A distributed energy producer and consumer peer-to-peer energy transaction method

Abstract

This invention proposes a peer-to-peer (P2P) energy trading method for distributed energy producers and consumers. It considers the long-term trend of credit scores and fully takes into account the interests of multiple stakeholders, including producers, consumers, and power grid companies. Through a more rational credit score mechanism, it addresses the problems of insufficient protection of regulatory interests and low participation rates among distributed energy producers and consumers in existing P2P trading mechanisms. This method more effectively incentivizes producers and consumers to increase P2P participation to reduce costs, standardizes interactive trading behavior, and actively participates in P2P transactions. Simultaneously, it effectively improves grid operation efficiency and promotes the improvement of the distributed energy producer-consumer P2P trading mechanism. This invention provides technical support for the efficient consumption and interactive trading of distributed energy.

Description

Technical Field

[0001] This invention belongs to the field of power distribution technology, specifically relating to a method for point-to-point energy trading among distributed energy producers and consumers. Background Technology

[0002] With the significant increase in the proportion of distributed renewable energy sources such as photovoltaics and wind power on the power distribution side, and the advancement of power market reforms, distributed energy and electricity users are increasingly merging and integrating. Traditional electricity users, acting as single consumers, are beginning to transform into distributed energy prosumers, evolving into dual roles as both energy producers and consumers, and becoming important participants in the production, distribution, and consumption of electricity. Peer-to-peer (P2P) trading among distributed energy prosumers is a crucial technology for promoting the local consumption and efficient utilization of distributed energy, and for ensuring the interests of distributed energy prosumers are enhanced. Therefore, designing a reasonable and effective P2P trading method for distributed energy prosumers has significant theoretical and practical implications.

[0003] However, encouraging distributed energy producers and consumers to trade on fully decentralized P2P platforms currently faces numerous challenges, including technical and institutional hurdles. Furthermore, fully decentralized distributed energy P2P trading poses significant challenges to the overall security of the power grid and regulatory mechanisms. Without incentive mechanisms in the distributed energy producer-consumer P2P trading system, producer behavior cannot be effectively regulated, and the interests of the distribution network cannot be effectively protected. Therefore, it is urgent to introduce a reputation value mechanism into distributed energy producer-consumer P2P trading. By adding a reputation value mechanism to distributed energy producer-consumer P2P trading, producers and consumers can be encouraged to regulate their trading behavior, ensuring the efficiency and reliability of distributed energy producer-consumer P2P trading.

[0004] Current research is exploring the introduction of reputation value mechanisms into P2P distributed energy trading to incentivize producers and consumers to engage in more rational transactions within P2P distributed energy networks. For example, the patent "A High-Reputation Distributed Trading Method for Integrated Energy Microgrids Based on Cross-Chain Interoperability" utilizes parallel main and secondary chains and cross-chain interoperability technology to achieve high-reputation distributed trading in integrated energy microgrids, effectively realizing distributed and trustworthy trading in energy systems. The literature "Design of Photovoltaic Trading Mechanism Based on Blockchain Incentive Mechanism" proposes a dynamic trading platform for photovoltaic systems based on blockchain technology, introducing a reputation value mechanism to ensure active and effective cooperation among nodes in the P2P photovoltaic trading network. The literature "Research on Distributed Photovoltaic Local Consumption Trading Mode Based on Blockchain" proposes a blockchain-based photovoltaic local consumption trading mode. It determines the internal electricity price through a Stackelberg game model, designs a reputation value-based local consumption trading mechanism, penalizes users with low local consumption levels, and encourages users to consume photovoltaic output through time-shiftable loads. The paper "Reputation-based jointscheduling of households appliances and storage in a microgrid with a shared battery" proposes a reputation-based centralized energy management system (EMS) to address the challenge of managing batteries and available energy fairly by considering the reputation of household users in redistributing available energy in shared storage units. The paper "Security scheduling and transaction mechanism of virtual power plants based on dual blockchains" proposes a reputation-based continuous bidirectional auction mechanism that integrates a reputation-based market segmentation mechanism, dividing participants according to reputation value to create a favorable trading environment. The paper "Reputation-based competitive pricing negotiation and power trading for grid-connected microgrid networks" proposes a distributed automatic control method to manage inter-node energy trading in grid-connected microgrids. Agents in the network form relationships, and the interaction between agents quantifies reputation using familiarity, acceptance, and historical knowledge of inter-node value.However, the literature that introduces credit scores only considers the costs and benefits of electricity energy users (prosumers and consumers), without addressing the benefits of the regulatory power grid company. Furthermore, the existing studies have a relatively short tracking period for credit score trends, mostly based on credit scores from the past few days, which cannot fully reflect the long-term impact of credit scores and has considerable limitations. Summary of the Invention

[0005] To overcome the shortcomings of existing technologies, this invention proposes a peer-to-peer (P2P) energy trading method for distributed energy producers and consumers. This method considers the long-term trend of credit scores and fully takes into account the interests of multiple stakeholders, including producers, consumers, and power grid companies. Through a more rational credit score mechanism, it addresses the problems of insufficient protection of regulatory interests and low participation rates among distributed energy producers and consumers in existing P2P trading mechanisms. It provides more reasonable incentives for distributed energy producers and consumers to increase P2P participation to reduce costs, standardize interactive trading behavior, and actively participate in P2P interactive transactions. Simultaneously, it effectively improves the efficiency of power grid operation and promotes the improvement of the distributed energy producer-consumer P2P trading mechanism. This invention provides technical support for the efficient consumption and interactive trading of distributed energy.

[0006] To achieve the above objectives, the present invention adopts the following technical solution:

[0007] A peer-to-peer energy trading method for distributed energy producers and consumers includes the following steps:

[0008] Step (1): Using the deviation of distributed energy producer-consumer P2P transactions and the distributed energy consumption rate as indicators, combined with the historical data of distributed energy producer-consumer P2P transactions, quantitatively analyze the reputation value of distributed energy producer-consumer P2P transactions.

[0009] Step (2): Apply the reputation value of distributed energy producer-consumer P2P transactions, which was statistically quantified in step (1), to the operating cost function of distributed energy producer-consumer P2P transactions in the form of a penalty coefficient, and construct a game model of distributed energy producer-consumer P2P transactions.

[0010] Step (3): Construct the optimal reaction function, convert the distributed energy producer-consumer P2P transaction model obtained in step (2) into a nonlinear optimization problem, obtain the Nash equilibrium solution of the distributed energy producer-consumer P2P transaction, and obtain the optimal operation scheme of the distributed energy P2P transaction.

[0011] Furthermore, step (1) specifically includes the following steps:

[0012] Step (1-1): Using the P2P transaction deviation of distributed energy producers and consumers and the distributed energy consumption rate as indicators, define the formula for the credit value index of distributed energy producers and consumers, as shown in equations (1) and (2):

[0013]

[0014]

[0015] Where V represents the deviation of electricity volume in P2P transactions between distributed energy producers and consumers, and P... actu P represents the actual amount of electricity traded in the electricity market. predi This refers to the electricity volume reported by the producer-consumer based on internal electricity and heat load forecasts and wind and solar power output forecasts. The smaller the V value, the higher the accuracy of the electricity volume reported by the distributed energy producer-consumer; the larger the V value, the lower the producer-consumer's credibility. The renewable energy non-consumption rate is used to represent the extent to which renewable energy is being utilized. λ represents the non-consumption percentage, measuring the wind and solar power absorption capacity of the producers and consumers themselves. E P P represents the remaining electricity for the buyer or seller after a single transaction. sum This represents the sum of electricity consumption by producers and consumers participating in a single market transaction.

[0016] Step (1-2): Statistically analyze the historical P2P transaction data of distributed energy producers and consumers, and calculate the intermediate reputation value reflecting the transaction reputation of distributed energy producers and consumers according to the reputation value index formula defined in step (1-1), as shown in formula (6):

[0017]

[0018] in, V represents the median credit rating of consumer i within time period t. i t The predicted electricity volume deviation for producer-consumer i during trading session t; η represents the unused renewable energy rate of producer-consumer i during trading period t; η and ξ are the weights of the two indicators, representing the importance of predicted power deviation and renewable energy consumption, respectively.

[0019] Steps (1-3): Based on the intermediate reputation values ​​of T time periods of the distributed energy producer-consumer P2P historical transactions, the final reputation value R of producer-consumer i is calculated using the extreme value averaging method, as shown in Equation (7). And based on the interval in which the final reputation value R of producer-consumer i is located, the penalty coefficient μ is determined, as shown in Equation (8).

[0020]

[0021] Where T=24 is the time period for trading within a day, C tLet C be the intermediate reputation value for any t∈T. min C is the minimum intermediate reputation value in T. max is the maximum intermediate reputation value in T.

[0022]

[0023] Furthermore, step (2) includes updating the operating cost function of distributed energy producer-consumer P2P transactions and constructing the distributed energy producer-consumer P2P transaction model.

[0024] Step (2-1): Apply the reputation value of the distributed energy producer-consumer P2P transaction statistically quantified in step (1) as a penalty coefficient to the distributed energy producer-consumer P2P transaction operating cost function, and update the distributed energy producer-consumer P2P transaction operating cost function, as shown in equation (11):

[0025]

[0026] Among them, C mf The costs or benefits for prosumers participating in P2P market transactions; C p2p For transaction costs between prosumers and other prosumers; C p2g For transaction costs between producers / consumers and the power grid; P is the on-grid electricity price at time t; t p2g The electricity traded between producers and consumers and the power grid at time t; P represents the intermediate electricity price at which transactions are completed between producers and consumers at time t; t p2p，i The transaction volume of P2P transactions involving prosumers at time t;

[0027] Step (2-2): Based on the distributed energy producer-consumer P2P transaction operating cost function updated in step (2-1), construct a distributed energy producer-consumer P2P transaction model that considers reputation value, as shown in equation (12):

[0028]

[0029] In the formula, C represents the total operating cost of distributed energy producers and consumers. op For the internal unit operating costs of distributed energy producers and consumers, C om For the maintenance costs of internal units of distributed energy producers and consumers, C mf The costs or benefits of distributed energy producers and consumers participating in P2P market transactions are calculated as shown in equation (11); p2p For distributed energy producer-consumer P2P transaction electricity prices, 2p and These represent the maximum and minimum transaction prices for electricity through P2P platforms. Let t be the transaction power of distributed energy producers and consumers. Let t be the power of the i-th producer-consumer interacting and trading with distributed energy producers-consumers. Let t be the power traded between distributed energy producers / consumers and the grid. These represent the photovoltaic and wind power generation outputs within the distributed energy producer-consumer at time t, respectively. p represents the total load power within the distributed energy producer-consumer at time t. min Let p be the minimum allowable transaction power for distributed energy producers and consumers at time t. max Let t be the maximum allowable transaction power of distributed energy producers and consumers. The subscripts min and max represent the minimum and maximum values ​​of the variable, respectively.

[0030] Furthermore, step (3) includes the steps of equivalent transformation of the transaction model based on the optimal reaction function and obtaining the optimal solution of the distributed energy producer-consumer P2P transaction model:

[0031] Step (3-1): Construct the optimal reaction function and convert the distributed energy producer-consumer P2P transaction model obtained in step (2) into a nonlinear optimization problem.

[0032] First, define the Nikaido-Isoda function, and then divide the payment function into C functions that change with the prosumer's strategy. mf (x i and constant unit operation and maintenance costs e i Prosumer payment function φ i (p i The specific representation is shown in equation (17):

[0033]

[0034] Where, μ i Let be the penalty coefficient for producer-consumer i, pi be the output of producer-consumer i at time t, pload and pload' be the electricity transaction amounts of producer-consumer i as the electricity buyer and seller, respectively, and cbuy and csell be functions of the electricity purchase and sale prices.

[0035] Wherein, the supply and demand function p during time period t loadj (c buy )p′ loadj (c sell This can be expressed as shown in equations (18) and (19):

[0036]

[0037]

[0038] Where: p load 0 and p load 0’ Let be the expected supply and demand during time period t; α and β represent the elasticity of demand and supply with respect to the purchase price, respectively. The purchase price and the selling price can be expressed as equations (20) and (21), respectively:

[0039]

[0040]

[0041] Step (3-2): Relax the nonlinear optimization problem constructed in step (3-1), transform it into an optimization problem as shown in equation (22), and use the relaxation algorithm to find the optimal solution to obtain the optimal operating scheme for distributed energy P2P transactions:

[0042]

[0043] In the formula: c i (y i ,p) represents the payoff of producer-consumer i after only changing its own strategy; c i (p i ) represents the payoff of producer i without changing any producer-consumer strategy. α and β represent the elasticities of demand and supply with respect to the purchase price, respectively; p i p is the strategy before the change of producer-consumer i. j ,p' j For prosumers j (j≠i), the unchanged strategies of acting as buyers and sellers respectively; y i The revised strategy for prosumer i; For prosumers i, the supply and demand expectations remain unchanged. Let j (j≠i) represent the expected supply and demand of producers and consumers, respectively, as buyers and sellers with unchanged strategies. The supply and demand expectations of producer-consumer i after changing their strategy.

[0044] Beneficial effects:

[0045] This invention, through an innovative method of measuring creditworthiness, not only considers the costs and benefits of electricity users as producers and consumers, but also the benefits of the regulatory power grid company. Furthermore, this invention has a long tracking period for changes in creditworthiness and can analyze stable creditworthiness over a considerable period of time to modify strategies in a timely manner. It not only reflects the long-term impact of creditworthiness, but also has strong flexibility.

[0046] This invention enables the standardization of distributed energy P2P trading behavior under the participation of multiple stakeholders and high penetration of renewable energy access. It helps to solve the problems in the design of existing distributed energy P2P trading mechanisms, such as the lack of protection for the interests of regulators and the low participation of distributed energy producers and consumers in P2P trading. It can more reasonably incentivize distributed energy producers and consumers to increase P2P participation to reduce costs, while effectively improving the efficiency of grid operation and ensuring the safe and stable supervision of distributed energy. It has good application prospects. Attached Figure Description

[0047] Figure 1 This is a flowchart illustrating the implementation of the distributed energy producer-consumer peer-to-peer energy trading method of the present invention. Detailed Implementation

[0048] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention. Furthermore, the technical features involved in the various embodiments of this invention described below can be combined with each other as long as they do not conflict with each other.

[0049] The distributed energy producer-consumer peer-to-peer energy trading method of this invention is a P2P distributed energy producer-consumer trading method that considers the long-term trend of credit value changes and fully takes into account the interests of multiple parties such as producers, consumers, and power grid companies. The flowchart is as follows. Figure 1 As shown, it includes the following steps:

[0050] Step (1) Using the deviation of distributed energy producer-consumer P2P transactions and the distributed energy consumption rate as indicators, combined with the historical data of distributed energy producer-consumer P2P transactions, the reputation value of distributed energy producer-consumer P2P transactions is quantitatively analyzed.

[0051] Step (2) The reputation value of distributed energy producer-consumer P2P transactions, which was statistically quantified in step (1), is applied to the operating cost function of distributed energy producer-consumer P2P transactions in the form of a penalty coefficient to construct a game model of distributed energy producer-consumer P2P transactions.

[0052] Step (3) Construct the optimal reaction function, and convert the distributed energy producer-consumer P2P transaction model obtained in step (2) into a nonlinear optimization problem. Find the Nash equilibrium solution of the distributed energy producer-consumer P2P transaction and obtain the optimal operation scheme of the distributed energy P2P transaction.

[0053] Specifically, such as Figure 1 As shown, step (1) specifically includes the following steps:

[0054] Step (1-1): Using the P2P transaction deviation of distributed energy producers and consumers and the distributed energy consumption rate as indicators, define the formula for the credit value index of distributed energy producers and consumers, as shown in equations (1) and (2).

[0055]

[0056]

[0057] Where V represents the deviation of electricity volume in P2P transactions between distributed energy producers and consumers, and P... actu P represents the actual amount of electricity traded in the electricity market. predi This refers to the electricity volume reported by the producer-consumer based on internal electricity and heat load forecasts and wind and solar power output forecasts. The smaller the V value, the higher the accuracy of the electricity volume reported by the distributed energy producer-consumer; the larger the V value, the lower the producer-consumer's credibility. The renewable energy non-consumption rate is used to represent the extent to which renewable energy is being utilized. λ represents the non-consumption percentage, measuring the wind and solar power absorption capacity of the producers and consumers themselves. E P P represents the remaining electricity for the buyer or seller after a single transaction. sum It represents the sum of electricity generated by producers and consumers participating in a single market transaction.

[0058] Step (1-2): Statistically analyze the historical data of distributed energy producer-consumer P2P transactions, and calculate the intermediate credit value reflecting the creditworthiness of distributed energy producer-consumer transactions based on the credit value index formula defined in step (1-1), as shown in formula (6).

[0059]

[0060] in, V represents the median credit rating of consumer i within time period t. i t The predicted electricity volume deviation for producer-consumer i during trading session t; η represents the renewable energy non-consumption rate of producer-consumer i during trading period t; η and ξ are the weights of the two indicators, representing the importance of predicted power deviation and renewable energy consumption, respectively.

[0061] Step (1-3): Based on the intermediate reputation values ​​of the T time periods of the P2P historical transactions of distributed energy producers and consumers, the final reputation value R of producer and consumer i is calculated by the extreme value averaging method, as shown in Equation (7). Based on the interval in which the final reputation value R of producer and consumer i is located, the penalty coefficient μ is determined, as shown in Equation (8).

[0062]

[0063] Where T=24 is the time period for trading within a day, C tLet C be the intermediate reputation value for any t∈T. min C is the minimum intermediate reputation value in T. max is the maximum intermediate reputation value in T.

[0064]

[0065] Step (2) mainly includes updating the operating cost function of distributed energy producer-consumer P2P transactions and constructing the distributed energy producer-consumer P2P transaction model.

[0066] Step (2-1): Apply the reputation value of the distributed energy producer-consumer P2P transaction statistically quantified in step (1) to the distributed energy producer-consumer P2P transaction operating cost function in the form of a penalty coefficient, and update the distributed energy producer-consumer P2P transaction operating cost function as shown in equation (11).

[0067]

[0068] Among them, C mf The costs or benefits for prosumers participating in P2P market transactions; C p2p For transaction costs between prosumers and other prosumers; C p2g For transaction costs between producers / consumers and the power grid; P is the on-grid electricity price at time t; t p2g The electricity traded between producers and consumers and the power grid at time t; P represents the intermediate electricity price at which transactions are completed between producers and consumers at time t; t p2p，i Let t be the transaction volume of P2P transactions involving prosumers.

[0069] Step (2-2): Based on the distributed energy producer-consumer P2P transaction operation cost function updated in step (2-1), construct a distributed energy producer-consumer P2P transaction model that considers reputation value, as shown in equation (12).

[0070]

[0071] In the formula, C represents the total operating cost of distributed energy producers and consumers. op For the internal unit operating costs of distributed energy producers and consumers, C om For the maintenance costs of internal units of distributed energy producers and consumers, C mf The costs or benefits of distributed energy producers and consumers participating in P2P market transactions are calculated as shown in equation (11); p2p For distributed energy producer-consumer P2P transaction electricity prices, and These represent the maximum and minimum transaction prices for electricity through P2P platforms. Let t be the transaction power of distributed energy producers and consumers. Let t be the power of the i-th producer-consumer interacting and trading with distributed energy producers-consumers. Let t be the power traded between distributed energy producers / consumers and the grid. These represent the photovoltaic and wind power generation outputs within the distributed energy producer-consumer at time t, respectively. p represents the total load power within the distributed energy producer-consumer at time t. min Let p be the minimum allowable transaction power for distributed energy producers and consumers at time t. max Let t be the maximum allowable transaction power of distributed energy producers and consumers. The subscripts min and max represent the minimum and maximum values ​​of the variable, respectively.

[0072] In step (3), an optimal reaction function is constructed, and the distributed energy producer-consumer P2P transaction model obtained in step (2) is equivalently transformed into a nonlinear optimization problem. The optimal solution of the distributed energy producer-consumer P2P transaction is obtained, and the optimal operating scheme of the distributed energy P2P transaction is acquired. This mainly includes the steps of equivalent transformation of the transaction model based on the optimal reaction function and obtaining the optimal solution of the distributed energy producer-consumer P2P transaction model.

[0073] Step (3-1): Construct the optimal reaction function and convert the distributed energy producer-consumer P2P transaction model obtained in step (2) into a nonlinear optimization problem.

[0074] First, define the Nikaido-Isoda function, and then divide the payment function into C functions that change with the prosumer's strategy. mf (x i and constant unit operation and maintenance costs e i Prosumer payment function φ i (p i The specific representation is shown in equation (17):

[0075]

[0076] Where, μ i Let p be the penalty coefficient for producer-consumer i. i For the output of producer i at time t, p load and p load Let 'i' represent the electricity transaction amount where the producer and consumer act as the electricity buyer and seller respectively, and let c represent the electricity purchase and sale price. buy and c sell The function.

[0077] The supply and demand function p during time period t load (c buy ),p' load (csell This can be expressed as shown in equations (18) and (19):

[0078]

[0079]

[0080] Where: p load 0 and p load 0’ Let be the expected supply and demand during time period t; α and β represent the elasticity of demand and supply with respect to the purchase price, respectively. The purchase price and the selling price can be expressed as equations (20) and (21), respectively:

[0081]

[0082]

[0083] Step (3-2): Relax the nonlinear optimization problem constructed in step (3-1) and convert it into an optimization problem as shown in equation (22). Use the relaxation algorithm to find the optimal solution and obtain the optimal operation scheme of distributed energy P2P transaction.

[0084]

[0085] In the formula: c i (y i ,p) represents the payoff of producer-consumer i after only changing its own strategy; c i (p i ) represents the payoff of producer i without changing any producer-consumer strategy; α and β represent the elasticities of demand and supply with respect to the purchase price, respectively; p i p is the strategy before the change of producer-consumer i. j ,p' j For prosumers j (j≠i), the unchanged strategies of acting as buyers and sellers respectively; y i The revised strategy for prosumer i; For prosumers i, the supply and demand expectations remain unchanged. Let j (j≠i) represent the expected supply and demand of producers and consumers, respectively, as buyers and sellers with unchanged strategies. The supply and demand expectations of producer-consumer i after changing their strategy.

[0086] Due to the rearranged Nikaido-Isoda function ψ(p) i ,y i ) contains information about variable p i The sum of positive square terms y i The negative square term, equation (22) is a weakly convex-concave function.

[0087] Those skilled in the art will readily understand that the above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A distributed energy producer-consumer peer-to-peer energy transaction method, characterized by, Comprise the following steps: Step (1), with distributed energy producer and consumer P2P transaction deviation and distributed energy consumption rate as index, combining with distributed energy producer and consumer P2P transaction historical data, the credit value of distributed energy producer and consumer P2P transaction is quantitatively analyzed; Step (2), the credit value of distributed energy producer and consumer P2P transaction quantitatively analyzed in step (1) is used in the form of penalty coefficient to the distributed energy producer and consumer P2P transaction operation cost function, and a game model of distributed energy producer and consumer P2P transaction is constructed; Step (3), an optimal response function is constructed, the distributed energy producer and consumer P2P transaction model obtained in step (2) is equivalently converted into a nonlinear optimization problem, the Nash equilibrium solution of distributed energy producer and consumer P2P transaction is solved, and the optimal operation scheme of distributed energy P2P transaction is obtained; The step (3) comprises the following steps based on the optimal response function: Step (3-1): an optimal response function is constructed, and the distributed energy producer and consumer P2P transaction model obtained in step (2) is equivalently converted into a nonlinear optimization problem: First, the Nikaido-Isoda function is determined, which divides the payoff function into C mf (x i ) that varies with the producer-consumer strategy and the constant unit operation and maintenance cost e i , the producer-consumer payoff function is specifically expressed as shown in equation (17): （17） wherein, is the penalty coefficient for producer-consumer i, pi is the output of producer-consumer i at time t, pload and pload' are the electricity transaction amounts of producer-consumer i as a buyer and a seller, respectively, and is a function of the buying and selling electricity prices cbuy and csell. Supply and demand functions over a period of time , are shown by equations (18), (19) (18) (19) where: p load 0 and p load 0’ are the expected demand and supply over the period t, respectively; a and β represent the elasticity of demand and supply with respect to the purchase and sale price, respectively; and p purchase and sale price, respectively. (20) (21) Step (3-2): the nonlinear optimization problem constructed in step (3-1) is relaxed, and is equivalently converted into an optimization problem as shown in formula (22), and a relaxation algorithm is used to solve the optimal solution, and the optimal operation scheme of distributed energy P2P transaction is obtained: （22） where: c i (y i , p) represents the payoff of producer-consumer i after changing only its own strategy; c i (p i ) represents the payoff of producer-consumer i without changing any producer-consumer strategy; is the unchanged strategy of producer-consumer i; and α and β represent the elasticity of demand and supply with respect to purchase and sale price, respectively; , is the unchanged strategy of producer-consumer j as a purchaser or seller, respectively, where j≠i; is the changed strategy of producer-consumer i; is the supply and demand expectation of producer-consumer i without changing the strategy; , is the supply and demand expectation of producer-consumer j as a purchaser or seller, respectively, without changing the strategy, where j≠i; is the supply and demand expectation of producer-consumer i after changing the strategy.

2. The method of claim 1, wherein the method further comprises: The step (1) specifically comprises the following steps: Step (1-1): with distributed energy producer and consumer P2P transaction deviation and distributed energy consumption rate as index, a credit value index formula of distributed energy producer and consumer is defined, as shown in formula (1) and (2): （1） （2） Wherein, V is the distributed energy producer and consumer P2P transaction power deviation, P actu is the actual transaction power in the electricity transaction; P predi is the power released by the producer and consumer according to the internal electricity and heat load prediction and the wind and light output prediction; is the renewable energy non-consumption rate, which is used to represent the consumption of renewable energy; λ is the non-consumption proportion, which measures the wind and light consumption capacity of the producer and consumer, E P is the remaining power of the buyer or seller after a single transaction, P sum is the sum of the power of the producer and consumer participating in the market transaction in a single transaction; Step (1-2): the historical data of distributed energy producer and consumer P2P transaction is statistically analyzed, and the intermediate credit value reflecting the transaction credit of distributed energy producer and consumer is calculated according to the credit value index formula defined in step (1-1), as shown in formula (6): （6） wherein, is the intermediate reputation value of producer-consumer i in time period t, is the predicted power deviation of producer-consumer i in trading period t; is the non-consumption rate of renewable energy of producer-consumer i in trading period t; η and ξ are the weights of the two indexes, respectively representing the importance of the predicted power deviation and the renewable energy consumption. Step (1-3): the final credit value R of producer and consumer i is calculated by using the extreme value average value method according to the intermediate credit value of T time periods of distributed energy producer and consumer P2P historical transaction, as shown in formula (7), and the penalty coefficient μ is determined according to the interval of the final credit value R of producer and consumer i, as shown in formula (8): （7） where T = 24 is the time period in a day during which transactions are carried out, is the intermediate reputation value at any t e T, is the minimum intermediate reputation value in T, is the maximum intermediate reputation value in T; （8）。 3. The method of claim 2, wherein the method further comprises: The step (2) comprises the following steps of distributed energy producer and consumer P2P transaction operation cost function updating and distributed energy producer and consumer P2P transaction model construction: Step (2-1): the credit value of distributed energy producer and consumer P2P transaction quantitatively analyzed in step (1) is used in the form of penalty coefficient to the distributed energy producer and consumer P2P transaction operation cost function, and the distributed energy producer and consumer P2P transaction operation cost function is updated, as shown in formula (11): （11） wherein, is the cost or benefit of the producer-consumer participating in the P2P market transaction; is the transaction cost of the producer-consumer with other producer-consumers; is the transaction cost of the producer-consumer with the grid; is the on-grid electricity price at time t; is the electricity quantity traded between the producer-consumer and the grid at time t; is the intermediate electricity price reached between the producer-consumers at time t; is the traded electricity quantity of the producer-consumer participating in the P2P transaction at time t; Step (2-2): based on the distributed energy producer and consumer P2P transaction operation cost function updated in step (2-1), a distributed energy producer and consumer P2P transaction model considering credit value is constructed, as shown in formula (12): （12） In the formula, The total operating cost for distributed energy producers and consumers, For the internal operating costs of distributed energy producers and consumers, For the maintenance costs of internal units of distributed energy producers and consumers, The cost or benefit of distributed energy producers and consumers participating in P2P market transactions is calculated as shown in Equation (11); For distributed energy producer-consumer P2P transaction electricity prices, and These represent the maximum and minimum transaction prices for electricity through P2P platforms. Let t be the transaction power of distributed energy producers and consumers. The first interaction and transaction between distributed energy producers and consumers at time t. The power of individual consumers Let t be the power traded between distributed energy producers / consumers and the grid. , These represent the photovoltaic and wind power generation outputs within the distributed energy producer-consumer at time t, respectively. Let t be the total load power within the distributed energy producer-consumer. Let t be the minimum allowable transaction power for distributed energy producers and consumers. Let t be the maximum allowable transaction power of distributed energy producers and consumers. The subscripts min and max represent the minimum and maximum values ​​of the variable, respectively.

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