23 results about "Energy trading" patented technology

This invention discloses a blockchain-based Virtual Power Plant Adjustable Resource (RWA) trading system and method, relating to the field of blockchain energy trading technology. The system determines the device's execution capability in the current power direction based on its operating parameters and generates a directional reachability flag. A security unit digitally signs the data containing the directional reachability flag to generate a hardware signature receipt. Blockchain nodes verify the receipt's validity and update the resource circuit breaker status in the distributed ledger based on the directional reachability flag. The system performs directional constraint verification on scheduling requests based on the current resource circuit breaker status. If the requested power direction is not blocked, a scheduling event is generated, and the device executes the action and provides feedback on the execution proof via a smart terminal. This invention achieves fine-grained management and asymmetric control of the execution capability of distributed energy devices, effectively improving the utilization rate of adjustable resources in a virtual power plant while ensuring security.

This invention discloses a renewable energy dispatching method, device, terminal, and medium based on risk perception. The method predicts photovoltaic (PV) power generation using confidence level, environmental data, and a PV power generation prediction model; constructs a two-layer optimization model to coordinate aggregator costs and building user costs during demand response; and obtains building flexible participation capacity and demand response incentive price by solving the two-layer optimization model. Based on confidence level, PV power generation, building flexible participation capacity, and demand response incentive price, it constructs an aggregator day-ahead bidding profit optimization model and supply and demand constraints, respectively. Based on the aggregator day-ahead bidding profit optimization model and supply and demand constraints, it optimizes the energy storage system's charging and discharging strategy and energy trading strategy. This solves the problem of existing technologies failing to consider the uncertainty of power generation, user participation costs in demand response, and willingness to respond, making it difficult to effectively balance the risks of power shortages and trading benefits, and achieving power dispatch optimization under uncertain conditions.

This invention discloses a method and system for coordinated optimization of carbon emissions in a multi-park integrated energy system, belonging to the field of integrated energy optimization and scheduling technology. The method includes: constructing a dynamic carbon trading and green certificate flexible offsetting mechanism; building a two-layer game optimization model with the system operator as the leader and multiple park agents as followers; the upper-layer system operator optimization model uses the DQN algorithm to dynamically optimize and publish energy trading prices; the lower-layer park agent optimization model uses the PPO algorithm to perform distributed optimization scheduling of equipment within the park based on the received energy trading prices; improving the Shapley value benefit allocation method based on carbon emission reduction contributions to fairly distribute the total alliance benefits generated by the coordinated operation of multiple parks; and conducting collaborative iterative training of the two-layer game optimization model until game equilibrium is reached, outputting the optimal energy trading price and scheduling strategies for each park. This invention achieves a synergistic improvement in both the overall economic benefits and carbon emission reduction levels of the system.

There is provided an energy transaction platform to facilitate a wholesale↔distribution↔DER owner marketplace for DER-based energy products, including contracting, delivery, and settlement, for example, based on blockchain technology. The platform provides components to enrol and verify DER owners having a DER device. Platform components define and communicate contracts for energy services for bidding by DER owners to facilitate e.g. EV charging, GHG reduction, or demand response goals. Contracts are cleared in response to bidding, for example, by DER owners. The platform triggers contract delivery, which delivery is monitored and confirmed. Settlement is made between contract parties according to confirmed participation. Participation rewards and / or credit tokens are transferrable to DER owners, which can be further transferred. DER owner credibility measures can encourage participation and assist with clearing contracts. The platform can use blockchain smart contracts for events and store data to the blockchain.

The application provides a kind of biomass energy participates in peak shaving green township power distribution network regional collaborative autonomy method, the method first obtains the source and load prediction data of each regional microgrid of green township, energy supply equipment basic parameters and local grid purchase and sale electricity price data;Then, according to the time scale characteristics of source and load prediction data, an uncertainty model of source and load is established;Then, a double-layer optimization model of green township power distribution network is constructed, including a regional microgrid energy trading price model based on the energy supply and consumption conditions of each regional microgrid as the upper layer strategy, and a two-stage robust optimization model of regional microgrid considering comprehensive constraint conditions with economic efficiency and clean energy consumption rate as optimization objectives based on the uncertainty model of source and load and regional microgrid energy trading price as the lower layer strategy;Finally, the double-layer optimization model of green township power distribution network is iteratively solved, and the distributed energy output, energy storage charging and discharging power and regional interaction power that make the system economic efficiency and clean energy consumption rate optimal are obtained.

The application discloses an interconnected park comprehensive energy system optimization method considering carbon trading. Firstly, the energy transaction mode of each park system and the superior energy network is determined, and the internal structure diagram of the park system is constructed; then, the internal equipment of each park system is modeled, and the operation cost of the independently operated park system is established; finally, the cooperation game optimization model of the interconnected park system for energy transaction is constructed, and the model is solved. The application has the advantages of reducing carbon emission, promoting new energy consumption, reducing the operation cost of each park system and reducing the energy supply pressure.

This invention belongs to the field of distribution network optimization and dispatching, and discloses a method and system for optimizing producer-consumer distributed peer-to-peer (P2P) transactions based on negotiated dynamic operating envelopes. This invention combines a producer-consumer P2P energy trading model with an optimal dynamic operating envelope optimization model of the distribution system operator (DSO) to construct a two-layer optimization framework model, effectively balancing the privacy and autonomy of producer-consumer transactions while achieving an organic coordination between distribution system security and market participant economics. This invention fully considers the trading preferences and autonomous behavior of producer-consumers, allowing them and the DSO to negotiate and obtain the optimal dynamic operating envelope (DOE). P2P transactions conducted under the negotiation results will not violate system security constraints. Based on the two-layer optimization framework model, this invention can dynamically and specifically adjust inlet and outlet power to guide producer-consumer P2P transactions, achieving a free P2P market while satisfying distribution network security.

This invention discloses a method and system for optimized scheduling of integrated energy systems based on a large language model in the field of integrated energy system optimization scheduling and intelligent control. The method includes: partitioning the integrated energy system according to power supply sources and energy conversion equipment; constructing an integrated energy system model, an energy trading matrix, and a carbon emission rights trading matrix; and calculating the total net carbon emissions and total green electricity certificates of the integrated energy system; constructing the state space, action space, and reward function of a deep reinforcement learning model; training the deep reinforcement learning model based on historical optimized scheduling data of the integrated energy system and the large language model; and generating optimized scheduling actions for the integrated energy system using the trained deep reinforcement learning model based on real-time operating data of the integrated energy system. This invention can improve the coordinated optimization capability of integrated energy systems and enhance the adaptability and flexibility of scheduling strategies.

The present application relates to a kind of blockchain-based vehicle networking energy security transaction method, belong to vehicle networking and distributed energy transaction field.The method aims at solving the problem of fair pricing under asymmetric information, user privacy protection and transaction efficiency.The gist is that: authority issues certificate for vehicle and roadside unit;Vehicle node signs broadcast transaction request;Roadside unit verifies and broadcasts demand and collects response, then triggers smart contract;The contract is based on bayesian Nash equilibrium model, and the optimal equilibrium transaction volume and price are obtained by solving the first-order condition of maximum expected utility, to realize automatic matching;Identity and data privacy are protected by combining ring signature and zero-knowledge proof;Hybrid consensus mechanism is used to improve efficiency;Finally, deposit escrow, settlement and default handling are automatically completed by contract.The method is used to build decentralized, safe and reliable vehicle networking energy transaction market.

The application provides a micro-grid energy transaction method and system based on multi-agent deep reinforcement learning, comprising: constructing a hierarchical micro-grid controller agent according to the demand of micro-grid participating in community market P2P energy transaction; wherein, the FDS controller is used for flexible demand scheduling in the micro-grid, and the ET controller is used for energy transaction between the micro-grid and other micro-grids; a hierarchical multi-agent deep neural network model is constructed, the optimal strategy learning task is decomposed into two sub-sequence tasks, and the neural network model is trained in combination with the priority experience replay and importance sampling mechanism; the trained model is combined with the intermediate market pricing strategy to control the micro-grid to complete the P2P energy transaction. The application can not only support the micro-grid to make autonomous quotation and quantity when participating in the energy transaction, ensure the new energy to be traded preferentially, and maximize the P2P transaction income. Meanwhile, the calculation complexity is reduced, and the efficiency of the agent making the optimal transaction sequence decision is improved.

This invention discloses a distributed energy trading method based on data center microgrids, relating to the field of energy technology. The method includes: identifying actions sampled from all agent data center microgrids as joint actions; performing state transitions on each agent data center microgrid through these joint actions, calculating joint state reward values, and adding the generated states and joint state reward values ​​after executing the joint actions to an environmental experience replay buffer; generating multi-step virtual trajectories using a trained world model to construct a model experience replay buffer; obtaining training samples from the model experience replay buffer and performing gradient updates on the parameters of the policy network and value network corresponding to each agent data center microgrid; and sampling actions for each agent data center microgrid based on local observation information at the current moment through the trained policy network. This method improves the accuracy of energy trading prediction for data center microgrids.

This invention provides a power planning method and system based on the electricity market and carbon market, comprising: constructing a typical scenario for the planning target year; establishing a two-layer power planning model based on the electricity market and carbon market; wherein: the upper-layer model is used to determine the power planning scheme and pass the power planning scheme to the lower-layer model; the lower-layer model simulates electricity market and carbon market transactions based on the power planning scheme, and passes the market clearing price to the upper layer; the lower-layer model is transformed into a problem, incorporating the equivalent constraints of electricity market and carbon market clearing into the upper-layer model, and solving the upper-layer model to obtain the final results in the power planning scheme. This invention fully considers the impact of the electricity market and carbon market, constructs corresponding trading models and embeds them as constraints into the power planning model, and can accurately assess the benefits and costs of power sources participating in carbon quota trading in the carbon market and electricity energy trading in the electricity market.

The application belongs to the technical field of block chain, and particularly relates to an energy transaction supervision method and system, which comprises the following steps: S1. initialization and supervision power distribution; S2. terminal registration and transient signature based on PUF; S3. privacy cleaning and aggregation of an edge gateway; S4. block consensus chaining based on chameleon hash; S5. compliance correction execution under multi-party consensus; the application has the advantages that a trusted supervision architecture of 'end-edge-cloud' three-layer cooperation is provided. The most key technical concept is to build a full-process trust closed loop from the physical world to the digital world and to the compliance supervision level.

The gradient correction method of incentive parameters of comprehensive energy system demand response, a comprehensive demand response model is constructed based on consumer psychology, and a master-slave game model of comprehensive energy system is established with comprehensive energy operator as leader and load aggregator as follower. The comprehensive energy operator uses the user energy information in the game process to correct the dead zone and saturation zone incentive threshold parameters of the comprehensive demand response model along the negative gradient direction, and uses the model as the approximate response function of the behavior of the load aggregator to optimize the decision in the next round of game. A response adjustment mechanism is designed to fine-tune the comprehensive demand response plan of the comprehensive energy operator to improve the executability of the comprehensive demand response plan. Different scenes are set for comparative analysis, and the example results show that the method can assist the comprehensive energy operator to make decisions in energy trading, improve the trading efficiency, reduce the threshold of the load aggregator to participate in the transaction, and improve the low-carbon nature and renewable energy consumption capacity of the system.

This invention provides a blockchain-based electricity trading method and electronic device. The method includes: receiving first electricity information submitted by a first power supply user node and second electricity information submitted by a power purchase user node on a local energy trading blockchain; calculating the location marginal price of the distribution network, and determining intermediate first power supply user nodes whose storage bidding and first reputation values ​​meet a first preset requirement based on the location marginal price and a reputation threshold; providing power demand to the power purchase user node based on the stored electricity of the intermediate first power supply user nodes; when the sum of the first electricity stored by all intermediate first power supply user nodes is less than the power demand, providing the power shortage electricity to the power purchase user node based on the second power supply user node on the renewable energy trading blockchain; wherein, the power shortage electricity is the difference between the power demand and the sum of the first electricity stored. This invention can effectively ensure the transaction security of the electricity market under stable and reliable energy trading conditions.

The present invention relates to a virtual power plant to which an optimal price determination and contract renewal system based on digital twin modeling and artificial intelligence is applied. More specifically, the invention comprises: an optimal price determination system including a power generation estimation module that estimates the power generation amount of a power source per reference period, a power demand estimation module that estimates the power consumption amount of a power demander, and a price determination module that derives an optimal price by reinforcement-learned artificial intelligence based on power generation and power consumption data estimated from the power generation estimation module and the power demand estimation module, respectively; a power source management module that manages data on real-time energy transactions and history of power sources and calculates a parameter value that compensates for the difference between the price determined by the optimal price determination system and the actual transaction price, and outputs it to the power generation estimation module; a demand source management module that manages data on real-time energy transactions and history of power demanders and calculates a parameter value that compensates for the difference between the price determined by the optimal price determination system and the actual transaction price, and outputs it to the power demand estimation module; and a monitoring module that manages the optimal price determination system, monitors real-time power transactions, and manages their history. A cloud data server module that processes and manages all data regarding power trading input / output from the above monitoring module and provides it to the user;A contract renewal system comprising: a supply-side digital twin modeling module that generates multiple supply-side contract renewal digital twin modules by modeling supply-side contract renewal conditions based on supply-side power supply history data for the entire contract period and maximum / minimum range data calculated therefrom from the cloud data server module; a demand-side digital twin modeling module that generates multiple demand-side contract renewal digital twin modules by modeling demand-side contract renewal conditions based on demand-side power demand or consumption history data for the entire contract period and maximum / minimum range data calculated therefrom from the cloud data server module; and a contract renewal module that combines multiple supply-side contract renewal digital twin modules and demand-side contract renewal digital twin modules to calculate and output contract matching condition ranges by artificial intelligence for each, wherein the power generation estimation module includes multiple supply-side digital twin models that model supply quantities for price based on data regarding estimated power supply and maximum / minimum range data calculated therefrom, and the power demand estimation module includes multiple demand quantities that model demand quantities for price based on data regarding estimated power demand and maximum / minimum range data calculated therefrom The present invention relates to a virtual power plant to which a system for optimal price determination and contract renewal by digital twin modeling and artificial intelligence is applied, wherein the system includes a demand-side digital twin model, and the price determination module determines an optimal price range by artificial intelligence from multiple prices determined by matching the supply and demand quantities for each price derived from multiple supply-side digital twin models and multiple demand-side digital twin models.

This invention provides a multi-entity energy trading method for distribution networks based on Stackelberg-Nash equilibrium and two-stage WDRO reconfiguration. It constructs a Stackelberg-GNE two-layer game model that simultaneously incorporates energy interactions between DSOs and microgrids, as well as among multiple microgrids. The objective function of the DSO and the uncertainties in the renewable energy output of each microgrid are transformed into a single-layer optimization problem and a two-stage robust model, respectively. The generalized Nash equilibrium model among the lower-level microgrids is equivalently transformed into a centralized optimization problem, and a distributed solution is performed using the alternating direction multiplier method combined with a column and constraint generation algorithm to obtain the energy interactions between multiple microgrids and between microgrids and DSOs. Through a fixed-point iteration method, the Stackelberg game equilibrium between the upper-level DSO and the lower-level microgrids is solved, yielding the market equilibrium solution for multi-entity energy trading in the distribution network. This provides a holistic characterization of the multi-entity energy interaction process, ensuring market equilibrium and effectively overcoming the problems of high computational complexity or insufficient robustness in existing WDRO methods.

This invention relates to the field of energy trading, specifically to a hybrid dynamic pricing scheme for electricity. Significant fluctuations in electricity demand have long been a problem for power grid companies, often increasing their costs. With the development of smart grids, real-time dynamic pricing for electricity, as a demand-side management technology, has attracted widespread attention in both academia and industry due to its ability to achieve peak shaving. To address the problem of significant electricity demand fluctuations and improve trading utility, this invention provides a hybrid dynamic pricing scheme based on Stackelberg game theory. Specifically, this invention designs a user dissatisfaction function arising from participation in peak shaving, an incentive cost model for power companies, utility functions for both power companies and users, and constructs an electricity demand fluctuation cost based on electricity demand. This cost is incorporated into the utility function of power companies, and the Sarima-Ann hybrid forecasting model is used to predict electricity demand. This invention can reduce peak electricity demand, improve the utility of both trading parties, and simultaneously improve the accuracy of electricity demand prediction and pricing results.