks for equipment monitoring; predictive analytics models for maintenance; and natural language processing systems for control interfaces.

[0049] In some aspects, the techniques described herein relate to a system, further including a DER fleet management framework implementing: graph neural networks modeling relationships between fleet members; deep learning models optimizing fleet-wide performance; multi-agent reinforcement learning algorithms; clustering techniques for operational groups; and federated learning for sharing operational insights.

[0050] In some aspects, the techniques described herein relate to a digital platform for DER management implementing Al systems for: integrated DER operations; performance optimization; maintenance scheduling; fleet coordination; market participation; and regulatory' compliance.

[0051] In some aspects, the techniques described herein relate to a platform, wherein the Al systems include: supervised learning systems; deep learning systems; natural language processing systems; intelligent agent systems; self-optimizing systems; and self-organizing systems.

[0052] In some aspects, the techniques described herein relate to a system for Al-enabled DER orchestration, including: an intelligent data layer architecture with: controlled data processing pipelines; intelligence services for data ingestion; pattern recognition capabilities; and predictive analytics functions.

[0053] In some aspects, the techniques described herein relate to a system, further including a user interface configured to: facilitate configuration of the data layer; manage algorithm portals; control data retention rules; prioritize resource usage; and maintain data security.

[0054] In some aspects, the techniques described herein relate to a system, wherein the system implements a cross-service resource optimization framework to: manage energy resources across subsystems; optimize computational resource allocation; coordinate distributed energy assets; and balance system-wide energy consumption.

[0055] In some aspects, the techniques described herein relate to a system implementing an energy edge convergence technology’ stack, including: a plurality of energy edge modules enabled at various layers by convergence of Al capabilities; a set of converging technology’ stack examples for energy edge scenarios; and Al-driven capabilities for energy' edge operations.

[0056] In some aspects, the techniques described herein relate to a system, wherein the Al capabilities include: expert systems; generative Al; routing capabilities; control capabilities; optimization capabilities; and generation capabilities.

[0057] In some aspects, the techniques described herein relate to a system, further including a digital platform for DER management module enabled by: expert systems utilizing rule-based analysis of energy profiles; generative Al for synthesizing energy’ operations proposals; transaction systems interfaces; and targeting and recommendation systems.

[0058] In some aspects, the techniques described herein relate to a system, wherein the digital platform for DER management module enables: Al optimization of DER product parameters; Al optimization of deployment parameters; optimization during initial design phase; and optimization during operation phase.

[0059] In some aspects, the techniques described herein relate to a converged, Al-based energy- aware workflow orchestration system, including: Al algorithms for transaction monitoring; machine learning techniques for risk assessment; robotics and process automation for repetitive tasks; and blockchain technology for transaction recording.

[0060] In some aspects, the techniques described herein relate to a system, wherein the system implements: deep neural networks for pattern recognition; predictive analytics; natural language processing for transaction documents; cloud computing infrastructure; and API integrations for system communication.

[0061] In some aspects, the techniques described herein relate to a system for automated governance of energy transactions and operations, including: modules for policy automation; regulatory framework monitoring; distributed energy resource governance; and energy grid governance capabilities.

[0062] In some aspects, the techniques described herein relate to a system for Al-based enterprise transactional decision support, including: strategic energy resource planning capabilities; transaction planning simulation; intelligent dashboards; and digital twins integrating operational data.

[0063] In some aspects, the techniques described herein relate to a system, wherein the system provides capabilities for: automated edge transaction orchestration; adjustment of transaction parameters; monitoring of marketplace conditions; and analysis of sensor data from energy entities.

[0064] In some aspects, the techniques described herein relate to an intelligent edge system for energy-optimized networking, including: Al systems in edge devices; expert systems enabling localized energy transactions; point-of-use transaction capabilities; and energy optimization algorithms.

[0065] In some aspects, the techniques described herein relate to a context-aware sensor fusion system for energy management, including: data fusion capabilities for marketplace data; operational data integration; Al classification systems; prediction systems; and optimization systems for computation-intensive industries.

[0066] In some aspects, the techniques described herein relate to a system for joint optimization of energy and computation, including: resource optimization modules; computation resource management; energy' resource management; and industry-specific optimization capabilities.

[0067] In some aspects, the techniques described herein relate to a system, wherein the resource optimization modules provide: real-time monitoring capabilities; predictive analytics; automated control systems; resource allocation optimization; and automated execution of optimization strategies.

[0068] In some aspects, the techniques described herein relate to a system implementing energy edge convergence capabilities, including: automated governance of energy transactions; Al-based enterprise decision support; digital platform for DER management; automated edge transaction orchestration; and converged workflow orchestration.

[0069] In some aspects, the techniques described herein relate to a system, further including: intelligent edge networking; context-aware sensor fusion; analytics integration; Al classification systems; and optimization systems.

[0070] In some aspects, the techniques described herein relate to a system for energy edge operations automation, including: modules for automated governance; policy automation systems; regulatory' compliance monitoring; transaction orchestration; and workflow optimization.

[0071] In some aspects, the techniques described herein relate to a system, wherein the modules implement: Al-based decision support; strategic planning capabilities; marketplace simulation; operational data integration; and digital twin modeling.

[0072] In some aspects, the techniques described herein relate to a system for intelligent orchestration of energy' edge resources, including: automated edge transaction systems; marketplace condition monitoring; sensor data analysis; energy optimization algorithms; and resource allocation systems.

[0073] In some aspects, the techniques described herein relate to a system, further including: context-aware data fusion; operational analytics; Al-driven classification; predictive modeling; and resource optimization.

[0074] In some aspects, the techniques described herein relate to a platform for energy edge convergence implementation, including: automated governance modules; transaction support systems; DER management capabilities; workflow orchestration; and optimization engines.BRIEF DESCRIPTION OF THE DRAWINGS

[0075] The present disclosure will become more fully understood from the detailed description and the accompanying drawings.

[0076] FIG. 1 is a schematic diagram that presents examples of platforms and main elements according to some embodiments.

[0077] FIGS. 2A and 2B are schematic diagrams that present an introduction of main subsystems of a major ecosystem, according to some embodiments.

[0078] FIG. 3 is a schematic diagram that presents more detail on distributed energy generation systems, according to some embodiments.

[0079] FIG. 4 is a schematic diagram that presents more detail on data resources, according to some embodiments.

[0080] FIG. 5 is a schematic diagram that presents more detail on configured energy edge stakeholders, according to some embodiments.

[0081] FIG. 6 is a schematic diagram that presents more detail on intelligence enablement systems, according to some embodiments.

[0082] FIG. 7 is a schematic diagram that presents more detail on Al-based energy orchestration, according to some embodiments.

[0083] FIG. 8 is a schematic diagram that presents more detail on configurable data and intelligence, according to some embodiments.

[0084] FIG. 9 is a schematic diagram that presents a dual -process learning function of a dualprocess artificial neural network, according to some embodiments.

[0085] FIGS. 10-37 are schematic diagrams of embodiments of neural net systems that may connect to. be integrated in, and be accessible by the platform for enabling intelligent transactions including ones involving expert systems, self-organization, machine learning, artificial intelligence and including neural net systems trained for pattern recognition, for classification of one or more parameters, characteristics, or phenomena, for support of autonomous control, and other purposes in accordance with embodiments of the present disclosure.

[0086] FIG. 38 is a schematic view of an exemplary embodiment of a quantum computing service according to some embodiments of the present disclosure.

[0087] FIG. 39 illustrates quantum computing service request handling according to some embodiments of the present disclosure.

[0088] FIG. 40 is a diagrammatic view of a thalamus sen ice and how it coordinates within the modules in accordance with the present disclosure.

[0089] FIG. 41 is another diagrammatic view of a thalamus sendee and how it coordinates within the modules in accordance with the present disclosure.

[0090] FIG. 42 is a diagrammatic view of a determination of attention by a machine learning model in accordance with the present disclosure.

[0091] FIG. 43 is a diagrammatic view of a transformer model in accordance with the present disclosure.

[0092] FIG. 44 is a diagrammatic view of an energy edge converging technology stack in accordance with the present disclosure.

[0093] FIG. 45 is a diagrammatic view of a set of capabilities of an energy edge convergence technology stack in accordance with the present disclosure.

[0094] Fig. 46 depicts a schematic of a Distributed Energy’ Resource (DER) platform.

[0095] Fig. 47 depicts a schematic of a configured DER provider.

[0096] Fig. 48 depicts a schematic of DER generator module.

[0097] Fig. 49 depicts a schematic of DER generator system.

[0098] Fig. 50 depicts a schematic of provider service layer.

[0099] Fig. 51 depicts a schematic of a generic DER assist layer.

[0100] Fig. 52 depicts a schematic of a client load.

[0101] Fig. 53 depicts a schematic of a client orchestration layer.

[0102] Fig. 54 depicts a schematic of details of the client assist library of the client orchestration layer.

[0103] Fig. 55 depicts a schematic of DER market orchestration layer.

[0104] Fig. 56 depicts a schematic of a market value analysis system and a DER market assist layer.

[0105] Fig. 57 depicts a schematic of an automated resource orchestration and control system.

[0106] Fig. 58 depicts a schematic of a power evaluation system.

[0107] Fig. 59 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.

[0108] FIG. 60 is a schematic view of an example Al convergence system of systems.

[0109] FIG. 61 is a schematic view of an example offering layer.

[0110] FIG. 62 is a schematic view of an example transactions layer.[OHl] FIG. 63 is a schematic view of an example operations layer.

[0112] FIG. 64 is a schematic view of an example network layer.

[0113] FIG. 65 is a schematic view of an example data layer.

[0114] FIG. 66 is a schematic view of an example data layer.

[0115] FIG. 67 is a schematic view of an example intelligent data layer architecture.

[0116] FIG. 68 is a schematic view of an example network layer.

[0117] FIG. 69 is a schematic view of an example Al subsystem integrator system.

[0118] FIG. 70 is a schematic view of an example multiplatform attention management system.DETAILED DESCRIPTIONFIG. 1: Introduction of Platform and Main Elements

[0119] In 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, sendees, layers, devices, components, machines, products, sub-systems, interfaces, connections, and other elements working incoordination 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.

[0120] 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 naturalgas-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 poyver transmission systems, wireless poyver transmission systems, or the like.

[0121] In 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 energy resources 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 strategy7, 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 stakeholder’s energy needs and capabilities, 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.

[0122] In embodiments, the platform 102 (and / or elements thereol) 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 energy7edge orchestration 110. These dataresources 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 1 10 ensures that optimizations are based on the most updated available data.

[0123] 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 sendees 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, optimization, and automation systems 114 transform the platform 102 into a dynamic entity', responsive to realtime changes and proactive in its strategies. The set of configurable data and intelligence modules and sendees 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.

[0124] 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.

[0125] 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 overseeand 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.

[0126] 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.

[0127] 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 set of 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 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.AI-Based Energy Orchestration, Optimization and Automation Systems

[0128] In 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 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 different times and / or locations.

[0129] In 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 identity areas of wastage or inefficiency. By way of example, they can evaluate how a building's energy consumption 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 Al-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.

[0130] The Al-based energy orchestration, optimization, and automation systems 114 may be enabled by the set of intelligence enablement systems 1 12 that provide functions and capabilities that support a range of applications and use cases.

[0131] In 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, uti 1 i ty) 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 w ay 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.

[0132] In 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 energyconsuming system may be a multi-dimensional vector that represents consumption type, purpose, 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 energyconsuming system, may include various parameters like consumption type, the purpose of consumption, the specific device consuming energy-, among others.

[0133] In embodiments, patterns of energy- usage may7include localized patterns, such as based on consumer’s work-a-day schedule. However, patterns of energy- usage may7be 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, a consumer’s work-a-day schedule, 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.

[0134] 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.Subsystems and Modules of Intelligence Enablement SystemsIntelligent Data Layers

[0135] The set of intelligence enablement systems 112 may include a set of intelligent data layers 130, such as a set of sen-ices (including microservices), APIs, interfaces, modules, applications, programs, and the like which may consume any of the data entities and ty pes 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 solutions108. The set of intelligent data layers 130, the set of intelligence enablement systems 1 12 more generally, and / or the configurable data and intelligence modules and services 1 18 may access data from various sources throughout the platform 102 and. in 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.

[0136] In 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 manufacturing plant 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 ty pes 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

[0137] 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 ofblockchain-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 sy stems 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.

[0138] In embodiments, the smart contract systems 132 may be used by the set of energy transaction enablement systems 144 (descnbed 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 and other transaction-relevant energy events, including information on energy consumption, generation, distribution, and storage. For example, a city 's energy grid having integrated renewable energysources, 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

[0139] Any entity, analytic results, output of artificial intelligence, state, operating condition, or other feature noted throughout this disclosure may, in 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 factones. 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, orotherwise adjust data presented based on real-time conditions, such as changes in energy costs, changes in operating behavior, or the like.

[0140] In 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 tw in of this environment, capturing every' detail of its energy' consumption patterns. Such digital twin can provide real-time information about the facility 's energy demands, 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 HVAC system's operation based on real-time occupancy data or optimizing lighting based on natural daylight availability can shift the energy costs considerably.

[0141] In 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 can create a digital twin of such city, capturing every' aspect of its energy consumption. This digital representation may include every thing 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 city's energy7footprint, 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 adequately7prepared 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.

[0142] In 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 byadjusting 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 their device's digital twin, 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 user’s account) 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, responsive, and tailored to individual preferences and real-world scenarios.

[0143] In 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 consumptionand / 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.

[0144] In 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 tower 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 areas to 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.

[0145] In embodiments, the platform 102 may7be 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 ty pes 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 w ay of example, in an urbansetting, 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 dunng 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.

[0146] In 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 energy7consumption 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 patterns, 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.

[0147] In 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 only does 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 non-renewable 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.Energy Simulation Systems

[0148] In embodiments, a set of energy simulation systems 136 is provided, such as to develop and evaluate detailed simulations of energy generation, demand response and chargemanagement, 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.

[0149] In embodiments, as more enterprises embrace hybrid infrastructure, 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 embodiments, 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.Introduction of Main Subsystems and Modules of AI-Based Energy Orchestration, Optimization, and Automation Systems

[0150] 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.Configurable Data and Intelligence Modules and Services

[0151] In 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 sendee (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.

[0152] In 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 the hke. 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 hke). By way of example, consider a household equipped w ith multiple energy-consuming devices, each ith 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 w ay of example, on a sunny day, solar panels in a house may be generating excess energy7, in such case, the platform 102 may utilize energy primarily from the solar panels, reducing energy7consumption from the grid.

[0153] In embodiments, the platform 102 may capture the energy consumption information from / via the edge devices and develop a data set that represents a plurality7of perspectives regarding consumed energy. Edge devices that may communicate (e.g., locally or in close proximity) w ith 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 thedevices 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

[0154] Configurable data and intelligence modules and services 118 may include a set of energy7transaction enablement systems 144. The set of energy7transaction 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 counterparty7transactions) 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 artificial intelligence, including to autonomously discover, configure, and execute transactions according to a strategy7and / 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 discovery7, 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).

[0155] As energy7is 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.

[0156] 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 P2Ptrading, wholesale contracts, renewable energy certificate (REC) tracking, and broader distributed energy provisioning, payment management and other transaction elements. In 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.

[0157] In 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.

[0158] In 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 w ay of example, in regions w here utility charges vary based on the time of use, the platform 102 can shift energy demand to periods when energy7is cheaper. In an example, smart home devices, linked to the platform 102, can identify periods when energy costs are lowest and adjust their operations accordingly, ensunng efficient and cost-effective energy consumption.Stakeholder Energy Digital Twins

[0159] The configurable data and intelligence modules and services 1 18 may include a set of stakeholder energy digital tw ins 148, which may, in 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); stakeholder information technology and netw orking 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 stakeholder’s supply chain, 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, particularly7relating to energy consumption, generation, storage, and or distribution.

[0160] 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 ofexample, 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

[0161] 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 and 2B: Introduction of Main Subsystems of Major Ecosystem Components Data Resources for Energy Edge Orchestration

[0162] 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, cleansing and / 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 least-cost 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.

[0163] 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 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

[0164] 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

[0165] In 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

[0166] 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, supply chain data, procurement data, pricing data, customer data, product data, operating data, and many others.Subsystems and Modules of Advanced Energy Resources and Systems

[0167] In 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 stand-alone applications.Transformed Energy Infrastructure

[0168] The advanced energy resources and systems 104 orchestrated by the platform 102 may include a set of transformed energy infrastructure sy stems 120. The energy edge will involve increasing digitalization of generation, transmission, substation, and distribution assets, which inturn will shape the operations, maintenance, and expansion of legacy grid infrastructure. In 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.

[0169] In 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 delivery7. This compatibility7ensures 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.

[0170] Any improvements to legacy grid assets, new grid-connected equipment, and supporting systems may, in embodiments, comply with regulatory standards from NERC, FERC, NIST, and other relevant authorities; positively impact the reliability of the grid; reduce the grid’s susceptibility 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.).

[0171] 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.

[0172] In embodiments, in order to maintain and improve existing energy infrastructure, the platform 102 may include various capabilities, including fully integrated predictive maintenance across utility-owned assets (i.e., generation, transmission, substations, and distribution); smart (Al / 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.

[0173] In embodiments, po 'er 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

[0174] In 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 new7levels 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-gnd environments that require alternative forms of generation, storage, or mobility of energy. In 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 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.

[0175] In 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.

[0176] In embodiments, the DERs 128 will be integrated into computational networks and infrastructure 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

[0177] In 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 ty pes) 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 beused 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.

[0178] In 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 energy7edge devices and systems using such protocols. Elerein, 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.Configured Stakeholder Energy Edge Solutions

[0179] 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 1 18 to enable benefits to particular stakeholders, such as private enterprises, non-govemmental 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 sendees, 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 of mobility7demand 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 energy7consumption, ensuring that office buildings are adequately powered during work hours while conserving energy7during off-hours.Enterprise Optimization Solutions

[0180] In 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 ways of optimizing energy7in 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 energy7consumption in real-time. In embodiments, this may enable increasing the operationalbottom 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.

[0181] In 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 each floor's energy consumption, 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 energywastage.

[0182] In 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 algonthms, can monitor and adjust energy consumption based on the specific needs of each building, optimizing energy use.

[0183] In 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, a building’s energy generation, storage and / or consumption 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 leams from these patterns, ensuring energy- is used efficiently.

[0184] In 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 safety7because 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, and intelligent 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 real-time, predicting potential w eak points, and suggesting timely maintenance and repair.

[0185] In 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 embodiments, enterprises can inspect pipelines for defects that can lead to dangerous failures and automatically alert pipeline operators.Energy Provisioning and Governance Solutions

[0186] 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 system for 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.

[0187] 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.

[0188] More detail on a variety of energy provisioning and governance solutions 156 is provided below.Localized Production Solutions

[0189] In 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 distnbution 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, isunreliable, 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 electncal grid may be absent or unreliable.

[0190] In 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 energy7load.FIG. 3: More Detail on Distributed Energy Generation Systems

[0191] 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 energy7storage (ETES), gravitybased 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 embodiments, the distributed energy storage systems 304 may be portable, such that units of energy may be transported to points of use. 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 retum-on-investment (ROI) on distributed energy resources (DERs), while providing reliability and flexibility for energy needs.

[0192] In embodiments, the DERs 128 may use various distributed energy7delivery methods and systems 308 having various energy delivery7capabilities, including transmission lines (e.g., conventional grid and building infrastructure), wireless energy transmission (including by coupled, resonant transfer between high-Q resonators, near-field energy7transfer and other methods), transportation of fluids, batteries, fuel cells, small nuclear systems, and the like), and others.

[0193] 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 othermobile 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.

[0194] 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 connectivity7, intelligent agents, and other onboard intelligence features) where intelligence features of the smart resources 318 can be used for energy7orchestration, 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 infrastructure 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, an loT 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 energy7generation, 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.

[0195] In 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.

[0196] 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 embodiments, flexible hybrid energy systems 324 may be provided that is adaptable to meet vary ing 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 temporary7, 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 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), smart buildings, and many others. The modular nuclear systems 330 may include SMRs and other reactor types. The advanced energy7resources 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 connectivity7for remote management, batteries with alternative chemistry (including green chemistry7, 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).FIG. 4: More Detail on Data Resources

[0197] Referring to FIG. 4, the data resources for energy edge orchestration 1 10 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 eventlogs 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 ).

[0198] In 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 energy7); 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, ty pe 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, and 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 velocity7data, wind direction data, storm data, barometric pressure data, and others).

[0199] In embodiments, the data resources for energy edge orchestration 1 10 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 performanceof 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.

[0200] In 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 w ider deployment of edge and loT devices to produce massive increases in the scale and granularity' of data of available data of the many ty pes 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 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-stakes enterprise activities), and the like). In 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 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 time-sensitive 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., timesensitive arbitrage opportunities or damage-mitigation needs)), and many others). In 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).

[0201] In 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.FIG. 5: More Detail on Configured Energy Edge Stakeholder Solutions Localized Production

[0202] 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.

[0203] 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 mining operations 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.

[0204] The set of localized production solutions 158 may include a set of transport cost mitigation solutions 524, such as ones where the cost of energy7required 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, hydrationsystems, 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).

[0205] 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).

[0206] 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 sendees of an enterprise.Enterprise Optimization Solutions

[0207] 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-govemmentalorganizations, 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., treatybased agencies), regulator}’, 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.

[0208] 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.

[0209] 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 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 embodiments, the platform 102 may be used to orchestrate routing and delivery’ of w ireless pow er to deliver energy’ to a point and time of use. Energy’ delivery’ optimization may be based on market prices (historical, current, futures market, and / or predicted), based on operational conditions (current and predicted), based on policies (e.g., dictating priority for certain uses) and many’ other factors.

[0210] 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 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 obj ectives, and contextual factors of anenterprise, 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.

[0211] 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, w orkflow s 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 tw in 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.Mobility Demand Solutions

[0212] 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.

[0213] The set of mobility demand solutions 510 may7include 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 energy7used 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 energy7costs and prices. For example, among many others, an electric or hybridpassenger 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.

[0214] 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.

[0215] 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.

[0216] 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

[0217] 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 theplatform 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.

[0218] 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 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 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.

[0219] 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 subj ect 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 are automatically 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 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 fractionof renewable energy), seting 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.

[0220] 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.

[0221] 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.

[0222] In 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 and governance 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.

[0223] In 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 totalcarbon 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 company's activities remain environmentally sustainable, even as it scales up its operations.

[0224] In 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 yvith metadata indicating its origin. If the energy’ was predominantly generated through green sources like yvind or solar, the associated carbon footprint would be low. Hoyvever, 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 yvith 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.

[0225] In embodiments, the edge device may monitor the amount of power being used by the set of doyvnstream devices and may determine the carbon output based on the energy source metadata and carbon output rate associated yvith 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 doyvn certain devices to reduce the energy’ spend, toggling energy’ usage between different devices, sending alerts 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 non-renewable energy, the platform 102 may shift more households in the residential community to solar power, despite potential added costs.

[0226] In 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 forsectors 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.

[0227] In 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.

[0228] In 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 project's strategic goals. By continuously monitoring decisions of these components, the platform 102 can realign any’ deviations, ensuring that the entire system works in harmony.

[0229] In 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 w ay 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.

[0230] In embodiments, the platform 102 may be configured to track and monitor human interaction w ith 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 of energy resources, and responses to changes in an environment and / or market. By way of example, in a scenario where a human operator regularly interacts with an energy management system in a factory, by tracking these interactions, the platform 102 can determine the consistency of decisions made by different agents, ensuring a harmonized approach across the factory’. Such tracking may, particularly, be useful for factories with multiple shifts, ensuring that energy decisions are consistent, regardless of the operating personnel.

[0231] In embodiments, the platform 102 may be configured to provide and / or facilitate detection and prevention of harm to wildlife by energy infrastructure. Infrastructure development often comes at an environmental cost. For energy projects located near forests or water bodies, there is a risk of harming wildlife. The platform 102 may be configured to detect any potential threats to wildlife due to the infrastructure, like birds flying into wind turbines or aquatic life being affected by hydropower plants, and take preventive actions.

[0232] In embodiments, the platform 102 may be configured to gather data related to patterns of wildlife and use the wildlife pattern data to perform optimization of energy generation and distribution. By way of example, in wind farms located near habitats of migratory' birds, the platform 102 can analyze data related to birds’ movement patterns. By understanding these patterns, it can optimize energy' generation schedules, reducing the risk of bird collisions with the blades of the yvind turbine, yvhich ultimately may also reduce infrastructure damage. By way of example, in extreme cases, during peak migration periods, the platform 102 can stop the operations of yvind turbines directly in path of movement of birds to minimize bird impacts.

[0233] In embodiments, the platform 102 may be configured to determine and / or manage energy needs related to space travel. The platform 102 may perform and / or provide improvements to poyver generation, storage, and distribution during space missions based on the determined energy needs. Space missions, like the Mars rovers, require precise energy management. The platform 102 can monitor solar panel efficiencies, battery storage levels, and energy consumption rates in such rovers. By way of example, during periods when there is no sunlight, the platform 102 can help optimize energy consumption ensuring essential systems remain functional.

[0234] In embodiments, the platform 102 may be configured to receive data from and / or transmit energy-related data to one or more satellites. The platform 102 may improve operation of one or more systems of components based on data received from the one or more satellites. By way of example, weather satellites provide crucial data that impacts energy' generation, especially for reneyvables (like cloud cover over an area yvhich can impact solar energy generation). By receiving data from these satellites, the platform 102 can forecast cloud cover, aiding solar farms to predict energy generation dips and adjust their distribution strategies accordingly.

[0235] In embodiments, the platform 102 may7be configured to use data received from the one or more satellites to perform and / or improve one or more of monitoring energy usage, predicting energy demand, and allocating energy resources. For example, satellite data can also be invaluable for energy' management. By way of example, by analyzing cloud movement patterns from satellites, the platform 102 can anticipate yvhen solar farms in a region may experience reduced sunlight and adjust energy distribution from other sources.

[0236] In embodiments, the platform 102 may be configured to plan and / or manage energy needs and resources related to asteroid mining operations. Asteroid mining is being explored as a future method to extract rare minerals. The platform 102 may consider energy requirements of extraction and / or transportation operations of the asteroid mining operations. In such operations, the platform 102 can manage energy for mineral extraction (like operating various tools formining operation) and transportation (like propulsion). By way of example, when extracting minerals from an asteroid bound for Earth, the platform 102 can optimize energy use for both the extraction process and subsequent transportation of the extracted minerals back.

[0237] In embodiments, the platform 102 may be configured to manage and / or track disposal of radioactive waste generated by nuclear power plants. The platform 102 may ensure safety and compliance with international standards and regulations. Herein, the platform 102 can track waste quantities, monitor storage conditions, and ensure that disposal methods are compliant with international standards. By way of example, after a reactor's fuel is spent, the platform 102 can monitor the cooling process to ensure safety of such cooling operation, and subsequent safe storage of the spent fuel.

[0238] In embodiments, the platform 102 may be configured to optimize solar power generation via advanced analytics. The platform 102 may ensure maximum efficiency and reliability' of solar power plants and distributed solar energy' resources. For example, in case of solar energy, solar power plants and solar installations have become increasingly' complex. To ensure their peak performance, the platform 102 can utilize advanced analytics to analyze the operational data of these systems. By doing so, the platform 102 can provide insights into panel efficiency, dirt accumulation, etc. By way of example, using the platform 102, operators can predict which panels may need maintenance, determine optimal panel angles based on the sun's position, and even predict energy generation based on weather forecasts.

[0239] In embodiments, the platform 102 may be configured to anticipate and respond to threats from hostile nation states, such as cyberattacks targeting energy grids and / or sabotage of energy resources. In an era of increasing cyber warfare, energy grids are potential targets. The platform 102 can monitor for unusual patterns for detecting cyber intrusions, ensuring that energy resources remain secure. By way of example, during a sudden grid shutdown, the platform 102 can identify if it's a technical failure or a cyberattack.

[0240] In embodiments, the platform 102 may be configured to plan and / or manage energy' needs related to land mine cleanup operations. The platform 102 may consider energy' required for detection, extraction, and / or safe disposal of land mines. Land mine cleanup is a dangerous and energy-intensive operation. The platform 102 can manage energy' needs for detection robots, ensuring they operate efficiently. By way of example, during a land mine detection operation in a large field, the platform 1 2 can optimize robot paths to minimize energy’ consumption.

[0241] In embodiments, the platform 102 may be configured to address legal and / or ethical implications of decisions made by the platform 102. The platform 102 may ensure compliance with laws and regulations, and / or may implement safeguards to prevent harm related to operation of the platform 102. The platform 102, with its Al systems, can make decisions impacting human lives. The platform 102 is configured to cross-check every decision with legal and ethical guidelines, ensuring that it does not even inadvertently cause harm. By way of example, in case of power shortage, before shutting off power to a critical facility, the platform 102 can assess the human impact, and may accordingly decide not to take such step and may try to divert power from other sources, and the like.

[0242] In embodiments, the platform 102 may be configured to manage data storage in compliance with regulatory requirements, thereby ensuring data privacy and security. Data storage, especially in the energy sector, involves a plethora of user-specific information that can be both sensitive and crucial for operations. Particularly, in regions with strict data regulations, like the EU with its GDPR, the platform 102 ensures that all stored energy consumption data complies with local regulations, safeguarding user privacy. Using the platform 102, this data can be stored with advanced encryption standards, and only be accessed when necessary.

[0243] In embodiments, the platform 102 may be configured to manage and respect requests from individual and / or groups of individuals for data anonymity in accordance with data privacy and protection laws. As energy' consumption data becomes more granular, and with smart home devices, it may become increasingly possible to understand behaviors of humans by analyzing his / her energy usage patterns. Considering that, individuals may demand that their data be anonymized. The platform 102 can ensure that individual energy consumption patterns aren't traceable back to specific users, adhering to privacy7norms.

[0244] In embodiments, the platform 102 may7be configured to manage and / or address scenarios in which Al entities and / or robotic entities may request anonymity. By way of example, a business employing Al entities for providing energy management support (like a chatbot) for its users may wish not to let their user know about the use of Al; in such case, the Al entities may send an anonymity request to the platform 102 in its interactions, and the platform 102 may be configured to ensure that its identity remains protected.

[0245] In embodiments, the platform 102 may store data related to DNA and perform handling of the DNA data in accordance with laws and regulations. By way of example, the platform 102 can store DNA data related to bio-energy proj ects, ensuring that this sensitive data is handled ethically and legally. In an example, with the platform 102, research institutions can store DNA sequences of algae species being used for biofuel production. This data can then be accessed and analyzed to determine which species produced the most biofuel under specific conditions, all while ensuring the sensitive genetic data remains protected.

[0246] In embodiments, the platform 102 may be configured to interact with bank systems to manage financial transactions related to energy trading. The platform 102 may ensure secure and / or efficient energy7trading operations. With the growth of energy7trading, the platform 102 can act as a bridge between energy' producers, traders, and consumers. The platform 102 can integrate with banking systems to streamline financial transactions. By way of example, during an energy7trade between two businesses, the platform 102 can manage the financial aspects, ensuring swift and secure payments.

[0247] In embodiments, the platform 102 may be configured to perform automated marketing operations. The platform 102 may provide and / or facilitate one or more of personalized customer engagement, predictive analytics related to marketing operations, and optimization of marketing campaigns. For example, the platform 102 can use energy consumption data to tailor marketing campaigns. In an example, if a region has high solar energy potential (say, for example, due to all-seasons sunlight availability ), the platform 102 can target consumers in such region with solarpanels product ads. In another example, if a region already has high solar energy adoption, the platform 102 can target consumers in such region with solar accessory product ads.

[0248] In embodiments, the platform 102 may be configured to provide and / or facilitate secure and compliant use of text messaging communications with one or both of customers and stakeholders. The platform 102 may adhere to regulations related to privacy and / or consent. For example, the platform 102 can manage text-based communications with stakeholders, ensuring every message sent complies with privacy and consent regulations. By way of example, before sending a promotional message to a user, the platform 102 can check if the said user has consented to such communications.

[0249] In embodiments, the platform 102 may be configured such that edge devices maymonitor movement of energy7production, storage, and consumption devices throughout an area served by an energy7grid. Movement and / or dispositioning of devices may be based on monitoring network traffic passing through / by the edge devices, such as network equipment and the like. Movement and / or dispositioning may also be based on changes in network activity, such as increases in localized network activity associated with energy production / storage / consumption devices.

[0250] In embodiments, the platform 102 may be configured to detect movement of energy7production devices. When energy producing devices are moved within a networked environment, such as by being detected in a new locale (different / new segment) of a networked environment, edge devices may use this information to adjust guidance / instructions for local energy systems regarding energy production, pricing, and the like. Depending on the nature of the newly positioned energy producing resources (e.g.. temporal or permanent) the rules or policies to govern energy production, storage, and utilization may be impacted. As an example, new energy production resources that are dedicated to a temporal event such as construction, a high attendance local event (e.g., a sports event), festival, and the like may suggest that demand on a local energy7infrastructure may be mitigated for / during the event. Although demand for energy7locally may increase substantially, due to the dedicated energy7sourcing resources being disposed locally, energy7policies may suggest taking some portion of the local energy grid and / or energy producing resources off-line for maintenance. If it appears that newly disposed energy producing resources have a more generalized local supply approach (including a long-term presence), such as when responding to an increase in demand and / or reduction in unreliable sourcing, edge devices that detect these new energy supply resources may act as moderator to temper an impact on local energy7supply providers, such as by limiting access to the new source of supply, alerting local energy authorities of the new sourcing presence, and the like.

[0251] In embodiments, the platform 102 may be configured to detect movement of energy consumption devices or of energy consumers based on movement of, for example, consumer mobile devices. This may be achieved through detecting an unusual increase in device presence in a localized network, such as in proximity to one or more cellular antennas, and the like. Increasing presence of potential energy7consumers, (e.g., such as at a social event, concert, sporting event, political event, and the like) in a localized network environment, once detected,may be responded to by the edge devices adjusting energy delivery infrastructure to make a corresponding amount of energy available in the impacted region. Another role that edge devices may play in such a scenario, is to increase radio transmit power and / or receive power across the affected region to accommodate the increase in device traffic. This may extend to signaling to energy providers that networked edge devices (within a region and / or as identified by specific identifier) will be increasing energy consumption in the near term.

[0252] In embodiments, the platform 102 may be configured such that edge devices may also detect and / or react to detecting an influx of energy storage systems, including without limitation, whole-home energy' storage systems. When new energy storage device(s) are detected by edge devices, an energy management plan for a region may be adjusted to take into consideration new energy' storage capabilities. This may involve managing energy' grid utilization to better take advantage of the increased storage capacity7. Local storage of energy7, particularly consumer- direct energy7, can be leveraged to off-load an energy grid during certain times, such as when demand is high, by7directing the local energy storage systems to give up their energy7to the grid at high demand times. Likewise, edge devices may configure communication channels between sourcing and storage to facilitate coordination among these resources.FIG. 6: More Detail on Intelligence Enablement Systems

[0253] Referring to FIG. 6, further detail is provided as to embodiments of the set of intelligence enablement systems 1 12, including the set of intelligent data layers 130, the distributed ledger and smart contract systems 132, the set of adaptive energy digital twin systems 134 and the set of energy simulation systems 136.

[0254] The set of intelligent data layers 130 may undertake any of the wide range of data processing capabilities noted throughout this disclosure and the documents incorporated by' reference herein, optionally autonomously, under user supervision, or with semi-supervision, including extraction, transformation, loading, normalization, cleansing, compression, route selection, protocol selection, self-organization of storage, filtering, timing of transmission, encoding, decoding, and many others. The set of intelligent data layers 130 may include energy' generation data layers 602 (such as producing and automatically configuring and routing streams or batches of data relating to energy' generation by a set of entities, such as operating assets of an enterprise), energy' storage data layers 604 (such as producing and automatically configuring and routing streams or batches of data relating to energy' storage by a set of entities, such as operating assets of an enterprise or assets of a set of customers), energy7delivery data layers 608 (such as producing and automatically7configuring and routing streams or batches of data relating to energy delivery7by a set of entities, such as delivery by transmission line, by pipeline, by portable energy storage, or others), and energy consumption data layers 610 (such as producing and automatically configuring and routing streams or batches of data relating to energy consumption by a set of entities, such as operating assets of an enterprise, a set of customers, a set of vehicles, or the like).

[0255] The distributed ledger and smart contract systems 132 may provide a set of underlying capabilities to enable energy-related transactions, such as purchases, sales, leases, futurescontracts, and the like for energy generation, storage, delivery, or consumption, as well as for related types of transactions, such as in renewable energy credits, carbon abatement credits, pollution abatement credits, leasing of assets, shared economy transactions for asset usage, shared consumption contracts, bulk purchases, provisioning of mobile resources, and many others. This may include a set of energy transaction blockchains 612 or distributed ledgers to record energy transactions, including generation, storage, delivery’, and consumption transactions. A set of energy transaction smart contracts 614 may operate on blockchain events and other input data to enable, configure, and execute the aforementioned ty pes of transactions and others. In embodiments, a set of energy' transaction intelligent agents 618 may be configured to design, generate, and deploy the set of energy transaction smart contracts 614, to optimize transaction parameters, to automatically discover counterparties, arbitrage opportunities, and the like, to recommend and / or automatically initiate steps to contract offers or execution, to resolve contracts upon completion based on blockchain data, and many other functions.

[0256] The set of adaptive energy digital twin systems 134 may’ include digital twins of energy- related entities, such as operating assets of an enterprise that generate, store, deliver, or consume energy, and may include may include energy generation digital twins 622 (such as displaying content from event logs, or from streams or batches of data relating to energy generation by a set of entities, such as operating assets of an enterprise), energy storage digital twins 624 (such as displaying energy storage status information, usage patterns, or the like for a set of entities, such as operating assets of an enterprise or assets of a set of customers), energy delivery digital twins 628 (such as displaying status data, events, workflows, and the like relating to energy’ delivery by a set of entities, such as delivery by transmission line, by pipeline, by portable energy’ storage, or others), and energy consumption digital twins 630 (such as displaying data relating to energy consumption by a set of entities, such as operating assets of an enterprise, a set of customers, a set of vehicles, or the like). The set of adaptive energy digital twin systems 134 may include various ty pes of digital twin described throughout this disclosure and / or the documents incorporated herein by reference, such as ones fed by data streams from edge and loT devices, ones that adapt based on user role or context, ones that adapt based on market context, ones that adapt based on operating context, and many’ others.

[0257] The set of energy simulation systems 136 may include a wide range of systems for the simulation of energy-related behavior based on historical patterns, current states (including contextual, operating, market and other information), and anticipated / predicted states of entities involved in generation, storage, delivery and / or consumption of energy. This may include an energy generation simulation 632. energy storage simulation 634. energy delivery simulation 638 and energy consumption simulation 640, among others. The set of energy simulation systems 136 may employ a wide range of simulation capabilities, such as 3D visualization simulation of behavior of physical, presentation of simulation outputs in a digital twin, generation of simulated financial outcomes for a set of different operational scenarios, generation of simulated operational outcomes, and many others. Simulation may be based on a set of models, such as models of the energy generation, storage, delivery and / or consumption behavior of a machine orsystem, or a fleet of machines or systems (which may be aggregated based on underlying models and / or based on projection to a larger set from a subset of models). Models may be iteratively improved, such as by feedback of outcomes from operations and / or by feedback comparing model-based predictions to actual outcomes and / or predictions by other models or human experts. Simulations may be undertaken using probabilistic techniques, by random walk or random forest algorithms, by projections of trends from past data on current conditions, or the like. Simulations may be based on behavioral models, such as models of enterprise or individual behavior based on various factors, including past behavior, economic factors (e.g., elasticity of demand or supply in response to price changes), energy utilization models, and others. Simulations may use predictions from artificial intelligence, including artificial intelligence trained by machine learning (including deep learning, supervised learning, semi-supervised learning, or the like). Simulations may be configured for presentation in augmented reality, virtual reality’ and / or mixed reality interfaces and systems (collectively referred to as “XR”), such as to enable a user to interact with aspects of a simulation in order to be trained to control a machine, to set policies, to govern a factory or other entity that includes multiple machines, to handle a fleet of machines or factories, or the like. As one example among many, a simulation of a factory may simulate the energy consumption of all machines in the factory while presenting other data, such as operational data, input costs, production costs, computation costs, market pricing data, and other content in the simulation. In the simulation, a user may configure the factory, such as by setting output levels for each machine, and the simulation may simulate profitability of the factory based on a variety of simulated market conditions. Thus, the user may be trained to configure the factory under a variety of different market conditions.FIG. 7: More Detail on AI-Based Energy Orchestration, Optimization, and Automation Systems

[0258] Referring to FIG. 7 more detail is provided with respect to the set of Al-based energy orchestration, optimization, and automation systems 114, each of which may use various other capabilities, services, functions, modules, components, or other elements of the platform 102 in order to orchestrate energy-related entities, workflows, or the like on behalf of an enterprise or other user. Orchestration may, for example, use robotic process automation to facilitate automated orchestration of energy-related entities and resources based on training data sets and / or human supervision based on historical human interaction data. As another example, orchestration may involve design, configuration, and deployment of a set of intelligent agents, which may automatically orchestrate a set of energy-related workflows based on operational, market, contextual and other inputs. Orchestration may involve design, configuration, and deployment of autonomous control systems, such as systems that control energy-related activities based on operational data collected by or from onboard sensors, edge devices, loT devices and the like. Orchestration may involve optimization, such as optimization of multivariate decisions based on simulation, optimization based on real-time inputs, and others. Orchestration may involve use of artificial intelligence for pattern recognition, forecasting and prediction, such as based on historical data sets and current conditions.

[0259] 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.

[0260] The set of energy generation orchestration systems 138 may include a set of generation timing orchestration systems 702 and a set of location orchestration systems 704. among others. The set of timing orchestration systems 702 may orchestrate the timing of energy generation, such as to ensure that timing of generation meets mission critical or operational needs, complies with policies and plans, is optimized to improve financial or operational metrics and / or (in the case of energy7generated for sale) is well-timed based on fluctuations of energy7market prices. Generation timing orchestration can be based on models, simulations, or machine learning on historical data sets. Generation timing orchestration can be based on current conditions (operating, market, and others).

[0261] The set of location orchestration systems 704 may orchestrate location of generation assets, including mobile or portable generation assets, such as portable generators, solar systems, wind systems, modular nuclear systems and others, as well as selection of locations for larger- scale. fixed infrastructure generation assets, such as power plants, generators, turbines, and others, such as to ensure that for any given operational location, available generation capacity (baseline and peak capacity) meets mission critical or operational needs, complies with policies and plans, is optimized to improve financial or operational metrics and / or (in the case of energy generated for sale) is well-located based on local variations in energy market prices. Generation location orchestration can be based on models, simulations, or machine learning on historical data sets. Generation location orchestration can be based on current conditions (operating, market, and others).

[0262] The set of energy7consumption orchestration systems 140 may include a set of consumption timing optimization systems 718 and a set of operational prioritization systems 720, among others. The set of consumption timing optimization systems 718 may orchestrate timing consumption, such as to shift consumption for non-critical activities to lower-cost energy7resources (e.g., by shifting to off-peak times to obtain lower electricity pricing for grid energy consumption, shifting to lower cost resources (e.g., renewable energy systems in lieu of the grid), to shift consumption to activities that are more profitable (e.g., to shift consumption to a machine that has a high marginal profit per time period based on current market and operating conditions (such as detected by a combination of edge and loT devices and market data sources), and the like).

[0263] The set of operational prioritization systems 720 may enable a user, intelligent agent, or the like to set operational priorities, such as by rule or policy, by setting target metrics (e.g., for efficiency, marginal profit production, or the like), by declaring mission-critical operations (e.g.. for safety, disaster recovery and emergency sy stems), by declaring priority among a set of operating assets or activities, or the like. In embodiments, energy consumption orchestration maytake inputs from operational prioritization to provide a set of recommendations or control instructions to optimize energy consumption by a machine, components, a set of machines, a factory, or a fleet of assets.

[0264] The set of energy storage orchestration systems 142 may include a set of storage location orchestration systems 708 and a set of margin of safety orchestration systems 710. The set of storage location orchestration systems 708 may orchestrate location of storage assets, including mobile or portable generation assets, such as portable batteries, fuel cells, nuclear storage systems and others, as well as selection of locations for larger-scale, fixed infrastructure storage assets, such as large-scale arrays of batteries, fuel storage systems, thermal energystorage systems (e.g., using molten salt), gravity-based storage systems, storage systems using fluid compression, and others, such as to ensure that for any given operational location, available storage capacity7meets mission critical or operational needs, complies with policies and plans, is optimized to improve financial or operational metrics and / or (in the case of energy7stored and provide for sale) is well-located based on local variations in energy7market prices. Storage location orchestration can be based on models, simulations, or machine learning on historical data sets, such as behavioral models that indicate usage patterns by individuals or enterprises. Storage location orchestration can be based on current conditions (operating, market, and others) and many other factors; for example, storage capacity can be brought to locations where grid capacity is offline or unusually constrained (e.g., for disaster recovery).

[0265] The set of margin of safety orchestration systems 710 may be used to orchestrate storage capacity to preserve a margin of safety, such as a minimum amount of stored energy to power mission critical systems (e g., life support systems, perimeter security systems, or the like) or high priority systems (e.g.. high-margin manufacturing) for a defined period in case of loss of baseline energy- capacity (e.g., due to an outage or brownout of the grid) or inadequate renewable energy production (e.g., when there is inadequate wind, water or solar power due to weather conditions, drought, or the like). The minimum amount may be set by rule or policy, or may be learned adaptively, such as by an intelligent agent, based on a training data set of outcomes and / or based on historical, current, and anticipated conditions (e.g., climate and weather forecasts). The set of margin of safety7orchestration systems 710 may, in embodiments, take inputs from the energy7provisioning and governance solutions 156.

[0266] The set of energy marketplace orchestration systems 146 may include a set of transaction aggregation systems 722 and a set of futures market optimization systems 724.

[0267] The set of transaction aggregation systems 722 systems may automatically orchestrate a set of energy-related transactions, such as purchases, sales, orders, futures contracts, hedging contracts, limit orders, stop loss orders, and others for energy generation, storage, delivery or consumption, for renewable energy credits, for carbon abatement credits, for pollution abatement credits, or the like, such as to aggregate a set of smaller transactions into a bulk transaction, such as to take advantage of volume discounts, to ensure current or day-ahead pricing when favorable, to enable fractional ownership by a set of owners, operators, or consumers of a block of energygeneration, storage, or delivery- capacity, or the like. For example, an enterprise may aggregateenergy purchases across a set of assets in different jurisdictions by use of an intelligent agent that aggregates a set of futures market energy purchases across the jurisdiction and represents the aggregated purchases in a centralized location, such as an operating digital twin of the enterprise.

[0268] The set of futures market optimization systems 724 may automatically orchestrate aggregation of a set of futures markets contracts for energy, renewable energy credits, for carbon offsets or abatement credits, for pollution abatement credits, or the like based on a forecast of future energy needs for an individual or enterprise. The forecast may be based on historical usage patterns, current operating conditions, current market conditions, anticipated operational needs, and the like. The forecast may be generated using a predictive model and / or by an intelligent agent, such as one based on machine learning on outcomes, on human output, on human-labeled data, or the like. The forecast may be generated by deep learning, supervised learning, semisupervised learning, or the like. Based on the forecast, an intelligent agent may design, configure, and execute a series of futures market transactions across various jurisdictions to meet anticipated timing, location, and type of needs.

[0269] The set of energy delivery orchestration systems 147 may include a set of delivery routing orchestration systems 712 and a set of energy delivery type orchestration systems 714.

[0270] The set of energy delivery routing orchestration systems 712 may use various components, modules, facilities, services, functions and other elements of the platform 102 to orchestrate routing of energy delivery, such as based on location, timing and type of needs, available generation and storage capacity at places of energy need, available energy sources for routing (e.g., liquid fuel, portable energy’ generation systems, portable energy storage systems, and the like), available routes (e.g., main pipelines, pipeline branches, transmission lines, wireless power transfer systems, and transportation infrastructure (roads, railways and waterways, among others)), market factors (price of energy, price of goods, profit margins for production activities, timing of events that require energy, and others), environmental factors (e.g., weather), operational priorities, and others. A set of artificial intelligence systems trained in various ways disclosed herein may be trained to recommend or to configure a route, such as based on the foregoing inputs and a set of training data, such as human routing activities, a route optimization model, iteration among a large number of simulated scenarios, or the like, or combination of any of the foregoing. For example, a set of control instructions may direct valves and other elements of an energy pipeline to deliver an amount of fluid-based energy to a location while directing mobile or portable resources to another location that would otherwise have reduced energy availability' based on the pipeline routing instructions.

[0271] The set of energy delivery type orchestration systems 714 may use various components, modules, facilities, services, functions and other elements of the platform 102 to orchestrate optimization of the type of energy delivery, such as based on location, timing and type of needs, available generation and storage capacity at places of energy need, available energy sources for routing (e.g., liquid fuel, portable energy generation systems, portable energy storage systems, and the like), available routes (e.g., main pipelines, pipeline branches, transmission lines, wireless power transfer systems, and transportation infrastructure (roads, railways andwaterways, among others)), market factors (price of energy, price of goods, profit margins for production activities, timing of events that require energy, and others), environmental factors (e.g.. weather), operational priorities, and others. A set of artificial intelligence systems trained in various ways disclosed herein may be trained to recommend or to configure a mix of energy types, such as based on the foregoing inputs and a set of training data, such as human type selection activities, a delivery type optimization model, iteration among a large number of simulated scenarios, or the like, or combination of any of the foregoing. For example, a set of recommendations or control instructions may select a set of portable, modular energy' resources that are compatible with needs (e.g., specifying renewable sources where there is high storage capacity7to meet operational needs, such that inexpensive, intermittent sources are preferred), while the instructions may select more expensive natural gas energy7where storage capacity7is limited or absent and usage is continuous (such as for a 24 / 7 data center that operates remotely from the energy grid).

[0272] Many other examples of Al-based energy orchestration, optimization, and automation systems 114 are provided throughout this disclosure.FIG. 8: More Detail on Configurable Data and Intelligence Modules and Services

[0273] Referring to FIG. 8 the set of configurable data and intelligence modules and sen ices 118 may include the set of energy transaction enablement systems 144, the set of stakeholder energy digital twins 148 and the set of data integrated microservices 150, among many others. These data and intelligence modules may include various components, modules, sendees, subsystems, and other elements needed to configure a data stream or batch, to configure intelligence to provide a particular type of output, or the like, such as to enable other elements of the platform 102 and / or various stakeholder solutions.

[0274] The set of energy transaction enablement systems 144 may include a set of counterparty7and arbitrage discovery7systems 802, a set of automated transaction configuration systems 804 and a set of energy investment and divestiture recommendation systems 808, among others. The set of counterparty and arbitrage discovery7systems 802 may be configured to operate on various data sources related to operating energy needs, contextual factors, and a set of energy market, renewable energy7credit, carbon offset, pollution abatement credit, or other energy-related market offers by a set of counterparties in order to determine a recommendation or selection of a set of counterparties and offers. An intelligent agent of the set of counterparty and arbitrage discovery systems 802 may initiate a transaction with a set of counterparties based on the recommendation or selection. Factors may include cost, counterparty reliability, size of counterparty7offer, timing, location of energy needs, and many others.

[0275] The set of automated transaction configuration systems 804 may automatically or under human supervision recommend or automatically configure terms for a transaction, such as based on contextual factors (e.g.. weather), historical, current, or anticipated / predicted market data (e.g., relating to energy7pricing, costs of production, costs of storage, and the like), timing and location of operating needs, and other factors. Automation may be by artificial intelligence, such as trained on human configuration interactions, trained by deep learning on outcomes, or trainedby iterative improvement through a series of trials and adjustments (e.g., of the inputs and / or weights of a neural network).

[0276] The set of energy investment and divestiture recommendation systems 808 may automatically or under human supervision recommend or automatically configure terms for an investment or divestiture transaction, such as based on contextual factors (e.g., weather), historical, current, or anticipated / predicted market data (e g., relating to energy pricing, costs of production, costs of storage, and the like), timing and location of operating needs, and other factors. Automation may be by artificial intelligence, such as trained on human configuration interactions, trained by deep learning on outcomes, or trained by iterative improvement through a series of trials and adjustments (e.g., of the inputs and / or weights of a neural network). For example, the set of energy7investment and divestiture recommendation systems 808 may output a recommendation to invest in additional modular, portable generation units to support locations of planned energy7exploration activities or the divestiture of relatively inefficient factories, where energy costs are forecast to produce negative marginal profits.

[0277] The set of stakeholder energy digital twins 148 may include a set of financial energy digital twins 810, a set of operational energy digital twins 812 and a set of executive energy digital twins 814, among many others. The set of financial energy7digital twins 810 may, for example, represent a set of entities, such as operating assets of an enterprise, along with energy - related financial data, such as the cost of energy being used or forecast to be used by a machine, component, factory7, or fleet of assets, the price of energy that could be sold, the cost or price of renewable energy7credits available through use of renewable energy7generation capacity, the cost or price of carbon offsets needed to offset current of future anticipated operations, the cost of pollution abatement offsets or credits, and the like. The set of financial energy digital twins 810 may be integrated with other financial reporting systems and interfaces, such as enterprise resource planning suites, financial accounting suites, tax systems, and others.

[0278] The set of operational energy7digital twins 812 may, for example, represent operational entities involved in energy7generation, storage, delivery7, or consumption, along w ith relevant specification data, historical, current or anticipated / predicted operating states or parameters, and other information, such as to enable an operator to view- components, machines, systems, factories, and various combinations and sets thereof, on an individual or aggregate level. The set of operational energy7digital twins 812 may display energy data and energy -related data relevant to operations, such as generation, storage, delivery and consumption data, carbon production, pollution emissions, waste heat production, and the like. A set of intelligent agents may provide alerts in the digital twins. The digital twins may automatically7adapt, such as by highlighting important changes, critical operations, maintenance, or replacement needs, or the like. The set of operational energy digital twins 812 may take data from onboard sensors, loT devices, and edge devices positioned at or near relevant operations, such as to provide real-time, current data.

[0279] The set of executive energy digital twins 814 may, for example, display entities involved in energy generation, storage, delivery or consumption, along with relevant specification data, historical, current or anticipated / predicted operating states or parameters, andother information, such as to enable an executive to view key performance metrics driven by energy with respect to components, machines, systems, factories, and various combinations and sets thereof, on an individual or aggregate level. The set of executive energy digital twins 814 may display energy data and energy-related data relevant to executive decisions, such as generation, storage, delivery and consumption data, carbon production, pollution emissions, waste heat production, and the like, as well as financial performance data, competitive market data, and the like. A set of intelligent agents may provide alerts in the digital twins, such as configured to the role of the executive (e.g., financial data to a CFO, risk management data to a chief legal officer, and aggregate performance data to a CEO or chief strategy officer. The set of executive energy digital twins 814 may automatically adapt, such as by highlighting important changes, critical operations, strategic opportunities, or the like. The set of executive energy digital twins 814 may take data from onboard sensors, loT devices, and edge devices positioned at or near relevant operations, such as to provide real-time, current data.

[0280] The set of data integrated microservices 150 may include a set of energy market data services 818, a set of operational data sendees 820 and a set of other contextual data services 822, among many others.

[0281] The set of energy market data services 818 may provide a configured, filtered and / or otherwise processed feed of relevant market data, such as market prices of the goods and services of an enterprise, a feed of historical, current and / or futures market energy prices in the operating jurisdictions of the enterprise (optionally weighted or ordered based on relative energy usage across the jurisdictions), a feed of historical and / or proposed transactions (optionally augmented with counterpart}’ information) configured according to a set of preferences of a user or enterprise (e.g., to show transactions relevant to the operating requirements or energy capacities of the enterprise), a feed of historical, current or future renewable energy credit prices, a feed of historical, current or future carbon offset prices, a feed of historical, current or future pollution abatement credit prices, and others.

[0282] The set of operational data services 820 may provide a configured, filtered and / or otherwise processed feed of operational data, such as historical, current, and anticipated / predicted states and events of operating assets of an enterprise, such as collected by sensors, loT devices and / or edge devices and or anticipated or inferred based on a set of models, analytic systems, and or operation of artificial intelligence systems, such as intelligent forecasting agents.

[0283] The set of other contextual data services 822 may provide a wide range of configured, filtered, or otherwise processed feeds of contextual data, such as weather data, user behavior data, location data for a population, demographic data, psychographic data, and many others.

[0284] The configurable data integrated microservices of various types may provide various configured outputs, such as batches and files, database reports, event logs, data streams, and others. Streams and feeds may be automatically generated and pushed to other systems, services may be queried and / or may be pulled from sources (e.g., distributed databases, data lakes, and the like), and may be pulled by application programming interfaces.

[0285] In embodiments, the platform 102 may include one or more virtual power plants. The virtual power plants may be or include one or more of: a virtual power plant for aggregating and managing multiple heterogeneous energy resources in one place, a virtual power plant wherein the energy resources include solar plants, battery storage systems, wind turbines, electric vehicle charging stations, demand and response management centers, and smart meters, and a virtual power plant for managing a set of small, isolated power generation points used for load-leveling, to absorb excess supply from intermittent renewables, and to deliver supply during shortages. Additional Concepts and Examples Adaptive Energy Data Pipeline

[0286] In embodiments, an Al-based platform for enabling intelligent orchestration and management of power and energy includes an adaptive energy data pipeline configured to communicate data across a set of nodes in a network. Each node of the set of nodes is adapted to operate on an energy data set associated with at least one of energy generation, energy storage, energy deliver}', or energy consumption. At least one node of the set of nodes is configured, by one or both of an algorithm or a rule set, to filter, compress, transform, error correct and / or route at least a portion of the energy data set based on at least one of a set of netw ork conditions, data size, data granularity7, or data content.

[0287] For example, the nodes may include a set of energy producers, and the adaptive energy data pipeline may be configured to adapt communication with each of the energy7producers, thereby causing the energy7producers to adapt the data on energy production that is reported to other nodes via the energy data pipeline. If netw ork bandwidth is low; the adaptive energy data pipeline may instruct one or more of the energy producers to compress data more tightly so that data may be delivered more efficiently: to report data with a lower frequency in order to reduce bandwidth consumption; and / or apply a form of error correction in order to reduce retransmissions of data that includes correctible errors.

[0288] For example, the nodes may include a set of energy consumers, and the adaptive energydata pipeline may instruct one or more of the energy7consumers to adapt data content to adapt reported data (such as energy7consumption types, rates, and / or uses) to focus on a particular consumption of data that is of higher priority7than other ty pes of consumption. If the focus includes climate control, the adaptive energy7data pipeline may instruct one or more of the energy consumers to increase reporting of energy consumption data that is associated with climate control and / or to reduce reporting of energy consumption data that is not associated with climate control. If the focus includes emissions, the adaptive energy7data pipeline may instruct one or more of the energy7consumers to increase reporting of energy consumption data that is associated with emissions and / or to reduce reporting of energy consumption data that is not associated with emissions. If the focus includes consumption of energy that involves other resources of interest, such as water, the adaptive energy data pipeline may instruct one or more of the energy consumers to increase reporting of energy consumption data that is associated with the resource of interest (e.g. , energy spent on water filtration and / or purification) and / or to reduce reporting of energy consumption data that is not associated with the resource of interest.

[0289] For example, the nodes may include a heterogeneous set of energy producers and energy consumers, and the adaptive energy data pipeline may instruct one or more of the energy producers and / or one or more of the energy consumers to communicate through one or more communication routes, such as one or more network paths. The communication route may include a direct communication path between an energy producer and an energy consumer that is consuming energy produced, at least in part, by the energy producer. The communication route may include an indirect communication path between an energy producer and an energy consumer that passes between one or more intermediary locations, such as an auditor or broker. The communication route may include a shared communication path among an energy consumer and two or more energy producers that are capable of producing energy needed by the energy' consumer, such that the energy' producers may negotiate and / or cooperate to determine the manner of providing energy to the energy consumer. The communication route may include a shared communication path among an energy producer and two or more energy' consumers that are capable of consuming energy' that is produced by the energy' producer, such that the energy' consumers may negotiate and / or cooperate to determine the manner of allocating consumption of the produced energy. The adaptive energy data pipeline may aid in determining communication routes (e.g., network topologies and / or allocation of bandwidth among a communicating set of resources) to enable an efficient, reliable, prioritized, and / or purposeful exchange of communication among the resources.

[0290] For example, the node may include a smart grid control center that manages multiple microgrids. In periods of high energy demand, the control center needs real-time or near-realtime energy’ usage data to manage load distribution effectively. During such high-demand periods, the adaptive energy data pipeline may prioritize the transmission of energy consumption data over less critical data. Conversely, during periods of low demand, the adaptive energy data pipeline may prioritize maintenance or status data.

[0291] For example, the node may be responsible for monitoring the health and safety' of energy infrastructure, like power plants or substations. If this node detects potential safety' hazards, the adaptive energy' data pipeline may prioritize the transmission of these critical alerts over routine data, ensuring rapid response to potential issues.

[0292] For example, the node may' be an industrial setting with multiple energy-consuming machinery, where not all machines may have equal priority of respective operations. For high- priority machines, the adaptive energy data pipeline may request detailed, granular data, such as minute-by-minute energy consumption metrics. For less critical machines, the adaptive energy data pipeline may only request hourly or daily summaries, which may suffice for such less critical machines.

[0293] In embodiments, the adaptive energy data pipeline is further configured to adapt a transport of data over a network and / or communication system. The adapting is based on one or more of, a congestion condition, a delay and / or latency condition, a packet loss condition, an error rate condition, a cost of transport condition, a quality-of-service (QoS) condition, a usage condition, a market factor condition, and a user configuration condition. For example, theadaptive energy data pipeline may adapt a network topology based on parameters of the network and / or communication system, such as deploying new communication routes among resources; increasing and / or decreasing bandwidth of a communication route among resources; routing or re-routing network communication among the available network routes; and / or scheduling, prioritizing, or otherwise configuring communication among the resources to make use of available communication resources based on the set of available communication conditions. The adapting may be based on short-term conditions and / or priorities (e.g., allocating currently available bandwidth to support current communication needs among the resources). The adapting may be based on long-term conditions and / or priorities (e.g., allocating development resources to plan the development, construction, maintenance, and transfer of infrastructure, such as new network deployments or the acquisition of wireless communication spectrum) based on current and / or projected needs.

[0294] In embodiments, the Al-based platform further includes an adaptive energy digital twin that represents one or more of, an energy stakeholder entity, an energy distribution resource, a stakeholder information technology, a networking infrastructure entity, an energy-dependent stakeholder production facility, a stakeholder transportation system, a market condition, or an energy usage priority’ condition. For example, the adaptive energy twin may make decisions about purchasing energy-related resources on behalf of an energy stakeholder entity and mayengage in transactions with other energy stakeholder entities, including other adaptive energy twins that represent such other energy stakeholder entities. The adaptive energy- twin may autonomously initiate, transact, complete, and / or record ledger entries for energy-related transactions, such as the purchase of raw energy, raw energy resources, energy production, energy transport, and / or energy consumption. The adaptive energy twin may determine a conformity of energy activities of an energy stakeholder entity with regard to an energy- usage policy, such as an energy- consumption policy or a carbon emissions policy. The adaptive energytwin may operate on a combination of a set of needs, priorities, and / or interests of an energystakeholder entity and one or more other parties, such as a government, a public body, an industry consortium, one or more entities that depend upon the energy stakeholder entity (e.g., consumers of energy that is produced by an energy producing entity), and / or the environment.

[0295] In embodiments, the Al-based platform further includes an adaptive energy digital twin that is configured to perform one or more of, providing a visual and / or analytic indicator of energy consumption by one or more energy consumers, filtering energy- data, highlighting energy data, or adjusting energy data. For example, the visual and / or analytic indicators may include energy availability alerts (e.g.. alerts of power outages, blackouts, brownouts, power surges, or the like arising from weather conditions, equipment failures, and / or maintenance operations). The visual and / or analytic indicators may- include excess consumption and / or cost alerts provided to the one or more energy consumers as to excess energy- consumption by certain activities (e.g, manufacturing activities or climate control activities). The visual and / or analytic indicators may include recommendations for adapting energy consumption based on various conditions (e.g., a recommendation to reduce energy- consumption during periods of energy scarcity). The visualand / or analytic indicators may be presented to one or more users (e.g., as visual alerts shown in a web browser page, an app on a user device, a display component of a display-equipped consumer device, an audio alert presented by an audio device, or the like). The visual and / or analytic indicators may include recommendations for improving an efficiency of energy’ consumption (e.g., replacing a particularly energy-inefficient appliance, such as an old refrigerator or HVAC unit, with a newer and more energy-efficient version of the appliance).

[0296] In embodiments, the Al-based platform further includes an adaptive energy digital twin that is configured to generate a visual and / or analytic indicator of energy consumption by one or more of, one or more machines, one or more factories, or one or more vehicles in a vehicle fleet. For example, the visual and / or analytic indicators may be processed by a vehicle owned by the user. The visual and / or analytic indicators may cause the vehicle to operate differently, such as causing an autonomous vehicle may drive more slowly and / or efficiently in order to reduce energy usage during periods of energy scarcity, such as fuel shortages or cost increases. The visual and / or analytic indicators may advise a user of emissions created by the consumer use of the vehicle, such as during periods of varying traffic and / or weather conditions. The visual and / or analytic indicators may inform the user of the comparative costs of using the vehicle during certain periods, such as a cost of traveling before, during, and / or after rush-hour traffic. The visual and / or analytic indicators may include a comparison of energy use and / or efficiency by various modes of transportation, such as energy use when traveling by car, truck, bus, motorcycle, airplane, helicopter, or the like. In an example, the adaptive energy digital twin may be employed by cities and municipalities to monitor and manage, and to provide visual and / or analytic indicators for public services, such as street lighting, public transport systems, and water supply. Herein, these visual and / or analytic indicators may show patterns of energy consumption during different times of the day or year, helping city managers optimize operations and reduce costs. In another example, the adaptive energy digital twin may be employed in hospitals or healthcare facilities. Herein, the adaptive energy’ digital twin may provide visual and / or analytic indicators on the energy’ consumption of different departments or equipment. Such insights may be utilized in prioritizing power supply during outages or emergencies. In yet another example, the adaptive energy’ digital twin may be employed in large industrial units. Herein, the adaptive energy digital twin may provide visual and / or analytic indicators related to the energy consumption of various production processes. This can assist in scheduling operations to take advantage of low energy rates or shift loads to off-peak hours.

[0297] In embodiments, the adaptive energy data pipeline is further configured to perform one or more of. extracting energy’ -related data, detecting and / or correcting errors in energy-related data, transforming, converting, normalizing, and / or cleansing energy-related data, parsing energy-related data, detecting patterns, content, and / or objects in energy -related data, compressing energy-related data, streaming energy-related data, filtering energy-related data, loading and / or storing energy-related data, routing and / or transporting energy-related data, or maintaining security of energy-related data. For example, the adaptive energy' data pipeline may adapt to cause certain kinds of data to be stored by, routed to, and / or processed by certainlocations, such as causing energy consumption data of particular energy consumers to be transmitted to and / or stored by energy producers that produce the energy consumed by the particular energy consumers. The adaptive energy data pipeline may adapt to cause certain kinds of data to be retained, analyzed, summarized, and / or discarded, such as an automated collection and / or curation of data by refrigeration systems in a region in furtherance of government research into incentivizing energy-efficient refrigeration policies.

[0298] In embodiments, the energy’ data set is based on one or more public data resources, the public data resources including one or more of, a weather data resource, a satellite data resource, a census, population, demographic, and / or psychographic data resource, a market data resource, or an ecommerce data resource. For example, the adaptive energy data pipeline may be configured to monitor public resources for information on climate conditions, pollution, governmental energy' policy, energy-related market conditions, or the like. The adaptive energy’ data pipeline may automatically search public data sources (e.g., the Internet) to discover sources of valuable energy-related data, and may develop a catalog of discovered sources, including the types of energy -related data, the accuracy and / or reliability of such data, the security and / or sensitivity of such data to various parties, or the like. The adaptive energy data pipeline may distribute the catalog (e.g., to digital twins of energy' stakeholder entities) and / or merge the catalog with similar catalogs from other sources (e.g.. indications of data sources provided by digital twins of energy stakeholder entities). The adaptive energy data pipeline may’ use the catalog to develop instructions for energy-related resources. For example, the adaptive energy data pipeline may receive data from a research group or government agency that describes energy-related driving behaviors associated with various objectives such as energy efficiency, safety', emissions, or the like. The adaptive energy data pipeline may use the data received from catalogued data sources to generate and / or adapt instructions for vehicles that adapt autonomous driving behavior in furtherance of the identified objectives. In an example, the adaptive energy data pipeline may utilize real-time traffic and public transit data to understand road congestion, public transport schedules, and traffic patterns. This data can help in optimizing energy' consumption for electric vehicles or public transit systems by suggesting optimal routes, speeds, and charging schedules. In another example, the adaptive energy data pipeline may utilize data on the production of renewable energy' sources, such as wind, solar, and hydro. By analyzing this data, the Al-based platform can predict the availability of renewable energy’ and adjust energy consumption or storage strategies accordingly. In yet another example, the adaptive energy data pipeline may utilize real-time air quality indices from environmental agencies. This data can provide insights into pollution levels, which can be valuable for optimizing energy generation in urban areas or adjusting operations of power generation facilities that may increase pollution during peak times.

[0299] In embodiments, the energy’ data set is based on one or more enterprise data resources, the enterprise data resources including one or more of, resource planning data, sales and / or marketing data, financial planning data, demand planning data, supply chain data, procurement data, pricing data, customer data, product data, or operating data. For example, the adaptiveenergy data pipeline may have access to organizational data of an energy stakeholder entity, such as a company, an educational institution, or a government. The organizational data can include, for example, organizational objectives such as reducing costs, improving energy efficiency, prioritizing energy availability for organizational processes, reducing emissions, shifting to renewable energy resources, establishing new resources in particular geographic regions, entering new markets, developing new products, undertaking new manufacturing processes, or the like. The adaptive energy data pipeline can adapt energy resources based on the organizational data, such as allocating energy resources or gathering energy -related data to match energy resource planning and development to the organizational objectives. The adaptive energy data pipeline can inform the organization as to policies that may impact one or more of the organizational objectives, such as informing the organization of the prospects for energy-related resource development and energy7availability' in a region where the organization is planning to develop or position new organizational resources.

[0300] In embodiments, the Al-based platform further includes at least one Al-based model and / or algorithm, wherein the at least one Al-based model and / or algorithm is trained based on a training data set, and the training data set is based on one or more of, one or more human tags and / or labels, one or more human interactions with a hardware and / or software system, one or more outcomes, one or more Al-generated training data samples, a supervised learning training process, a semi-supervised learning training process, or a deep learning training process. For example, the adaptive energy data pipeline may generate a training data set based on discovered data sources, such as research groups and / or government agencies. The adaptive energy data pipeline may initiate new training processes based on the newly developed training data sets, such as training or retraining models of autonomous vehicle control based on new studies regarding the energy-efficiency, safety, and / or emissions of certain autonomous vehicle driving behaviors. The adaptive energy' data pipeline may identify certain areas of error, weakness, or loss of confidence in new or in-use Al-based models, such as driving patterns by autonomous vehicles in certain ty pes of conditions (e.g, rain, snow, or nighttime) that relate to energy' -related objectives (e.g., conserving fuel resources and / or reducing emissions). The adaptive energy data pipeline may' generate new Al models, adapt existing Al models, and / or initiate training or retraining procedures of Al models, wherein these processes are carried out to include the new or adjusted Al models in the autonomous driving control systems of autonomous vehicles.

[0301] In embodiments, at least one node of the set of nodes is configured to orchestrate delivery of energy to one or more points of consumption, and the delivery of the energy includes one or more of, one or more fixed transmission lines, one or more instances of wireless energy transmission, one or more deliveries of fuel, or one or more deliveries of stored energy. For example, the adaptive energy’ data pipeline may be configured to schedule delivery of fuel resources to various depots. The adaptive energy data pipeline may be configured to schedule transmission of quantities of power from some power sources or power stores (e.g., factories or batteries) to other power stores or power consumers. The adaptive energy data pipeline may be configured to schedule use of energy' by energy consumers based on the availability', transfer,and / or costs of such energy. The adaptive energy data pipeline may be configured to develop energy-related policies in order to satisfy the needs and / or objectives of energy producers, energy stores, energy transporters, and / or energy consumers, such as ensuring the availability of power resources for essential operations of an energy stakeholder entity and / or reducing excessive consumption for low-priority uses during periods of energy scarcity.

[0302] In embodiments, at least one node of the set of nodes is further configured to record, in a distributed ledger and / or blockchain, one or more energy-related events, the one or more energy-related events including one or more of, an energy purchase and / or sale event, a service charge associated with an energy purchase and / or sale event, an energy consumption event, an energy generation event, an energy distribution event, an energy' storage event, a carbon emission production event, a carbon emission abatement event, a renewable energy credit event, a pollution production event, or a pollution abatement event. For example, the adaptive energy' data pipeline may generate entries on a distributed ledger to indicate the offer, negotiation, acceptance, and / or completion of an energy-related transaction between one or more energy producers and one or more energy consumers. The adaptive energy data pipeline may generate one or more smart contracts by which energy-related transactions are carried out, and / or may record such one or more smart contracts on the distributed ledger. The adaptive energy data pipeline may audit a distributed ledger to develop data and information that may inform various energy-related analyses, such as an analysis of energy transactions recorded on a distributed ledger to guide the development of new energy production and / or storage infrastructure resources in view of an indication of energy supply, energy demand, energy usage, energy cost, or the like.

[0303] In embodiments, at least one node of the set of nodes is deployed in an off-grid environment, and the off-grid environment includes one or more of, an off-grid energy generation system, an off-grid energy storage system, or an off-grid energy mobilization system. For example, off-grid nodes may include residences, mobile homes, encampments, or the like that develop, store, transport, and / or consume energy supplied by renewable energy' resources. The adaptive data energy data pipeline may adapt energy resources based on the needs of such nodes. For example, the adaptive energy data pipeline may provide supplemental and / or emergency energy generation, storage, and / or transport facilities that can provide power in case the off-grid renewable energy resources fail to meet demand. The adaptive energy data pipeline may provide energy generation, storage, and / or transport facilities that can make use of excess power that is generated by one or more off-grid nodes beyond the energy consumption needs of such nodes. The adaptive energy data pipeline may coordinate the development of energy grid resources based on the nodes of the off-grid environment, such as adjusting the capacity, scale, and / or development of new energy plants, storage facilities, and / or transmission channels based on the initiation, expansion, reduction, and / or collapse of communities of nodes in the off-grid environment.

[0304] In embodiments, the adaptive energy data pipeline is further configured to monitor one or both of, an overall energy consumption by at least a portion of the set of nodes, or a role of at least one node of the set of nodes in an overall energy consumption by at least a portion of the setof nodes, and based on the monitoring, perform one or more of, managing an energy consumption by the set of nodes, forecasting an energy consumption by the set of nodes, or provisioning resources associated with energy consumption by the set of nodes. For example, manufacturing organizations may adapt the roles of manufacturing resources such as facilities, warehouses, data centers, and vehicles. Such roles may inform the energy generation, storage, transport, and / or consumption needs and priorities of such resources. For example, a repurposing of a manufacturing plant from using a first manufacturing process to using a second manufacturing process, and the change of manufacturing processes may change the forecasted demand for energy. The adaptive energy data pipeline may respond to changes in forecasted demand for energy based on the roles of the manufacturing organization, such as allocating new power plants and / or energy storage resources in the vicinity of the manufacturing resource to accommodate the change in forecasted energy demand. For example, in case of EV charging stations, if a particular charging station node gets upgraded to a fast-charging station or if its usage frequency increases due to a new transit route nearby, its energy consumption pattern can change significantly. The adaptive energy data pipeline can recognize this and may prioritize energy supply to such charging stations during peak commuting hours or facilitate faster grid connections. For example, in cities, nodes like street-lights may be retrofitted with additional functionalities, like turning them into Wi-Fi hotspots. This multifunctionality alters their energy consumption profile. The adaptive energy data pipeline can recognize this and may ensure that these multi-purpose nodes are sufficiently powered, especially during times when their additional functionalities are in high demand, such as providing Wi-Fi during public events or the like.

[0305] In embodiments, the set of nodes in the network that comprise the adaptive energy’ data pipeline comprise a set of edge networking devices that govern at least one of energy generation, energy storage, energy delivery' or energy consumption by a set of operating devices that are controlled via the edge networking devices. For example, the edge devices may include a set of loT devices in a facility’, wherein each loT device includes a set of computing resources that can be used for various forms of computation that consume energy’. The adaptive energy data pipeline may adapt the energy generation, storage, and / or delivery to accommodate the consumption of energy’ by the loT devices. For example, a power-over-Ethemet (PoE) network may be adapted to provide power to various loT devices, some of which may’ have energy storage resources, such as local batteries or capacitors. The adaptive energy’ data pipeline may schedule the delivery’ of power over the PoE network such that loT devices are supplied with enough power to perform scheduled computation, and, optionally, to maintain power in local energy storage resources. For example, a first loT device that performs significant computation, but that also includes a battery. The adaptive energy data pipeline may be configured to schedule delivery of energy to the loT device at sufficient intervals to allow the loT device to perform its computation while avoiding depletion of the battery. A second loT device may perform a periodic monitoring function, such as applying a computer vision (CV) model to a camera input. The periodic monitoring function may involve significant expenditure of energy, and the loT device may not have a local battery'. The adaptive energy data pipeline may be configured toschedule a supply of energy to the loT device over the PoE network so that it has enough power to perform the periodic monitoring function. The adaptive energy data pipeline can also adapt the schedule of the loT device so that the monitoring function is performed during periods of sufficient energy supply and / or delivery, and is not performed during periods of energy’ scarcity.

[0306] In embodiments, the adaptive energy data pipeline is further configured to automatically select a least-cost route for data communicated across the set of nodes, the selection being based on a low-priority energy use related to the data. For example, the adaptive energy’ data pipeline may be configured to assign costs to available routes for data communication, including wired local-area and wide-area network routes, wireless local-area network (WLAN) routes, cellular communication routes, and satellite routes. “Cost” may be determined based on a variety of factors, such as energy’ expenditure, bandwidth expenditure, and / or use of limited resources. The adaptive energy’ data pipeline may also determine the value of various forms of communication, such as a value of communicating reports of occurrences of energy generation, storage, transport, and / or consumption; a value of communicating reports of audits of energy resources, such as status, capacity, and usage of energy’ generation, storage, and / or transport resources; and a value of communicating energy-based transactions, such as recordation of energy-related events on a distributed ledger. The adaptive energy data pipeline may match the value of each communication with the costs of the routes associated with each such communication. The adaptive energy data pipeline may perform the matching on an ad-hoc basis to determine a route for a particular communication. The adaptive energy data pipeline may perform the matching on a holistic basis to determine routes for all current and / or future communications among a set of nodes. The adaptive energy’ data pipeline may prioritize the occurrence and / or frequency of communications based on the matching (e g., increasing a reporting occurrence and / or frequency of reports having a high value / cost ratio, and decreasing a reporting occurrence and / or frequency of reports having a low value / cost ratio). In some cases, the adaptive energy' data pipeline may be capable of identifying and using least-cost routes for all current and / or forecasted communications. In some cases, the adaptive energy' data pipeline may have to switch from a least-cost route to a higher-cost route for a particular communication (<?.g, in case the least-cost route is entirely consumed by a first energy consumer that transmits large volumes of data, such that a higher-cost route has to be used by a second energy' consumer that transmits only low volumes of intermittent data).

[0307] In embodiments, the adaptive energy data pipeline is further configured to automatically select a high-quality of service route for data communicated across the set of nodes, the selection being based on a high-prionty energy use related to the data. For example, the adaptive energy data pipeline may be configured to determine a quality of service of each available route for data communication, including wired local-area and wide-area network routes, wireless local-area network (WLAN) routes, cellular communication routes, and satellite routes. “Quality of service” may be determined based on a variety of factors, such as speed, bandwidth, latency, capacity, reliability', demand, and / or security. The adaptive energy' data pipeline may also determine the quality-of-service needs of various forms of communication, such as a quality-of-service need ofcommunicating reports of occurrences of energy generation, storage, transport, and / or consumption; a quality-of-service need of communicating reports of audits of energy resources, such as status, capacity, and usage of energy generation, storage, and / or transport resources; and a quality-of-service need of communicating energy-based transactions, such as recordation of energy-related events on a distributed ledger. The adaptive energy data pipeline may match the value of each communication with the quality-of-service needs of the routes associated with each such communication. The adaptive energy data pipeline may perform the matching on an ad-hoc basis to determine a route for a particular communication. The adaptive energy data pipeline may perform the matching on a holistic basis to determine routes for all current and / or future communications among a set of nodes. The adaptive energy data pipeline may prioritize the occurrence and / or frequency of communications based on the matching (e.g., choosing higher- QoS routes for communications having a high value / QoS-need product, and choosing lower-QoS routes for communications having a low- value / QoS-need product). In some cases, the adaptive energy data pipeline may be capable of identifying and using least-cost routes for all current and / or forecasted communications. In some cases, the adaptive energy data pipeline may have to switch from a least-cost route to a higher-cost route for a particular communication in order to meet a QoS need for the communication (e.g. , in case the bandwidth and / or latency associated with the least-cost route are not suitable for an urgent communication, such as an indication of a detected or imminent failure of an energy resource or an urgent demand for energy by an energy consumer).

[0308] In embodiments, the adaptive energy data pipeline includes a set of artificial intelligence capabilities, the capabilities being configured to adapt the pipeline to enable optimization of elements of data transmission in coordination with energy orchestration needs. For example, various energy resources, such as energy producers, energy stores, energy transporters, and energy consumers may include one or more machine learning models that adapt the capabilities of such resources to energy availability and / or costs. Each energy- resource may have to retrain its machine learning models to account for new- data, new- market conditions, new7usage patterns, or the like. Such retraining of machine learning models also consumes energy7. Accordingly, the adaptive energy data pipeline may7coordinate the retraining of the machine learning models based on energy availability, need, and / or value. For example, the adaptive energy data pipeline may instruct the energy resources to schedule retraining during periods of lower energy demand, such as off-peak hours. The adaptive energy data pipeline may instruct a particular energy resource to retrain its machine learning model urgently based on a mismatch between a performance of the energy resource and the environment (e.g., behaviors of the machine learning model that do not correspond to energy market conditions, and therefore causes the energy resource to produce, store, transport, and / or consume too much or too little energy based on updated energy market conditions). Further, such retraining may be based on the communication of information to the energy resource, such as up-to-date information about energy market conditions. The adaptive energy data pipeline may adapt the transmission of information to the energy resource to provide up-to-date information for the retraining of its machine learningmodel(s). Further, the adaptive energy data pipeline may be utilized to anticipate energy demands and adjust data transmission processes accordingly. For example, the adaptive energy data pipeline can forecast a spike in energy demand due to impending weather conditions like a heatwave. Based on this prediction, the adaptive energy data pipeline may prioritize data transmission from energy storage systems, to ensure they are prepared to dispatch energy’ efficiently. Moreover, by optimizing data transmission, the adaptive energy data pipeline ensures that energy distribution centers receive real-time consumption data without delay, enabling them to make instantaneous adjustments in energy' supply.

[0309] In embodiments, the adaptive energy data pipeline includes a self-organizing data storage, the data storage being configured to store data on a device based on one or more of patterns of the data, content of the data, or context of the data. For example, information about patterns of energy’ production, storage, transportation, and / or consumption may be stored by various devices, wherein such devices may have dynamic access to available storage resources. The adaptive energy data pipeline may adapt the provisioning of data storage to satisfy the storage needs of the energy resources. For example, the adaptive energy data pipeline may provision a pool of data storage devices such that the data storage needs of energy producers are sufficient to hold information about current or forecasted energy consumption. The provisioned data storage may be used to adapt the current and / or future operation of data production, storage, and / or transport by the energy resource. Additionally, the provisioned data storage may be used to store labeled data in a training data set to update one or more machine learning models of such energy resources, such as a machine learning model used by an energy producer to forecast energy demand cycles. The adaptive energy data pipeline may ensure that sufficient data storage is provisioned for the energy resource to accommodate the data needed to retrain the machine learning model. Such retraining may occur on a periodic basis (e.g., once a month) and / or on demand (e.g., when drift is detected), and the adaptive energy data pipeline may schedule the provisioning of data storage accordingly (e.g, increasing a provisioning of data storage capacity’ for the energy’ resource in anticipation of an imminent retraining period, or upon detecting drift that will likely necessitate retraining of the machine learning model). If such provisioning is detected or projected to be insufficient, the adaptive energy’ data pipeline may alert one or more administrators of the insufficiency, and / or may arrange for the acquisition of additional data storage capacity (e.g., by completing transactions for additional data storage via the execution of smart contracts and recordation for transactions on a distributed ledger).

[0310] In embodiments, the adaptive energy data pipeline is configured to perform automated, adaptive networking, the adaptive networking including one or more of adaptive protocol selection, adaptive routing of data based on RF conditions, adaptive filtering of data, adaptive slicing of network bandwidth, adaptive use of cognitive network capacity, or adaptive use of peer-to-peer network capacity. For example, the adaptive networking may involve switching between protocols based on a determination that a current protocol is insufficient. Such insufficiency may include, for example, excessive latency; excessive errors and / or retransmission; excessive overhead and / or bandwidth usage; and / or inadequate security', such as aprotocol that uses a cryptography technique that has been compromised. The adaptive energy data pipeline may determine an alternative protocol that may reduce or eliminate the insufficiency of the current protocol. The determination may be based on a comparison of the features of the protocols; testing and / or metering of the protocols; historical data of the performance of various protocols under various conditions; and / or simulations and / or heuristics regarding the performance of different protocols in a particular scenario. The adaptive energy data pipeline may automatically switch a network from the current protocol to the alternative protocol based on the determination. The switch may include one or more of: reconfiguring a piece of communication hardware to use an updated set of communication parameters; changing a driver of a piece of communication hardware; reconfiguring a communication stack; changing communication libraries used by a piece of communication equipment; substituting a first piece of communication hardware of a device with a second piece of communication hardware of the device (e.g., switching from a wired connection to a wireless connection or vice versa); acquiring new hardware and / or software to be added to the set of communication resources used by a device; and / or requesting and / or recommending a development or acquisition of new communication resources for a device.

[0311] In embodiments, the adaptive energy data pipeline is configured to perform enterprise contextual adaptation by automatically processing data based on one or more of an operating context of an enterprise, a transactional context of an enterprise, or a financial context of an enterprise. For example, the enterprise may pursue one or more enterprise objectives such as reducing costs, improving energy efficiency, prioritizing energy’ availability for organizational processes, reducing emissions, shifting to renewable energy resources, establishing new resources in particular geographic regions, entering new markets, developing new products, undertaking new manufacturing processes, or the like. The adaptive energy data pipeline may be configured to interpret energy -related data in the context of the enterprise objectives. For example, a current use of energy' by the enterprise may be undesirably high, but the energy' usage may be in furtherance of developing and / or deploying renewable energy’ resources that are forecasted to reduce energy usage considerably for the long-term future. Thus, the adaptive energy data pipeline may prioritize the production, storage, and / or transport of energy on behalf of the enterprise today, in order to achieve rapid efficiency gains in energy production and / or use in the near-term future that benefits both the enterprise and the broader set of energy producers, stores, transporters, and consumers.Automatically Optimizing Energy Used in Edge Data Pipeline

[0312] In embodiments, an Al-based platform for enabling intelligent orchestration and management of power and energy includes a set of adaptive, autonomous data handling systems. Each of the adaptive, autonomous data handling systems is configured to collect data relating to energy generation, storage, or delivery from a set of edge devices that are in operational control of a set of distributed energy resources. Each of the adaptive, autonomous data handling systems is configured to autonomously adjust, based on the collected data, a set of operational parameters for such operational control.

[0313] For example, the data collected by each of the adaptive, autonomous data handling systems may include various properties of energy generation, such as total power capacity, peak power generation, surge power generation capacity, per-unit power generation cost, or the like. The data collected by each of the adaptive, autonomous data handling systems may include various properties of energy storage, such as total power storage capacity, current power storage, power storage density, per-unit power storage cost, or the like. The data collected by each of the adaptive, autonomous data handling systems may include various properties of energy deliver}’, such as peak power delivery, surge power delivery' capacity, per-unit power delivery cost, or the like.

[0314] For example, the operational parameters may include a schedule of a set of processes, including computational, industrial, research, engineering, and / or auditing processes. Each adaptive, autonomous data handling system may be configured to determine the schedule of the set of processes based on the priorities and needs of the adaptive, autonomous data handling systems, and / or of other systems of the same or other energy generators, stores, transporters, and / or consumers. For example, during periods of energy scarcity, an adaptive, autonomous data handling system may be configured to increase and / or prioritize communication with edge devices relating to surveying their energy consumption needs and priorities, and may issue instructions to adapt the processes performed by such edge devices to address energy scarcity based on the results of such surveys. During periods of energy inefficiency, an adaptive, autonomous data handling system may be configured to increase and / or prioritize communication with edge devices relating to surveying the efficiency of their energy consumption, and may issue instructions to such edge devices to improve their energy consumption efficiency based on the results of such surveys. During periods of energy resource planning (e.g, provisioning the development of new energy resources), an adaptive, autonomous data handling system may be configured to increase and / or prioritize communication with edge devices relating to surv eying their projected energy needs, and may inform the energy’ resource planning based on such forecasts.

[0315] In embodiments, each of the adaptive, autonomous data handling systems is further configured to adapt a transport of data over a network and / or communication system, wherein the adapting is based on one or more of, a congestion condition, a delay and / or latency condition, a packet loss condition, an error rate condition, a cost of transport condition, a quality-of-service (QoS) condition, a usage condition, a market factor condition, or a user configuration condition.

[0316] In embodiments, each of the adaptive, autonomous data handling systems includes an adaptive energy digital twin that represents one or more of, an energy stakeholder entity, an energy distribution resource, a stakeholder information technology, a networking infrastructure entity, an energy-dependent stakeholder production facility, a stakeholder transportation system, a market condition, or an energy usage priority condition.

[0317] In embodiments, each of the adaptive, autonomous data handling systems includes an adaptive energy' digital twin that is configured to perform one or more of. providing a visualand / or analytic indicator of energy consumption by one or more energy consumers, filtering energy data, highlighting energy data, or adjusting energy data.

[0318] In embodiments, each of the adaptive, autonomous data handling systems includes an adaptive energy digital twin that is configured to generate a visual and / or analytic indicator of energy consumption by one or more of, one or more machines, one or more factories, or one or more vehicles in a vehicle fleet.

[0319] In embodiments, each of the adaptive, autonomous data handling systems is further configured to perform one or more of, extracting energ -related data, detecting and / or correcting errors in energy -related data, transforming, converting, normalizing, and / or cleansing energy- related data, parsing energy-related data, detecting patterns, content, and / or objects in energy- related data, compressing energy -related data, streaming energy-related data, filtering energy- related data, loading and / or storing energy -related data, routing and / or transporting energy- related data, or maintaining security of energy -related data.

[0320] In embodiments, the energy' edge data is based on one or more public data resources, the public data resources including one or more of, weather data resource, a satellite data resource, a census, population, demographic, and / or psychographic data resource, a market data resource, or an ecommerce data resource.

[0321] In embodiments, the energy' edge data is based on one or more enterprise data resources, the enterprise data resources including one or more of, resource planning data, sales and / or marketing data, financial planning data, demand planning data, supply chain data, procurement data, pricing data, customer data, product data, or operating data.

[0322] In embodiments, the Al-based platform further includes at least one Al-based model and / or algorithm, wherein the at least one Al -based model and / or algorithm is trained based on a training data set, and the training data set is based on one or more of, one or more human tags and / or labels, one or more human interactions with a hardware and / or softw are system, one or more outcomes, one or more Al-generated training data samples, a supervised learning training process, a semi-supervised learning training process, or a deep learning training process.

[0323] In embodiments, each of the adaptive, autonomous data handling systems is further configured to orchestrate delivery of energy' to one or more points of consumption, and the delivery of the energy includes one or more of, one or more fixed transmission lines, one or more instances of wireless energy transmission, one or more deliveries of fuel, or one or more deliveries of stored energy.

[0324] In embodiments, each of the adaptive, autonomous data handling systems is further configured to record, in a distributed ledger and / or blockchain, one or more energy-related events, the one or more energy-related events including one or more of, an energy purchase and / or sale event, a service charge associated with an energy purchase and / or sale event, an energy consumption event, an energy generation event, an energy distribution event, an energy storage event, a carbon emission production event, a carbon emission abatement event, a renewable energy' credit event, a pollution production event, or a pollution abatement event.

[0325] In embodiments, at least one of the adaptive, autonomous data handling systems is deployed in an off-grid environment, and the off-grid environment includes one or more of, an off-grid energy generation system, an off-grid energy storage system, or an off-grid energy mobilization system.

[0326] In embodiments, the platform further comprises an adaptive energy data pipeline configured to communicate data across a set of nodes in a network.

[0327] In embodiments, the set of nodes in the network that comprise the adaptive energy’ data pipeline comprise a set of edge networking devices that govern at least one of energy consumption, energy storage, energy' delivery or energy consumption by a set of operating devices that are controlled via the edge networking devices.

[0328] In embodiments, the adaptive energy' data pipeline is further configured to automatically select a least-cost route for data communicated across the set of nodes, the selection being based on a low-priority7energy use related to the data.

[0329] In embodiments, the adaptive energy data pipeline is further configured to automatically select a high-quality of service route for data communicated across the set of nodes, the selection being based on a high-priority energy use related to the data.

[0330] In embodiments, the adaptive energy data pipeline includes a set of artificial intelligence capabilities, the capabilities being configured to adapt the pipeline to enable optimization of elements of data transmission in coordination with energy orchestration needs.

[0331] In embodiments, the adaptive energy data pipeline includes a self-organizing data storage, the data storage being configured to store data on a device based on one or more of patterns of the data, content of the data, or context of the data.

[0332] In embodiments, the adaptive energy data pipeline is configured to perform automated, adaptive networking, the adaptive networking including one or more of adaptive protocol selection, adaptive routing of data based on RF conditions, adaptive filtering of data, adaptive slicing of network bandwidth, adaptive use of cognitive network capacity7, or adaptive use of peer-to-peer network capacity.

[0333] In embodiments, the adaptive energy7data pipeline is configured to perform enterprise contextual adaptation by’ automatically processing data based on one or more of an operating context of an enterprise, a transactional context of an enterprise, or a financial context of an enterprise.Automated and Coordinated Governance of a Grid and a Distributed Edge (Non-Grid) Resource Set

[0334] In embodiments, an Al-based platform for enabling intelligent orchestration and management of power and energy includes a system configured to perform automated and coordinated governance of a set of energy entities that are operationally coupled within an energy7grid and a set of distributed edge energy resources, wherein at least one of the distributed edge energy7resources is operationally independent of the energy7grid.

[0335] For example, governance of the energy7grid may involve managing and responding to varying energy7demands. In this scenario, the Al-based platform can be integrated with a systemof smart meters deployed across residential and commercial properties. These smart meters continuously transmit consumption data to the Al-based platform. When the Al-based platform detects a peak demand period, perhaps due to extreme weather conditions, it can initiate demand response strategies. This may involve sending signals to smart home systems, prompting them to temporarily adjust thermostats or delay the operation of high-energy-consuming appliances like washing machines. Further, the Al-based platform may incentivize the factories to reschedule some of their energy-intensive operations to non-peak hours. This governance approach ensures that the grid does not get overloaded.

[0336] For example, governance of the energy grid may involve a determination and / or ranking of priorities such as energy grid capacity, energy grid reliability, energy' grid cost reduction, energy' grid efficiency, energy grid security', and / or energy' grid emissions reduction. The priorities may be based on policies developed by a nation, government, organization, or research group, such as global, national, and / or regional targets for reducing emissions. The priorities may be based on market conditions, such as current and / or forecasted costs of planning, building, developing, using, and / or maintaining renewable vs. non-renewable energy' resources. The AI- based platform may adapt its orchestration and management of power and energy' based on the priorities, such as adapting computation performed by various edge devices in view of the overall priorities of the Al-based platform. For example, the Al-based platform may allocate processing of the distributed edge energy resources over a certain time period, such that a total amount of energy consumed by the distributed edge energy’ resources remains within an energy consumption cap that is projected to satisfy an emissions target for the time period.

[0337] For example, governance of the energy grid may involve the Al -based platform to constantly monitor production rates of the DERs like solar panels, wind turbines, and battery storage systems, and adjusting grid input accordingly. By way of example, on a particularly sunny day, if there is excess energy’ production from solar panels across a locality', the Al-based platform may either store the excess energy in grid-connected battery' systems or redirect it to areas with higher demand. Conversely, if there is a forecasted drop in renewable energy' production due to weather conditions, the Al-based platform may use stored energy or manage demand to prevent grid instability. Additionally, the Al-based platform can predict maintenance needs for these DERs, ensuring they' operate optimally and contribute efficiently to the grid.

[0338] In embodiments, the system is further configured to adapt a transport of data over a network and / or communication system, wherein the adapting is based on one or more of, a congestion condition, a delay and / or latency condition, a packet loss condition, an error rate condition, a cost of transport condition, a quality-of-service (QoS) condition, a usage condition, a market factor condition, or a user configuration condition.

[0339] In embodiments, the Al-based platform further includes an adaptive energy digital twin that represents one or more of, an energy’ stakeholder entity, an energy distribution resource, a stakeholder information technology, a networking infrastructure entity, an energy-dependent stakeholder production facility, a stakeholder transportation system, a market condition, or an energy usage priority condition.

[0340] In embodiments, the Al-based platform further includes an adaptive energy digital twin that is configured to perform one or more of, providing a visual and / or analytic indicator of energy consumption by one or more energy consumers, filtering energy data, highlighting energy data, or adjusting energy data.

[0341] In embodiments, the Al-based platform further includes an adaptive energy digital twin that is configured to generate a visual and / or analytic indicator of energy consumption by one or more of, one or more machines, one or more factories, or one or more vehicles in a vehicle fleet.

[0342] In embodiments, the system is further configured to perform one or more of, extracting energy-related data, detecting and / or correcting errors in energy -related data, transforming, converting, normalizing, and / or cleansing energy-related data, parsing energy-related data, detecting patterns, content, and / or objects in energy-related data, compressing energy-related data, streaming energy -related data, filtering energy -related data, loading and / or storing energy- related data, routing and / or transporting energy-related data, or maintaining security of energy- related data.

[0343] In embodiments, the Al-based platform further includes at least one Al-based model and / or algorithm, wherein the at least one Al-based model and / or algorithm is trained based on a training data set, and the training data set is based on one or more of, one or more human tags and / or labels, one or more human interactions with a hardware and / or software system, one or more outcomes, one or more Al-generated training data samples, a supervised learning training process, a semi-supervised learning training process, or a deep learning training process.

[0344] In embodiments, the system is further configured to orchestrate delivery of energy’ to one or more points of consumption, and the delivery’ of the energy’ includes one or more of, one or more fixed transmission lines, one or more instances of wireless energy transmission, one or more deliveries of fuel, or one or more deliveries of stored energy'.

[0345] In embodiments, the system is further configured to record, in a distributed ledger and / or blockchain, one or more energy-related events, the one or more energy -related events including one or more of, an energy purchase and / or sale event, a service charge associated with an energy' purchase and / or sale event, an energy’ consumption event, an energy generation event, an energy' distribution event, an energy storage event, a carbon emission production event, a carbon emission abatement event, a renewable energy' credit event, a pollution production event, or a pollution abatement event.

[0346] In embodiments, at least one of the distributed energy edge resources is deployed in an off-grid environment, and the off-grid environment includes one or more of, an off-grid energy' generation system, an off-grid energy storage system, or an off-grid energy mobilization system.

[0347] In embodiments, the system is configured to facilitate governance of a mining operation.

[0348] In embodiments, the system includes mine-level Internet of Things (loT) sensing of the mining environment, ground-penetrating sensing of unmined portions of the mining environment, mass spectrometry and computer vision-based sensing of mined materials, asset tagging of smart containers, wearable device for detecting physiological status of miners, secure recording and resolution of transactions and transaction-related events, smart contracts for automaticallyallocating proceeds derived from the mining environment, and an automated system for recording, reporting, and assessing compliance with contractual, regulatory, and legal policy requirements.

[0349] In embodiments, the system includes a set of carbon-aware energy edge solutions, the solutions including exploring, configuring, and implementing a set of policies regarding carbon generation.

[0350] In embodiments, the solutions require energy production by a mining operation to be monitored to track carbon emissions generated by the mining operation.

[0351] In embodiments, the solutions require energy production by a mining operation to require offsetting carbon generation by the mining operation.

[0352] In embodiments, the platform includes a user interface and system includes a set of automated energy policy deployment solutions, the solutions being configurable via user interaction with the user interface.

[0353] In embodiments, the system includes an intelligent agent trained to generate policies related to governance of the mining operation, the intelligent agent being trained on a training set of historical data, feedback from outcomes, and human policy-setting interactions.

[0354] In embodiments, the system facilitates governance of the mining operation by implementing policies including one or more of, 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, setting profit margin minimums based on energy and other marginal costs for a production entity, or setting minimum storage baselines for energy storage entities.

[0355] In embodiments, the system includes a set of energy governance smart contract solutions configured to allow a user of the platform to design, generate, and deploy a smart contract that automatically provides a degree of governance of a set of energy transaction.

[0356] In embodiments, the system includes a set of automated energy financial control solutions configured to allow a user of the platform to design, generate, configure, or deploy a policy related to control of financial factors related to one or more of energy generation, storage, delivery, or utilization.Adaptive, Autonomous Data Handling Systems in the Energy Edge

[0357] In embodiments, an Al-based platform for enabling intelligent orchestration and management of power and energy includes a set of adaptive, autonomous data handling systems, wherein each of the adaptive, autonomous data handling systems is configured to collect data relating to energy generation, storage, or delivery from a set of edge devices that are in operational control of a set of distributed energy resources and is configured to autonomously adjust, based on the collected data, a set of operational parameters for such operational control.

[0358] For example, in an industrial facility such as a manufacturing plant, the set of operational parameters may include an allocation of resources to produce various products. Themanufacturing plant may perform various manufacturing tasks to produce each of the various products, and may be configured to adapt the selection of products to be produced based on a variety of inputs, such as resource costs, product demand, market conditions, the operating status and capacity of various machines of the manufacturing plant, or the like. The Al-based platform may determine an allocation of resources to produce various products that is consistent with both manufacturing objectives of the manufacturing plant (e.g., a completion of certain quantities of manufactured units within a designated time frame) and the needs of the various manufacturing tasks (e.g, a delivery of manufacturing materials to various manufacturing machines to keep them supplied, and / or a performance of a maintenance task to a manufacturing machine while it is out of operation). The Al-based platform may further determine the allocation based on the collected data related to energy generation, storage, and / or delivery' from the set of edge devices that are in operational control of the distributed energy resources. For example, the Al-based platform may configure the edge devices to generate, store, and / or deliver energy' in synchrony with the allocation of products to be produced, and / or to coordinate the allocation of products to be produced based on the availability and / or cost of generated, stored, and / or transported energy.

[0359] As another example, in an industrial facility such as a manufacturing plant, the set of operational parameters may include a schedule of operating various manufacturing equipment and / or performing various manufacturing processes. The manufacturing plant may perform various manufacturing tasks according to various times and / or under various conditions, such as a speed of a manufacturing machine or an assembly line, or a schedule of transporting manufacturing materials within the manufacturing plant. The Al -based platform may determine a schedule of the manufacturing tasks that is consistent with both manufacturing objectives of the manufacturing plant (e.g., a completion of certain quantities of manufactured units within a designated time frame) and the needs of the various manufacturing tasks (e g., a delivery of manufacturing materials to various manufacturing machines to keep them supplied, and / or a performance of a maintenance task to a manufacturing machine while it is out of operation). The Al-based platform may further determine the schedule based on the collected data related to energy generation, storage, and / or delivery from the set of edge devices that are in operational control of the distributed energy^ resources. For example, the Al-based platform may configure the edge devices to generate, store, and / or deliver energy in synchrony with the schedule of operational processes, and / or to coordinate the schedule of operational processes based on the availability and / or cost of generated, stored, and / or transported energy.

[0360] As yet another example, in a residential community with multiple homes, the set of operational parameters may include the allocation and distribution of energy during various peak and non-peak times. Homes within the community may have various energy consumption patterns, some may have solar panels for energy generation with energy’ storage devices like home batteries, while others may rely solely on grid power. The Al-based platform collects data regarding individual home energy consumption, battery storage levels, solar energy generation, and grid energy' prices. By analyzing this data, the Al-based platform may adjust operationalparameters such as when to draw energy from the grid, when to use stored energy, and even when to sell excess energy back to the grid.

[0361] As still another example, in a commercial building, such as a shopping mall or business complex, the operational parameters may include the allocation of energy resources across various retail outlets, central air conditioning systems, lighting, and other utilities. The Al-based platform may continuously gather data from a multitude of sensors distributed throughout the building, monitoring energy consumption patterns of individual outlets, lighting systems, HVAC units, and more. The Al-based platform may identify that certain outlets or areas have higher footfall and energy consumption during specific hours. Using this data, the Al-based platform may adapt operational parameters to prioritize energy distribution to these high-footfall areas during peak hours, ensuring optimal lighting, temperature, and operational efficiency. Moreover, if the commercial building has renewable energy sources like rooftop solar panels, the Al-based platform can make decisions on when to use the generated energy , when to store it, or even when to feed it back to the grid, ensuring optimal energy usage and cost efficiency.

[0362] In embodiments, each of the adaptive, autonomous data handling systems is further configured to adapt a transport of data over a netw ork and / or communication system, wherein the adapting is based on one or more of, a congestion condition, a delay and / or latency condition, a packet loss condition, an error rate condition, a cost of transport condition, a quahty-of-service (QoS) condition, a usage condition, a market factor condition, or a user configuration condition.

[0363] In embodiments, each of the adaptive, autonomous data handling systems includes an adaptive energy digital twin that represents one or more of, an energy stakeholder entity, an energy distribution resource, a stakeholder information technology, a networking infrastructure entity , an energy-dependent stakeholder production facility, a stakeholder transportation system, a market condition, or an energy usage priority condition.

[0364] In embodiments, each of the adaptive, autonomous data handling systems includes an adaptive energy7digital twin that is configured to perform one or more of, providing a visual and / or analytic indicator of energy' consumption by one or more energy' consumers, filtering energy data, highlighting energy' data, or adjusting energy' data.

[0365] In embodiments, each of the adaptive, autonomous data handling systems includes an adaptive energy digital twin that is configured to generate a visual and / or anafytic indicator of energy consumption by one or more of, one or more machines, one or more factories, or one or more vehicles in a vehicle fleet.

[0366] In embodiments, each of the adaptive, autonomous data handling systems is further configured to perform one or more of. extracting energy-related data, detecting and / or correcting errors in energy-related data, transforming, converting, normalizing, and / or cleansing energy- related data, parsing energy-related data, detecting patterns, content, and / or objects in energy- related data, compressing energy-related data, streaming energy-related data, filtering energy- related data, loading and / or storing energy-related data, routing and / or transporting energy- related data, or maintaining security of energy-related data.

[0367] In embodiments, the energy edge data is based on one or more public data resources, the public data resources including one or more of, a weather data resource, a satellite data resource, a census, population, demographic, and / or psychographic data resource, a market data resource, or an ecommerce data resource.

[0368] In embodiments, the energy’ edge data is based on one or more enterprise data resources, the enterprise data resources including one or more of, resource planning data, sales and / or marketing data, financial planning data, demand planning data, supply chain data, procurement data, pricing data, customer data, product data, or operating data.

[0369] In embodiments, the Al-based platform further includes at least one Al-based model and / or algorithm, wherein the at least one Al -based model and / or algorithm is trained based on a training data set, and the training data set is based on one or more of, one or more human tags and / or labels, one or more human interactions with a hardware and / or software system, one or more outcomes, one or more Al-generated training data samples, a supervised learning training process, a semi-supervised learning training process, or a deep learning training process.

[0370] In embodiments, each of the adaptive, autonomous data handling systems is further configured to orchestrate delivery of energy to one or more points of consumption, and the delivery of the energy includes one or more of, one or more fixed transmission lines, one or more instances of wireless energy transmission, one or more deliveries of fuel, or one or more deliveries of stored energy.

[0371] In embodiments, each of the adaptive, autonomous data handling systems is further configured to record, in a distributed ledger and / or blockchain, one or more energy-related events, the one or more energy -related events including one or more of, an energy purchase and / or sale event, a service charge associated with an energy purchase and / or sale event, an energy consumption event, an energy' generation event, an energy distribution event, an energy storage event, a carbon emission production event, a carbon emission abatement event, a renewable energy’ credit event, a pollution production event, or a pollution abatement event.

[0372] In embodiments, at least one of the adaptive, autonomous data handling systems is deployed in an off-grid environment, and the off-grid environment includes one or more of, an off-grid energy7generation system, an off-grid energy storage system, or an off-grid energy’ mobilization system.

[0373] In embodiments, the platform further comprises an adaptive energy data pipeline configured to communicate data across a set of nodes in a network.

[0374] In embodiments, the set of nodes in the network that comprise the adaptive energy data pipeline comprise a set of edge networking devices that govern at least one of energy consumption, energy storage, energy’ delivery or energy consumption by a set of operating devices that are controlled via the edge networking devices.

[0375] In embodiments, the adaptive energy data pipeline is further configured to automatically select a least-cost route for data communicated across the set of nodes, the selection being based on a low-priority energy use related to the data.

[0376] In embodiments, the adaptive energy data pipeline is further configured to automatically select a high-quality of service route for data communicated across the set of nodes, the selection being based on a high-prionty energy use related to the data.

[0377] In embodiments, the adaptive energy data pipeline includes a set of artificial intelligence capabilities, the capabilities being configured to adapt the pipeline to enable optimization of elements of data transmission in coordination with energy orchestration needs.

[0378] In embodiments, the adaptive energy data pipeline includes a self-organizing data storage, the data storage being configured to store data on a device based on one or more of patterns of the data, content of the data, or context of the data.

[0379] In embodiments, the adaptive energy data pipeline is configured to perform automated, adaptive networking, the adaptive networking including one or more of adaptive protocol selection, adaptive routing of data based on RF conditions, adaptive filtering of data, adaptive slicing of network bandwidth, adaptive use of cognitive network capacity7, or adaptive use of peer-to-peer network capacity.

[0380] In embodiments, the adaptive energy data pipeline is configured to perform enterprise contextual adaptation by automatically processing data based on one or more of an operating context of an enterprise, a transactional context of an enterprise, or a financial context of an enterprise.Digital Twin of a Mine

[0381] In embodiments, an Al-based platform for enabling intelligent orchestration and management of power and energy includes a digital twin system having a digital twin of a mine, wherein the digital twin includes at least one parameter that is detected by a sensor of the mine.

[0382] For example, the at least one parameter detected by a sensor of the mine may include at least one physical property of the mine, temperature, humidity, pressure, strain, the presence of chemicals and / or radiation, or the like. The at least one parameter may include at least one physical property of a resource of the mine, such as a location, size, composition, or extraction status of an oil deposit. The at least one parameter may include at least one property7of a machine of the mine, such as a location, condition, and / or operating state of a pump, drill, or vehicle. The at least one parameter may include at least one property7of a process associated with the mine, such as an objective, set of requirements, allocation of resources, operating status, and / or projected result of an oil extraction process. The at least one parameter may include at least one property of an individual associated with the mine, such as an identity, type, skill set, current task, and / or health condition of a mine worker. The at least one parameter may include at least one property of a data set associated with the mine, such as a content, generation date, update date, and / or usage of a survey of an oil deposit or land feature of the mine.

[0383] For example, the mine may include industrial operations for surveying, accessing, and extracting minerals from areas of a mining site. The industrial operations may be associated with various pieces of equipment, such as lighting, cameras, ventilating fans, heating and cooling systems, drills, pumps, refineries, storage containers, transports, and the like. Each piece of equipment may have various energy-related needs, such as an energy type, quantity7, storagecapacity, and consumption rate. Some pieces of equipment may also be associated with one or more sensors that detect various properties, such as environmental sensors that detect temperature, humidity, pressure, strain, the presence of chemicals and / or radiation, or the like. The detected properties may relate to the piece of equipment (e.g., a speed, operating condition, or health state of the piece of equipment), a user of the piece of equipment (e g, a presence, identity, activity, or health state of the user), the environment (e.g. , an ambient or weather condition), or the like. The Al-based platform may orchestrate and manage energy in view of the energy needs of each piece of equipment of the mine based, at least in part, on the properties detected by the sensors. For example, the Al-based platform may monitor energy usage by each piece of equipment over the course of a period of time. The Al -based platform may then determine a schedule for generating, storing, and / or transporting energy' to the pieces of the equipment, based on the monitoring, in order to meet the energy' needs of the equipment over a future corresponding period of time. The schedule may be based, in part, on simulated operation of each piece of equipment, based on a corresponding digital twin and the properties detected by' the sensors associated with the piece of equipment.

[0384] In embodiments, the at least one parameter is associated with one or more of, an unmined portion of the mine, a mining of materials from the mine, a smart container event involving a smart container associated with the mine, a physiological status of a miner associated with the mine, a transaction-related event associated with the mine, or a compliance of the mine with one or more contractual, regulatory, and / or legal policies.

[0385] In embodiments, the digital twin system of the Al-based platform additionally represents one or more of, an energy stakeholder entity, an energy’ distribution resource, a stakeholder information technology, a networking infrastructure entity, an energy-dependent stakeholder production facility, a stakeholder transportation system, a market condition, or an energy usage priority condition.

[0386] In embodiments, the digital twin system of the Al-based platform is further configured to perform one or more of, providing a visual and / or analytic indicator of energy' consumption by one or more energy' consumers, filtering energy data, highlighting energy' data, or adjusting energy data.

[0387] In embodiments, the digital twin system of the Al-based platform is further configured to generate a visual and / or analytic indicator of energy consumption by one or more of, one or more machines, one or more factories, or one or more vehicles in a vehicle fleet.

[0388] In embodiments, the parameter is based on one or more public data resources, the public data resources including one or more of. a weather data resource, a satellite data resource, a census, population, demographic, and / or psychographic data resource, a market data resource, or an ecommerce data resource.

[0389] In embodiments, the parameter is based on one or more enterprise data resources, the enterprise data resources including one or more of, resource planning data, sales and / or marketing data, financial planning data, demand planning data, supply chain data, procurement data, pricing data, customer data, product data, or operating data.

[0390] In embodiments, the digital twin system of the Al-based platform includes at least one Al-based model and / or algorithm, wherein the at least one Al-based model and / or algorithm is trained based on a training data set, and the training data set is based on one or more of, one or more human tags and / or labels, one or more human interactions with a hardware and / or software system, one or more outcomes, one or more Al -generated training data samples, a supervised learning training process, a semi-supervised learning training process, or a deep learning training process.

[0391] In embodiments, the digital twin system of the Al-based platform is further configured to orchestrate delivery of energy to one or more points of consumption, and the delivery of the energy includes one or more of, one or more fixed transmission lines, one or more instances of wireless energy' transmission, one or more deliveries of fuel, or one or more deliveries of stored energy'.

[0392] In embodiments, the digital twin system of the Al-based platform is further configured to record, in a distributed ledger and / or blockchain, one or more energy -related events, the one or more energy-related events including one or more of, an energy purchase and / or sale event, a service charge associated with an energy' purchase and / or sale event, an energy consumption event, an energy generation event, an energy' distribution event, an energy storage event, a carbon emission production event, a carbon emission abatement event, a renewable energy credit event, a pollution production event, or a pollution abatement event.

[0393] In embodiments, the digital twin system of the Al-based platform is deployed in an off- grid environment, and the off-grid environment includes one or more of, an off-grid energy’ generation system, an off-grid energy storage system, or an off-grid energy mobilization system.

[0394] In embodiments, the mine is a data mine.

[0395] In embodiments, the mine is a set of resources for conducting computational operations.

[0396] In embodiments, the Al-based platform includes mine-level Internet of Things (loT) sensing of the mining environment, ground-penetrating sensing of unmined portions of the mining environment, mass spectrometry' and computer vision-based sensing of mined materials, asset tagging of smart containers, wearable device for detecting physiological status of miners, secure recording and resolution of transactions and transaction-related events, smart contracts for automatically allocating proceeds derived from the mining environment, and an automated system for recording, reporting, and assessing compliance with contractual, regulatory, and legal policy requirements.

[0397] In embodiments, the Al-based platform includes a set of carbon-aware energy edge solutions, the solutions including exploring, configuring, and implementing a set of policies regarding carbon generation.

[0398] In embodiments, the Al-based platform requires energy production by a mining operation to be monitored to track carbon emissions generated by the mining operation.

[0399] In embodiments, the Al-based platform requires energy production by a mining operation to require offsetting carbon generation by the mining operation.

[0400] In embodiments, the Al-based platform includes a user interface and platform includes a set of automated energy policy deployment solutions, the solutions being configurable via user interaction with the user interface.

[0401] In embodiments, the Al-based platform includes an intelligent agent trained to generate policies related to governance of a mining operation, the intelligent agent being trained on a training set of historical data, feedback from outcomes, and human policy-setting interactions.

[0402] In embodiments, the Al-based platform facilitates governance of a mining operation by implementing policies including one or more of, 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, setting profit margin minimums based on energy' and other marginal costs for a production entity, or setting minimum storage baselines for energy storage entities.AI-Based Platform for Automated Labor Law Compliance Associated With Mining Operations

[0403] An Al-based platform for enabling intelligent orchestration and management of power and energy includes a governance system for a mining operation; and a reporting system for conveying at least one parameter that is sensed by a sensor of a mine of the mining operation, wherein the at least one parameter is associated with a compliance of the mining operation with a set of labor standards.

[0404] For example, the labor standard may include a set of tasks that a laborer with a particular background is trained, competent, and / or authorized to perform. The Al-based platform may adapt parameters associated w ith the operation of the mine to ensure compliance with the labor standard, such as adjusting parameters of an allocation of laborers to tasks to be performed in the mine, such that laborers are only allocated to tasks that they are trained, competent, and / or authorized to perform based on the labor standard.

[0405] As another example, the labor policy may include a set of work requirements for a laborer to perform a particular task, such as a maximum length of a work period, an allocation of breaks during the work period, a performance of a safety check during the work period, and / or an availability of a piece of safety equipment during the work period. The Al-based platform may adapt parameters associated with the operation of the mine to ensure compliance with the labor standard, such as adjusting parameters of an allocation of a laborer to a task to be performed in the mine, such that the work period of the laborer does not exceed a maximum length, includes an allocation of breaks, includes a required safety check, and / or is allocated only when a required piece of safety equipment is available, based on the labor standard.

[0406] As yet another example, the labor standard may specify that laborers working in certain zones of the mine with high risks, like deeper mine shaft, must undergo periodic training and certification. The Al-based platform can maintain a digital record of training and certification status of each laborer. Before a particular laborer is allocated to a task in these high-risk zones,the Al-based platform can verify that his / her training is up-to-date and have the required certification. If not, the Al-based platform may re-route the concerned laborer to another task, and may further flag that particular laborer for training before he / she can be assigned to the high- risk zone. This ensures that only adequately trained laborers work in areas with high risks to as to maintain compliance with the labor standards.

[0407] As still another example, the labor standard may include health monitoring requirements for laborers who are exposed to certain hazardous environments in the mine, such as areas with high levels of harmful gases. The Al -based platform, integrated with health monitoring devices like wearable sensors, may continuously monitor vital signs of laborers, ensuring that any irregularities, such as elevated heart rates, are detected in real time. If such anomalies are detected, the Al-based platform may initiate corresponding protocols, such as alerting onsite medical personnel, or even halting certain mining operations temporarily. This ensures that health of the laborers is not compromised and that the mining operation remains compliant with health monitoring standards.

[0408] In embodiments, the Al-based platform retrieves information about the labor standard from a labor standard information source, such as a labor policy library associated with a geographic region of the mine. The Al-based platform may determine and execute one or more processes for assessing compliance of the mining operation with the set of labor standards based on the available sensors and parameters. For example, the labor standards may include a safety standard for a labor condition associated with a miner, such as a work schedule, a determined physical health state, a determined mental and / or emotional health state, or an exposure of the miner to various health hazards such as radiation or pollution. The Al-based platform may determine, based on labor policy information, which labor standards apply to the miner. The AI- based platform may determine detectable parameter thresholds that apply to such standards (e.g, a maximum exposure to radiation over a given period of time). The Al -based platform may then identify' sensors in the mine that are capable of detecting the detectable parameters (e.g, among a set of distributed radiation sensors, which radiation sensors are capable of providing data that is indicative of the exposure of the miner to radiation). The Al-based platform may orchestrate and manage the collection of information from the identified sensors in order to ensure that the collective data is indicative of the exposure of the miner to radiation over a period of time. Such orchestration and management may include scheduling and executing a generation, storage, and / or transport of power to each of the identified sensors so that sufficient data is reported to the Al-based platform to carry out its labor standard auditing function and to achieve governance of the mining operation.

[0409] In embodiments, the reporting system is further configured to adapt a transport of data over a network and / or communication system, wherein the adapting is based on one or more of, a congestion condition, a delay and / or latency condition, a packet loss condition, an error rate condition, a cost of transport condition, a quality-of-service (QoS) condition, a usage condition, a market factor condition, or a user configuration condition.

[0410] In embodiments, the Al-based platform includes an adaptive energy digital twin that represents one or more of, an energy stakeholder entity, an energy distribution resource, a stakeholder information technology, a networking infrastructure entity, an energy-dependent stakeholder production facility, a stakeholder transportation system, a market condition, or an energy usage priority condition.

[0411] In embodiments, the Al-based platform includes an adaptive energy digital twin that is configured to perform one or more of, providing a visual and / or analytic indicator of energy consumption by one or more energy consumers, filtering energy data, highlighting energy data, adjusting energy data, or generating a visual and / or analytic indicator of energy consumption by one or more of, one or more machines, one or more factories, or one or more vehicles in a vehicle fleet.

[0412] In embodiments, the reporting system is further configured to perform one or more of, extracting energy-related data, detecting and / or correcting errors in energy-related data, transforming, converting, normalizing, and / or cleansing energ -related data, parsing energy- related data, detecting patterns, content, and / or objects in energy-related data, compressing energy-related data, streaming energy-related data, filtering energy -related data, loading and / or storing energy-related data, routing and / or transporting energy-related data, or maintaining security of energy-related data.

[0413] In embodiments, the reporting system is further configured to record, in a distributed ledger and / or blockchain, one or more energy-related events, the one or more energy -related events including one or more of. an energy’ purchase and / or sale event, a service charge associated with an energy purchase and / or sale event, an energy consumption event, an energy generation event, an energy distribution event, an energy storage event, a carbon emission production event, a carbon emission abatement event, a renewable energy' credit event, a pollution production event, or a pollution abatement event.

[0414] In embodiments, at least one of the at least one parameter is based on one or more of, one or more public data resources, the one or more public data resources including one or more of, a weather data resource, a satellite data resource, a census, population, demographic, and / or psychographic data resource, a market data resource, or an ecommer...

Claims

CLAIMS1. An Al -based platform for enabling intelligent orchestration and management of distributed energy resources (DERs), comprising: a DER client interface configured to provide client demand forecasts to one or more DER marketplace orchestration layers; a client energy transaction orchestration and execution system including a client smart contract system configured to negotiate smart contracts to meet DER client energy needs; and a client assist layer including a configured intelligence system and a client API / GUI.

2. The platform of claim 1, wherein the DER client interface includes a client broadcast / poll system configured to receive proposals from the DER marketplace orchestration layers.

3. The platform of claim 2, wherein the proposals include: specific contract proposals to meet a client demand forecast; or information regarding available energy solutions.

4. The platform of claim 1 , further comprising a client needs analysis system configured to analyze energy' requirements.

5. The platform of claim 1, further comprising a market demand system including transaction aggregation systems configured to automatically orchestrate energy -related transactions.

6. The platform of claim 5, wherein the energy-related transactions include: purchases; sales; orders; futures contracts; hedging contracts; limit orders; or stop loss orders.

7. The platform of claim 1, further comprising a DER market orchestration layer including one or more energy marketplaces.

8. The platform of claim 7, wherein the energy' marketplaces are based on: type of energy; or location of energy'.

9. The platform of claim 7, wherein the DER market orchestration layer includes: a market forming system; a market demand system; a market response system; and a market value analysis system.

10. The platform of claim 7, wherein the DER market orchestration layer includes a DER market interface configured to broadcast information regarding current and forecasted energy capacity7and pricing.

11. The platform of claim 10, wherein the DER market interface is configured to receive information from DER clients including:requests for power to meet immediate needs; or requests for bids on projected future needs.

12. The platform of claim 1. further comprising a digital platform for DER management module that leverages expert systems and generative artificial intelligence.

13. The platform of claim 12, wherein the expert systems utilize a rule-based approach to analyze energy profiles and transaction histories.

14. The platform of claim 12, wherein the generative Al algorithms synthesize data to create and propose energy operations and transaction offerings.

15. The platform of claim 12, wherein the module implements targeting and recommendation use cases through a dynamic feedback loop.

16. The platform of claim 15, wherein interactions with offerings and proposals are continuously fed back into the system to refine Al models.

17. The platform of claim 1, further comprising a set of futures market optimization systems configured to automatically orchestrate aggregation of futures markets contracts based on a forecast of future energy needs.

18. The platform of claim 17, wherein the forecast is based on: historical usage patterns; current operating conditions; current market conditions; or anticipated operational needs.

19. An Al-based platform for enabling intelligent orchestration and management of distributed energy resources (DERs), comprising: a DER client interface providing client demand forecasts; a client energy transaction system with smart contract capabilities; a DER market orchestration layer including energy marketplaces; a market demand system with transaction aggregation capabilities; and a digital platform for DER management leveraging expert systems and generative Al.

20. A system for managing distributed energy resources (DERs), comprising: a market forming system; a market demand system; a market response system; a market value analysis system; and a market orchestration assist layer.

21. The system of claim 20, wherein the market demand system includes transaction aggregation systems configured to automatically orchestrate energy-related transactions for: energy generation; energy storage; energy delivery; energy consumption; renewable energy credits;carbon abatement credits; or pollution abatement credits.

22. The system of claim 20, further comprising a set of intelligent data layers configured to: produce energy generation data layers; produce energy storage data layers; produce energy delivery data layers; and produce energy consumption data layers.

23. The system of claim 22, wherein the intelligent data layers are configured to perform: extraction; transformation; loading; normalization; cleansing; compression; route selection; protocol selection; self-organization of storage; filtering; timing of transmission; encoding; or decoding.

24. A distributed energy resource (DER) management platform, comprising: a market transaction and orchestration system; a set of transaction aggregation systems; a set of futures market optimization systems; and a predictive model configured to generate forecasts using machine learning on outcomes, human output, or human-labeled data.

25. The platform of claim 24, wherein the predictive model is configured to design, configure, and execute a series of futures market transactions across various jurisdictions to meet anticipated timing, location, and type of needs.

26. A system for distributed energy resource (DER) market orchestration, comprising: a market broadcast / poll system for providing information regarding current and forecasted energy capacity and pricing; a market value analysis system; and a market orchestration assist layer configured to coordinate multiple DER providers and DER clients.

27. The system of claim 26, further comprising distributed ledger and smart contract systems configured to enable: energy-related transactions; purchases;sales; leases; futures contracts; renewable energy- credits; carbon abatement credits; pollution abatement credits; leasing of assets; shared economy transactions; shared consumption contracts; bulk purchases; or provisioning of mobile resources.

28. The system of claim 26, further comprising energy' transaction intelligent agents configured to: design smart contracts; generate smart contracts; deploy smart contracts; optimize transaction parameters; discover counterparties; discover arbitrage opportunities; recommend contract execution steps; or resolve contracts upon completion based on blockchain data.

29. A system for Al convergence in distributed energy- resource management, comprising: an Al system generation module configured to generate one or more generative Al systems; an Al system orchestration module configured to manage allocation of computational resources; and operations modules configured to generate and invoke Al systems for energy-related operations.

30. The system of claim 29, wherein the Al system generation module is configured to: generate primary content Al systems; generate supplemental content Al systems; generate metadata Al systems; and generate content review Al systems.

31. The system of claim 29, wherein the Al system orchestration module is configured to: adjust allocations of computational resources; provision resources to enable Al systems to fulfill requests; manage acquisition and decommissioning of resources; and reorganize resources based on changes in processing demands.

32. The system of claim 29, wherein the operations modules are configured to add executive "smart" features to energy-related operations, including autonomously executing transactions to acquire energy resources in anticipation of shortages.

33. A platform for Al-enabled distributed energy resource management, comprising: a data layer system of systems including: transact on / market-oriented capabilities; market platforms; transaction flows; user interfaces; and content providers.

34. The platform of claim 33, wherein the data layer system includes an intelligent data layer architecture comprising: an ingestion stage; an analysis stage; a derived intelligence stage; and a consumer visualization portal.

35. The platform of claim 34, wherein the ingestion stage is configured to: receive data from multiple sources; parse content; determine structure; determine relationships among data elements; and determine intended meaning of data elements.

36. The platform of claim 33, further comprising a cross-service resource optimization system comprising Al agents trained to: manage subsystems; configure subsystems; deploy subsystems; provision subsystems; and optimize subsystems operating within a linked system.

37. The platform of claim 36, wherein the cross-service resource optimization system is configured to measure and allocate energy used across platforms, including: battery storage by devices; energy use by GPUs in cloud computing; and energy use by data centers for generative Al workloads.

38. A system for Al -based distributed energy resource orchestration, comprising: a digital twin platform configured to provide an environment for decision making; an adaptive energy digital twin representing energy stakeholder entities; and a decision making framework for distributing authority among human beings, human-AI systems, and autonomous Al systems.

39. The system of claim 38, wherein the decision making framework is selected from:a hierarchical framework; a rules-based framework; a simulation framework; an enterprise planning framework; an algorithmic framework; a principles-based framework; a collaborative framework; a peer-to-peer framework; or a competitive framework.

40. The system of claim 38, wherein the digital twin platform includes: an interface system for designating trainers for Al system creation; a system for displaying training metrics; and an embedded intelligent agent system for discovering available systems.

41. A distributed energy resource management platform with Al convergence capabilities, comprising: a mobile distributed energy resources intelligence framework implementing Al systems to: optimize localized demand response through mobile energy assets; predict local energy demand patterns; and optimize positioning and dispatch of mobile DERs.

42. The platform of claim 41, wherein the framework employs: deep learning models analyzing grid conditions; weather data analysis; historical usage pattern analysis; reinforcement learning for dynamic routing; predictive maintenance models; and federated learning techniques.

43. The platform of claim 41, further comprising an energy trading systems intelligence platform including: neural networks for automated energy trading; deep learning models for price prediction; natural language processing systems for market analysis; and reinforcement learning algorithms for trading optimization.

44. A smart distributed energy resources intelligence system, comprising: neural networks for autonomous DER operation; deep reinforcement learning models for real-time control; computer vision networks for equipment monitoring; predictive analytics models for maintenance; and natural language processing systems for control interfaces.

45. The system of claim 44, further comprising a DER fleet management framework implementing: graph neural networks modeling relationships between fleet members; deep learning models optimizing fleet-wide performance; multi-agent reinforcement learning algorithms; clustering techniques for operational groups; and federated learning for sharing operational insights.

46. A digital platform for DER management implementing Al systems for: integrated DER operations; performance optimization; maintenance scheduling; fleet coordination; market participation; and regulatory compliance.

47. The platform of claim 46, wherein the Al systems include: supervised learning systems; deep learning systems; natural language processing systems; intelligent agent systems; self-optimizing systems; and self-organizing systems.

48. A system for Al-enabled DER orchestration, comprising: an intelligent data layer architecture with: controlled data processing pipelines; intelligence services for data ingestion; pattern recognition capabilities; and predictive analytics functions.

49. The system of claim 48, further comprising a user interface configured to: facilitate configuration of the data layer; manage algorithm portals; control data retention rules; prioritize resource usage; and maintain data security.

50. The system of claim 48, wherein the system implements a cross-service resource optimization framework to: manage energy resources across subsystems; optimize computational resource allocation; coordinate distributed energy assets; and balance system-wide energy consumption.

51. A system implementing an energy edge convergence technology stack, comprising:a plurality of energy edge modules enabled at various layers by convergence of Al capabilities; a set of converging technology stack examples for energy edge scenarios; andAl-driven capabilities for energy edge operations.

52. The system of claim 51, wherein the Al capabilities include: expert systems; generative Al; routing capabilities; control capabilities; optimization capabilities; and generation capabilities.

53. The system of claim 51, further comprising a digital platform for DER management module enabled by: expert systems utilizing rule-based analysis of energy profiles; generative Al for synthesizing energy operations proposals; transaction systems interfaces; and targeting and recommendation systems.

54. The system of claim 53, wherein the digital platform for DER management module enables:Al optimization of DER product parameters;Al optimization of deployment parameters; optimization during initial design phase; and optimization during operation phase.

55. A converged. Al-based energy-aware workflow orchestration system, comprising:Al algorithms for transaction monitoring; machine learning techniques for risk assessment; robotics and process automation for repetitive tasks; and blockchain technology for transaction recording.

56. The system of claim 55, wherein the system implements: deep neural networks for pattern recognition; predictive analytics; natural language processing for transaction documents; cloud computing infrastructure; andAPI integrations for system communication.

57. A system for automated governance of energy transactions and operations, comprising: modules for policy automation; regulatory framework monitoring; distributed energy resource governance; and energy grid governance capabilities.

58. A system for Al-based enterprise transactional decision support, comprising: strategic energy resource planning capabilities;transaction planning simulation; intelligent dashboards; and digital twins integrating operational data.

59. The system of claim 58, wherein the system provides capabilities for: automated edge transaction orchestration; adjustment of transaction parameters; monitoring of marketplace conditions; and analysis of sensor data from energy' entities.

60. An intelligent edge system for energy -optimized networking, comprising:Al systems in edge devices; expert systems enabling localized energy' transactions; point-of-use transaction capabilities; and energy optimization algorithms.

61. A context-aware sensor fusion system for energy management, comprising: data fusion capabilities for marketplace data; operational data integration;Al classification systems; prediction systems; and optimization systems for computation-intensive industries.

62. A system for joint optimization of energy and computation, comprising: resource optimization modules; computation resource management; energy resource management; and industry -specific optimization capabilities.

63. The system of claim 62, wherein the resource optimization modules provide: real-time monitoring capabilities; predictive analytics; automated control systems; resource allocation optimization; and automated execution of optimization strategies.

64. A system implementing energy edge convergence capabilities, comprising: automated governance of energy transactions;Al-based enterprise decision support; digital platform for DER management; automated edge transaction orchestration; and converged workflow orchestration.

65. The system of claim 64, further comprising: intelligent edge networking; context-aware sensor fusion; analytics integration;Al classification systems; and optimization systems.

66. A system for energy edge operations automation, comprising: modules for automated governance; policy automation systems; regulatory’ compliance monitoring; transaction orchestration; and workflow optimization.

67. The system of claim 66, wherein the modules implement:Al-based decision support; strategic planning capabilities; marketplace simulation; operational data integration; and digital twin modeling.

68. A system for intelligent orchestration of energy edge resources, comprising: automated edge transaction systems; marketplace condition monitoring; sensor data analysis; energy optimization algorithms; and resource allocation systems.

69. The system of claim 68, further comprising: context-aware data fusion; operational analytics;Al-driven classification; predictive modeling; and resource optimization.

70. A platform for energy’ edge convergence implementation, comprising: automated governance modules; transaction support systems;DER management capabilities; workflow orchestration; and optimization engines.