Point-to-point power transaction method and system in block chain environment

Abstract

The invention provides a point-to-point electricity transaction method and system in a block chain environment, and relates to the field of electricity transactions, and the method comprises the steps: obtaining the historical transaction information of a producer and a soldier, determining the electricity production and consumption constitution condition and environmental benefits of the producer and the soldier, and calculating a green electricity incentive factor; analyzing marginal costs of different types of power; optimizing the initial price of each type of power to obtain a target price and a consumption and transaction sequence of each type of power; performing weighted combination on the green electricity excitation factor and the target price of the producer to obtain a green electricity excitation price; and determining transaction matching information of the producer, and according to the transaction sequence, the green electricity incentive price and the transaction matching information, sequentially carrying out electricity transaction through a preset transaction platform. According to the invention, by means of the green electricity incentive pricing and efficient transaction matching method, producers and sellers are incentive to participate in the green electricity transaction, the green electricity transaction income is increased, the permeability of green electricity in the transaction is improved, and stable, low-carbon and efficient point-to-point electricity transaction is realized.

Description

Technical Field

[0001] The present application relates to the technical field of power trading, and specifically to a peer-to-peer power trading method and system in a blockchain environment. Background Art

[0002] The high proportion of green electricity access has promoted the continuous optimization of the power structure, and green electricity has become an important part of the power system. Peer-to-peer electricity trading is an important way to promote green electricity trading; in peer-to-peer electricity trading, the user role has gradually changed from consumers to producers and sellers. Producers and sellers of different green electricity production scales can directly trade by buying and selling green electricity, which effectively matches the supply and demand of green electricity and improves the utilization efficiency of green electricity. Blockchain is an encrypted distributed database in which all peers share information in a decentralized and secure manner. In order to further improve peer-to-peer electricity trading, blockchain has become an effective supporting technology for peer-to-peer electricity trading with its decentralized, tamper-proof, open and transparent characteristics.

[0003] However, in the process of peer-to-peer electricity trading based on blockchain, green electricity incentives and value measurement will have an impact on the entire peer-to-peer electricity trading revenue and carbon emissions. Therefore, it is necessary to comprehensively consider the green electricity incentives and transaction prices of both the supply and demand sides of the producers and sellers. For both the supply and demand sides of the producers and sellers, reasonable green electricity incentives play a vital role in increasing the enthusiasm of producers and sellers to participate in green electricity trading and increasing the revenue of green electricity trading. Current incentives include monetary incentives that provide subsidies and price incentives that provide discounts.

[0004] In existing point-to-point electricity trading, the penetration and utilization levels of green electricity by producers and sellers are not accurately measured, the green electricity incentives for producers and sellers are inaccurate, and the competitive advantages of green electricity cannot be fully utilized; pricing strategies are difficult to effectively reflect the value of green electricity; and the transaction matching methods in related technologies have too many iterations and are time-consuming to calculate, resulting in low transaction matching efficiency. Summary of the invention

[0005] In response to the shortcomings of the existing technology, the present application provides a peer-to-peer electricity trading method and system in a blockchain environment, which solves the problems of inaccurate green electricity incentives for producers and sellers and time-consuming transaction matching process.

[0006] To achieve the above objectives, the present invention is implemented through the following technical solutions:

[0007] On the first aspect, an embodiment of the present application provides a peer-to-peer electricity trading method in a blockchain environment, which includes: obtaining historical transaction information of producers and sellers, determining the electricity production and consumption structure and environmental benefits of producers and sellers, and calculating the green electricity incentive factor of producers and sellers to measure the value of green electricity to determine the incentive level; according to the equivalent marginal cost pricing theory, analyzing and determining the marginal cost of different types of electricity; according to the marginal cost of different types of electricity, optimizing the initial price of each type of electricity to obtain the target price and the consumption and transaction order of various types of electricity; weightedly combining the green electricity incentive factor and the target price of producers and sellers to obtain the green electricity incentive price; calculating the cost-effectiveness of producers and sellers according to a preset market clearing algorithm, and determining the transaction matching information of producers and sellers; and conducting electricity transactions in sequence through a preset trading platform according to the transaction order, green electricity incentive price and transaction matching information.

[0008] According to the first aspect of the embodiment of the present application, the green electricity incentive factor includes a power structure factor and an environmentally friendly factor, and the weights corresponding to the power structure factor and the environmentally friendly factor are adjusted by daily deviation; the green electricity incentive factor satisfies the expression:

[0009]

[0010] In the formula, G i,t represents the green electricity incentive factor, β i,t Represents the environmental friendly factor, is the target weight corresponding to the environmental friendly factor, α i,t represents the power structure factor;

[0011] The daily deviation represents the ratio of the number of photovoltaic transactions in the power transaction to the actual number of photovoltaic transactions in the total time period, so as to ensure the degree of participation of producers and sellers in power transactions; the daily deviation satisfies the expression:

[0012]

[0013] Where, d i is the daily deviation, is the number of photovoltaic transactions in electricity trading, is the actual number of photovoltaic transactions, t represents time, and T is the total time period.

[0014] According to the first aspect of the embodiment of the present application, the relationship between the target weight and the daily deviation satisfies the expression:

[0015]

[0016] The power structure factor satisfies the expression:

[0017]

[0018] In the formula, represents the amount of green electricity produced and consumed, It represents the amount of all energy consumed in production;

[0019] The environmental friendly factor satisfies the expression:

[0020]

[0021] In the formula, γ energy represents the regional carbon emission factor, D i,t Indicates the amount of electricity required by producers and sellers, S i,t Indicates the amount of electricity supplied by producers and sellers, Indicates the amount of green electricity stored in the transaction.

[0022] According to the first aspect of the embodiment of the present application, the pricing process of the green electricity incentive price is divided into an electricity cost measurement stage for different transaction types and an automatic bidding decision-making stage; in the electricity cost measurement stage, each producer and seller participating in the electricity transaction is equipped with a photovoltaic power generation system and an energy storage system, and the costs of the producers and sellers include photovoltaic power generation costs and energy storage costs; photovoltaic power generation costs include initial investment costs and operating and maintenance costs; energy storage costs consist of energy storage power costs and energy storage loss costs.

[0023] According to the first aspect of the embodiment of the present application, the photovoltaic power generation cost satisfies the expression:

[0024]

[0025] In the formula, is the photovoltaic power generation cost, is the initial investment cost, is the operation and maintenance cost, n PV represents the life of the photovoltaic system, r represents the discount rate;

[0026] The initial investment cost satisfies the expression:

[0027]

[0028] The operation and maintenance cost satisfies the expression:

[0029]

[0030] In the formula, represents the unit capacity price of photovoltaic power generation system, Represents the capacity of the photovoltaic power generation system, T PV Indicates the annual equivalent operating hours of the photovoltaic power generation system. Indicates the amount of electricity generated by the photovoltaic power generation system, represents the basic electricity price of photovoltaic power transformers, ε represents the operating cost of photovoltaic power generation system, ω represents the fixed maintenance cost of photovoltaic power generation system, and δ represents the fixed maintenance cost of photovoltaic power generation system;

[0031] The capacity of the photovoltaic power generation system satisfies the expression:

[0032]

[0033] The basic electricity price of photovoltaic power transformers meets the expression:

[0034]

[0035] The annual equivalent operating hours of the photovoltaic power generation system satisfy the expression:

[0036]

[0037] Where a represents the basic charge coefficient of the photovoltaic power generation system.

[0038] According to the first aspect of the embodiment of the present application, the energy storage power cost satisfies the expression:

[0039]

[0040] In the formula, is the energy storage electricity cost, represents the amount of electricity purchased from the grid, Represents the price of purchasing grid electricity, represents the income from selling photovoltaic power through transactions, represents the revenue from selling photovoltaic power to the grid, Represents the available capacity of the energy storage system, represents the price of purchasing photovoltaic power;

[0041] The energy storage loss cost satisfies the expression:

[0042]

[0043] In the formula, is the energy storage loss cost, θ represents the loss cost per kilowatt-hour of the energy storage system charging and discharging operation, represents the loss cost of the energy storage system per kilowatt-hour charging operation, Indicates the number of kilowatt-hours of energy storage system charging. It represents the discharge quantity of the energy storage system per kilowatt-hour, and Δt represents the time period corresponding to the calculated energy storage loss cost.

[0044] According to a first aspect of an embodiment of the present application, the charging and discharging of the energy storage system satisfies a preset rule, and the preset rule satisfies the expression:

[0045]

[0046] N min ≤N t ≤N max

[0047] In the formula, Indicates the maximum charging capacity of the energy storage system. represents the power stored in the energy storage system at t0, represents the power stored in the energy storage system at t1, η c Represents the charging efficiency of the energy storage system, η d Represents the discharge efficiency of the energy storage system, N t represents the power stored in the energy storage system at time t, N min Indicates the minimum capacity to prevent deep discharge of the energy storage system, N max Indicates the maximum capacity to prevent deep discharge of the energy storage system.

[0048] According to the first aspect of the embodiment of the present application, the above-mentioned analysis and determination of the marginal cost of different types of electricity based on the equivalent marginal cost pricing theory may include the following steps: drawing the load duration curve MCC and the minimum cost curve LDC of the supply-side production and consumption area and the demand-side production and consumption area respectively; sorting the marginal costs of different types of electricity from low to high according to the minimum cost curve LDC to form the slope of each load duration curve MCC segmentation; wherein the minimum cost curve LDC is used to characterize the load level and duration that the power system needs to bear in a specific time period; the target price satisfies the expression:

[0049] P i ele =P i +κ i

[0050] Where P i represents the initial price, P i ele represents the target price, κ i Represents marginal cost.

[0051] According to the first aspect of the embodiment of the present application, the optimization matching method corresponding to the market clearing algorithm includes an information collection and organization stage, a cost-effectiveness calculation stage and an order matching stage; in the information collection and organization stage, the power composition and bidding information of the supply-side producers and sellers and the demand-side producers and sellers are collected to calculate the green electricity incentive factors of the producers and sellers' incentive prices.

[0052] According to the first aspect of the embodiment of the present application, in the cost-utility calculation stage, the optimal order is selected and the power trading scheme is optimized through a preset cost-utility function, and the cost-utility function satisfies the expression:

[0053]

[0054] Where U ef is the cost-utility function, represents the photovoltaic power generation cost of producer and seller i at time t, represents the amount of electricity generated by the photovoltaic system of producer and seller i at time t, represents the energy storage cost of producer and seller i at time t, represents the energy storage system transaction volume corresponding to producer and seller i at time t, represents the amount of electricity purchased by producer and seller i from the grid at time t, represents the amount of electricity sold by producer and seller i to the grid at time t, represents the price at which producer and seller i purchases electricity from the grid at time t, M represents the price at which producer and seller i sells electricity to the grid at time t; i,t represents the cost paid by producer and seller i for transmitting electricity using the physical layer of the trading platform at time t, and m represents the cost paid for using the information layer of the trading platform; represents the clearing price for purchasing photovoltaic power, Indicates the clearing amount of electricity purchased from photovoltaic power, represents the clearing price for selling PV power, Indicates the clearing electricity volume of photovoltaic power sold; represents the price at which producer and seller i purchases energy storage electricity at time t, represents the amount of energy storage electricity purchased by producer and seller i at time t, represents the price at which producer and seller i sells stored electricity at time t, represents the quantity of energy storage electricity sold by producer and seller i at time t; Fixed cost of remote service fee, τ Fixed cost of remote service fee, S i represents the distance between producer and seller i and the trading platform, represents the load of producer and marketer i at time t; represents the maximum storage capacity of producer and seller i at time t, represents the maximum photovoltaic power generation of producer and seller i at time t; It represents the transaction price of producers and sellers on the supply side, represents the transaction price of producers and sellers on the demand side, represents the photovoltaic transaction volume of the supply-side producers and sellers, Represents the PV transaction volume of demand-side producers and sellers.

[0055] According to the first aspect of the embodiment of the present application, the trading platform includes a market layer and an information layer. The information layer is used to transmit and store transaction information and interact with other producers and sellers and grids by establishing an aggregator; the market layer is responsible for the physical exchange of energy and capital after the transaction is completed; the market layer and the information layer are connected through a blockchain.

[0056] On the second aspect, an embodiment of the present application provides a peer-to-peer electricity trading system in a blockchain environment, which includes a calculation module, a determination module, an optimization module, a weighting module, a matching module and a trading module; wherein the calculation module is used to obtain historical transaction information of producers and sellers, determine the electricity production and consumption structure and environmental benefits of producers and sellers, and calculate the green electricity incentive factor of producers and sellers to measure the value of green electricity to determine the incentive level; the determination module is used to analyze and determine the marginal cost of different types of electricity according to the equivalent marginal cost pricing theory; the optimization module is used to optimize the initial price of each type of electricity according to the marginal cost of different types of electricity, and obtain the target price and the consumption and transaction order of various types of electricity; the weighting module is used to weightedly combine the green electricity incentive factor and the target price of the producer and seller to obtain the green electricity incentive price; the matching module is used to calculate the cost-effectiveness of the producer and seller according to a preset market clearing algorithm, and determine the transaction matching information of the producer and seller; the trading module is used to conduct electricity transactions in sequence through a preset trading platform according to the transaction order, green electricity incentive price and transaction matching information.

[0057] In a third aspect, an embodiment of the present application provides an electronic device, comprising: a processor, a memory, and a program stored in the memory and executable on the processor, wherein when the program is executed by the processor, the peer-to-peer electricity trading method in the blockchain environment of the first aspect is implemented.

[0058] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, on which a program or instruction is stored. When the program or instruction is executed by a processor, the peer-to-peer electricity trading method in the blockchain environment of the aforementioned first aspect is implemented.

[0059] This application provides a peer-to-peer electricity trading method and system in a blockchain environment. Compared with the prior art, it has the following beneficial effects:

[0060] This application calculates the respective green electricity incentive factors and quantifies the green electricity level of producers and sellers based on the predicted transaction information of producers and sellers on both sides of supply and demand, combined with the electricity production and consumption structure and environmental benefits of producers and sellers. Secondly, this application calculates the final target price of producers and sellers on both sides of supply and demand based on the green electricity incentive level of producers and sellers, combined with the marginal cost of different types of electricity of producers and sellers, and weights the target price by the green electricity incentive factor to obtain the green electricity incentive price to promote the green transformation of electricity transactions of producers and sellers. Furthermore, this application uses a market clearing algorithm to optimize the transaction matching process; improve the efficiency of point-to-point electricity transaction matching, and promote the balance of supply and demand in transactions. This application encourages producers and sellers to participate in green electricity transactions, increase green electricity transaction revenue, and increase the penetration rate of green electricity in transactions based on green electricity incentive pricing and efficient transaction matching methods, so as to achieve stable, low-carbon, and efficient point-to-point electricity transactions. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0062] Figure 1 It is a flowchart of a peer-to-peer electricity trading method in a blockchain environment provided by an embodiment of the present application;

[0063] Figure 2 is a schematic diagram of an exemplary pricing process of green electricity incentive prices provided in an embodiment of the present application;

[0064] Figure 3 It is a structural schematic diagram of a peer-to-peer power trading system in a blockchain environment provided by an embodiment of the present application;

[0065] Figure 4 It is an exemplary structural diagram of the transaction module provided in the embodiment of the present application. DETAILED DESCRIPTION

[0066] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention are clearly and completely described. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0067] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0068] The embodiments of the present application solve the problems of inaccurate green electricity incentives for producers and sellers and time-consuming transaction matching process by providing a peer-to-peer electricity trading method and system in a blockchain environment.

[0069] The technical solution in the embodiment of the present application is to solve the above technical problems, and the overall idea is as follows:

[0070] The high proportion of green electricity access has promoted the continuous optimization of the power structure, and green electricity has become an important part of the power system. Peer-to-peer power trading is an important way to promote green electricity trading. In peer-to-peer power trading, the user role has gradually changed from consumers to producers and sellers. Producers and sellers of different green electricity production scales can directly trade by buying and selling green electricity, which effectively matches the supply and demand of green electricity and improves the utilization efficiency of green electricity. At the same time, through peer-to-peer trading of green electricity, the challenges brought to the power grid by the incorporation of a large number of green electricity producers and sellers into the power trading system are alleviated.

[0071] As an encrypted distributed database, all peers in the blockchain share information in a decentralized and secure manner. To further improve peer-to-peer electricity trading, blockchain has become an effective supporting technology for peer-to-peer electricity trading due to its decentralized, tamper-proof, open and transparent characteristics. However, in the process of peer-to-peer electricity trading based on blockchain, green electricity incentives and value measurement will have an impact on the entire peer-to-peer electricity trading revenue and carbon emissions. Therefore, it is necessary to comprehensively consider the green electricity incentives and transaction prices of both the supply and demand sides.

[0072] For producers and sellers on both the supply and demand sides, reasonable green electricity incentives play a vital role in increasing producers and sellers' enthusiasm for participating in green electricity transactions and increasing the benefits of green electricity transactions. Current incentives include monetary incentives for providing subsidies and price incentives for providing discounts. At the same time, as one of the most sensitive and effective regulatory strategies in the electricity trading market, price strategies can effectively measure the value of different types of electricity. In the context of peer-to-peer electricity trading, the production and consumption of green electricity by producers and sellers is converted into a series of mathematical representations and integrated into the transaction price, which better reflects the value of green electricity.

[0073] Therefore, comprehensively considering green electricity incentive pricing, launching effective incentives for green electricity and adopting reasonable pricing strategies in point-to-point electricity transactions based on this is an important foundation for future point-to-point green electricity transactions. It can better promote the development and popularization of green electricity, increase the enthusiasm of producers and sellers to participate in green electricity transactions, and achieve stable, low-carbon and efficient point-to-point green electricity transactions.

[0074] In the existing peer-to-peer electricity trading, the penetration and utilization level of green electricity by producers and sellers is not accurately measured, resulting in inaccurate incentives for green electricity for producers and sellers, and the inability to give full play to the competitive advantages of green electricity. There is a lack of comprehensive consideration of the value of green electricity and transaction pricing, and the pricing strategies involved are difficult to effectively reflect the value of green electricity. The existing transaction matching methods have too many iterations and are time-consuming to calculate, resulting in low transaction matching efficiency.

[0075] In order to better understand the above technical solution, the above technical solution will be described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0076] The following first introduces a peer-to-peer electricity trading method in a blockchain environment provided by an embodiment of the present application.

[0077] The present application embodiment provides a flowchart of a peer-to-peer electricity trading method in a blockchain environment, such as Figure 1 As shown, the method may include the following steps S110-S160:

[0078] S110. Obtain historical transaction information of producers and sellers, determine the composition of electricity production and consumption and environmental benefits of producers and sellers, calculate the green electricity incentive factor of producers and sellers to measure the value of green electricity and determine the incentive level.

[0079] S120. Based on the equivalent marginal cost pricing theory, analyze and determine the marginal costs of different types of electricity.

[0080] S130. According to the marginal costs of different types of electricity, the initial price of each type of electricity is optimized to obtain the target price and the consumption and transaction sequence of various types of electricity.

[0081] S140. Perform a weighted combination of the green electricity incentive factor and the target price of the producer and seller to obtain a green electricity incentive price.

[0082] S150. Calculate the cost-effectiveness of the producer and seller according to a preset market clearing algorithm, and determine the transaction matching information of the producer and seller.

[0083] S160. Electricity transactions are carried out in sequence through a preset transaction platform according to the transaction sequence, green electricity incentive price and transaction matching information.

[0084] In the embodiments of the present application, it can be understood that producers and sellers are residential, commercial and industrial users who actively buy and sell electricity in the peer-to-peer electricity trading market, and their respective purchasing behaviors depend on whether they have excess supply and additional demand; according to the supply and demand of electricity, producers and sellers are divided into supply-side producers and sellers and demand-side producers and sellers, and the typical configuration of producers and sellers includes photovoltaic systems, energy storage systems, loads and smart meters.

[0085] The above is the specific implementation method of the peer-to-peer electricity trading system under the blockchain environment provided in the embodiment of the present application. This application calculates the respective green electricity incentive factors based on the predicted transaction information of the producers and sellers on both the supply and demand sides, combined with the electricity production and consumption structure and environmental benefits of the producers and sellers, and quantifies the green electricity levels of the producers and sellers.

[0086] Furthermore, this application calculates the final target price for producers and sellers on both the supply and demand sides based on the green electricity incentive level of producers and sellers and the marginal cost of different types of electricity of producers and sellers, and weights the target price by the green electricity incentive factor to obtain the green electricity incentive price to promote the green transformation of electricity trading among producers and sellers.

[0087] It should be noted that this application uses a market clearing algorithm to optimize the transaction matching process, improve the efficiency of point-to-point power transaction matching, and promote the balance of transaction supply and demand. This application encourages producers and sellers to participate in green power transactions, increase green power transaction revenue, and improve the penetration rate of green power in transactions through green power incentive pricing and efficient transaction matching methods, thereby achieving stable, low-carbon, and efficient point-to-point power transactions.

[0088] In one example, the trading platform includes a market layer and an information layer. The information layer is used to transmit and store transaction information and interact with other producers and sellers and grids by establishing an aggregator. The market layer is responsible for the physical exchange of energy and capital after the transaction is completed. The market layer and the information layer are connected through a blockchain.

[0089] In some embodiments, the green electricity incentive factor includes a power structure factor and an environmentally friendly factor, and the weights corresponding to the power structure factor and the environmentally friendly factor are adjusted by daily deviation; the green electricity incentive factor satisfies the expression:

[0090]

[0091] In the formula, G i,t represents the green electricity incentive factor, β i,t Represents the environmental friendly factor, is the target weight corresponding to the environmental friendly factor, α i,t Represents the power structure factor.

[0092] The daily deviation represents the ratio of the number of photovoltaic transactions in the power transaction to the actual number of photovoltaic transactions in the total time period, so as to ensure the degree of participation of producers and sellers in power transactions; the daily deviation satisfies the expression:

[0093]

[0094] Where, d i is the daily deviation, is the number of photovoltaic transactions in electricity trading, is the actual number of PV transactions, t represents time, and T is the total time period;

[0095] The relationship between the target weight and the daily deviation satisfies the expression:

[0096]

[0097] The power structure factor satisfies the expression:

[0098]

[0099] In the formula, represents the amount of green electricity produced and consumed, It represents the amount of all energy consumed in production;

[0100] The environmental friendly factor satisfies the expression:

[0101]

[0102] In the formula, γ energy represents the regional carbon emission factor, D i,t represents the electricity consumption of producers and sellers on the demand side, S i,t It represents the electricity consumption of producers and sellers on the supply side. Indicates the amount of green electricity stored in the transaction.

[0103] In the embodiments of the present application, it can be understood that the present application regards green electricity as photovoltaic power generation based on the typical configuration of the prosumer and its energy composition. The electricity purchased and stored in the energy storage by the prosumer during the peak and trough of the power grid electricity price, as well as the electricity purchased from the power grid, are not green electricity. The green electricity incentive factor, as an incentive coefficient reflecting the value of green electricity to the prosumer, affects the pricing and cost-effectiveness of the prosumer. The power structure factor and the environmental friendly factor are updated once after each transaction. The green electricity incentive factor is the sum of the power structure factor and the environmental friendly factor multiplied by their respective weights.

[0104] In order to more accurately measure the incentive level of green electricity for producers and sellers, daily deviation is introduced as an intermediate variable to determine the weights of the two factors: power structure factor and environmental friendliness factor; daily deviation represents the ratio of the number of photovoltaic transactions in point-to-point electricity trading to the actual number of photovoltaic transactions in the total time period T, which is used to indicate the degree of participation of producers and sellers in point-to-point electricity trading.

[0105] In order to reflect the electricity consumption structure of producers and marketers and guide them to improve their electricity consumption behavior, this application designs an electricity structure factor; the larger the electricity structure factor, the greener the electricity structure of the producers and marketers, which has a positive impact on the prices of the producers and marketers. On the contrary, the smaller the electricity structure factor, the negative impact it has on the prices of the producers and marketers.

[0106] The consumption of grid electricity by prosumers will generate corresponding carbon emissions. In order to reflect the carbon emissions generated by prosumers' consumption of grid electricity and measure the environmental value of prosumers, an environmental friendly factor based on the carbon emission responsibility of prosumers is introduced. The larger the environmental friendly factor, the greater the positive impact on the prosumer's price. On the contrary, the smaller the environmental friendly factor, the negative impact it has on the prosumer's price.

[0107] It should be noted that in order to reduce the electricity transaction costs of producers and sellers and optimize electricity trading plans, this application develops green electricity incentive pricing for point-to-point electricity trading based on the equivalent marginal cost pricing theory of regional heating pricing. The electricity costs of various types of producers and sellers are calculated based on their electricity production structure.

[0108] In some embodiments, please refer to Figure 2 The pricing process of green power incentive price is divided into the electricity cost measurement stage of different transaction types and the automatic bidding decision stage; in the electricity cost measurement stage, each producer and seller participating in the electricity transaction is equipped with a photovoltaic power generation system and an energy storage system. The costs of the producers and sellers include photovoltaic power generation costs and energy storage costs; photovoltaic power generation costs include initial investment costs and operation and maintenance costs; energy storage costs consist of energy storage power costs and energy storage loss costs; among them, the photovoltaic power generation cost satisfies the expression:

[0109]

[0110] In the formula, is the photovoltaic power generation cost, is the initial investment cost, is the operation and maintenance cost, n PV represents the life of the photovoltaic system, and r represents the discount rate.

[0111] The initial investment cost satisfies the expression:

[0112]

[0113] The operation and maintenance cost satisfies the expression:

[0114]

[0115] In the formula, represents the unit capacity price of photovoltaic power generation system, Represents the capacity of the photovoltaic power generation system, T PV Indicates the annual equivalent operating hours of the photovoltaic power generation system. Indicates the amount of electricity generated by the photovoltaic power generation system, represents the basic electricity price of photovoltaic power transformers, ε represents the operating cost of photovoltaic power generation system, ω represents the fixed maintenance cost of photovoltaic power generation system, and δ represents the fixed maintenance cost of photovoltaic power generation system;

[0116] The photovoltaic system capacity satisfies the expression:

[0117]

[0118] The basic electricity price of photovoltaic power transformers meets the expression:

[0119]

[0120] The annual equivalent operating hours of the photovoltaic system satisfy the expression:

[0121]

[0122] Where a represents the basic charge coefficient of the photovoltaic system;

[0123] The energy storage electricity cost satisfies the expression:

[0124]

[0125] In the formula, is the energy storage electricity cost, represents the amount of electricity purchased from the grid, Represents the price of purchasing grid electricity, represents the income from selling photovoltaic power through transactions, represents the revenue from selling photovoltaic power to the grid, Represents the available capacity of the energy storage system, represents the price of purchasing photovoltaic power;

[0126] In the embodiments of the present application, it can be understood that when the photovoltaic return generated by the producer and seller after selling it to the power grid is greater than the average return of participating in the peer-to-peer power transaction, the energy storage power composition of the demand-side producer and seller is all electricity purchased at peak and valley electricity prices. On the contrary, the energy storage power composition of the supply-side producer and seller is the photovoltaic power generation remaining after participating in the peer-to-peer power transaction plus the electricity purchased at the peak and valley electricity prices. The electricity in the energy storage can be sold to the producers and sellers and consumers on the demand side, and participate in the peer-to-peer power trading market based on the maximization combination of economic benefits and social benefits, so as to eliminate the instability of power supply caused by the uncertainty of green electricity as much as possible.

[0127] It should be noted that energy storage improves the regulation capability of the green power reactor peak characteristics, but the charging and discharging operations will lead to a decrease in energy storage performance, resulting in energy storage loss costs.

[0128] In one example, the energy storage loss cost satisfies the expression:

[0129]

[0130] In the formula, is the energy storage loss cost, θ represents the loss cost per kilowatt-hour of the energy storage system charging and discharging operation, represents the loss cost of the energy storage system per kilowatt-hour charging operation, Indicates the number of kilowatt-hours of energy storage system charging. It represents the discharge quantity of the energy storage system per kilowatt-hour, and Δt represents the time period corresponding to the calculated energy storage loss cost.

[0131] In another example, the charging and discharging of the energy storage system satisfies a preset rule, and the preset rule satisfies the expression:

[0132]

[0133] N min ≤N t ≤N max

[0134] In the formula, Indicates the maximum charging capacity of the energy storage system. represents the power stored in the energy storage system at t0, represents the power stored in the energy storage system at t1, η c Represents the charging efficiency of the energy storage system, η d Represents the discharge efficiency of the energy storage system, N t represents the power stored in the energy storage system at time t, N minIndicates the minimum capacity to prevent deep discharge of the energy storage system, N max Indicates the maximum capacity to prevent deep discharge of the energy storage system.

[0135] It should also be noted that in the same peer-to-peer electricity trading market, different types of electricity can replace each other, and it is necessary to determine the order of consumption of various types of electricity. According to the characteristics of peer-to-peer electricity trading, the entire peer-to-peer trading market is divided into two areas, namely the supply-side production and consumption area and the demand-side production and consumption area.

[0136] In some embodiments, the above-mentioned analysis and determination of the marginal costs of different types of electricity based on the equivalent marginal cost pricing theory, that is, the above-mentioned S120 may specifically include:

[0137] S210, respectively draw the load continuity curve LDC and the minimum cost curve MCC of the supply-side production and consumption zone and the demand-side production and consumption zone; wherein the minimum cost curve MCC is constructed by sorting the load levels from high to low and is independent of the time series of the load.

[0138] S220. Sort the marginal costs of different types of electricity from low to high according to the minimum cost curve MCC to form the slope of each load duration curve LDC segment; wherein the minimum cost curve MCC is used to characterize the load level and duration that the power system needs to withstand within a specific time period.

[0139] It should be noted that the intercepts on the vertical axis of each section of the load continuity curve LDC represent the shadow capacity cost of each unit under the optimization theory. The load continuity curve LDC is used to determine the lowest point of the supplier's power generation cost under different power generation method combinations. Based on the marginal cost of different types of electricity in the load continuity curve LDC, the target price of each type of electricity producer and seller is obtained, and the consumption and trading order of various types of electricity is determined according to the ranking of various types of electricity.

[0140] In one example, the target price satisfies the expression:

[0141] P i ele =P i +κ i

[0142] Where P i represents the initial price, P i ele represents the target price, κ i It is understandable that the target price used by producers and sellers for bidding is the sum of the initial price and the corresponding marginal cost; this can bring considerable economic returns to producers and sellers, effectively shorten the investment payback period, promote self-production and self-use of electricity, and has the dual advantages of achieving economic benefits and electricity self-sufficiency.

[0143] In another example, the marginal cost of producers and sellers of various types of electricity is κ i It can be calculated by the Lagrange equation and the first-order partial derivative of the Lagrange equation:

[0144]

[0145] In the formula, C represents the unit output value cost, C i represents the cost of the i-th producer and seller, Indicates the output power. Represents the amount of electricity obtained from peer-to-peer electricity trading. Indicates the amount of electricity sold to peer-to-peer electricity trading. represents the required electricity, and L is used to characterize the Lagrange equation.

[0146] It should also be noted that when the power source for peer-to-peer electricity trading is energy storage or electricity purchased from the grid, the marginal costs of energy storage and the grid can be obtained by replacing the parameters of the above equation about photovoltaic power generation with the parameters of the corresponding power source.

[0147] In some embodiments, in the weighting module 140, the process of weighting the green electricity incentive factor and the target price of the producer and seller to obtain the green electricity incentive price satisfies the expression:

[0148]

[0149] Where P i inc represents the green electricity incentive price, P i ele represents the target price, G i,t Represents the green electricity incentive factor, i=e means that if the prosumer i is a supply-side prosumer, then i=e; i=f means that if the prosumer i is a demand-side prosumer, then i=f.

[0150] It is understandable that based on the uncertainty characteristics of green electricity production by producers and sellers, they participate in transactions after determining their own electricity supply and demand. Producers and sellers conduct electricity transactions in sequence according to the determined transaction order, calculated green electricity incentive price and matching results. In order to optimize the transaction matching of producers and sellers and minimize costs, this application has developed an optimization matching method based on a typical market clearing algorithm, which uses a step-by-step iterative method for order matching and transactions between producers and sellers; through market order matching in the trading platform, producers and sellers can achieve optimal trading decisions and optimize their electricity trading plans.

[0151] In some embodiments, the optimization matching method corresponding to the market clearing algorithm includes an information collection and organization stage, a cost-utility calculation stage, and an order matching stage; in the information collection and organization stage, the power composition and bidding information of the supply-side producers and sellers and the demand-side producers and sellers are collected to calculate the green electricity incentive factors of the producers and sellers' incentive prices.

[0152] In the cost-utility calculation stage, the optimal order is selected and the power trading scheme is optimized through the preset cost-utility function, which satisfies the expression:

[0153]

[0154] In the formula, represents the cost of photovoltaic power generation, represents the storage cost of storing photovoltaic electricity, represents the cost of trading with the grid, Represents the cost of participating in the peer-to-peer electricity trading platform, represents the cost of conducting PV transactions with other producers and sellers, Represents the cost of energy storage transactions with other users.

[0155] U ef is the cost-utility function, represents the photovoltaic power generation cost of producer and seller i at time t, represents the amount of electricity generated by the photovoltaic system of producer and seller i at time t, represents the energy storage cost of producer and seller i at time t, represents the energy storage system transaction volume corresponding to producer and seller i at time t, represents the amount of electricity purchased by producer and seller i from the grid at time t, represents the amount of electricity sold by producer and seller i to the grid at time t, represents the price at which producer and seller i purchases electricity from the grid at time t, M represents the price at which producer and seller i sells electricity to the grid at time t; i,t represents the cost paid by producer and seller i for transmitting electricity using the physical layer of the trading platform at time t, and m represents the cost paid for using the information layer of the trading platform; represents the clearing price for purchasing photovoltaic power, Indicates the clearing amount of electricity purchased from photovoltaic power, represents the clearing price for selling PV power, Indicates the clearing electricity volume of photovoltaic power sold; represents the price at which producer and seller i purchases energy storage electricity at time t, represents the amount of energy storage electricity purchased by producer and seller i at time t, represents the price at which producer and seller i sells stored electricity at time t, represents the quantity of energy storage electricity sold by producer and seller i at time t; Fixed cost of remote service fee, τ Fixed cost of remote service fee, S i represents the distance between producer and seller i and the trading platform, represents the load of producer and marketer i at time t; represents the maximum storage capacity of producer and seller i at time t, represents the maximum photovoltaic power generation of producer and seller i at time t; It represents the transaction price of producers and sellers on the supply side, represents the transaction price of producers and sellers on the demand side, represents the photovoltaic transaction volume of the supply-side producers and sellers, Represents the PV transaction volume of demand-side producers and sellers.

[0156] In some embodiments, at the order matching stage, after calculating the optimal cost utility of all producers and sellers participating in the transaction, the supply and demand parties are matched to select their trading partners. The matching vectors of the producers and sellers of the supply and demand parties are as shown in the following formula:

[0157]

[0158] in, and A binary variable representing the matching of producers and sellers on both the supply and demand sides, and represents the matching vector of production and sales on both the supply and demand sides, and Depending on the minimum cost utility on the supply side and the demand side, a matching matrix between the supply and demand sides is formed in the trading platform:

[0159]

[0160] in,[·] T is the matrix transpose operation, V S With V D represents the matching matrix of producers and sellers on both sides of the supply and demand. , determine the supply and demand match and complete the transaction; E represents the set of producers and sellers corresponding to the supply side, and F represents the set of producers and sellers corresponding to the demand side; when the supply and demand of the producers and sellers are successfully matched, the matching sequence ends; the producers and sellers who have not successfully matched enter the iteration and recalculate the cost-utility of the remaining producers and sellers to be matched. The number of iterations K = min(E, F) is to ensure that a pair of matched producers and users is formed in each iteration.

[0161] In some embodiments, the present application also provides a peer-to-peer power trading system 300 in a blockchain environment, please refer to Figure 3 The system 300 may specifically include the following modules:

[0162] The calculation module 310 is used to obtain the historical transaction information of the prosumer, determine the composition of the power production and consumption of the prosumer and the environmental benefits, and calculate the green power incentive factor of the prosumer to measure the value of green power and determine the incentive level;

[0163] A determination module 320 is used to analyze and determine the marginal costs of different types of electricity according to the equivalent marginal cost pricing theory;

[0164] An optimization module 330, for optimizing the initial price of each type of electricity according to the marginal costs of different types of electricity, and obtaining a target price and a consumption and transaction sequence of various types of electricity;

[0165] A weighting module 340 is used to perform a weighted combination of the green electricity incentive factor and the target price of the producer and seller to obtain a green electricity incentive price;

[0166] A matching module 350, configured to calculate the cost-utility of the prosumer according to a preset market clearing algorithm, and determine the transaction matching information of the prosumer;

[0167] The transaction module 360 ​​is used to carry out power transactions in sequence through a preset transaction platform according to the transaction sequence, the green electricity incentive price and the transaction matching information.

[0168] Any multiple modules among the calculation module 310, the determination module 320, the optimization module 330, the weighting module 340, the matching module 350 and the transaction module 360 ​​may be combined into one module for implementation, or any one of the modules may be split into multiple modules. Alternatively, at least part of the functions of one or more of the modules may be combined with at least part of the functions of other modules and implemented in one module.

[0169] In some embodiments, the determination module 320 may be specifically configured to:

[0170] Draw the load duration curve LDC and minimum cost curve MCC of the supply-side prosumer zone and the demand-side prosumer zone respectively;

[0171] The marginal costs of different types of electricity are sorted from low to high according to the minimum cost curve MCC to form the slope of each load duration curve LDC segment; wherein the minimum cost curve MCC is used to characterize the load level and duration that the power system needs to withstand in a specific time period.

[0172] Figure 3 Each module in the system shown has the function of implementing each step in the aforementioned peer-to-peer electricity trading method under the blockchain environment and can achieve its corresponding technical effect. For the sake of concise description, it will not be repeated here.

[0173] In some embodiments, the peer-to-peer power trading system 300 may also include a regulatory agency, which uses credit delegation Byzantine fault tolerance as a consensus mechanism, acts as a certificate issuance center for the federated chain, and supervises identity registration, certificate issuance, and certificate revocation within the blockchain. In addition, the regulatory agency is also used to formulate regulatory policies, maintain infrastructure, and manage emergencies to ensure the resilient development and stable operation of peer-to-peer power trading based on blockchain.

[0174] In some embodiments, please refer to Figure 4 , the transaction module 360 ​​includes a prosumer bidding unit 410, a contract generation unit 420, a transaction execution unit 430, a transaction verification unit 440 and a transaction settlement unit 450; specifically, the prosumer bidding unit 410 is used to send the predicted photovoltaic power generation information of the prosumers of both the supply and demand sides to the corresponding prosumers through a preset format; the preset format satisfies the expression:

[0175]

[0176] in, Indicates the amount of electricity that can be sold. When producers and sellers have excess electricity that cannot meet their needs or need electricity, they will choose to sell or buy electricity through the peer-to-peer electricity trading platform and send the transaction information to the endorsing node in the following format:

[0177] <issue,Suser / Duser,ID,time,Q i,t ,P i,t >

[0178] Among them, issue represents the transaction release operation.

[0179] The contract generation unit 420 is used to create a transaction contract, register energy transaction information, and guide the producers and sellers to the smart contract trading platform. After the demand-side producers and sellers confirm the transaction request and the supply-side producers and sellers agree to the transaction request, the trading platform generates an energy transaction block, the format of which is as follows:

[0180] <buy / sell,Duser / Suser,ID,Q ef ,P ef ,U ef ,time>

[0181] Among them, buy / sell represents transaction purchase / sale operations, Q ef Represents the clearing power, P ef represents the clearing price, U ef Represents liquidation utility.

[0182] The transaction execution unit 430 is used to execute the transaction contract at the market level by producers and sellers on both the supply and demand sides after the transaction block is generated at the information level; the power selling node transmits the power agreed in the contract to the power purchasing node, and the smart meter monitors the power consumption, time, and other information of both parties to the transaction in real time.

[0183] After the transaction is completed, the transaction verification unit 440 performs verification based on the real-time transaction data of the smart meter. The verification information format is as follows:

[0184]

[0185] in, Indicates the agreed transaction volume. Indicates the actual transaction volume. Indicates the agreed transaction price. Indicates the actual transaction price; during the transaction verification phase, the execution of the transaction contract is verified. If the real-time transaction data of the smart meter is inconsistent with the transaction contract agreement, the endorsement node will find the defaulting producer and seller and identify the nature of the default by the producer and seller.

[0186] If the predicted energy generation is inconsistent with the actual energy generation, it will not be attributed to the category of point-to-point transaction default; in other cases, such as the producer and seller failing to comply with the contract, the credit value of the producer and seller will be reduced through the transaction rules, affecting the next producer and seller transaction.

[0187] The transaction settlement unit 450 automatically performs transaction settlement by calling the smart contract. If the producer and seller breaches the contract, the transaction will not be executed; if the transaction is executed normally, it will enter the payment stage. The transaction settlement will be transferred from the power purchase node account to the power sales node account through contract scheduling in accordance with regulations; at the same time, the energy transaction information generated by the endorsement node is stored in the blockchain as a settlement block.

[0188] It should be clear that the present application is not limited to the specific configuration and processing described above and shown in the figures. For the sake of simplicity, a detailed description of the known method is omitted here. In the above embodiments, several specific steps are described and shown as examples. However, the method process of the present application is not limited to the specific steps described and shown, and those skilled in the art can make various changes, modifications and additions, or change the order between the steps after understanding the spirit of the present application.

[0189] In summary, compared with the prior art, this application has the following beneficial effects:

[0190] 1. This application calculates the respective green electricity incentive factors based on the predicted transaction information of producers and sellers on both the supply and demand sides, combined with the electricity production and consumption structure and environmental benefits of the producers and sellers, and quantifies the green electricity levels of the producers and sellers. In the process of calculating the green electricity incentive factors, the power structure factor and the environmental friendly factor are coupled according to the weights determined by the daily deviation, which effectively reflects the green electricity production and consumption of the producers and sellers.

[0191] 2. This application is based on the green electricity incentive level of producers and sellers, combined with the marginal costs of different types of electricity of producers and sellers, and by determining the size of the marginal costs of different types of electricity, it helps producers and sellers to give priority to low-cost electricity types for trading; the final target price of producers and sellers on both the supply and demand sides is calculated based on the marginal cost, and the target price is weighted by the green electricity incentive factor to obtain the green electricity incentive price to promote the green transformation of electricity trading by producers and sellers; the coupling of green electricity incentives and pricing methods is effectively considered, the overall benefits of producers and sellers participating in green electricity trading are increased, and the utilization efficiency of green electricity is improved.

[0192] 3. This application uses a market clearing algorithm to optimize the transaction matching process; improve the efficiency of point-to-point power transaction matching, and promote the balance of transaction supply and demand. This application optimizes market resource allocation, encourages producers and sellers to participate in green power transactions, increases green power transaction revenue, and improves the penetration rate of green power in transactions, thereby achieving stable, low-carbon, and efficient point-to-point power transactions based on green power incentive pricing and efficient transaction matching methods.

[0193] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A peer-to-peer electricity trading method in a blockchain environment, characterized in that: include: Obtain historical transaction information of producers and sellers, determine the composition of electricity production and consumption and environmental benefits of producers and sellers, calculate the green electricity incentive factor of the producers and sellers to measure the value of green electricity and determine the incentive level; According to the equivalent marginal cost pricing theory, analyze and determine the marginal cost of different types of electricity; Based on the marginal costs of different types of electricity, the initial price of each type of electricity is optimized to obtain the target price and the consumption and trading sequence of various types of electricity; A green electricity incentive factor and the target price of the producer and seller are weighted together to obtain a green electricity incentive price; Calculating the cost utility of the producer and seller according to a preset market clearing algorithm, and determining the transaction matching information of the producer and seller; According to the transaction sequence, the green electricity incentive price and the transaction matching information, electricity transactions are carried out in sequence through a preset transaction platform.

2. The peer-to-peer electricity trading method in a blockchain environment as claimed in claim 1, characterized in that: The green electricity incentive factor includes a power structure factor and an environmentally friendly factor. The weights corresponding to the power structure factor and the environmentally friendly factor are adjusted by daily deviation. The green electricity incentive factor satisfies the expression: In the formula, G i,t represents the green electricity incentive factor, β i,t represents the environmental friendly factor, is the target weight corresponding to the environmental friendly factor, α i,t represents the power structure factor; The daily deviation represents the ratio of the number of photovoltaic transactions in the power transaction to the actual number of photovoltaic transactions in the total time period, so as to ensure the degree of participation of producers and sellers in the power transaction; the daily deviation satisfies the expression: Where, d i is the daily deviation, is the number of photovoltaic transactions in electricity trading, is the actual number of PV transactions, t represents time, and T is the total time period; The relationship between the target weight and the daily deviation satisfies the expression: The power structure factor satisfies the expression: In the formula, represents the amount of green electricity produced and consumed, It represents the amount of all energy consumed in production; The environmental friendly factor satisfies the expression: In the formula, γ energy represents the regional carbon emission factor, D i,t represents the electricity consumption of producers and sellers on the demand side, S i,t It represents the electricity consumption of producers and sellers on the supply side. Indicates the amount of green electricity stored in the transaction.

3. The peer-to-peer electricity trading method in a blockchain environment as claimed in claim 1, characterized in that: The pricing process of the green electricity incentive price is divided into the electricity cost measurement stage for different transaction types and the automatic bidding decision stage; in the electricity cost measurement stage, each producer and seller participating in the electricity transaction is equipped with a photovoltaic power generation system and an energy storage system, and the costs of the producer and seller include photovoltaic power generation costs and energy storage costs; the photovoltaic power generation costs include initial investment costs and operation and maintenance costs; the energy storage costs are composed of energy storage power costs and energy storage loss costs; wherein, the photovoltaic power generation costs satisfy the expression: In the formula, is the photovoltaic power generation cost, is the initial investment cost, is the operation and maintenance cost, n PV represents the life of the photovoltaic power generation system, r represents the discount rate; The initial investment cost satisfies the expression: The operation and maintenance cost satisfies the expression: In the formula, represents the unit capacity price of photovoltaic power generation system, Represents the capacity of the photovoltaic power generation system, T PV Indicates the annual equivalent operating hours of the photovoltaic power generation system. Indicates the amount of electricity generated by the photovoltaic power generation system, represents the basic electricity price of photovoltaic power transformers, ε represents the operating cost of photovoltaic power generation system, ω represents the fixed maintenance cost of photovoltaic power generation system, and δ represents the fixed maintenance cost of photovoltaic power generation system; The photovoltaic power generation system capacity satisfies the expression: The basic electricity price of the photovoltaic power transformer satisfies the expression: The annual equivalent operating hours of the photovoltaic power generation system satisfy the expression: In the formula, a represents the basic charge coefficient of the photovoltaic power generation system; The energy storage power cost satisfies the expression: In the formula, is the energy storage electricity cost, represents the amount of electricity purchased from the grid, Represents the price of purchasing grid electricity, represents the income from selling photovoltaic power through transactions, represents the revenue from selling photovoltaic power to the grid, Represents the available capacity of the energy storage system, represents the price of purchasing photovoltaic power; The energy storage loss cost satisfies the expression: In the formula, is the energy storage loss cost, θ represents the loss cost per kilowatt-hour of the energy storage system charging and discharging operation, represents the loss cost of the energy storage system per kilowatt-hour charging operation, Indicates the number of kilowatt-hours of energy storage system charging. represents the discharge quantity of the energy storage system per kilowatt-hour, and Δt represents the time period corresponding to the calculated energy storage loss cost.

4. The peer-to-peer electricity trading method in a blockchain environment as claimed in claim 3, characterized in that: The charging and discharging of the energy storage system meets the preset rules, and the preset rules meet the expression: N min ≤N t ≤N max In the formula, Indicates the maximum charging capacity of the energy storage system. represents the power stored in the energy storage system at t0, represents the power stored in the energy storage system at t1, η c Represents the charging efficiency of the energy storage system, η d Represents the discharge efficiency of the energy storage system, N t represents the power stored in the energy storage system at time t, N min Indicates the minimum capacity to prevent deep discharge of the energy storage system, N max Indicates the maximum capacity to prevent deep discharge of the energy storage system.

5. The peer-to-peer power trading method in a blockchain environment according to any one of claims 1 to 3, characterized in that: According to the equivalent marginal cost pricing theory, the marginal costs of different types of electricity are analyzed and determined, including: Draw the load duration curve LDC and minimum cost curve MCC of the supply-side prosumer zone and the demand-side prosumer zone respectively; The marginal costs of different types of electricity are sorted from low to high according to the minimum cost curve MCC to form the slope of each load duration curve LDC segment; wherein the minimum cost curve MCC is used to characterize the load level and duration that the power system needs to bear in a specific time period; The target price satisfies the expression: P i ele =P i +k i Where P i represents the initial price, P i ele represents the target price, κ i represents the marginal cost.

6. The peer-to-peer power trading method in a blockchain environment according to any one of claims 1 to 3, characterized in that: The optimization matching method corresponding to the market clearing algorithm includes an information collection and organization stage, a cost-utility calculation stage, and an order matching stage; in the information collection and organization stage, the power composition and bidding information of the supply-side producers and sellers and the demand-side producers and sellers are collected to calculate the green power incentive factor of the producers and sellers' incentive price; In the cost-utility calculation stage, the optimal order is selected and the power trading scheme is optimized through a preset cost-utility function, and the cost-utility function satisfies the expression: Where U ef is the cost-utility function, represents the photovoltaic power generation cost of producer and seller i at time t, represents the amount of electricity generated by the photovoltaic system of producer and seller i at time t, represents the energy storage cost of producer and seller i at time t, represents the energy storage system transaction volume corresponding to producer and seller i at time t, represents the amount of electricity purchased by producer and seller i from the grid at time t, represents the amount of electricity sold by producer and seller i to the grid at time t, represents the price at which producer and seller i purchases electricity from the grid at time t, represents the price at which producer and seller i sells electricity to the grid at time t; M i,t represents the cost paid by producer and seller i for transmitting electricity using the physical layer of the trading platform at time t, and m represents the cost paid for using the information layer of the trading platform; represents the clearing price for purchasing photovoltaic power, Indicates the clearing amount of electricity purchased from photovoltaic power, represents the clearing price for selling PV power, Indicates the clearing electricity volume of photovoltaic power sold; represents the price at which producer and seller i purchases energy storage electricity at time t, represents the amount of energy storage electricity purchased by producer and seller i at time t, represents the price at which producer and seller i sells stored electricity at time t, represents the quantity of energy storage electricity sold by producer and seller i at time t; Fixed cost of remote service fee, τ Fixed cost of remote service fee, S i represents the distance between producer and seller i and the trading platform, represents the load of producer and marketer i at time t; represents the maximum storage capacity of producer and seller i at time t, represents the maximum photovoltaic power generation of producer and seller i at time t; It represents the transaction price of producers and sellers on the supply side, represents the transaction price of producers and sellers on the demand side, represents the photovoltaic transaction volume of the supply-side producers and sellers, Represents the PV transaction volume of demand-side producers and sellers.

7. The peer-to-peer electricity trading method in a blockchain environment as claimed in claim 1, characterized in that: The trading platform includes a market layer and an information layer. The information layer is used to transmit and store transaction information and interact with other producers and sellers and grids by establishing an aggregator. The market layer is used for the physical exchange of energy and capital after the transaction is completed. The market layer and the information layer are connected through a blockchain.

8. A peer-to-peer electricity trading system in a blockchain environment, characterized in that: include: A calculation module, used to obtain historical transaction information of the prosumer, determine the composition of the prosumer's electricity production and consumption and the environmental benefits, and calculate the prosumer's green electricity incentive factor to measure the value of green electricity and determine the incentive level; A determination module, used to analyze and determine the marginal costs of different types of electricity based on the equivalent marginal cost pricing theory; An optimization module, for optimizing the initial price of each type of electricity according to the marginal costs of different types of electricity, and obtaining a target price and a consumption and transaction sequence of various types of electricity; A weighting module, used for weighting the green electricity incentive factor and the target price of the producer and seller to obtain a green electricity incentive price; A matching module, used to calculate the cost-utility of the prosumer according to a preset market clearing algorithm, and determine the transaction matching information of the prosumer; The transaction module is used to carry out electricity transactions in sequence through a preset transaction platform according to the transaction sequence, the green electricity incentive price and the transaction matching information.

9. An electronic device, characterized in that: include: A processor, a memory, and a program stored in the memory and executable on the processor, wherein when the program is executed by the processor, the peer-to-peer electricity trading method in a blockchain environment as described in any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores programs or instructions, and when the programs or instructions are executed by the processor, the peer-to-peer electricity trading method in the blockchain environment as described in any one of claims 1-7 is implemented.

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