720 results about "Energy resources" patented technology

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.

The invention discloses a cross-regional virtual power plant cooperative scheduling method and device, a medium and a product, and relates to the field of data processing. The method comprises the following steps: acquiring real-time characteristic data such as space-time positions, output / demand prediction and the like of distributed energy resources and loads, and determining dynamic weights of characteristic dimensions based on a global optimization target and data of a current scheduling period; generating a dynamic resource cluster division instruction containing a member list and a coordination constraint condition according to the dynamic weight and the real-time data, and sending an initial cross-regional coordination scheduling instruction containing a net exchange power target value and the like and a compensation price signal to each dynamic resource cluster local agent; after aggregation response boundary information returned by the local agent is received, an instruction and a signal are updated, a target collaborative scheduling instruction is obtained and finally sent to each dynamic resource cluster for execution, and effective control over cross-regional virtual power plant resources is achieved. According to the method, the problem that the adaptability of the cross-regional virtual power plant collaborative scheduling instruction and the actual resource capacity is insufficient can be relieved.

The invention discloses a distributed power supply storage and charging integrated system, and belongs to the technical field of power system management. The system comprises a clean energy access module, an energy storage control scheduling module, an intelligent charging control module, an energy management scheduling module, a communication network interconnection module and an equipment state monitoring module, the battery state is dynamically evaluated from multiple dimensions, and a matching strategy is called through a real-time environment sensing mechanism; the charging and discharging path and rate are more in line with the actual load demand and the energy supply and demand state of the system, the battery circulation loss is reduced, the energy utilization rate is maximized, the non-critical load reduction power of the user is dynamically guided, and the specific charging demand of the user in the future time period is dynamically predicted. Therefore, the system has the capability of allocating energy resources in advance, centralized coordination is carried out on the multiple charging piles, imbalance of the energy resources in spatial distribution is avoided, the scheduling flexibility of the whole system is improved, and the self-adaptive capability of the system to complex power consumption behaviors is enhanced.

The invention discloses a power grid real-time optimization scheduling system and method based on digital twinning, and relates to the technical field of power grid scheduling. A sensor is deployed to collect environmental parameters of key nodes in real time, a dynamic environmental condition coefficient is constructed, a power grid state is analyzed in combination with frequency stability and a relative strength index, and a multi-model fusion prediction mechanism is established, so that space-time two-dimensional accurate prediction of load and power generation is realized. A multi-objective optimization model is adopted to take'maximization of new energy consumption + minimization of scheduling cost 'as a core objective, a genetic algorithm is introduced to solve an optimal scheduling scheme, and a feasible solution is screened in combination with forward simulation of a digital twin model. Through abnormal early warning triggering, environment correlation analysis and model iterative optimization, a scheduling strategy is dynamically adjusted, and the power supply efficiency and the emergency response capability in an extreme scene are improved. According to the method, multi-source heterogeneous data are effectively fused, and real-time sensing of a power grid operation state, collaborative optimization of multiple energy resources and adaptive iteration of a scheduling model are realized.

A system and method for integrated optimization of autonomous electric vehicle fleets across transportation and electricity networks which employs artificial intelligence to dynamically allocate autonomous electric vehicles between mobility services and electricity grid services based on real-time conditions. The platform acquires data including energy mix forecasts, earth observation measurements, vehicle owner schedules, and emission-based route penalties to generate coordinated allocation decisions. Vehicle owners specify availability through a scheduling interface. The system optimizes vehicle utilization through a hierarchical optimization approach implementing mobility demand-side flexibility and electricity demand-side flexibility simultaneously. Multi-objective genetic algorithm optimization balances revenue generation, energy costs, emissions reduction, and battery health. The integrated approach maximizes value creation across both transportation and energy domains, reducing urban emissions while enhancing grid stability through coordinated management of distributed energy resources in autonomous electric vehicle fleets.

The invention discloses a wind energy resource prediction method, system and device and a storage medium, and relates to the field of renewable energy sources, and the method comprises the steps: obtaining the three-dimensional wind field observation data of a target region; performing space-time alignment processing operation on the three-dimensional wind field observation data to generate a space-time sequence; performing multi-source data fusion on the space-time sequence, and constructing a multi-dimensional wind field matrix; establishing a wind energy potential prediction model based on the multi-dimensional wind field matrix; and predicting to-be-predicted wind energy data based on the wind energy potential prediction model to obtain a wind energy resource prediction result. And efficient, accurate and reliable resource evaluation is provided for wind energy development of various terrain areas by constructing a machine learning model.

The invention relates to the technical field of carbon footprint evaluation, and discloses a carbon footprint optimization method and system based on LCA-GLSS coupling. The carbon footprint optimization method comprises the following steps: calculating carbon footprints of all stages of a product in a full life cycle, carbon footprint contribution ratios corresponding to materials, energy sources, resources and wastes involved in all process units in the full life cycle, and carbon footprint sensitivities corresponding to the materials, the energy sources, the resources and the wastes; a high-carbon emission link found in the carbon footprint evaluation process is optimized, waste and variation in the carbon emission process are recognized and eliminated, a carbon footprint prediction model is established, the carbon emission trend in the future is predicted and simulated, and the prediction accuracy is improved through model optimization and parameter adjustment; the evaluation result is comprehensively analyzed and explained, main contribution factors and key links of the carbon footprint are identified, and emission reduction measures and optimization suggestions are formulated. The method has both quantitative analysis and process improvement, and can effectively improve the efficiency and accuracy of carbon footprint management.

The invention relates to the technical field of virtual power plant scheduling, and discloses a distributed collaborative optimization scheduling method for a virtual power plant. The method includes collecting an operating state data set of a target virtual power plant. And performing distributed collaborative model construction processing on the operation state data set to generate collaborative scheduling features covering power distribution balance degree, constraint matching closeness and interactive response sensitivity. And calling a pre-trained optimization scheduling model to carry out multi-target collaborative optimization processing on the collaborative scheduling features to obtain an optimization scheduling result and a key collaborative region identifier. And based on the association relationship between the load demand fluctuation sequence and the equipment adjustment capability, performing operation environment compensation correction processing on the optimization scheduling result, and generating a corrected result. And generating a virtual power plant scheduling strategy set including a power transfer path adjustment scheme and an energy storage equipment configuration processing scheme according to the key cooperative region identifier. According to the method, distributed energy resources are effectively integrated through multi-dimensional collaborative optimization and dynamic correction.

The invention discloses a big data driven dynamic resource scheduling method for an IPv6 edge computing node, and relates to the technical field of edge computing, and the scheduling method comprises the following specific steps: S100, resource data collection and integration: deploying monitoring equipment at the IPv6 edge computing node, collecting computing, network, storage and energy resource data, and transmitting the data to a server; through multi-dimensional resource data acquisition and storage, comprehensive monitoring and quantitative integration of calculation, network, storage and energy resource data are realized, the resource scheduling is more accurate and efficient through the comprehensive data acquisition mode, and through construction of the energy consumption performance model, the performance value can be accurately calculated, and the energy consumption performance is improved. And the model is trained and optimized by using big data, so that the balance of energy consumption and performance in the resource scheduling process is realized, the resource utilization efficiency is improved, the energy consumption cost is reduced, and powerful support is provided for green and sustainable development of edge computing nodes.

The invention relates to the technical field of wind energy resource evaluation, in particular to a method and a system for calculating a hydrodynamics wind field numerical value based on high-precision landform, and the method comprises the steps: generating a three-dimensional unstructured grid for CFD wind field simulation based on high-precision landform data; analyzing an atmospheric boundary layer structure, and establishing a turbulence calculation model; according to the turbulence calculation model, the influence of atmospheric stability on the mountain flowing wind acceleration factor is researched, and a mountain flowing model is developed; performing CFD wind field simulation according to the three-dimensional unstructured grid and the mountain flow model, and correcting parameters of the mountain flow model; a refined grid is made through terrain elevation data; and according to the refined grid, driving the corrected mountain flow model to carry out numerical simulation, and carrying out wind power plant wind resource evaluation. Through the method, the complex characteristics of the wind field can be reflected more comprehensively, the precision and the calculation efficiency of wind resource evaluation are remarkably improved, and the efficiency and the accuracy of numerical simulation of the complex terrain wind field are improved.

The invention discloses a source network load storage oriented regional collaborative scheduling method and system, and relates to the technical field related to power resource scheduling, and the method comprises the steps: traversing target region source network load storage to carry out real-time data collection, and constructing a source network load storage multi-dimensional data set to carry out multi-scale layered optimization; operation constraint conditions are set, limitation is carried out in combination with a power grid topological structure, a cooperative scheduling instruction set is determined, cooperative control feedback is carried out, and regional power scheduling feedback parameters are generated; and multi-channel dynamic correction is carried out, and a cooperative scheduling optimization strategy is generated to carry out energy balance cooperative scheduling on the source network load storage in the target area. The technical problems that in the prior art, intermittent fluctuation of new energy is difficult to consume, the collaboration of all links of source network load storage is poor, the dispatching flexibility of a power system is insufficient, and the resource configuration efficiency is low are solved. The technical effects of optimizing regional energy resource allocation and improving the clean energy consumption level, the power grid operation stability and the source grid load storage cooperative response capability are achieved.

The invention discloses an electricity meter metering error correction method and system based on time sequence anomaly detection and an electricity meter, relates to the technical field of electricity meter metering error correction, and provides an intelligent anomaly detection method integrating multi-source data preprocessing, online learning and federation cooperation for a power system in which distributed energy and diversified loads coexist. The method comprises the following steps of: 1, dynamically filtering and caching original data by adopting a suspicious degree function, and outputting high-quality input; secondly, online learning is carried out through multi-algorithm fusion and expert knowledge constraint, and accurate judgment of traditional and emerging modes is achieved; 3, realizing multi-region cooperative gain based on difference parameter sharing and global aggregation; and 4, deploying a lightweight model at the edge end by using feedback evaluation and model distillation, thereby improving the real-time performance and reliability. In conclusion, the false alarm rate and the missing report rate can be remarkably reduced, various changes of load behaviors are effectively adapted, the operation and maintenance efficiency of a power grid is improved, and meanwhile data privacy safety and collaboration are guaranteed.

The invention provides a wind power data determination method and device based on geographical constraint attention. The wind power data determination method comprises the steps of obtaining topographic data, surface vegetation coverage data and wind power data; for the wind power data, combining the topographic data and the surface vegetation data, performing feature fusion through an adaptive attention mechanism to obtain a fusion feature containing multi-source information; performing shallow feature extraction, deep feature extraction and image up-sampling on the fusion features in sequence, wherein the deep feature extraction realizes multi-scale feature extraction and geographic constraint fusion through a multi-stage residual fusion module RFSTB, a wind field frequency domain decoupling module CFB and a geographic perception global attention module GAB; verifying the effectiveness of the model by using a peak signal-to-noise ratio (PSNR) and a structural similarity index (SSIM); and performing super-resolution reconstruction on the wind power data based on the trained model to generate wind power data with predetermined precision. The method provides support for fine evaluation and efficient development of wind energy resources in complex terrain areas.

The invention provides a multi-modal communication edge aggregation gateway system for a virtual power plant, and the system comprises a smart energy unit which serves as edge gateway equipment and is used for carrying out the bidirectional communication with a user branch load, a distributed energy source and a flexible load; the measurement monitoring unit is arranged on a user branch load loop or a user side equipment end and is used for acquiring power utilization data of a branch line and an equipment operation condition; a multi-mode communication module; and the self-adaptive protocol switching unit is used for dynamically selecting communication protocols and realizing switching among the protocols according to the communication environment parameters, the equipment types and the data transmission requirements. The invention thoroughly solves the problem that the traditional communication system cannot adapt to different application scenes and equipment type communication requirements by adopting a single communication protocol, realizes the goal of dynamically adjusting the communication strategy according to the network environment change, and effectively avoids the phenomenon that the communication quality is reduced or even interrupted.

A meter socket adapter (MSA) for connecting a battery energy storage system (BESS) to a household microgrid through a meter combo load center. The MSA includes: a housing matching an electricity meter and a meter socket in the meter combo load center; a plurality of power conductors that connect among the electricity meter, the meter socket, and BESS; a meter interconnection device (MID) switch for connecting a utility grid or one or more distribution energy resource (DER) devices to supply power to household loads in the household microgrid; a MID driving circuit for driving the MID switch to close or open; and a thermal management circuit couple with a plurality of temperature sensors disposed at each high current connection point to prevent overheat.

The invention relates to the field of power, and provides a distributed resource evaluation and aggregation method and device for a virtual power plant and electronic equipment, and the method comprises the steps: dynamically calculating the adjustment capability score and the close degree of each distributed resource according to the adjustment capability index of each distributed resource of the virtual power plant; based on the adjustment capability score and the close degree of each distributed resource, selecting a distributed resource meeting a threshold condition from each distributed resource to participate in aggregation scheduling; and optimizing the runnable boundary of each distributed resource participating in aggregation scheduling based on the power of each distributed resource participating in aggregation scheduling and a pre-established double-layer optimization model. According to the method, efficient aggregation and operation boundary optimization of distributed energy resources by the virtual power plant can be realized through a dynamic scoring mechanism and a hierarchical optimization architecture, and the method has the advantages of high efficiency, flexibility and accuracy.

Accurate estimation of distributed energy resources (DER) hosting capacity of electrical distribution network becomes pivotal to ensure reliable and stable operation of network. Existing techniques either do not capture real-life scenarios or use approximation techniques to simplify computation which leads to improper estimation of hosting capacity. Present disclosure provides method and system for determining optimal DER hosting capacity of electrical distribution network. The system first receives network configuration data, historical temporal data of DER generation, historical temporal data of load, and capacity information of each DER as input which is then utilized to compute bus and line admittance matrix for determining power flow constraints. Then, system models plurality of constraints. Thereafter, system models objective function for minimizing losses and maximizing DER utilization which is then used along with modelled constraints to create an optimization model. Finally, system solves optimization model to obtain DER hosting capacity of electrical distribution network.

A method for generating an optimized regional architecture for efficient national transport is provided. The method integrates vehicle powertrain architectures, operations logistics, and energy pathways to optimize both behind-the-fence and public access energy dispensing solutions supported by local distributed energy resource equipment and centralized fuel sourcing. More specifically, the method provides an optimal regional architecture for electrified national transport modeling. The method assimilates critical data for seasonal operating scenarios to provide a regional specific constrained-optimal infrastructure deployment solution. As discussed herein, the present invention provides local government agencies, industry end users, energy suppliers, and equipment providers with a flexible planning tool to navigate the deployment of electrified freight transportation systems in view of localized constraints.

The invention discloses a distributed energy resource intelligent scheduling method based on the Internet of Things, and relates to the technical field of the Internet of Things, and the method comprises the steps: collecting operation data of distributed energy equipment, carrying out the preprocessing of the operation data, obtaining an equipment operation data set, carrying out the group intelligent initialization of the equipment operation data set, and generating an initial pheromone matrix; carrying out local optimization on the equipment operation data set and the initial pheromone matrix to generate an updated pheromone matrix and an equipment candidate scheduling scheme, and carrying out screening and verification to generate a local optimal scheduling scheme; receiving a local optimal scheduling scheme by adopting a quantum computing node, taking the local optimal scheduling scheme as an initial solution, constructing a global optimization problem, outputting a global optimal scheduling scheme, and converting the global optimal scheduling scheme into a global scheduling instruction; according to the method, by formulating a global optimization problem, equipment interaction, economic factors and multi-dimensional constraints in a network range are captured, coordination and constraint compliance among equipment are realized, and the problem of instability of a power grid is prevented.

The invention relates to the technical field of offshore wind turbines, in particular to a submersible mud floating type offshore wind turbine system. The submersible mud floating type offshore wind power structure system can be flexibly suitable for different water depths and marine environments, the whole structure system is in a suspended state during normal work, the structure system enables the structure to sink to be in a mud floating state through an anchor chain and gas-water replacement operation in an extreme marine environment, and after the extreme marine environment, the structure system is in a floating state. The structure floats upwards and is in a suspended state through an anchor chain and gas-water replacement operation, and work continues to be carried out; the suspension state of the submersible mud floating type offshore wind power structure system is divided into a semi-submersible state and a full-submersible state, and the submersible mud floating type offshore wind power structure system is suitable for wind energy resources of different heights and has the advantage of fully developing offshore wind energy resources; the buoy of the submersible mud floating type offshore wind power structural system can be filled with water and air, and state conversion of the structure is achieved through air-water replacement.

The invention discloses a source-grid-load-storage multidirectional cooperation method and system in a new energy high-permeability power grid, and belongs to the technical field of source-grid-load-storage collaboration.The method comprises the steps that a historical data set and social economic data of a power system in a region are obtained, and a prediction model is constructed to obtain a load prediction result; acquiring solar radiation quantity, wind energy resource distribution data and historical meteorological data in the region to obtain a power supply prediction result; obtaining a load prediction correction value based on the power supply prediction result; constructing a power balance model based on the load prediction correction value and the power supply prediction result; solving the established power balance model based on a genetic algorithm, and determining an operation scheme; and evaluating the determined operation scheme, and if the operation scheme meets requirements, outputting the operation scheme for source-network-load-storage multidirectional collaboration. According to the method, the load change rule can be comprehensively captured, and the problem of inaccurate load prediction caused by single data in the prior art is effectively solved.

The invention relates to the technical field of industrial park green low-carbon development, and discloses an industrial park green low-carbon multi-dimensional performance evaluation index system construction method. Setting evaluation dimensions of energy, resources, economy, environment, management and the like, determining a core index, and calculating a comprehensive weight based on objective and subjective weights; after multi-source data are collected and processed, a TOPSIS method and a grey correlation analysis method are adopted for comprehensive evaluation, and an industry comparison report is generated; establishing a real-time monitoring system to track, evaluate and display key data and indexes; meanwhile, an index system and a weight distribution mechanism are periodically optimized in combination with a machine learning technology, core indexes are screened through clustering analysis, variance contribution evaluation, correlation matrix construction and dynamic threshold adjustment, and dynamic adaptive optimization of the indexes and the weights is realized by utilizing a feature importance analysis and reinforcement learning algorithm. According to the invention, scientific evaluation and intelligent management of park green low-carbon performance are realized, and the accuracy, adaptability and continuous optimization capability of evaluation are improved.

The invention relates to the technical field of power systems, and discloses an active power distribution network intra-day economic optimization scheduling method, system, device, medium and product. The method takes the comprehensive operation cost minimization, the voltage fluctuation minimization, the load peak-valley difference minimization and the energy storage life loss minimization of an active power distribution network as multiple optimization objectives; and determining constraint conditions of the multiple optimization targets according to a reducible load balance state, a transferable load balance state and a power distribution network operation balance state, constructing a power distribution network optimization scheduling model, optimizing and solving the power distribution network optimization scheduling model, and determining an intra-day scheduling optimal strategy of the active power distribution network according to an optimal solution. Therefore, the comprehensive operation cost, the voltage fluctuation, the load peak-valley difference and the energy storage life loss are comprehensively considered, continuously changing load requirements and energy resources can be better dealt with, the resource utilization rate and the flexibility of the optimal scheduling strategy of the active power distribution network are improved, and the stability and the economical efficiency of power supply are ensured.

The invention provides an energy complex flexible dynamic aggregation method and system based on reinforcement learning, and the method comprises the steps: collecting the real-time operation parameters of electric, gas, heat and multi-energy coupling equipment in an energy complex through an industrial Ethernet protocol, and constructing a continuous time dynamic system model through employing a Shenchang differential equation process modeling technology; and the nonlinear dynamic characteristics of various energy resources are accurately described. Modeling an aggregation process into a Markov decision process, and training and optimizing by adopting an improved twin delay depth deterministic strategy gradient algorithm to generate a reinforcement learning aggregation strategy model. The system realizes self-adaptive dynamic optimization through an online learning updating mechanism, and the renewable energy consumption rate and the system operation efficiency are remarkably improved.

The invention discloses a double-layer non-cooperative demand response method based on asynchronous multi-agent reinforcement learning, and the method comprises the steps: S1, constructing a double-layer non-cooperative game framework: constructing a double-layer dynamic game model between a public utility company and a consumer; s2, completing modeling of the partially observable Markov game, and converting the double-layer non-cooperative game into the partially observable Markov game; s3, designing an asynchronous multi-agent reinforcement learning algorithm as a game equilibrium solving engine; and S4, implementing centralized training and priority experience playback to realize asynchronous multi-agent reinforcement learning algorithm efficiency optimization. The method is suitable for a dynamic game scene between a public utility company and a consumer group in an intelligent power grid environment, and can realize efficient consumption and load fluctuation suppression of distributed energy in a power grid.

The systems and methods provide techniques for a distributed energy resource management system (DERMS). The system can optimize energy distribution by leveraging Distributed Energy Resources (DERs) (such as thermostats for HVAC systems, electric vehicle (EV) chargers, and home battery storage) by shifting energy among devices and home. During peak demand, the VPP can minimize the cost to the company, reduce demand on the grid, and ultimately reduce long-term consumer costs. The system can utilize home sensors to determine a state of occupancy (such as a home is occupied, unoccupied, or the residents are on vacation). The system can consider a desired comfort level. The various sources of data (e.g., energy data, cost data, and home sensor data) can be used to manage energy distribution in a VPP and generate demand response events or other instructions for energy usage based on real-time occupancy data.

A meter socket adapter (MSA) for connecting a battery energy storage system (BESS) to a household microgrid through a meter combo load center. The MSA includes: a housing matching an electricity meter and a meter socket in the meter combo load center; a plurality of power conductors that connect among the electricity meter, the meter socket, and BESS; a meter interconnection device (MID) switch for connecting a utility grid or one or more distribution energy resource (DER) devices to supply power to household loads in the household microgrid; a MID driving circuit for driving the MID switch to close or open; and a MID connection detection circuit to verify an electrical connection of a closed MID switch.

An electric meter is disclosed. The electric meter comprises a first set of conductive paths configured to connect a first phase of an electric distribution power source, a first phase of a first distributed energy resource device, a first phase of a second distributed energy resource device, and a first phase of a load. The electric meter comprises a second set of conductive paths configured to connect a second phase of the electric distribution power source, a second phase of the first distributed energy resource device, a second phase of the second distributed energy resource device, and a second phase of the load. Also disclosed is a method of use of the electric meter and an electric meter system.

The invention discloses a space-time collaborative optimization-based virtual power plant adjustable capability evaluation method and system. The method comprises the following steps of S10, obtaining operation parameters of distributed energy resources in a virtual power plant; s20, performing space-time analysis on the operation parameters, extracting space distribution characteristics and time change characteristics of the distributed energy resources, and generating a space-time distribution matrix; s30, constructing a joint scheduling model of the virtual power plant based on the spatial-temporal distribution matrix, and obtaining an adjustable capability evaluation result of the virtual power plant by solving the joint scheduling model; and S40, generating a regulation and control instruction according to the adjustable capability evaluation result, and sending the regulation and control instruction to the distributed energy resources to realize collaborative optimization scheduling. According to the method, the operation parameters of the time dimension and the space dimension of the distributed energy resources are jointly scheduled, and the dynamic data of the virtual power plant are acquired in real time for analysis and evaluation, so that the regulation and control process is more comprehensive, and the evaluation result of the adjustable capability of the virtual power plant is optimized.

One aspect of the present disclosure relates to a distributed energy data platform process, and more particularly, to a distributed energy data platform process capable of increasing efficiency of energy resources and ensuring flexibility in management by configuring an AIoT process based on a distributed energy data platform. The distributed energy data platform process includes a data collection unit for collecting IoT sensor data of an IoT sensor installed in an energy-consuming device in real time; a database for loading the IoT sensor data collected in real time from the data collection unit; a visualization unit for interacting with the database so as to visualize the IoT sensor data to monitor the IoT sensor data in real time; and a training unit for configuring an artificial intelligence life cycle operation for the IoT sensor data.