255 results about "Energy trading" patented technology

The invention provides a micro-grid energy-saving scheme generation method and system based on energy storage optimization scheduling, and relates to the technical field of energy storage optimization scheduling, and the method comprises the steps: collecting historical operation data, real-time operation data and external environment data of a micro-grid system, and carrying out the multi-scene prediction through a Monte Carlo simulation method, and generating a multi-scene prediction result; according to a multi-scene prediction result, a fuzzy decision theory and an analytic hierarchy process are adopted, and an optimal microgrid energy-saving scheme is screened out from the solution set; and converting the optimal energy storage scheduling scheme into an intelligent energy-saving operation instruction set, recording a microgrid operation state, an energy storage scheduling result and energy transaction information by using a block chain, calculating a comprehensive performance index based on data recorded by the block chain, and performing distributed evaluation by using a federated learning method according to the comprehensive performance index. And continuously optimizing the micro-grid energy-saving scheme according to the evaluation result of the comprehensive performance index.

The invention discloses a virtual power plant system design method based on multilevel federated learning, and relates to the technical field of power system planning, and the design method comprises the steps: designing and constructing a virtual power plant system architecture comprising an energy resource module, a local controller, a regional coordinator, a central coordinator and a regional block chain network. Intelligent prediction and optimal control of distributed energy resources are realized by using a deep learning framework and a model training algorithm, the intelligent level of the system is improved, automatic management of energy transaction and execution of an intelligent contract are realized by using a block chain technology, transparency and security of the transaction are ensured, and multi-level federated learning is adopted, so that the efficiency of the system is improved. According to the invention, distributed cooperation of data distributed processing and model training is realized, risks of data leakage and privacy invasion are effectively avoided, cooperative work of a local controller and a regional coordinator is cooperated, intelligent management and optimal scheduling of distributed energy resources are realized, and manual intervention and management cost are reduced.

The invention relates to the technical field of new energy operation, and discloses a distributed new energy settlement and control system based on a block chain, and the system collects new energy power generation, energy storage and user power utilization data through an Internet of Things collector, and builds a network model comprising new energy production, energy storage, consumption and power grid nodes. And a Markov decision process is adopted for modeling. The system further defines a state space, an action space and a reward function, and a deep reinforcement learning algorithm optimization strategy is applied to maximize long-term rewards. Besides, a block chain technology is introduced, transparent and safe recording of new energy production, transaction, consumption and settlement processes is realized, transaction settlement is automatically executed through an intelligent contract, and the efficiency and reliability of energy transaction are improved.

The invention discloses an energy consumption analysis method and system based on a micro-grid resource physical digital space model, and the method comprises the steps: obtaining first monitoring data of distributed energy equipment and second monitoring data of electric equipment in a micro-grid, and fusing the data to construct the micro-grid resource physical digital space model; constructing a distributed energy transaction platform based on the block chain based on the model; an energy consumption traceability function is integrated in the transaction platform, an energy consumption traceability tree model is constructed, then the energy consumption contribution degree of each distributed energy device and the energy consumption responsibility sharing proportion of each electric device are calculated, and an energy consumption traceability report is generated and is linked for evidence storage; by constructing the energy consumption traceability tree model, fine traceability and responsibility sharing of micro-grid energy flow can be realized, accurate data support is provided for energy consumption analysis and management, and transparency and fairness of micro-grid energy management are remarkably improved.

The invention discloses an energy storage equipment management method and system based on the Internet of Things, and relates to the technical field of energy storage management, and the method comprises the steps: constructing an initial topological graph mathematical model processed by concurrent block energy storage equipment based on the Internet of Things and a block chain network; according to the method, energy distribution is efficiently optimized by means of a directed acyclic graph, and under the condition that the management decision effect is not reduced, the operation steps are greatly reduced, and meanwhile, the decision and the state of the energy storage equipment are deeply optimized and managed; meanwhile, powerful support is provided for scenes such as equipment state monitoring, scheduling decision making and energy transaction, the complexity of concurrent block processing in the block chain system is effectively reduced through the topological graph, redundant reference relations are reduced, so that sorting logic is simplified, the method is suitable for shared energy storage power station scenes needing to rapidly process high-frequency energy storage services, and the efficiency of the shared energy storage power station scenes is improved. Multi-dimensional state vectors are utilized to effectively represent interaction information of energy storage equipment and a power grid, and accurate control is performed in combination with discrete and continuous action instructions.

A system and method are disclosed for AI-orchestrated multi-source renewable energy harvesting and decentralized trading. A plurality of energy harvesting nodes convert renewable resources, including solar irradiance, wind, hydrodynamic flow, salinity gradients, geothermal heat, and waste-heat streams, into electrical power. Each node includes a sensor array that measures operating and environmental parameters and an edge AI processing unit that predicts performance degradation and computes control actions to maximize net power output subject to stress constraints. A power conversion subsystem conditions the generated power for delivery to storage, microgrids, or utility grids. A blockchain-based orchestration layer receives validated energy summaries, tokenizes discrete energy quanta as digital energy tokens with provenance metadata, and executes smart contracts for peer-to-peer trading, dynamic pricing, and programmable revenue distribution. A fleet-level AI coordination module performs federated learning across nodes and coordinates dispatch and curtailment among heterogeneous modalities, improving portfolio efficiency, grid stability, and environmental accounting.

The invention discloses a collaborative optimization decision-making method for an active power distribution network including production and consumer renting shared energy storage, and belongs to the technical field of power distribution network dispatching. Comprising the following steps: constructing a multi-agent collaborative transaction architecture containing energy storage shared by diasters; based on the multi-agent collaborative transaction architecture, through a multi-agent two-stage collaborative pricing strategy, the bargaining ability of a shared energy storage operator and a producer and a consumer is quantified; taking social benefit maximization and cooperative benefit distribution maximization as targets, and based on preset constraint conditions, constructing a multi-agent collaborative optimization model based on improved Nash bargaining; and optimizing the multi-agent collaborative optimization model in combination with bargaining capabilities of a shared energy storage operator and a producer and a consumer to obtain an optimal energy transaction volume and an optimal energy transaction price. The DDS-production and consumer-SESO collaborative decision-making framework provided by the invention can realize multi-agent cooperation and win-win, and reduce the scheduling difficulty of the power distribution network; the problems of non-uniform income distribution and high optimization scheduling difficulty in the existing electricity market transaction are solved.

The invention relates to the technical field of electric power data transaction, in particular to an integrated energy service electric power transaction method and system. The method comprises the following steps: acquiring power use data; performing consumer behavior pattern graph construction on the power use data to generate a consumer energy use behavior pattern graph; performing comprehensive energy demand prediction on the standard power use data through the consumer energy use behavior mode map to generate consumer comprehensive energy demand prediction data; and building a block chain energy transaction platform based on the comprehensive energy demand prediction data of the consumer, and generating a block chain energy transaction contract. According to the invention, through personalized energy demand prediction, block chain transaction platform construction, electric energy type analysis, fluctuation response strategy, automatic transaction monitoring and data visualization, the flexibility and efficiency of electric power transaction are improved.

The invention provides a voltage control strategy modeling and voltage online regulation and control method and system for a power distribution network, and is applied to the technical field of power distribution network regulation and control. The method comprises the steps that region division is carried out on the power distribution network based on the electrical distance between nodes of the power distribution network, multiple regions are obtained, and one region corresponds to one virtual power plant; determining the regional electricity price of each region based on the marginal pricing of the distribution region; taking each region as an object of energy transaction with the power transmission network, and constructing an optimization model by taking profit maximization of each region as a target on the basis of considering the safety of circuit elements in each region; performing optimization strategy modeling on the optimization model based on an observable Markov decision process to obtain an optimization strategy model; and training an optimization strategy model based on the regional electricity price of each region and a maximum entropy reinforcement learning algorithm. The method solves the problem that the complexity of the power distribution network affects the optimality and efficiency of the collaborative optimization problem of a plurality of virtual power plants in the power distribution network.

The invention discloses a micro-grid scheduling optimization method and system, a terminal and a storage medium. A user model of each user node in a micro-grid is established in advance; the user model comprises a plurality of energy models: a heating ventilation and air conditioning system model, a load model, a clean energy model, an energy storage system model, a user-power grid energy transaction model and an inter-user energy transaction model; determining a balance relation between energy loss and output according to the user model, and determining constraint conditions; establishing a user cost function of each user node according to the cost function of each energy model; and establishing an optimization problem according to the user cost function of each user node, obtaining an optimal solution according to the constraint condition and the optimization problem through an alternating direction multiplier algorithm, and determining an optimal scheduling scheme of the microgrid. All users are designed as peer-to-peer nodes, and a distributed scheduling algorithm is adopted, so that the autonomy of the system can be enhanced, the communication burden can be reduced, and meanwhile, the efficient privacy protection level can be improved.

The invention discloses an integrated energy system low-carbon economic optimization method and system considering fused magnesium load. The method comprises the following steps: determining a load matching index based on an actual load value and a wind-light-storage combined output value; based on the wind and light storage installed capacity and the operation and maintenance cost coefficient, combining a pre-constructed average electricity cost model to obtain an average electricity cost; with the load matching degree and the average cost per kilowatt-hour as optimization objectives, an NSGA-II algorithm is adopted for optimization to obtain the optimal wind and light storage installed capacity; establishing a master-slave game energy transaction model based on the optimal wind and light storage installed capacity and demand response, and solving the master-slave game energy transaction model based on an improved grey wolf algorithm to obtain a comprehensive energy system low-carbon economic optimization scheme; wherein the pre-constructed average cost-per-kilowatt-hour model is constructed by taking the minimum cost-per-kilowatt-hour as an optimization target and combining constraint conditions. According to the invention, optimization solution is carried out based on the improved grey wolf algorithm to obtain the optimal scheme, and the carbon emission and carbon transaction cost of the system are reduced.

The invention discloses a virtual power plant false data detection method and system based on a block chain and a medium, and relates to the technical field of data detection, and the method comprises the steps: obtaining distributed energy managed by each virtual power plant, and building a block network diagram; acquiring an energy transaction weight and an energy acquisition preference corresponding to the virtual power plant, and establishing a preference dimension graph and a transaction dimension graph; detecting data of the virtual power plant based on the transaction dimension graph to obtain a false data detection position of the virtual power plant; the method is used for solving the problems that in an existing false data detection method for the virtual power plant, all data of the virtual power plant needs to be analyzed, so that the time lower limit needed by data detection is high, and the detection speed is low due to the fact that the detection direction of data detection cannot be determined when a large amount of data exist.

The invention provides a hydrogen energy transaction and traceability management method and system based on a block chain, and relates to the technical field of block chains, and the method comprises the steps: obtaining hydrogen energy static creation data, generating a data abstract and an identity code, packaging the data into a digital asset voucher, and publishing the digital asset voucher to a block chain network; a supplier and a demander carry out matched transaction through an intelligent contract to realize complete or partial delivery of asset vouchers, and the intelligent contract triggers an atomization segmentation program to generate sub-vouchers containing a traceability index. According to the invention, the whole hydrogen energy transaction process is traceable, the transaction transparency and efficiency are improved, and the transaction risk is reduced.

The invention discloses an energy digital management method and system, and the method comprises the steps: receiving a multi-source heterogeneous energy data collection signal, and generating an initial energy characteristic spectrum based on a space-time correlation feature extraction algorithm; performing graph neural network coding on the initial energy feature graph, constructing an energy knowledge graph vector set, and decomposing a high-dimensional vector in the knowledge graph vector set into a plurality of subspace vectors; performing real-time scheduling decision on the subspace vectors by using an energy flow optimization model driven by reinforcement learning to generate an energy distribution strategy set; a digital twinborn verification algorithm is applied to the energy distribution strategy set, physical-digital space consistency verification is carried out, and a trusted energy scheduling scheme is generated; and performing block chain evidence storage and intelligent contract execution on the credible energy scheduling scheme to generate a non-tampering energy transaction record. By utilizing the embodiment of the invention, the scheduling precision and the response speed of the energy system can be improved, and full-link credible management from decision-making to execution is realized.

An interconnected photovoltaic micro-grid group decentralized energy transaction system based on a block chain is characterized in that the system is provided with a photovoltaic micro-grid group and a block chain energy transaction platform which are in bidirectional connection, and the photovoltaic micro-grid group comprises N photovoltaic micro-grids, each photovoltaic micro-grid is provided with an information collection module, a prediction function module, a transaction function module and a block verification module; the information collection module is used for collecting electricity consumption information of all power consumers, generating capacity information of all photovoltaic power stations and environmental meteorological information; the prediction function module is used for predicting photovoltaic power generation information and power consumption demand information of the current photovoltaic micro-grid at the next transaction moment; the transaction function module is used for executing point-to-point energy transaction in the block chain energy transaction platform and generating an intelligent contract; and the block verification module is used for executing a block chain consensus mechanism and realizing block verification or generation of the energy transaction intelligent contract. The method has the advantages that the stability and efficiency of a power transaction network are effectively improved.

The invention discloses a micro-grid group scheduling method and system based on hierarchical regulation and elastic load response, and belongs to the technical field of power system collaborative optimization and energy transaction, and the method comprises the steps: obtaining the power generation potential of a power generation side and the power utilization regulation potential of a demand side based on the operation constraint of the power generation side and the response constraint of the demand side; constructing a micro-grid group layered interactive coupling model based on the execution rule and the information interaction constraint; constructing a marketization transaction strategy model based on the transaction mode, the bidding mechanism and the income distribution mechanism; improving the particle swarm algorithm through model prediction control to obtain an optimized particle swarm algorithm; and based on the power generation of the power generation side, the power utilization regulation potential of the demand side, the micro-grid group interactive coupling model and the marketization transaction strategy model, obtaining an optimal scheduling scheme by using an optimized particle swarm algorithm. The technical problem that the feasibility of a scheduling scheme is difficult to improve in the prior art is solved.

A crowdsourced energy system includes a plurality of distributed energy resources managed by crowdsourcees of the system, a power network to which the distributed energy resources are connected, and a system operator that manages energy trading transactions and energy delivery within the system, the system operator operating at least one computing device configured to: obtain day-ahead peer-to-peer energy trading transaction requests from crowdsourcees for energy to be delivered from the distributed energy resources, estimate day-ahead energy load and solar forecasts, determine optimal power flow for the delivery of energy, and schedule delivery of energy from the distributed energy resources across the power network based upon the energy trading transaction requests, the estimated forecasts, and the determined optimal power flow.

The invention provides a distributed scheduling method for railway traction stations and peripheral micro-energy grids, and belongs to the technical field of energy supply of micro-energy grid groups containing the railway traction stations, and the method comprises the steps: building a micro-energy grid sharing mutual aid grid structure containing the railway traction stations, obtaining a power model of each railway traction station, and optimally configuring energy storage equipment, obtaining operation constraints of the energy storage equipment; constructing an energy sharing benefit function by taking each micro-energy network agent as a game main body and taking individual benefit maximization as a target, setting constraint conditions, and establishing an energy sharing non-cooperative game model; and solving the energy sharing non-cooperative game model by adopting an alternating direction multiplier algorithm to obtain a distributed energy transaction behavior result and a price result. According to the method, distributed solution is carried out on the micro-energy network energy sharing non-cooperative game model based on the alternating direction multiplier method, original centralized optimization is converted into coupling optimization among a plurality of micro-energy network agents through interactive variables, the efficiency of the whole iteration process is guaranteed, and the privacy of each energy network is protected.

The invention relates to the technical field of data security protection, in particular to an energy internet data security transmission and authentication system, and provides a scheme that the storage efficiency is improved by adjusting the capacity of a self-adaptive block and dynamically adjusting the size of the block according to the energy data flow. And a credibility rating mechanism is adopted to allocate the data to the main block chain or the auxiliary block chain, so that storage resources are optimized and the data credibility is improved. In a cross-chain interaction process, technologies such as identity verification, zero-knowledge proof and time lock are combined, so that the security and tamper-proof capability of data transmission are improved. Block storage is optimized based on the energy supply and demand balance state, the write-in load is reduced, and efficient storage of data in different load environments is ensured. The method improves the storage flexibility, safety and credibility of energy data, and can be widely applied to the fields of smart power grids, distributed energy transactions, carbon emission monitoring and the like.

The invention discloses a power distribution network service provider-multi-microgrid distribution robust optimization cooperative scheduling method considering multiple uncertainty. The method comprises the following steps: constructing Wasserstein fuzzy set constraints of purchase and sale electricity price of a superior power grid and renewable output; constructing a distribution robust model of the Wasserstein distance of the power distribution and sale service provider; constructing a multi-microgrid system operation model; constructing a P2P transaction network game model between a power distribution network service provider and multiple microgrids based on Wasserstein distribution robust optimization; and solving the P2P transaction network game model between the power distribution network service provider and the multiple microgrids by combining a KKT, a duality principle, a large M method and a stationary point method hybrid solving method. According to the method, the P2P network game model between the power distribution network service provider and the multiple microgrids is established, benefits of all parties are balanced, new energy in the microgrids is fully consumed, and active participation of the service provider and the microgrids in electric carbon energy transaction is effectively mined.

The invention belongs to the field of power distribution network optimization scheduling, and provides a power distribution network voltage coordination method and system considering P2P transaction risk, and the method comprises the steps: constructing a producer and consumer P2P energy transaction model and an optimization decision model of a power distribution system operator DSO; constructing a coordination model based on the guidance of the fusion dynamic operation envelope DOE and the risk network crossing cost RC-NUC; the method comprises the following steps: constructing a double-layer coordination optimization framework of multi-agent interaction of a power distribution system operator DSO and a producer and disagger based on a coordination model guided by fusion of a dynamic operation envelope DOE and risk network cost RC-NUC; and solving the double-layer coordination optimization framework of the multi-agent interaction of the operator DSO and the producer and the consumer of the power distribution system to obtain a coordination decision of the multi-agent. The DOE and RC-NUC are utilized to guide the producer and the consumer to adjust the transaction strategy so as to ensure the network security, only price and power information is exchanged among multiple subjects, the privacy and autonomy of the transaction are considered, and the coordinated optimization of the power distribution network security and the economic benefits of market participants is realized.

The invention discloses a building energy scheduling method and platform based on regional energy carbon management, and relates to the technical field of building energy scheduling. The method comprises the steps of multi-dimensional data acquisition, energy-carbon collaborative optimization, dynamic scheduling execution, block chain energy transaction and community-based energy collaboration. The platform comprises a multi-dimensional data acquisition module, an energy-carbon collaborative optimization module, a dynamic scheduling execution module, a block chain energy transaction module and a community collaborative module, and adopts a cloud edge collaborative architecture and a digital twin system. According to the method, double optimization of economy and carbon targets is realized through a cooperation mechanism of a genetic whale algorithm and an NSGA-II algorithm, an energy and carbon credit closed-loop system is constructed by using a block chain, idle energy storage resources are activated in combination with community cooperation, dynamic mapping and early warning are realized by means of digital twinning, and the method is high in practicability. The problems that in the prior art, economy and carbon targets are difficult to balance, energy transaction trust is lost, the user participation degree is low, and carbon management and control lack perspectiveness are solved.

The invention discloses a multi-microgrid intelligent scheduling method and system based on state adversarial reinforcement learning and distributed optimization. The method comprises the following steps: 1) modeling a scheduling problem of each microgrid as a Markov decision process; 2) optimizing the Markov decision process by using a state confrontation soft behavior reviewer to obtain an optimal scheduling strategy of each micro-grid; and 3) performing energy transaction and cost sharing optimization on the optimal scheduling strategy of each micro-grid by using a multiplier alternating direction method to obtain a multi-micro-grid intelligent scheduling strategy. The system comprises a scheduling problem modeling unit, a single-microgrid scheduling unit and a multi-microgrid intelligent scheduling unit. According to the method, SA-SAC and ADMM are combined, so that intelligentization, robustness and efficient optimization of a multi-microgrid system are realized, and an innovative solution is provided for sustainable operation of multiple microgrids.

The invention relates to a decentralized microgrid energy transaction method based on a two-way auction mechanism, and the method comprises the steps: building a local energy transaction model which is responsible for automatically completing the bidding and load scheduling task of P2P energy transaction; a decentralized electric energy transaction method is established, and tokens are used for transaction to realize information exchange of electric energy ownership; then, a simulation case of a power distribution network containing multiple micro-grids is researched, a scheduling strategy is formulated, and decentralized micro-grid energy transactions based on a two-way auction mechanism are subjected to optimization scheduling; the method has the advantages that the local energy transaction model is established, the energy consumption demand is effectively balanced, the decentralized electric energy transaction method is adopted, and the benefits of the two parties are guaranteed.

The invention discloses a point-to-point energy transaction method and system based on a virtual community and distributed power generation, and relates to the technical field of intelligent power grids, and the method comprises the steps: building a building model, and enabling the building model to be aggregated into a virtual community model according to the position of a power distribution network; establishing a distributed power generation model, and introducing a distributed power generation source into the virtual community model; when the virtual community and the distributed power generation source carry out point-to-point transaction, establishing a power distribution system operator model; and integrating the building model, the virtual community model, the distributed power generation model and the power distribution system operator model into a framework model of P2P energy transaction to obtain the point-to-point energy transaction method. According to the method, the utilization rate of renewable energy sources is maximized, and dependence on an external power grid is reduced. The energy transaction cost is remarkably reduced, the flexibility and expandability of the system are improved, the stability of the power distribution network is effectively maintained, network overload is prevented, and privacy protection and a reliable transaction mechanism are provided.

The invention relates to a multi-body robust optimization scheduling method based on a bidirectional master-slave game, and belongs to the field of comprehensive utilization of energy. A bidirectional master-slave game model is provided, an integrated energy system operator (IESO) is used as a leader to optimize energy pricing, an energy storage operator (ESO) is used as a secondary leader, a load aggregator (LA) is used as a follower, and each main body formulates an energy transaction strategy according to the own state and benefit target to optimize the internal running state of the system, so that various load requirements are met. Uncertain factors are added into a proposed bidirectional master-slave game model, the uncertainty of wind and light output and electricity price of an electricity market is considered, a safety constraint economic optimization model under the worst condition is established, and the problem is converted into an MILP formula by using a strong duality theory. And finally, solving the proposed bidirectional master-slave game model by adopting a golden section method, only exchanging limited energy quantity and price information during transaction, and protecting data information privacy of each subject.

The invention provides a flexible distributed energy network multi-agent collaborative optimization method based on MARL, and the method comprises the steps: building a distributed energy network multi-agent interaction framework which comprises a local energy transaction model for distinguishing an intelligent soft switching path, a network cost model based on a power transmission distribution factor, and a power distribution network operator and user income model; a physical system operation constraint model is established and covers intelligent soft switch operation characteristics, network topology radial constraint, energy storage charging and discharging dynamic states, controllable distributed power supply and photovoltaic output limitation, translational load scheduling and DistFlow power flow constraint. And finally, constructing a collaborative optimization mechanism based on a multi-agent near-end strategy optimization algorithm, modeling the system into a partially observable Markov decision process, adopting a centralized training distributed execution framework, and guaranteeing transaction security and data privacy through block chain evidence storage, RSA encryption and an intelligent contract. The system economy and the operation flexibility are improved.

The invention discloses an energy transaction protection method and system based on block chain online transaction, and particularly relates to the technical field of data management. Energy data are collected regularly, an energy data sequence is formed, a credibility evaluation model is adopted, a transaction data link stability index and an energy consumption deviation value are integrated, low-credibility data are identified and abnormal marking is carried out, and abnormal data points are compared with adjacent energy transaction equipment data, so that the energy transaction efficiency is improved. According to the method and the system, whether the abnormity is caused by malicious tampering or not is judged, for the data which are confirmed to be tampered, if the credibility of all the data meets a set threshold value, transaction data are stored in an uplink mode, and settlement is automatically executed by an intelligent contract. The method and the system can effectively prevent man-in-the-middle attack, side channel attack and data forgery; the authenticity and credibility of the energy transaction data are improved, and the fairness and long-term stability of the block chain energy market are ensured.

An electricity-carbon-green certificate market transaction method based on chaotic mapping considering carbon capturing, comprising: building a transaction strategy for describing the interaction of the market entities in a multi-element market, the multi-element market comprises an electricity market, a carbon market and a green certificate market; and building an upper-layer electricity-carbon-green certificate multi-market combined transaction model considering carbon emission settlement based on the transaction strategy. With the method proposed in the present invention, the model is built according to the energy transaction flow existing in among the transaction entities, efficient utilization of renewable energies and low carbon emissions can be realized, national requirement on low carbonization of market transaction can be satisfied, and a multi-element market transaction mechanism that complies with the dual-carbon target is provided for the existing energy transaction market.

The invention discloses an interconnected micro-grid online distributed point-to-point electric energy management method and system, relates to the technical field of interconnected micro-grid management, and aims to construct an electric energy transaction model by taking the minimum energy transaction cost as an optimization target and taking the electric energy of each micro-grid as a decision variable. Respectively inputting the working condition parameters of each micro-grid and the corresponding historical operation data into a preset electric energy optimization model to obtain initial electric energy corresponding to each micro-grid, and performing distributed iterative optimization on the electric energy transaction model according to the working condition parameters and the initial electric energy corresponding to each micro-grid, and obtaining the target energy transaction cost and the target electric energy corresponding to each micro-grid. The problem that a traditional micro-grid electric energy management method is generally low in convergence speed is solved, the real-time requirement of online management is difficult to meet in a large-scale interconnected micro-grid cluster scene, and the running reliability of a micro-grid is reduced.