36 results about "Prosumer" patented technology

The invention relates to an electric energy dispatching method for virtual power plant multi-subject system game, which comprises the following steps: acquiring and constructing a Wasserstein distance fuzzy set of electricity price and renewable energy output based on historical power plant data, and respectively establishing a superior power grid electricity price predicted value and a fuzzy set constraint of renewable energy output; establishing a VPP multi-subject collaborative transaction optimization model among an operator, an electric vehicle charging station and a producer, and reconstructing the VPP multi-subject collaborative transaction optimization model based on a Wasserstein distance fuzzy set and a maximum harsh lower bound method; and for the reconstructed VPP multi-agent collaborative transaction optimization model, establishing a game equilibrium model based on a non-cooperative game theory, and solving through a stationary point optimization method to obtain an electric energy scheduling scheme among multiple agents of the virtual power plant. Compared with the prior art, the method has the advantages that the robustness of game decision results is improved, and the flexibility and intelligence of electric energy utilization are improved.

The invention discloses a power distribution network collaborative optimization scheduling method and system considering asymmetric Nash bargaining and producer and consumer renting shared energy storage. The method comprises the following steps: constructing a wholesale market, a retail market and a P2P market, and a multi-agent collaborative transaction framework of mutual influence among a power distribution network operator, a shared energy storage operator and producer and consumer, wherein the multi-agent collaborative transaction framework comprises the wholesale market, the retail market, the P2P market and the mutual influence among the power distribution network operator, the shared energy storage operator and the producer and consumer; constructing a multi-agent optimization scheduling model for renting shared energy storage by the consumer and introducing a bargaining factor to measure the contribution degree of each consumer during electric energy transaction; a cooperative game Nash bargaining theory is introduced to re-model an original multi-body optimization scheduling model, and the model is decomposed into two sub-problems of total cost minimization and payment benefit maximization; a self-adaptive step size ADMM algorithm is adopted to perform distributed optimization solution on the sub-problems to obtain the electricity quantity purchased and sold from the power grid by the producer and the consumer and the shared energy storage, the P2P transaction electricity quantity and the transaction electricity price; and an improved IEEE33 node power distribution system is adopted to carry out example analysis, and a scheduling plan arrangement of multi-agent collaborative optimization operation is obtained.

The invention discloses a multi-main-body energy system cooperative operation asynchronous decentration method, and relates to the technical field of multi-main-body energy system control cooperation. The asynchronous decentration method for cooperative operation of the multi-agent energy system comprises the following steps of building thermodynamic modeling, interaction of a wind power plant and a power grid, shared energy storage and load adjustment, bidirectional information interaction, construction of a Nash negotiation game model, consideration of fraudulent behaviors, dual judgment acceleration asynchronous decentration ADMM algorithm solving, asynchronous calculation and real-time communication. According to the method, a cooperation framework of the production and disappearing person community PComs, the wind power plant WF and the shared energy storage ESS system is established, resources are coordinated through two-way communication, the energy efficiency is improved, the thermal inertia of a building in the production and disappearing person community PComs, the independent power generation capacity of the wind power plant WF and the resource sharing capacity of the shared energy storage ESS system are considered in the framework, and the operation flexibility of the system is improved.

A multi-agent reinforcement learning framework for managing energy transactions in microgrids that includes three layers of agents, each pursuing different objectives. The first layer, including prosumers and consumers, minimizes the total energy cost. The other two layers control the energy price to decrease the carbon emission impact while balancing the consumption and production of both renewable and conventional energy. The framework takes into account fluctuations in energy demand and supply due to household supplied energy from renewable energy sources and energy storage levels in household energy storage devices.

The invention discloses a method and a system for optimizing power consumption of a producer and a consumer based on a load clustering algorithm, and the method comprises the steps: collecting power consumption data, load data and photovoltaic power generation data of the producer and the consumer in a preset time period, and generating load curve data according to a time sequence; performing feature extraction on the load curve data, and classifying load features of each producer based on a clustering algorithm to obtain load modes of different energy demand responses; based on the load modes obtained through classification, the user self-sufficiency rate and the power product total cost of the producer and the consumer under various scheduling models are calculated respectively, and the user self-sufficiency rate is used for representing the renewable energy source proportion in the user load; and calculating a comprehensive index value according to the self-sufficiency rate of the user and the total cost of the power product, and selecting the scheduling model with the highest comprehensive index value to optimize the power consumption of the producer and the consumer. The optimization method provided by the invention can be used for improving the utilization rate of the spontaneous energy by the disappearing people.

The invention discloses a P2P energy interaction method considering production and disappearance differentiation contribution degree and voltage regulation and control, and the method comprises the steps: building an objective function and constraint conditions of a production and disappearance person output optimization model according to a current carbon emission penalty value when a production and disappearance person receives electric quantity from a superior power grid and supplies the electric quantity; constructing a producer and consumer consumer cluster energy consumption minimization sub-model, and solving the producer and consumer consumer cluster energy consumption minimization sub-model to obtain an electric energy optimal scheme of producer and consumer consumer energy consumption minimization; a DNO voltage maintenance model is established, and the DNO voltage maintenance model is utilized to carry out voltage safety inspection on the optimal scheme of the electric energy of the producer and the consumer; according to the optimal electric energy scheme for minimizing the energy consumption of the consumer, establishing a consumer differentiation model and obtaining negotiation weights of different consumers; and constructing a producer and consumer cluster differentiation negotiation sub-model and solving the sub-model to obtain the final power consumption. According to the method, the voltage safety can be ensured, and meanwhile, the difference of producers and diasters can be effectively quantified and distinguished, so that the energy interaction is more fair.

The invention discloses a distributed energy storage capacity optimization configuration method, system and device. The method comprises the steps of obtaining photovoltaic data corresponding to each producer at each time point and first power data of a community; the photovoltaic data are input into a preset first capacity optimization model, energy storage configuration capacity optimization is carried out with the lowest first cost corresponding to a community as the target, the first energy storage capacity corresponding to the community is obtained, and the community comprises a plurality of producers; determining a power transaction value in the community based on the first power data, inputting the first energy storage capacity and the power transaction value into a preset second capacity optimization model, and performing energy storage configuration capacity optimization by taking the lowest second cost corresponding to each producer as a target to obtain a second energy storage capacity corresponding to each producer, and configuring the energy storage capacity of each producer according to the second energy storage capacity. According to the invention, optimal configuration of community distributed shared energy storage can be realized.

This invention discloses a pricing method and system for distributed electricity transactions in industrial parks that considers the bounded rationality game theory of prosumers and consumers, relating to the field of distributed transaction technology. The method includes: designing a distributed transaction structure for industrial parks, clarifying the transaction identities of prosumers and consumers and the roles of operators; constructing a prosumer payoff model based on prospect theory, deriving the payoff prospect value function, and calculating risk preference parameters through questionnaires and maximum likelihood estimation; constructing a non-cooperative game theory model of prosumers and consumers; and designing a distributed transaction pricing algorithm for industrial parks to maximize the total electricity transaction volume by iteratively updating transaction prices. This invention can capture the bounded rationality characteristics of prosumers and consumers, promote low-price and efficient electricity consumption, improve transaction stability and economy, and provide an effective solution for distributed transaction pricing in industrial parks.

The invention discloses a post-fault reliability improvement method for embedded multi-master multi-slave end-to-end transaction and power distribution network operator-producer interaction and cooperation. The method comprises the following steps: step 1, establishing a producer energy consumption benefit measurement model considering differentiated reliability preference; 2, in combination with the production and consumer energy consumption benefit measurement model, establishing a post-fault power distribution network operator-production and consumer double-layer interaction reliability improvement model of the embedded multi-master multi-slave end-to-end transaction; and step 3, based on the balance proof of the multi-master multi-slave game, and based on a sample boundary compression algorithm, solving the power distribution network operator-production and consumer double-layer interaction reliability improvement model after the fault, and finally outputting a network reconstruction strategy of the power distribution network operator and an operation strategy of the production and consumer after the power distribution network fault. According to the technical scheme provided by the embodiment of the invention, mutual benefit and win-win of a power distribution network operator and a producer after a power distribution network fault event occurs are realized, and the social welfare level is effectively improved.

This application discloses a method and apparatus for optimizing energy consumption by prosumers that considers energy storage life loss, including: obtaining the electricity purchase price and electricity sales price of the prosumer from the power grid; obtaining the electricity cost of the prosumer based on the electricity purchase price, electricity sales price, and the power purchased and sold by the prosumer from the power grid; constructing a total operating cost function for the prosumer based on the electricity cost, the comfort loss cost caused by energy consumption changes, the energy storage life loss cost, and the photovoltaic system operation and maintenance cost; solving the total operating cost function for the prosumer based on total energy consumption optimization constraints, energy storage system constraints, interruptible load constraints, transferable load constraints, and power balance constraints to determine the prosumer's energy consumption strategy. By incorporating the energy storage life loss cost into the construction of the total operating cost function for the prosumer and solving it, the prosumer's energy consumption optimization strategy obtained is accurate and feasible while considering the full life cycle cost of the energy storage equipment.

The invention discloses a producer and consumer and shared energy storage economic dispatching method based on a master-slave game, and belongs to the technical field of power system economic dispatching operation. The scheduling method comprises the following steps: firstly, establishing a shared energy storage charging and discharging scheduling model; secondly, establishing a producer and consumer electric quantity balance scheduling model which is responsible for solving the problem of optimal scheduling of renewable energy consumption of producers and consumer and meeting of load requirements, and the optimization target is that the operation cost of the producers and consumer is minimum; and thirdly, establishing a producer and disappearing person and shared energy storage economic dispatching model based on the master-slave game. And 4, converting the double-layer optimization model into a single-layer optimization model by using a KKT condition, and linearizing the nonlinear constraint by using a Big-M method. And finally, establishing a shared energy storage and consumer internal transaction price updating model. According to the invention, an optimized operation strategy method is established for the shared energy storage and the producer, the net load between the producer and the consumer can be communicated, the producer and the consumer can be promoted to improve the utilization rate of renewable energy power generation, and the economic dispatching of the shared energy storage is realized.

The present invention discloses a green community energy sharing method considering carbon credits and electricity traceability, belonging to the technical field of energy market analysis. The present invention includes: (1) determining the prosumer parameters and the operating characteristic constraints of distributed resources; (2) establishing an energy clearing model considering the electricity traceability path based on each prosumer parameter; (3) establishing a carbon credit clearing model based on the established energy clearing model; (4) establishing a green community energy-carbon credit joint clearing model based on the established energy clearing model and carbon credit clearing model; (5) using the alternating direction method of multipliers to solve the energy-carbon credit joint clearing model to obtain the optimal trading plan for the green community and the energy low-carbon management method. The proposed method can provide technical and mechanism references for motivating prosumers on the distribution side to actively conduct low-carbon energy management and promote the consumption substitution of green electricity at the system terminal.

The present application provides a multi-state switch planning method for multi-agent game in a market environment. When executing the method, a planning decision model for multiple objectives is first established respectively; wherein the decision model of the power grid company includes the capacity planning variables of the multi-state switch, and then a dynamic game model is established according to the transmission relationship of the multiple objectives, and then the dynamic game model is solved based on the preset constraints to obtain the capacity planning of the multi-state switch, and a multi-state switch planning scheme that takes into account the market environment and the benefits of the power grid company is obtained in a dynamic solution manner. In this way, by combining the capacity planning variables of the multi-state switch, the DG operator, the power grid company and the prosumer, the effect of improving the utilization rate and cost-effectiveness of the multi-state switch in the market environment is achieved. In this way, a multi-state switch planning can be provided to improve the utilization rate and cost-effectiveness of the multi-state switch in a scenario of multi-agent interactive transactions in a market environment.

A cost allocation method and system for community-shared energy storage. The method comprises: acquiring a charging power of prosumers, a discharging power of prosumers, and a shared power of community-shared energy storage (S11); constructing a shared contribution quantification algorithm on the basis of the charging power of prosumers, the discharging power of prosumers, and the shared power of community-shared energy storage (S12); on the basis of the shared contribution quantification algorithm, quantifying the shared contribution degrees of different prosumers to an alliance (S13); and on the basis of the shared contribution degrees, constructing a cost allocation algorithm based on a generalized Nash bargaining theory, and determining allocation costs for different prosumers on the basis of the cost allocation algorithm (S14).

The present invention provides a multi-agent MEMG scheduling method based on distributed robust optimization, the method comprising: constructing a multi-agent MEMG-prosumer operation model with MEMG as the upper layer and prosumers as the lower layer; constructing an objective function minCoM for a single-agent MEMGi based on minimizing its own operation cost; MGi ; The minCoM MGi represents the minimum value of the total operating cost of MEMGi; optimizes the multiple uncertain subjects in the multi-agent MEMG-prosumer operation model based on the Wasserstein sphere; constructs the objective function of the multi-agent MEMG microgrid alliance based on the Nash negotiation theory; constructs the objective function PR for the prosumer J within MEMGi based on its own minimization operation cost model i,J , the PR i,J represents the total cost of the Jth prosumer within the MEMGi; the MEMG-prosumer model is equivalently converted and solved. This invention improves the overall operational economics of multi-agent MEMGs through cooperative game-playing.

The invention belongs to the technical field of electricity price formulating, and particularly relates to a P2P energy transaction electricity price formulating method and device considering market contribution degree, and the method comprises the steps: constructing a user model and a shared energy storage model for an energy community comprising a shared energy storage SES, a micro-grid user and a micro-grid operator; a personalized pricing strategy is established based on the energy supply-demand ratio and the market contribution degree; constructing a bilateral energy transaction model including utility maximization and dynamic electricity price in an energy community; energy transaction interaction between the micro-grid user and the SES is simulated by using a Stackelberg game, a bilateral energy transaction model is converted into a solvable form, and the micro-grid user is a producer and a consumer which both produce energy and consume energy; and solving the bilateral energy transaction model, and obtaining a personalized electricity price formulating scheme and an energy transaction strategy in the micro-grid group. According to the method, the user can be stimulated to participate in P2P transaction, the energy utilization efficiency is improved, and the power utilization cost is reduced.

A method and system for electrical energy transfer and allocation that includes a blockchain systems for identifying and tracking energy demands and requests by geographical and coordinates and other characteristics through a usage and / or consumption chain that includes consumers and producers (prosumers). A microgrid identifier can be used to tag a consumer and relate the consumer to a producer by geography or demand. Based on demand and location data an energy factor can be calculated and thereby serve to match consumers and producers. A federated learning model can be used to accommodate an energy sharing stage on the blockchain and thereby facilitate, by a server computer, the transport of excess available energy from one energy producer in a first microgrid to either an energy consumer in the first microgrid or an energy storage unit of a second microgrid.

The invention belongs to the technical field of power transmission and distribution prices of power systems, and relates to a power grid power transmission and distribution price pricing method suitable for diasters. Comprising the following steps: dividing power grid users into disagger users and general users; on the basis of a shallow cost recovery principle, acquiring the exclusive access facility cost of the consumer, and determining the access cost; obtaining the permitted income of the power grid public network, and constructing a public network use fee pricing model based on the incidence relation between the load rate and the coincidence rate; and based on the access fee and the public network usage fee pricing model, determining the power transmission and distribution price undertaken by the consumer. According to the method provided by the invention, a pricing mechanism of'access fee + selectable load rate public network use fee 'is constructed, so that the problems of poor applicability of traditional power transmission and distribution price and serious cross subsidy caused by'large capacity and low load rate' characteristics of the producer and the consumer are solved, and accurate allocation and recovery of the cost are realized.

An adaptive system for managing, in an integrated way, multiple EC with variable configuration, with prosumer and / or proconstomer and / or constorer nodes that are dynamically aggregated over the time, through a partitionable digital platform that includes logics for the automatic management. To each platform portion corresponds an EC and an oriented combination of logics in turn selected, sequenced and parametrized according to the optimization purposes provided by the EC, to locally implement, through the controller of each node, the commands imparted to its devices and optimize energy flows, by first adapting to EC logics and then to single node logics. The oriented combination is continually recalculated, within 50 milliseconds from the reading of the data of each aggregated node, to adapt in real time to the variable configuration of an EC and its nodes.

The invention discloses a distributed transaction method based on a block chain technology and target cascade analysis, and relates to the technical field of distributed energy transaction, and the method comprises the steps: constructing a producer and consumer energy model according to a local energy-standby market operation mechanism of a microgrid; a Nash negotiation model is constructed by considering the optimal transaction and price mutual relation of time for a plurality of parturients according to the parturient ability model; considering the charge-discharge efficiency and the battery loss efficiency of the storage battery, and constructing a dynamic operation model of the storage battery; constructing a bottom price model by considering the fairness of the distributed transaction to the buyer and the seller; and according to the producer and disagger energy model, the Nash negotiation model, the storage battery dynamic operation model and the bottom price model, analyzing a target cascade analysis theory, constructing a distributed transaction model based on the target cascade analysis theory, and solving the model to obtain a distributed transaction strategy. According to the invention, the income of the consumer and the enthusiasm of market participation in transaction can be improved, and distributed energy consumption is promoted.

The invention relates to a coscheduling method and a coscheduling system for privacy protection of productive disappears based on group learning and multi-agent reinforcement learning. The coscheduling method comprises the following steps of: constructing a coscheduling architecture between a productive disappears model and productive disappears; based on the architecture, carrying out partial observable Markov game modeling of multi-producer collaborative scheduling under incomplete information, and constructing a group learning-based decentralized privacy protection collaborative scheduling framework; and an optimal transaction strategy is solved in a decentralized privacy protection collaborative scheduling framework through a PS-MAPPO (Privacy Protection State Enhanced Multi-agent Near-end Policy Optimization Algorithm), so as to realize multi-yield disagger privacy protection collaborative scheduling. The method can provide core support for constructing a next-generation virtual power plant and a distributed energy aggregation system which are safe, credible, efficient, cooperative and elastically extensible, and has wide application prospects in the fields of novel power system construction, park-level comprehensive energy management, multi-micro-grid interconnection operation and the like.

This invention belongs to the field of power grid technology and discloses a collaborative optimization method, system, equipment, and medium based on virtual power plant operators and prosumers, including the following steps: S1: Establish a collaborative management framework for electricity carbon emissions between virtual power plant operators and prosumers; S2: Based on the collaborative management framework, construct a two-layer game model; the upper layer is the operator, and the lower layer is the prosumer. The operator calculates the node marginal electricity price and node carbon intensity through optimal power flow and distributes them to the prosumer. The prosumer optimizes scheduling based on the node marginal electricity price and node carbon intensity and submits the power purchase and sale curve; S3: Solve the two-layer game model using a distributed iterative algorithm with a penalty function, and achieve adaptive optimization of the model solution by introducing a penalty term to obtain the optimal collaborative electricity carbon emissions strategy.

The present invention relates to a scheduling method and device for an integrated energy system based on multi-layer game, belonging to the technical field of energy scheduling in power systems. The method includes: constructing a shared energy storage model, an energy system model, and a prosumer model. The shared energy storage model includes a first power constraint and a first objective function. The energy system model includes a second power constraint and a second objective function. The prosumer alliance model includes an electric and thermal load constraint and a third objective function; constructing a three-layer game model based on the shared energy storage model, the energy system model, and the prosumer alliance model; solving the three-layer game model to obtain the scheduling result of the integrated energy system. The present invention constructs a three-layer game model for the shared energy storage system, the integrated energy system, and the prosumer alliance, thereby balancing the interests of the three parties and solving the problem of unbalanced interest distribution among individual or multiple subjects of the same level in the integrated energy system due to collaborative optimization.

This application belongs to the field of energy market trading technology, specifically involving a dynamic pricing optimization method for multi-energy resources based on layered game theory and blockchain-based notarization. The method includes: constructing a three-layer Stackelberg game architecture involving operators (TSOs), aggregators (AGGs), and prosumers (Prosumers); designing a two-way bid-and-clear protocol; developing a distributed solution algorithm based on improved KKT conditions and duality theory to achieve efficient computation of master-slave game equilibrium solutions; and establishing a notarization and verification mechanism for the game process based on blockchain smart contracts. This invention solves the problems of slow convergence speed and untraceable game results in traditional pricing models, significantly improving the computational efficiency of multi-energy resources in industrial parks.

The invention discloses a community-distribution network energy optimization method considering flexible resources and internal transactions, and the method comprises the steps: obtaining the internal transaction power of community members through a community internal production consumer transaction model, obtaining the active power and reactive power of a production consumer side through a community day-ahead optimization scheduling model, and obtaining the day-ahead active power and reactive power curves of the production consumer side. The active and reactive power curves of the producer and the consumer during real-time operation are obtained through the active and reactive power flexibility boundary models, then the active and reactive power flexibility margins are obtained, and the branch current violation amount, the node voltage violation amount and the cost for purchasing the flexible power are minimized through the power distribution network system optimization model. According to the community-distribution network energy management method, the violation amount of the community electricity charge and the voltage and current of the distribution network is reduced through resource sharing optimization, the community electricity utilization cost can be effectively reduced, the power factor and the operation stability are considered, and a safe, efficient and intelligent solution is provided for community-distribution network energy management.

The present invention discloses a method and system for joint optimization and scheduling of microgrids integrating heterogeneous flexible resources. The method comprises: establishing a photovoltaic load model; establishing a day-ahead energy storage leasing model; establishing a prosumer optimization model based on the photovoltaic load model and the day-ahead energy storage leasing model; establishing an intraday virtual energy storage model; establishing an intraday shared energy storage model; establishing a microgrid operator optimization model based on the prosumer optimization model, the intraday virtual energy storage model, and the intraday shared energy storage model; converting the microgrid operator optimization model into a pre-established Markov decision process; solving the Markov decision process, iteratively updating scheduling parameters, and achieving optimized scheduling of the microgrid. The present invention can stimulate the regulation potential of multiple types of flexible resources and enhance the dynamic regulation capability of flexible loads.

This invention belongs to the field of distribution network optimization and scheduling technology, specifically disclosing a two-stage distributed bar optimization method and system based on NDOE-guided DSO-prosumer dynamic negotiation optimization. The method includes acquiring relevant distribution network data, inputting the data into a pre-constructed NDOE-guided distribution network-prosumer dynamic negotiation optimization model, obtaining a distribution network optimization scheduling scheme involving DSO and prosumers, and defining the NDOE-guided distribution network-prosumer dynamic negotiation optimization model as a lower-level prosumer objective function and an upper-level DSO objective function constructed using the ATC algorithm. The method involves solving the lower-level prosumer objective function to obtain the prosumer response DOE, substituting this DOE into the upper-level DSO objective function, and solving the upper-level DSO objective function to obtain the target DOE. The method iterates until the ATC algorithm meets the convergence requirement. This invention allows prosumers to actively participate in optimization, effectively avoiding voltage limit exceedance problems caused by trading disturbances, while also considering the economic interests of market participants.

The invention relates to a prosumer system (100) for supplying to a gas network (300) and receiving from the gas network (300) at least one gas, comprising: - a bypass (110) designed to guide the at least one gas past a final pressure regulator (120) into the gas network (300) in an introduction mode (I), wherein the final pressure regulator (120) is designed to reduce the pressure of the at least one gas from a network pressure to a final pressure in an off-take mode (II), a counter (130) designed to count the delivery of the at least one gas to the gas network (300) in the introduction mode (I) and to count the intake of the at least one gas from the gas network (300) in the off-take mode (II). The invention further relates to a network system (200), a gas network (300), an operating method (400) and a control method (500).