End-to-end energy trading method, system, device and storage medium

Abstract

The present invention provides an end-to-end energy trading method, system, device, and storage medium, relating to the field of electric power. The end-to-end energy trading method includes acquiring data and transferring the acquired data as parameters into an optimization model; the electric energy for the end-to-end transaction needs to be transmitted through an existing power network, and the electric energy for the end-to-end transaction is used to calculate the network transmission fee for each transaction; in order to optimize the electricity consumption behavior of each user while suppressing the strategic behavior of each user to falsely quote prices; a VCG mechanism is used to calculate the replacement cost of each user, and the Nash negotiation method is applied to the VCG mechanism to reduce the computational difficulty of the VCG mechanism; the present invention optimizes the replacement cost of power users in the end-to-end energy trading market while considering network constraints, ensuring that the energy consumption needs of users are met while avoiding the users' strategic quotations, and verifying the rationality of considering the network transmission fee in the end-to-end transactions of users.

Description

Technical Field

[0001] The present invention relates to the field of electric power technology, and in particular to an end-to-end energy trading method, system, device and storage medium. Background Art

[0002] In recent years, with the development of renewable energy generation (REGs), Internet of Things (IoT) communication technologies, and user-level control infrastructure, passive users in traditional power systems have become active users (energy prosumers) capable of controlling their loads and power generation. Current power market participants can produce and consume energy according to their own schedules. Furthermore, users can effectively trade energy with other local energy market participants, transforming a system-centric power market into a participant-centric one. However, when participating in end-to-end energy transactions, power market participants need to transmit the traded electricity through the existing power grid. Furthermore, the clearing entities within the entire power system may not align with the interests of individual users, leading users to falsely bid to further obtain higher market returns. Summary of the Invention

[0003] (1) Technical problems solved

[0004] In response to the shortcomings of the existing technology, the present invention provides an end-to-end energy trading method, system, equipment and storage medium, which solves the problem that when participants in the power market participate in end-to-end energy transactions, they need to transmit the traded electricity through the existing power grid, and the clearing entities in the entire power system may not be consistent with the direction of maximizing the interests of each user, so each user may falsely bid in order to further obtain higher market returns.

[0005] (2) Technical solution

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

[0007] In one aspect, a method for end-to-end energy trading is provided, the method comprising:

[0008] Obtain user load data, real-time electricity prices, network structure data, each user's energy storage data, distributed power supply data and user energy demand, and pass the obtained data as parameters into the optimization model;

[0009] Transmitting end-to-end traded electricity through the existing power network, and using the end-to-end traded electricity to calculate the network transmission fee for each transaction;

[0010] The VCG mechanism is used to calculate the replacement cost of each user, and the Nash negotiation method is applied to the VCG mechanism to reduce the computational difficulty of the VCG mechanism, thereby optimizing the electricity consumption behavior of each user and suppressing the strategic behavior of each user to falsely report prices.

[0011] By assuming that each user holds distributed energy storage, distributed power, and flexible load resources, each resource has scheduling costs and maintenance costs when participating in scheduling, a model is established to minimize the individual costs of each user;

[0012] Establish a utility maximization model for each user and market operator in the market, and use the best response-based algorithm to solve the optimal transaction results;

[0013] Energy consumption costs, flexible resource scheduling and power network usage are sent to corresponding market participants and market operators to obtain the final market transaction situation.

[0014] Furthermore, the user load data includes the user's load data throughout the year, and the minimum data collection interval is 15 minutes; the real-time electricity price adopts the national unified peak, valley and flat three-hour electricity price, and the demand price charging period is one month; the network structure data mainly includes the line flow coefficient of the DC flow of the transmission network, and the flow coefficient is a constant matrix; the energy storage data of each user mainly includes the rated power, rated capacity, and charging and discharging loss coefficient of the energy storage; the distributed power supply data mainly includes the rated output power and output time of each distributed power supply; the energy demand of the user is the scheduling model of its flexible load.

[0015] Furthermore, the calculation of the network fee for each transaction specifically includes:

[0016] The transmission fee of end-to-end transactions is directly related to the amount of electricity traded and the electrical distance of the electricity transmission. The transmission fee is set to be proportional to the electrical distance and the amount of electricity transmitted.

[0017] G(d i,j ,p i,j ):=λ i,j d i,j p i,j

[0018] Among them, d i,j is the electrical distance between energy seller i and energy buyer j, p i,j is the amount of energy transmitted between energy buyers and sellers, λ i,j The network access fee corresponding to unit distance and unit power consumption;

[0019] The electrical distance is defined as the sum of the transmission coefficients between the nodes of energy buyers and sellers, and the electrical distance is expressed as

[0020]

[0021] in, is the transmission coefficient between energy seller i and energy buyer j. The electrical distance between energy seller i and energy buyer j is obtained by adding the transmission coefficients of all relevant branches;

[0022] After obtaining the network fee required for a transaction, the buyer and seller of the transaction share the network fee equally. The rules for sharing are:

[0023]

[0024] Among them, Δt is the time granularity of the entire market operation.

[0025] Furthermore, the VCG mechanism is used to calculate the replacement cost of each user, and the Nash negotiation method is applied to the VCG mechanism to reduce the computational difficulty of the VCG mechanism, thereby optimizing the electricity consumption behavior of each user and suppressing the strategic behavior of each user to falsely report prices. Specifically, the VCG mechanism includes:

[0026] If each user cannot purchase the corresponding electricity to meet the electricity demand in the end-to-end energy trading market, then the user purchases electricity from the upper grid according to the node marginal price. The total cost model is:

[0027]

[0028] Where, α n is the vector of decision variables of user n, c n is the cost of user n in the end-to-end transaction market, p m is the power purchased by user m from the upper grid, is the electricity purchase expenditure of user m from the upper grid, S I∪J is the total social cost;

[0029] If there is no end-to-end trading market, each user only purchases electricity from the upper grid according to the node marginal price, and the total cost of electricity purchase is

[0030]

[0031] Where S I∪J\P2P represents the total social cost when there is no end-to-end transaction;

[0032] The total replacement benefit of end-to-end transactions is expressed as the change in the total cost of each user not participating in the market when there is no end-to-end market and when there is an end-to-end market. The model is

[0033]

[0034] Where, is the total cost for each user when they only participate in the end-to-end energy trading market;

[0035] After obtaining the total substitution cost, the Nash negotiation method is used to fairly distribute the total cost to each market participant. The corresponding Nash negotiation model is:

[0036]

[0037] st

[0038]

[0039]

[0040] Where, F n is the total market revenue of user n participating in the end-to-end and traditional markets, d n is the income of user n who only participates in the traditional market, μ n is the Nash negotiation coefficient of user n;

[0041] The remaining total cost is redistributed to each market player, and the distribution mechanism is modeled as

[0042] μ n :=S I∪J\P2P -S {I∪J\P2P}∪{n∈P2P}

[0043]

[0044] Where, the negotiation parameter μ n It is set to be proportional to the total cost change after the user participates in the end-to-end transaction, Δc n is the total cost change after user n participates in the end-to-end transaction, It is the total cost change after all users participate in the end-to-end transaction.

[0045] Furthermore, by assuming that each user holds distributed energy storage, distributed power, and flexible load resources, each resource has scheduling costs and maintenance costs when participating in scheduling. A model for minimizing the individual costs of each user is established, including:

[0046] The constraints of the mathematical model established based on the characteristics of each user holding distributed energy storage, distributed power and flexible load resources are as follows:

[0047]

[0048]

[0049]

[0050]

[0051]

[0052]

[0053]

[0054]

[0055]

[0056]

[0057]

[0058] Where, They represent the electricity sold and purchased by user n at time t respectively; They respectively represent the user's electricity selling status, electricity buying status, renewable power generation status, energy storage charging status, discharge status, and flexible load resource usage status; when the corresponding state variable is 1, the user is in that state, and when the corresponding state variable is 0, the user is not in that state; They represent the output power of renewable power generation, energy storage charging, energy storage discharging and flexible load resources of user n at time t respectively; represents the upper and lower limits of the transaction power of user n at time t; users cannot buy and sell electricity at the same time, and the user's storage energy cannot be charged and discharged at the same time, then The user's flexible load resources are limited, so the upper limit of the flexible load adjustment is defined is the energy storage capacity of user n at time t, is the energy storage charging efficiency of user n at time t, is the energy storage discharge efficiency of user n at time t, Defined as the upper and lower limits of energy storage capacity during the entire charging and discharging process, is the upper limit of energy storage discharge power of user n at time t, is the upper limit of energy storage charging power for user n at time t.

[0059] Furthermore, by assuming that each user holds distributed energy storage, distributed power, and flexible load resources, each resource has scheduling costs and maintenance costs when participating in scheduling. The model for minimizing the individual costs of each user also includes:

[0060] Users pay network access fees to market operators. Each market operator attempts to maximize its own revenue and minimize the total social cost. The operator's objective function is:

[0061]

[0062] Where, The sum of the costs for users to participate in traditional markets and end-to-end markets, caused by end-to-end transactions;

[0063] The constraints for establishing a mathematical model based on energy trading volume and transaction price restrictions are:

[0064]

[0065]

[0066]

[0067]

[0068] Where, The power purchased from the upper grid for the entire end-to-end transaction, mainly the total power purchased by all users Subtract the total electricity sold by all users get; The unit price of the network access fee of the market operator is limited, B is the admittance matrix of the entire system, θ t is the voltage phase angle of each node in the entire system, P t is the injection power of each node at time t, F l max is the upper limit of active power transmission of line l.

[0069] Furthermore, the establishment of a utility maximization model for each user and market operator in the market and the use of an optimal response-based algorithm to solve for the optimal transaction result specifically include:

[0070] A large number of Boolean variables are introduced to represent the switching status of each user's distributed power supply, and the original problem is decomposed according to each user;

[0071] Fix the trading plans of other users as constants and optimize user n. Only the integer variable of user n is included in the single optimization. The difficulty of solving the entire problem is greatly reduced. After traversing all users, the optimal trading plan of N users in the entire market is obtained.

[0072] In each iteration, the optimization models of other users are solved using the optimization results of user n as parameters. The iterative convergence of the entire system satisfies the condition:

[0073]

[0074] The Hessian matrix of the above function is first-order positive definite, which makes the optimal response of the entire end-to-end trading market converge.

[0075] In another aspect, there is provided an end-to-end energy trading system, comprising:

[0076] The acquisition module is used to obtain user load data, real-time electricity prices, network structure data, energy storage data of each user, distributed power supply data and user energy demand;

[0077] a transmission module, which inputs the data acquired by the acquisition module as parameters into the optimization model, transmits the end-to-end traded electric energy through the existing power network, and sends the energy cost, flexible resource scheduling status and power network usage status to the corresponding market participants and market operators to obtain the final market transaction status;

[0078] The calculation module is used to calculate the network cost of each transaction using the end-to-end transaction energy and calculate the replacement cost of each user using the VCG mechanism;

[0079] Optimization module, used to optimize the electricity consumption behavior of each user and curb the strategic behavior of each user to falsely report prices;

[0080] Model building module, which builds a model to minimize the individual cost of each user and a model to maximize the utility of each user and market operator in the market;

[0081] The solution module is used to solve the best trading results using the best response-based algorithm.

[0082] In another aspect, a device is provided, comprising:

[0083] one or more processors;

[0084] a memory for storing one or more programs,

[0085] When the one or more programs are executed by the one or more processors, the one or more processors are enabled to perform the end-to-end energy trading method.

[0086] On the other hand, a computer-readable storage medium storing a computer program is provided, which implements the end-to-end energy trading method when executed by a processor.

[0087] (3) Beneficial effects

[0088] The present invention provides an end-to-end energy trading method, system, device and storage medium, which optimize the replacement cost of power users in the end-to-end energy trading market while taking network constraints into account, ensuring that the user's energy demand is met while avoiding the user's strategic quotation, verifying the rationality of considering network costs in the user's end-to-end transaction, and having strong practical value. BRIEF DESCRIPTION OF THE DRAWINGS

[0089] Figure 1 Schematic diagram of the process of the present invention;

[0090] Figure 2 This is a diagram of the end-to-end transaction market structure corresponding to the present invention;

[0091] Figure 3 It is a schematic diagram of each energy producer and consumer maximizing their own benefits corresponding to the present invention;

[0092] Figure 4 It is a schematic diagram of the market equilibrium solution method proposed by the present invention based on the best response principle;

[0093] Figure 5 It is a schematic diagram of the convergence conditions of the best response algorithm of the present invention. DETAILED DESCRIPTION

[0094] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0095] Example

[0096] See Figure 1-2 , which shows the end-to-end transaction market structure diagram of the present invention, the method includes the following steps:

[0097] (1) Obtaining data such as user load, real-time electricity prices, network structure data, energy storage of each user, distributed power generation data, and user energy demand, and passing the collected data as parameters into the optimization model; the optimization model refers to an optimization model that maximizes the individual benefits of users participating in the transaction;

[0098] Furthermore, the user load data includes the user's load data for the entire year, with a minimum data collection interval of 15 minutes;

[0099] Furthermore, the real-time electricity price adopts the national unified peak, valley and flat electricity price, and the demand price charging cycle is one month;

[0100] Furthermore, the network structure data mainly includes the line power flow coefficient of the DC power flow of the transmission network. Since the parameters of each line are fixed after construction, the power flow coefficient is a constant matrix.

[0101] Furthermore, the user's energy storage data mainly includes the rated power, rated capacity, and charge and discharge loss coefficient of the energy storage;

[0102] Furthermore, the user's distributed power data mainly includes the rated output power and output time of each distributed power source;

[0103] Furthermore, the user's energy demand is the scheduling model of his flexible load, which is the flexible load resources that the user can dispatch while meeting the user's own rigid load requirements.

[0104] (2) The electric energy for end-to-end transactions needs to be transmitted through the existing power network. Therefore, the present invention uses the electric energy for end-to-end transactions to calculate the network transmission fee for each transaction. The existing power network refers to the top-down transmission network that has been built. The present invention uses the existing transmission network to transmit the transaction electric energy in the point-to-point transaction market.

[0105] (21) The network fee for end-to-end transactions is directly related to the amount of electricity traded and the electrical distance over which the electricity is transmitted. Therefore, the present invention sets the network fee to be proportional to the electrical distance and the amount of electricity transmitted as follows:

[0106] G(d i,j ,p i,j ):=λ i,j d i,j p i,j

[0107] Where, d i,j is the electrical distance between energy seller i and energy buyer j, p i,j is the amount of energy transmitted between energy buyers and sellers, λ i,j It is the network access fee corresponding to unit distance and unit electricity.

[0108] (22) It is worth noting that the present invention defines electrical distance as the sum of the transmission coefficients between the nodes of the energy buyer and seller, so the electrical distance can be written as follows:

[0109]

[0110] Where, is the transmission coefficient between energy seller i and energy buyer j. By adding the transmission coefficients on all relevant branches, the present invention can obtain the electrical distance between energy seller i and energy buyer j.

[0111] (23) After obtaining the network fee required for a transaction, the buyer and seller of the transaction need to share the network fee equally. The rules for sharing are as follows:

[0112]

[0113] Where Δt is the time granularity of the entire market operation. By dividing the entire payment amount into half, each energy buyer and seller can equally bear the cost of the entire grid connection fee.

[0114] (3) In order to optimize the electricity consumption behavior of each user while suppressing the strategic behavior of each user to falsely report prices, the present invention uses the VCG mechanism to calculate the replacement cost of each user, and applies the Nash negotiation method to the VCG mechanism to reduce the computational difficulty of the VCG mechanism. The schematic diagram of maximizing the individual interests of each user is shown in Figure 3 ;

[0115] (31) If each user cannot purchase the corresponding electricity to meet their electricity needs in the end-to-end energy trading market, they need to purchase electricity from the upper-level power grid according to the node marginal electricity price. Therefore, the total cost model is as follows:

[0116]

[0117] Where, α n is the vector of decision variables of user n, c n is the cost of user n in the end-to-end transaction market, p m is the power purchased by user m from the upper grid, is the electricity purchase expenditure of user m from the upper grid, S I∪J is the total social cost.

[0118] If there is no end-to-end trading market, each user will only purchase electricity from the upper grid according to the node marginal price. The total cost of electricity purchase is as follows:

[0119]

[0120] Where S I∪J\P2P represents the total social cost when there is no end-to-end transaction.

[0121] (32) The total replacement benefit of the end-to-end transaction can be expressed as the change in the total cost of each user not participating in the market when there is no end-to-end market and when there is an end-to-end market. The model is as follows:

[0122]

[0123] Where, is the total cost when each user only participates in the end-to-end energy trading market.

[0124] (33) After obtaining the total substitution cost, the present invention uses the Nash negotiation method to fairly distribute the total cost to each market participant. The corresponding Nash negotiation model is as follows:

[0125]

[0126] st

[0127]

[0128]

[0129] Where, F n is the total market revenue of user n participating in the end-to-end and traditional markets, d n is the income of user n who only participates in the traditional market, μ n is the Nash negotiation coefficient of user n. The present invention sets the negotiation breakdown point as the total revenue of the user when participating in the traditional market alone according to the node marginal electricity price. If the total revenue of the user after participating in the end-to-end transaction is lower than the total revenue when participating in the traditional market, the user will not participate in the end-to-end transaction. Therefore, the Nash negotiation model conforms to the user's rational assumption and ensures the user's market utility.

[0130] (34) It is worth noting that the VCG mechanism cannot guarantee the balance of income and expenditure of the system. That is, when paying according to this mechanism, the total expenditure of users is lower than the total cost of sellers. Therefore, we need to redistribute the remaining total cost to each market player. The distribution mechanism can be modeled as follows:

[0131] μ n :=S I∪J\P2P -S {I∪J\P2P}∪{n∈P2P}

[0132]

[0133] Where, the negotiation parameter μ n It is set to be proportional to the total cost change after the user participates in the end-to-end transaction, Δc n is the total cost change after user n participates in the end-to-end transaction, It is the change in total cost after all users participate in end-to-end transactions. By distributing this change to each market participant, the income and expenditure of the entire market can be balanced.

[0134] (4) The present invention assumes that each user holds distributed energy storage, distributed power, and flexible load resources. Each resource has scheduling costs and maintenance costs when participating in scheduling. Therefore, the model for minimizing the individual cost of each user can be established as follows:

[0135]

[0136] Where, α n is the transaction power in the end-to-end market, k n is the switching state of each distributed power source, which is a Boolean variable, p n It is the power value of the distributed power supply. is the electricity selling cost of user n at time t, is the corresponding switch state, is the electricity purchase cost of user n at time t, is the corresponding switch state, is the network access fee of user n at time t, is the renewable energy generation cost of user n at time t, is the energy storage charging and discharging cost of user n at time t, is the cost of flexible load scheduling for user n at time t. and They represent the on / off status of renewable energy, distributed energy storage and user-side flexible load resources participating in the power market. When the corresponding parameter is 1, the distributed resource participates in power market transactions. When the variable is 0, the distributed resource does not participate in power market transactions.

[0137] (41) Considering the above three types of user-side distributed resources, the present invention establishes the following constraints on the mathematical model based on the characteristics of these three types of distributed resources:

[0138]

[0139]

[0140]

[0141]

[0142]

[0143]

[0144]

[0145]

[0146]

[0147]

[0148]

[0149] In the formula, the meaning of each variable is consistent with the corresponding variable in the objective function of (4). They represent the electricity sold and purchased by user n at time t, These variables represent the user's electricity selling and purchasing status, renewable power generation, energy storage charging and discharging, and flexible load resource utilization. When the corresponding state variable is 1, the user is in that state; when the corresponding state variable is 0, the user is not in that state. They represent the output power of renewable power generation, energy storage charging, energy storage discharging and flexible load resources of user n at time t respectively. Indicates the upper and lower limits of the transaction power of user n at time t. Because users cannot buy and sell electricity at the same time, and the user's storage energy cannot be charged and discharged at the same time, so Because the user's flexibility load resources are limited, the present invention defines the upper limit of the flexibility load adjustment is the energy storage capacity of user n at time t, is the energy storage charging efficiency of user n at time t, is the energy storage discharge efficiency of user n at time t, Defines the upper and lower limits of energy storage capacity during the entire charging and discharging process. is the upper limit of energy storage discharge power of user n at time t, is the upper limit of energy storage charging power for user n at time t.

[0150] (42) Since users need to pay network access fees to market operators, each market operator will try to maximize its own revenue and minimize the total social cost. Therefore, the operator's objective function is as follows:

[0151]

[0152] Where, is the sum of the costs for users to participate in the traditional market and the end-to-end market. The variables in this expression are consistent with those in (31). This is caused by end-to-end transactions, and the meaning of the variables here is consistent with the corresponding variables in (23).

[0153] (43) Considering the power balance of the entire system and the line flow constraints, the present invention establishes the following constraints on the mathematical model based on the energy trading volume and transaction price restrictions:

[0154]

[0155]

[0156]

[0157]

[0158] Where, The power purchased from the upper grid for the entire end-to-end transaction, mainly the total power purchased by all users Subtract the total electricity sold by all users You can get it. It limits the unit price of the network access fee of the market operator, B is the admittance matrix of the entire system, θt is the voltage phase angle of each node in the entire system, P t is the injection power of each node at time t, F l max is the upper limit of active power transmission of line l.

[0159] (5) After establishing the utility maximization model of each user and market operator in the market, the present invention uses Figure 4 The best response-based algorithm solves the best trading results.

[0160] (51) It is worth noting that in order to ensure the flexibility and incentive compatibility of each market player in the entire market, the present invention introduces a large number of Boolean variables to characterize the switching state of each user's distributed power supply. In order to accurately and efficiently solve the planning problem containing these switching states, the present invention decomposes the original problem according to each user.

[0161] (52) The present invention first fixes the transaction plans of other users as constants, and then optimizes user n. Since only the integer variable of user n is included in the single optimization, the difficulty of solving the entire problem is greatly reduced. After traversing all users, the optimal transaction plan of N users in the entire market can be obtained.

[0162] (53) Since in each iteration, the optimization model of other users requires the optimization result of user n as a parameter to solve, the iterative convergence of the entire system needs to meet Figure 5 , the corresponding mathematical conditions are as follows:

[0163]

[0164] The Hessian matrix of the above function is first-order positive definite, which can make the optimal response of the entire end-to-end trading market converge.

[0165] (6) Energy consumption costs, flexible resource scheduling and power network usage are sent to corresponding market participants and market operators to obtain the final market transaction situation.

[0166] This invention is applicable to power grids with a high penetration rate of renewable energy, and maximizes the benefits of each market participant while taking into account network constraints and network access costs. From the perspective of market participants, this invention analyzes energy consumption costs, the subjective willingness to participate in market transactions, and the scheduling characteristics of different distributed power sources. It replaces the original cost function with the user's substitution cost, optimizing P2P transactions in the market while ensuring that each user does not inflate prices to profit. This invention provides a new approach to the design of transaction mechanisms in end-to-end markets, effectively promoting the application of P2P energy transactions on the user side.

[0167] In another aspect, an end-to-end energy trading system for use in the above embodiment is provided, comprising:

[0168] The acquisition module is used to obtain user load data, real-time electricity prices, network structure data, energy storage data of each user, distributed power supply data and user energy demand;

[0169] a transmission module, which inputs the data acquired by the acquisition module as parameters into the optimization model, transmits the end-to-end traded electric energy through the existing power network, and sends the energy cost, flexible resource scheduling status and power network usage status to the corresponding market participants and market operators to obtain the final market transaction status;

[0170] The calculation module is used to calculate the network cost of each transaction using the end-to-end transaction energy and calculate the replacement cost of each user using the VCG mechanism;

[0171] Optimization module, used to optimize the electricity consumption behavior of each user and curb the strategic behavior of each user to falsely report prices;

[0172] Model building module, which builds a model to minimize the individual cost of each user and a model to maximize the utility of each user and market operator in the market;

[0173] The solution module is used to solve the best trading results using the best response-based algorithm.

[0174] In another aspect, a device is provided, comprising:

[0175] one or more processors;

[0176] a memory for storing one or more programs,

[0177] When the one or more programs are executed by the one or more processors, the one or more processors are enabled to execute the end-to-end energy trading method in the above embodiments.

[0178] On the other hand, a computer-readable storage medium storing a computer program is provided, and when the program is executed by a processor, the end-to-end energy trading method in the above embodiment is implemented.

[0179] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply the existence of any such actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article, or device comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or elements inherent to such process, method, article, or device. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of other identical elements in the process, method, article, or device comprising the element.

Claims

1. A peer-to-peer energy trading method, characterized in that: The method comprises: Obtain user load data, real-time electricity prices, network structure data, each user's energy storage data, distributed power supply data and user energy demand, and pass the obtained data as parameters into the optimization model; Transmitting end-to-end traded electricity through the existing power network, and using the end-to-end traded electricity to calculate the network transmission fee for each transaction; The VCG mechanism is used to calculate the replacement cost of each user, and the Nash negotiation method is applied to the VCG mechanism to reduce the computational difficulty of the VCG mechanism. This is used to optimize the electricity consumption behavior of each user and curb the strategic behavior of users who falsely report prices, including: If each user cannot purchase the corresponding electricity to meet the electricity demand in the end-to-end energy trading market, then the user purchases electricity from the upper grid according to the node marginal price. The total cost model is: Where, α n is the vector of decision variables of user n, c n is the cost of user n in the end-to-end transaction market, p m is the power purchased by user m from the upper grid, is the electricity purchase expenditure of user m from the upper grid, is the total social cost; If there is no end-to-end trading market, each user only purchases electricity from the upper grid according to the node marginal price, and the total cost of electricity purchase is Where, represents the total social cost when there is no end-to-end transaction; The total replacement benefit of end-to-end transactions is expressed as the change in the total cost of each user not participating in the market when there is no end-to-end market and when there is an end-to-end market. The model is Where, is the total cost for each user when they only participate in the end-to-end energy trading market; After obtaining the total substitution cost, the Nash negotiation method is used to fairly distribute the total cost to each market participant. The corresponding Nash negotiation model is: st Where, F n is the total market revenue of user n participating in the end-to-end and traditional markets, d n is the income of user n who only participates in the traditional market, μ n is the Nash negotiation coefficient of user n; The remaining total cost is redistributed to each market player, and the distribution mechanism is modeled as Where, the negotiation parameter μ n It is set to be proportional to the total cost change after the user participates in the end-to-end transaction, Δc n is the total cost change after user n participates in the end-to-end transaction, The total cost change after all users participate in the end-to-end transaction; By assuming that each user holds distributed energy storage, distributed power, and flexible load resources, each resource has scheduling costs and maintenance costs when participating in scheduling, a model is established to minimize the individual costs of each user; Establish a utility maximization model for each user and market operator in the market, and use the best response-based algorithm to solve the optimal transaction results; Energy consumption costs, flexible resource scheduling and power network usage are sent to corresponding market participants and market operators to obtain the final market transaction situation.

2. The end-to-end energy trading method according to claim 1, characterized in that: The user load data includes the user's load data for the whole year, and the minimum data collection interval is 15 minutes; the real-time electricity price adopts the national unified peak, valley and flat three-hour electricity price, and the demand price charging period is one month; the network structure data includes the line flow coefficient of the DC flow of the transmission network, and the flow coefficient is a constant matrix; the energy storage data of each user includes the rated power, rated capacity and charging and discharging loss coefficient of the energy storage; the distributed power supply data includes the rated output power and output time of each distributed power supply; the energy demand of the user is the scheduling model of its flexible load.

3. The end-to-end energy trading method according to claim 2, characterized in that: The calculation of the network fee for each transaction specifically includes: The transmission fee of end-to-end transactions is directly related to the amount of electricity traded and the electrical distance of the electricity transmission. The transmission fee is set to be proportional to the electrical distance and the amount of electricity transmitted. G(d i,j ,p i,j ):❝λ i,j d i,j p i,j Among them, d i,j is the electrical distance between energy seller i and energy buyer j, p i,j is the amount of energy transmitted between energy buyers and sellers, λ i,j The network access fee corresponding to unit distance and unit electricity; The electrical distance is defined as the sum of the transmission coefficients between the nodes of energy buyers and sellers, and the electrical distance is expressed as in, is the transmission coefficient between energy seller i and energy buyer j. The electrical distance between energy seller i and energy buyer j is obtained by adding the transmission coefficients of all relevant branches; After obtaining the network fee required for a transaction, the buyer and seller of the transaction share the network fee equally. The rules for sharing are: Among them, Δt is the time granularity of the entire market operation.

4. The end-to-end energy trading method according to claim 3, characterized in that: By assuming that each user holds distributed energy storage, distributed power, and flexible load resources, each resource has scheduling costs and maintenance costs when participating in scheduling. The model for minimizing individual costs for each user is established, including: The constraints of the mathematical model established based on the characteristics of each user holding distributed energy storage, distributed power and flexible load resources are as follows: Where, They represent the electricity sold and purchased by user n at time t respectively; They respectively represent the user's electricity selling status, electricity buying status, renewable power generation status, energy storage charging status, discharge status, and flexible load resource usage status; when the corresponding state variable is 1, the user is in that state, and when the corresponding state variable is 0, the user is not in that state; They represent the output power of renewable power generation, energy storage charging, energy storage discharging and flexible load resources of user n at time t respectively; represents the upper and lower limits of the transaction power of user n at time t; users cannot buy and sell electricity at the same time, and the user's storage energy cannot be charged and discharged at the same time, then The user's flexible load resources are limited, so the upper limit of the flexible load adjustment is defined is the energy storage capacity of user n at time t, is the energy storage charging efficiency of user n at time t, is the energy storage discharge efficiency of user n at time t, Defined as the upper and lower limits of energy storage capacity during the entire charging and discharging process, is the upper limit of energy storage discharge power of user n at time t, is the upper limit of energy storage charging power for user n at time t.

5. The end-to-end energy trading method according to claim 4, characterized in that: By assuming that each user holds distributed energy storage, distributed power, and flexible load resources, each resource has scheduling costs and maintenance costs when participating in scheduling. The model for minimizing individual costs for each user also includes: Users pay network access fees to market operators. Each market operator attempts to maximize its own revenue and minimize the total social cost. The operator's objective function is: Where, The sum of the costs for users to participate in traditional markets and end-to-end markets, Cost of network fees incurred for end-to-end transactions; The constraints for establishing a mathematical model based on energy trading volume and transaction price restrictions are: Where, The power purchased from the upper grid for the entire end-to-end transaction, based on the total power purchased by all users Subtract the total electricity sold by all users get; The unit price of the network access fee of the market operator is limited, B is the admittance matrix of the entire system, θ t is the voltage phase angle of each node in the entire system, P t is the injection power of each node at time t, F l max is the upper limit of active power transmission of line l.

6. The end-to-end energy trading method according to claim 5, characterized in that: The establishment of a utility maximization model for each user and market operator in the market and the use of an optimal response-based algorithm to solve for the optimal transaction result specifically include: A large number of Boolean variables are introduced to represent the switching status of each user's distributed power supply, and the original problem is decomposed according to each user; Fix the trading plans of other users as constants and optimize user n. Only the integer variable of user n is included in the single optimization. The difficulty of solving the entire problem is greatly reduced. After traversing all users, the optimal trading plan of N users in the entire market is obtained. In each iteration, the optimization models of other users are solved using the optimization results of user n as parameters. The iterative convergence of the entire system meets the following conditions: The Hessian matrix of the above function is first-order positive definite, which makes the optimal response of the entire end-to-end trading market converge.

7. An end-to-end energy trading system, characterized in that: include: The acquisition module is used to obtain user load data, real-time electricity prices, network structure data, energy storage data of each user, distributed power supply data and user energy demand; a transmission module, which inputs the data acquired by the acquisition module as parameters into the optimization model, transmits the end-to-end traded electric energy through the existing power network, and sends the energy cost, flexible resource scheduling status and power network usage status to the corresponding market participants and market operators to obtain the final market transaction status; The calculation module is used to calculate the network cost of each transaction using the end-to-end transaction energy and calculate the replacement cost of each user using the VCG mechanism; Optimization module, used to optimize the electricity consumption behavior of each user and curb the strategic behavior of each user to falsely report prices; Model building module, which builds a model to minimize the individual cost of each user and a model to maximize the utility of each user and market operator in the market; The solution module is used to solve the best trading results using the best response-based algorithm; The system is used to implement an end-to-end energy trading method as described in any one of claims 1-6.

8. A device, characterized in that The device comprises: one or more processors; a memory for storing one or more programs, When the one or more programs are executed by the one or more processors, the one or more processors are enabled to execute the end-to-end energy trading method according to any one of claims 1 to 6.

9. A computer-readable storage medium storing a computer program, characterized in that: When the program is executed by a processor, the end-to-end energy trading method according to any one of claims 1 to 6 is implemented.

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