Optical storage charging station V2G pricing method considering carbon transaction based on evolutionary game

Abstract

The invention provides an evolutionary game-based optical storage charging station V2G pricing method and system considering carbon transaction, a storage medium and electronic equipment, and relates to the technical field of V2G. The V2G pricing method is oriented to the V2G pricing problem of the CSO of the EV user and the charging station operator. The method comprises the following steps: firstly, adopting Monte Carlo simulation to obtain EV user data, and simulating a decision behavior of an EV user in a real scene; the Minkowski method is adopted to aggregate feasible power areas of the CSO, the total power boundary and the capacity boundary of the CSO in the day-ahead stage are derived, and comprehensive modeling is carried out on the EV user and the CSO; then, on the premise of initializing a pricing strategy, CSO, charging and discharging power and a price scheme are selected for a finite rationality EV user through an evolutionary game; secondly, taking stepped carbon transactions into consideration, and performing pricing optimization by adopting an improved multi-target whale optimization algorithm among the CSOs to realize maximization of economic benefits and social benefits; and finally, returning the pricing strategy to the EV user, carrying out multiple iterations to realize convergence, and finally outputting a global optimal V2G price.

Description

Technical Field

[0001] The present invention relates to the field of V2G technology, and in particular to a V2G pricing method, system, storage medium and electronic device for a photovoltaic charging station taking carbon trading into consideration based on evolutionary game. Background Art

[0002] As an innovative energy solution, V2G (Vehicle-to-Grid) technology not only optimizes the use of electricity by realizing two-way energy exchange between electric vehicles and the power grid, but also provides new ideas for the consumption of renewable energy and grid load regulation.

[0003] In the related technology, there are

[0004] For example, patent CN117593027A proposes a V2G demand response pricing method for industrial and commercial power users based on cooperative game, which maximizes the interests of both EV owners and industrial and commercial power users. For another example, patent CN117952641A proposes a pricing method that considers the master-slave game between power grid companies and electric vehicle aggregators, and solves the optimal real-time pricing strategy of electric vehicle aggregators and the optimal participation of electric vehicles, which helps power grid companies to reduce peak loads and smooth load fluctuations.

[0005] However, existing technologies do not take into account photovoltaic equipment and carbon emissions to improve environmental benefits. Summary of the invention

[0006] 1. Technical issues to be resolved

[0007] In view of the shortcomings of the prior art, the present invention provides a V2G pricing method, system, storage medium and electronic device for a photovoltaic charging station taking carbon trading into consideration based on evolutionary game, which solves the technical problem that the prior art does not take photovoltaic equipment and carbon emissions into consideration.

[0008] (II) Technical solution

[0009] To achieve the above objectives, the present invention is implemented through the following technical solutions:

[0010] A V2G pricing method for photovoltaic charging stations with energy storage and carbon trading based on evolutionary game theory includes:

[0011] S1. Monte Carlo method is used to simulate EV user data to initialize the charging and discharging power set of EV users; and the charging station operator CSO is regarded as a virtual energy storage device aggregated by EVs, and the Minkowski method is used to aggregate the feasible area for CSO to aggregate the behavior boundary of each type of EV user, and derive the total power boundary and capacity boundary of CSO in the day-ahead stage;

[0012] S2. Taking maximizing revenue through demand response as the optimization goal, construct an EV user model; and introduce a photovoltaic output model and a ladder-type carbon trading model, taking economic and social benefits as the collaborative optimization goals, and combining the total power boundary and capacity boundary of the CSO to construct a CSO model;

[0013] S3, randomly generate the V2G price for each CSO for each type of EV user;

[0014] S4, using the charging and discharging power set and the current V2G price as inputs of the corresponding EV user model, and selecting the optimal CSO for each type of EV user through evolutionary game, and determining the optimal charging and discharging efficiency and price scheme;

[0015] S5. Taking the optimal charge and discharge efficiency as input, and using the improved multi-objective whale optimization algorithm to solve the CSO model, to obtain the current optimal V2G price;

[0016] S6. If the current optimal V2G price meets the preset accuracy requirement, the global optimal V2G price is output; otherwise, the process returns to S4.

[0017] Preferably, the EV user model includes:

[0018] Objective function:

[0019]

[0020] Among them, formula (1) represents the maximization of revenue through demand response; represents the total benefit of the nth EV user participating in the V2G activity of the i-th CSO, which is composed of the charging and discharging benefits Government subsidies Service fee expenditure And the cycle loss of 1Ah electric vehicle battery Cd in V2G activities Composition; of which:

[0021]

[0022] T energy =L N s n，st η D (7)

[0023]

[0024] in, They represent the charging and discharging power of the nth EV user participating in the V2G activity of the ith CSO and the V2G price of the ith CSO for the nth EV user respectively;

[0025] W awardrepresents the unit price of government subsidies; represents the charging and discharging power of the nth type EV user during the peak period of electricity consumption;

[0026] C d，n Represents the cycle loss of the 1Ah battery of the nth EV user;

[0027] C c，n represents the battery investment of the nth EV user; T energy Indicates the cumulative capacity of all completed charge / discharge cycles; L N Indicates the total number of reference charge / discharge cycles when the depth of discharge is 0.8 within the battery life; η D is the impact factor of V2G on battery cycle life; x1 and x2 are constants representing battery loss characteristics;

[0028] Indicates the number of battery cycles;

[0029] And the constraints:

[0030]

[0031]

[0032] Among them, constraint (9) indicates that the V2G price of the i-th CSO for the n-th EV user is within the prescribed maximum limit q n，max and the minimum limit q n，min Inside;

[0033] Constraint (10) represents the changing relationship of the battery remaining power SOC ratio in adjacent time periods; η ch , η dis Respectively represent the charging efficiency and discharging efficiency; s n Indicates the total battery capacity of the nth EV user; SOC t , SOC t+1 Respectively represent the battery remaining power SOC ratio in period t and period t+1;

[0034] Constraint (11) indicates that the remaining battery capacity SOC of the nth EV user at different time periods is within the specified maximum limit and minimum limit Inside.

[0035] Preferably, the photovoltaic output model and the ladder-type carbon trading model are respectively expressed as:

[0036] (1) Photovoltaic output model

[0037] Assuming that the radiation intensity follows the Beta distribution within a specific time window, a probability model of light intensity is constructed, which is expressed as follows:

[0038]

[0039] in, is the light intensity received by the ith CSO during period t; Γ(·) is the gamma function, r max is the maximum value of the light intensity in a certain period of time; α and β are determined by the expected μ and variance σ of the light intensity in the selected time period. 2 The result is as shown in the following formula:

[0040]

[0041] Based on the probability model of light intensity, when the total area and efficiency of the photovoltaic system PVS are known, the photovoltaic output is approximately expressed as:

[0042]

[0043] in, represents the output power of the PV unit of the i-th CSO during period t; η pv is the photoelectric conversion efficiency; is the total area of ​​the PV array of the i-th CSO;

[0044] (2) Ladder-type carbon trading model

[0045] The carbon trading cost is defined as follows:

[0046]

[0047] Among them, ξ represents the length of the carbon emission interval; Indicates that the actual carbon emissions of the i-th CSO exceed the carbon emission quota part; λ represents the reward coefficient, τ is the penalty coefficient; p c It’s the carbon price.

[0048] Preferably, the CSO model includes:

[0049] Objective function:

[0050]

[0051] Among them, formula (17) represents the economic benefit; represents the total operating cost of the ith CSO, subsidized by the government The sum of the difference between the charging and discharging income and service fee expenditure paid by all EV users to CSO Operation and maintenance costs of photovoltaic units and energy storage systems Grid interaction costs Carbon trading costs Composition; of which:

[0052]

[0053] Among them, N represents the number of types of EV users, and I represents the number of CSOs;

[0054] min means minimization function; F aw_limit represents the upper limit of government subsidies to the i-th CSO, W g is the unit price of the policy subsidy of CSO in period t;

[0055] represents the total charging and discharging power of EV users in the i-th CSO;

[0056] They represent the operation and maintenance costs of photovoltaic and energy storage of the i-th CSO respectively; represents the output power of the photovoltaic device of the i-th CSO within one cycle; represents the input power of the energy storage device of the i-th CSO within one cycle; C E Indicates the unit price of energy storage capacity; represents the energy storage configuration capacity of the ith CSO; c bs Indicates the operation and maintenance unit price of the photovoltaic system;

[0057] They represent the prices of electricity purchased and sold by the ith CSO to the grid; The power of electricity purchased and sold by the ith CSO to the grid respectively;

[0058] Formula (18) represents the peak reduction and shifting benefits used to characterize social benefits; They represent the peak electricity price of the corresponding EV before and after the nth EV user participates in the V2G activity of the i-th CSO. The charge and discharge power under It indicates that the corresponding EV has low electricity price before and after the nth EV user participates in the V2G activity of the i-th CSO. The charge and discharge power under

[0059] And the constraints:

[0060]

[0061] Among them, constraint (25) represents the power balance constraint; Take the i-th CSO The positive and negative values ​​in, i.e., charging power and discharging power; and They are The value at time period t; represents the charging and discharging power of the energy storage device during period t, and Take separately The positive and negative values ​​in the t period, that is, the charging power and the discharging power; q basic,t It represents the power of the conventional load in the station during period t;

[0062] Constraint (26) represents the energy storage device constraint; Respectively represent the charge state of the energy storage device at time t and t+1; Respectively represent the maximum charging and discharging power of the energy storage device; β t It is a Boolean variable. When it is 1, it indicates the charging state, and when it is 0, it indicates the discharging state. Respectively represent the minimum and maximum charge states of the energy storage device;

[0063] Constraint (27) represents the total power boundary and capacity boundary of CSO; Determine the aggregate feasible area of ​​the ith CSO as a virtual energy storage device, Positive They represent the charge and discharge power of the ith CSO in period t, respectively. Equations (28) and (29) represent their calculation processes, respectively. and They represent the maximum charge and discharge power of the i-th CSO in period t; S i，t represents the energy level of the ith CSO at time t, and is the upper and lower limits; ΔS i，t represents the energy change of the i-th CSO caused by EV connection / disconnection during period t; Δt is the duration of the change; η ch and η dis Respectively represent the charging efficiency and discharging efficiency; η ref is the flow replenishment coefficient, which is determined by the flow loss.

[0064] Preferably, the evolutionary game in S4 refers to:

[0065] Assume that each CSO provides K strategies for each type of EV user, and the nth type of EV user selects the kth charging and discharging power and price strategy of the utility model of the i-th CSO, expressed as The kth strategy The fitness function is:

[0066]

[0067] Among them, x k represents the proportion of individuals who choose the kth strategy among the nth type of EV users. The proportion of the initial strategy is randomly set to x o , satisfying 0≤x k≤1, and

[0068] Calculate the conditional switching rate of each strategy:

[0069]

[0070] in, represents a switch from policy θ to policy The conditional switching probability is , φ is the noise level;

[0071] According to the dynamic evolution equation of the electric vehicle group, the proportion of individual strategies is updated to solve the evolutionary stable strategy; wherein the dynamic evolution equation of the electric vehicle group is:

[0072]

[0073] The distributed iterative algorithm is used to solve the evolutionary equilibrium, and the dynamic evolution equation of the electric vehicle group given by the above formula is discretized to obtain the final dynamic evolution equation of the electric vehicle group:

[0074]

[0075] in, represents the number of iterations of the evolutionary game; λ represents the iteration step of the evolutionary game.

[0076] Preferably, the S4 selects the optimal CSO for each type of EV user through evolutionary game, and determines the optimal charging and discharging efficiency and price plan; including:

[0077] Initialize the selection probability of each type of EV user based on the charging and discharging power set and the current V2G price, calculate the benefits to evaluate the utility of each type of EV user selecting different CSOs, and update the selection status of the EV user group;

[0078] Iterate until the selection state of each type of EV user converges and reaches evolutionary equilibrium, and obtains the strategy probability of different CSOs for each type of EV user after clustering. The strategy with the largest probability is selected as the equilibrium strategy. Finally, the optimal CSO is selected for each type of EV user, and the optimal charging and discharging efficiency and price plan are determined.

[0079] Preferably, in S5, the optimal charging and discharging efficiency is used as input, and an improved multi-objective whale optimization algorithm is used to solve the CSO model to obtain the current optimal V2G price; including:

[0080] S51. Initialize the group:

[0081] Input basic data, including population size, maximum number of iterations, inertia weight, and randomly generate the population using the logistic chaos mapping method; expressed as:

[0082]

[0083] Among them, s represents the number of iterations, ys, ys + 1 represents the population proportion at the sth iteration and the population proportion at the s+1th iteration respectively; is the branch parameter;

[0084] S52, calculate fitness:

[0085] Calculate the fitness value of the objective function, form a multi-objective fitness matrix, and record the optimal population;

[0086] S53, non-dominated sorting and calculation of congestion:

[0087] According to the multi-objective fitness matrix, the population is divided into different Pareto levels and the crowding degree is calculated; the crowding degree formula is:

[0088]

[0089] Among them, A[g-1].h is the hth objective function value in the solution set A, sorted by the hth objective function value, which is lower than the hth objective function value of the i-th solution; are the maximum and minimum values ​​of the h-th objective function respectively;

[0090] S54. Select operation:

[0091] Select the new top A groups;

[0092] S55, Update location:

[0093] According to the selected whale, a new candidate solution is generated by updating the position; the variable spiral method is used to update the position; the parameter b is set as the logarithmic drop line shape constant, and the parameter b is dynamically adjusted with the number of iterations to increase the whale's ability to explore the unknown area, which is expressed as:

[0094]

[0095] Where X(u+1) represents the position of the whale in the next iteration, X*(u) represents the optimal position of the whale in the current iteration; D* represents the distance between the current whale and the prey; l represents a random number between [-1, 1]; b0 is the initial parameter value; k is the rate of adjusting the parameters; u is the current iteration number;

[0096] S56, update the external archive to store the Pareto frontier solution set;

[0097] S57, repeat the iteration and output the result, and finally decode to obtain the current optimal V2G price.

[0098] A V2G pricing system for photovoltaic charging stations considering carbon trading based on evolutionary game theory includes:

[0099] A simulation and aggregation module, for executing S1, simulating EV user data by Monte Carlo method to initialize the charging and discharging power set of EV users; and considering the charging station operator CSO as a virtual energy storage device aggregated by EVs, and aggregating the feasible area for CSO by Minkowski method to aggregate the behavior boundary of each type of EV user, and deriving the total power boundary and capacity boundary of CSO in the day-ahead stage;

[0100] A construction module is used to execute S2, construct an EV user model with the optimization goal of maximizing revenue through demand response; and introduce a photovoltaic output model and a ladder-type carbon trading model, with economic and social benefits as the collaborative optimization goals, and construct a CSO model in combination with the CSO total power boundary and capacity boundary;

[0101] A random generation module, used to execute S3 and randomly generate a V2G price for each CSO for each type of EV user;

[0102] An evolutionary game module, used to execute S4, use the charging and discharging power set and the current V2G price as inputs of the corresponding EV user model, select the optimal CSO for each type of EV user through evolutionary game, and determine the optimal charging and discharging efficiency and price plan;

[0103] An optimization solution module, used to execute s5, take the optimal charging and discharging efficiency as input, and solve the CSO model using an improved multi-objective whale optimization algorithm to obtain the current optimal V2G price;

[0104] The judgment module is used to execute S6. If the current optimal V2G price reaches the preset accuracy requirement, the global optimal V2G price is output; otherwise, the process returns to S4.

[0105] A storage medium stores a computer program for V2G pricing of a photovoltaic and storage charging station taking into account carbon trading based on evolutionary game, wherein the computer program enables a computer to execute the V2G pricing method of the photovoltaic and storage charging station as described above.

[0106] An electronic device, comprising:

[0107] One or more processors; a memory; and one or more programs, wherein the one or more programs are stored in the memory and are configured to be executed by the one or more processors, and the programs include methods for executing the V2G pricing method for a photovoltaic charging station as described above.

[0108] (III) Beneficial effects

[0109] The present invention provides a V2G pricing method, system, storage medium and electronic device for photovoltaic charging stations taking carbon trading into consideration based on evolutionary game. Compared with the prior art, it has the following beneficial effects:

[0110] The present invention first uses Monte Carlo simulation to obtain EV user data and simulates the decision-making behavior of EV users in real scenarios; and uses the Minkowski method to aggregate the feasible power area of ​​CSO, derive the total power boundary and capacity boundary of CSO in the day-ahead stage, and comprehensively model EV users and CSOs; then, under the premise of initializing the pricing strategy, the evolutionary game is used to select CSOs and charging and discharging power and price plans for finitely rational EV users; then, considering the step-by-step carbon trading, the improved multi-objective whale optimization algorithm is used to optimize pricing between each CSO to maximize economic and social benefits; finally, the pricing strategy is returned to the EV user, and multiple iterations are performed to achieve convergence, and finally the global optimal V2G price is output. For the V2G pricing problem of EV users and charging station operators CSO, the present invention takes pricing optimization as the core goal and the realization of CSO operation management as the ultimate goal, to assist in achieving peak shaving and valley filling and smoothing load fluctuations. BRIEF DESCRIPTION OF THE DRAWINGS

[0111] 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.

[0112] Figure 1 A block diagram of a V2G pricing method for a photovoltaic charging station with energy storage and carbon trading based on evolutionary game provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0113] 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.

[0114] The embodiments of the present application solve the technical problem that the prior art does not consider photovoltaic equipment and carbon emissions by providing a V2G pricing method, system, storage medium and electronic device for photovoltaic charging stations that consider carbon trading based on evolutionary game.

[0115] The technical solution in the embodiment of the present application is to solve the above technical problems, and the overall idea is as follows:

[0116] Different from the prior art which does not consider photovoltaic equipment and carbon emissions in the construction of vehicle-to-grid (V2G) charging station infrastructure, the embodiments of the present invention focus on the V2G pricing issues for electric vehicle (EV) users and charging station operators (CSO) to improve environmental benefits.

[0117] The embodiment of the present invention is based on evolutionary game based on limited rationality and limited information, which can simulate the decision-making behavior of EV users on electricity prices and charging and discharging power more realistically. CSOs use the improved Non-Dominated Sorting Whale Optimization Algorithm (NSWOA) algorithm to achieve multi-objective optimization of economic and social benefits. The intelligent optimization algorithm has the advantages of simple optimization mechanism, strong search ability, and few parameters. The economic and social benefits are optimized in a coordinated manner, which can improve the reliability and economy of V2G charging and discharging scheduling. The game equilibrium state is reached between CSOs and EV users, which is in line with the actual situation.

[0118] 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.

[0119] Embodiment 1:

[0120] like Figure 1 As shown, an embodiment of the present invention provides a V2G pricing method for a photovoltaic storage charging station considering carbon trading based on evolutionary game, including:

[0121] S1. Monte Carlo method is used to simulate EV user data to initialize the charging and discharging power set of EV users; and the charging station operator CSO is regarded as a virtual energy storage device aggregated by EVs, and the Minkowski method is used to aggregate the feasible area for CSO to aggregate the behavior boundary of each type of EV user, and derive the total power boundary and capacity boundary of CSO in the day-ahead stage;

[0122] S2. Taking maximizing revenue through demand response as the optimization goal, construct an EV user model; and introduce a photovoltaic output model and a ladder-type carbon trading model, taking economic and social benefits as the collaborative optimization goals, and combining the total power boundary and capacity boundary of the CSO to construct a CSO model;

[0123] S3, randomly generate the V2G price for each CSO for each type of EV user;

[0124] S4, using the charging and discharging power set and the current V2G price as inputs of the corresponding EV user model, and selecting the optimal CSO for each type of EV user through evolutionary game, and determining the optimal charging and discharging efficiency and price scheme;

[0125] S5. Taking the optimal charge and discharge efficiency as input, and using the improved multi-objective whale optimization algorithm to solve the CSO model, to obtain the current optimal V2G price;

[0126] S6. If the current optimal V2G price meets the preset accuracy requirement, the global optimal V2G price is output; otherwise, the process returns to S4.

[0127] The embodiments of the present invention take photovoltaic equipment and carbon emissions into consideration, guide CSOs to actively participate in the V2G market and the carbon market, achieve energy conservation and emission reduction, and take into account both the economic and social benefits of the operation of charging stations, thus having practical application value.

[0128] Next, we will introduce the steps of the above scheme in detail:

[0129] In step S1, the Monte Carlo method is used to simulate EV user data to initialize the charging and discharging power set of EV users; and the charging station operator CSO is regarded as a virtual energy storage device aggregated by EVs, and the Minkowski method is used to aggregate the feasible area for CSO to aggregate the behavior boundary of each type of EV user, and the total power boundary and capacity boundary of CSO in the day-ahead stage are derived.

[0130] It should be noted that the CSO business model proposed in the embodiment of the present invention is applicable to large parking lots, such as parking lots in office buildings or supermarkets. Electric vehicle users of this business model usually have a fixed charging and discharging pattern, including the start and end time of charging and discharging, daily driving distance, etc.

[0131] According to the data of a national transportation survey (National Household Travel Survey, NHTS), the departure time and return time of EV users every day satisfy the characteristics of log-normal distribution, and their probability density functions are shown in equations (1) and (2) respectively. For any EV user j:

[0132]

[0133] in, For EV users The probability density function of departure time; For EV users The probability density function of the return time at the time of departure is μ1, σ1 is the mean and standard deviation of the departure time of EV user j every day; μ2, σ2 is the mean and standard deviation of the return time of EV user j every day. Time has come Run between moments.

[0134] As mentioned above, the charging and discharging behavior of EV users is mainly affected by factors such as daily mileage, grid connection and disconnection time, initial SOC, expected SOC and battery characteristics. At the same time, based on NHTS data, it can be seen that the daily mileage obeys the log-normal distribution, and its probability density function is:

[0135]

[0136] Among them, D j is the daily mileage of the EV, u j is the mean and σ is the variance.

[0137] Combining the above analysis of the probability distribution of EV users’ daily mileage and network access time, we can derive the initial battery remaining capacity (State-of-Charge, SOC) of each EV user at the start of charging.

[0138]

[0139] in, is the initial SOC of EV user j, The range of an EV when fully charged.

[0140] Based on the above model, this step uses the Monte Carlo algorithm to sample the charging load and daily mileage requirements of each EV user to determine the complete behavior boundary of the EV.

[0141] In addition, in order to quickly and accurately describe the aggregated feasible power region of CSO involving nonlinear variables, this step borrows the idea of ​​"Minkowski summation" and regards CSO as a virtual energy storage device aggregated by EVs, including:

[0142] The data of all EV users are clustered into N classes, and the data record of the nth class of EV users is:

[0143]

[0144] Among them, the invention cycle is 24 periods a day. are the departure time and return time of the nth EV user every day; s n is the total battery capacity of the nth EV user; s n，st ,s n，en are the battery power of the nth type EV user when entering and leaving the grid; are the minimum and maximum power consumption of the nth type EV user, respectively; are the maximum charging and discharging power of the nth type EV user in each period; is the initial SOC of the nth EV user at the start of charging.

[0145] The overall behavior boundaries of each type of EV are aggregated by Minkowski summation, and the total power boundary and energy boundary of the day-ahead and real-time phases are derived based on equation (7). In the day-ahead phase, the parameters of each CSO as a virtual energy storage device are calculated by equation (6) based on the EV information from equation (5). Finally, these calculated parameters are used to obtain the posterior day-ahead aggregated feasible region of the CSO, as shown below:

[0146]

[0147] in, The aggregate feasible area of ​​the i-th CSO as a virtual energy storage device is determined. and They represent the charging and discharging power of the i-th CSO in period t, and They represent the maximum charge and discharge power of the i-th CSO in period t; S i，t represents the energy level of the ith CSO at time t, and is the upper and lower limits; ΔS i,t represents the energy change of the ith CSO caused by the connection / disconnection of the electric vehicle at time t, Δt is the duration of the change; η ch and η dis Respectively represent the charging efficiency and discharging efficiency; η ref is the flow replenishment coefficient, which is determined by the flow loss.

[0148] In step s2, an EV user model is constructed with the optimization goal of maximizing revenue through demand response; and a photovoltaic output model and a ladder-type carbon trading model are introduced, and a CSO model is constructed with economic and social benefits as the collaborative optimization goals, combined with the CSO total power boundary and capacity boundary.

[0149] In the embodiment of the present invention, the final decision of the EV user is based on the net income brought by the V2G service, that is, the V2G income minus the total cost caused by battery loss, service fees, parking fees and driving costs. The electric vehicle can only perform one activity, charging or discharging, during the t period.

[0150] Specifically, the EV user model constructed in this step includes:

[0151] Objective function (the dispatch plan of EV users is only reflected in the day-ahead stage, and its goal is to maximize revenue through demand response):

[0152]

[0153] Among them, formula (8) represents the maximization of revenue through demand response; represents the total benefit of the nth EV user participating in the V2G activity of the i-th CSO, which is composed of the charging and discharging benefits Government subsidies Service fee expenditure And the cycle loss of 1Ah electric vehicle battery Cd in V2G activities Composition; of which:

[0154]

[0155]

[0156] in, They represent the charging and discharging power of the nth EV user participating in the V2G activity of the ith CSO and the V2G price of the ith CSO for the nth EV user respectively;

[0157] W award represents the unit price of government subsidies; represents the charging and discharging power of the nth type EV user during the peak period of electricity consumption;

[0158] C d，n Represents the cycle loss of the 1Ah battery of the nth EV user;

[0159] C c，n represents the battery investment of the nth EV user; T energy Indicates the cumulative capacity of all completed charge / discharge cycles; L N Indicates the total number of reference charge / discharge cycles when the depth of discharge is 0.8 within the battery life; η D is the impact factor of V2G on battery cycle life; x1 and x2 are constants representing battery loss characteristics;

[0160] Indicates the number of battery cycles.

[0161] And the constraints:

[0162]

[0163] Among them, constraint (16) indicates that the V2G price of the i-th CSO for the n-th EV user is within the prescribed maximum limit q n，max and the minimum limit q n，min Inside;

[0164] Constraint (17) represents the changing relationship of the battery remaining power SOC ratio in adjacent time periods; η ch , η dis Respectively represent the charging efficiency and discharging efficiency; sn Indicates the total battery capacity of the nth EV user; SOC t , SOC t+1 Respectively represent the battery remaining power SOC ratio in period t and period t+1;

[0165] Constraint (18) indicates that the remaining battery capacity SOC of the nth EV user at different time periods is within the specified maximum limit and minimum limit Inside.

[0166] It should be noted that in the real-time stage, CSO will optimize energy plans, including charging and discharging of battery energy storage systems (BESS), renewable energy generation, V2G charging piles, etc., as well as purchasing and selling electricity in the real-time power market according to the demand response of EV users to minimize operating costs. Prior to this, for the CSO model, the embodiment of the present invention considers tiered carbon trading and takes into account the economic benefits and social benefits of CSO, in which the minimum peak-to-valley difference is used as the social benefit.

[0167] Specifically, this step first introduces a photovoltaic output model and a ladder-type carbon trading model, which are respectively expressed as:

[0168] (1) Photovoltaic output model

[0169] The SARIMA model effectively addresses this challenge by capturing seasonal fluctuations, long-term trends, and random noise. Assuming that the radiation intensity follows a Beta distribution within a specific time window, a probabilistic model of light intensity is constructed, which is expressed as follows:

[0170]

[0171] in, is the light intensity received by the ith CSO during period t; Γ(·) is the gamma function, r max is the maximum value of the light intensity in a certain period of time; α and β are determined by the expected μ and variance σ of the light intensity in the selected time period. 2 The result is as shown in the following formula:

[0172]

[0173] Based on the probability model of light intensity, when the total area and efficiency of the photovoltaic system (PVS) are known, the photovoltaic output is approximately expressed as:

[0174]

[0175] in, represents the output power of the PV unit of the i-th CSO during period t; η pv is the photoelectric conversion efficiency; is the total area of ​​the PV array of the i-th CSO.

[0176] (2) Ladder-type carbon trading model

[0177] The embodiment of the present invention constructs a ladder-type carbon trading model to further solve the problem of carbon emissions, and defines the carbon trading cost as follows:

[0178]

[0179] Among them, ξ represents the length of the carbon emission interval; Indicates that the actual carbon emissions of the i-th CSO exceed the carbon emission quota part; λ represents the reward coefficient, τ is the penalty coefficient; pc is the carbon price.

[0180] On this basis, this step continues to build the CSO model, including:

[0181] Objective function (on the one hand, it focuses on economic benefits; on the other hand, it focuses on social benefits - peak reduction and peak shifting benefits, that is, considering that without adopting an orderly charging strategy, the charging behavior of electric vehicles is completely determined by the car owners, showing low-peak power demand during the day and peak power demand at night):

[0182]

[0183] Among them, formula (24) represents the economic benefit; represents the total operating cost of the ith CSO, subsidized by the government The sum of the difference between the charging and discharging income and service fee expenditure paid by all EV users to CSO Operation and maintenance costs of photovoltaic units and energy storage systems Grid interaction costs Carbon trading costs Composition; of which:

[0184]

[0185] Among them, N represents the number of types of EV users, and I represents the number of CSOs;

[0186] min means minimization function; F aw_limit represents the upper limit of government subsidies to the i-th CSO, W g is the unit price of the policy subsidy of CSO in period t;

[0187] represents the total charging and discharging power of EV users in the i-th CSO;

[0188] They represent the operation and maintenance costs of photovoltaic and energy storage of the i-th CSO respectively; represents the output power of the photovoltaic device of the i-th CSO within one cycle; represents the input power of the energy storage device of the i-th CSO within one cycle; C E Indicates the unit price of energy storage capacity; represents the energy storage configuration capacity of the ith CSO; c bs Indicates the operation and maintenance unit price of the photovoltaic system;

[0189] They represent the prices of electricity purchased and sold by the ith CSO to the grid; The power of electricity purchased and sold by the ith CSO to the grid respectively;

[0190] Formula (25) represents the peak reduction and shifting benefits used to characterize social benefits; They represent the peak electricity price of the corresponding EV before and after the nth EV user participates in the V2G activity of the i-th CSO. The charge and discharge power under It indicates that the corresponding EV has low electricity price before and after the nth EV user participates in the V2G activity of the i-th CSO. The charge and discharge power under .

[0191] And the constraints:

[0192]

[0193] Wherein, constraint (32) represents the power balance constraint; Take the i-th CSO The positive and negative values ​​in, i.e., charging power and discharging power; and They are The value at time period t; represents the charging and discharging power of the energy storage device during period t, and Take separately The positive and negative values ​​in the t period, i.e., charging power and discharging power; qbasic,t represents the power of the conventional load in the station during the t period;

[0194] Constraint (33) represents the energy storage device constraint; Respectively represent the charge state of the energy storage device at time t and t+1; They represent the maximum charging and discharging power of the energy storage device respectively; βt is a Boolean variable, which represents the charging state when it is 1 and the discharging state when it is 0. Respectively represent the minimum and maximum charge states of the energy storage device;

[0195] Constraint (34), i.e., the above formula (7), represents the total power boundary and capacity boundary of CSO; Determine the aggregate feasible area of ​​the ith CSO as a virtual energy storage device, and They represent the charge and discharge power of the ith CSO in period t, respectively. Equations (35) and (36) represent their calculation processes, respectively. and They represent the maximum charge and discharge power of the i-th CSO in period t; S i，t represents the energy level of the ith CSO at time t, and is the upper and lower limits; ΔS i，t represents the energy change of the i-th CSO caused by EV connection / disconnection during period t; Δt is the duration of the change; η ch and η dis Respectively represent the charging efficiency and discharging efficiency; η ref is the flow replenishment coefficient, which is determined by the flow loss.

[0196] In step s3, the V2G price of each CSO for each type of EV user is randomly generated;

[0197] This step performs the pricing strategy initialization operation to facilitate the subsequent execution of the evolutionary game operation.

[0198] In step S4, the charging and discharging power set and the current V2G price are used as inputs of the corresponding EV user model, and the optimal CSO is selected for each type of EV user through evolutionary game, and the optimal charging and discharging efficiency and price scheme are determined.

[0199] In the evolutionary game process of EV users choosing charging stations, EV users obtain economic benefits based on electricity prices and charging and discharging power simulation results to evaluate the utility of CSOs, and use evolutionary games to select the best CSO. After users make stable choices, an evolutionary game equilibrium state is formed.

[0200] Assume that each CSO provides K strategies for each type of EV user, and the nth type of EV user selects the kth charging and discharging power and price strategy of the utility model of the i-th CSO, expressed as The kth strategy The fitness function is:

[0201]

[0202] Among them, xk represents the proportion of individuals who choose the kth strategy among the nth type of EV users, and the proportion of the initial strategy is randomly set to x o , satisfying 0≤x k≤1, and

[0203] Calculate the conditional switching rate of each strategy:

[0204]

[0205] in, represents a switch from policy θ to policy The conditional switching probability is , φ is the noise level;

[0206] According to the dynamic evolution equation of the electric vehicle group, the proportion of individual strategies is updated to solve the evolutionary stable strategy; wherein the dynamic evolution equation of the electric vehicle group is:

[0207]

[0208] The distributed iterative algorithm is used to solve the evolutionary equilibrium, and the dynamic evolution equation of the electric vehicle group given by the above formula is discretized to obtain the final dynamic evolution equation of the electric vehicle group:

[0209]

[0210] in, represents the number of iterations of the evolutionary game; λ represents the iteration step of the evolutionary game.

[0211] On this basis, in this step, the optimal CSO is selected for each type of EV user through evolutionary game, and the optimal charging and discharging efficiency and price plan are determined; including:

[0212] Initialize the selection probability of each type of EV user based on the charging and discharging power set and the current V2G price, calculate the benefit to evaluate the utility of each type of EV user selecting different CSOs, and update the selection status of the EV user group; wherein, it can be understood that during the first operation, the current V2G price is the V2G price randomly generated in step S1, and in each subsequent operation, the current V2G price is the current optimal V2G price obtained in the previous iteration process.

[0213] Iterate until the selection state of each type of EV user converges and reaches evolutionary equilibrium, and obtains the strategy probability of different CSOs for each type of EV user after clustering. The strategy with the largest probability is selected as the equilibrium strategy. Finally, the optimal CSO is selected for each type of EV user, and the optimal charging and discharging efficiency and price plan are determined.

[0214] In step S5, the optimal charging and discharging efficiency is used as input, and the CSO model is solved by using an improved multi-objective whale optimization algorithm to obtain the current optimal V2G price.

[0215] In this step, the whale optimization algorithm of non-dominated sorting is used to solve the multi-objective optimization problem of economic and social benefits of charging stations, including:

[0216] S51. Initialize the group:

[0217] Input basic data, including population size, maximum number of iterations, inertia weight, and randomly generate the population using the logistic chaos mapping method; expressed as:

[0218]

[0219] Among them, s represents the number of iterations, ys, ys + 1 represents the population proportion at the sth iteration and the population proportion at the s+1th iteration respectively; is the branch parameter;

[0220] S52, calculate fitness:

[0221] Calculate the fitness value of the objective function, form a multi-objective fitness matrix, and record the optimal population;

[0222] S53, non-dominated sorting and calculation of congestion:

[0223] According to the multi-objective fitness matrix, the population is divided into different Pareto levels and the crowding degree is calculated; the crowding degree formula is:

[0224]

[0225] Among them, A[g-1].h is the hth objective function value in the solution set A, sorted by the hth objective function value, which is lower than the hth objective function value of the i-th solution; are the maximum and minimum values ​​of the h-th objective function respectively;

[0226] S54. Select operation:

[0227] Select the new top A groups;

[0228] S55, Update location:

[0229] According to the selected whale, a new candidate solution is generated by updating the position; the variable spiral method is used to update the position; the parameter b is set as the logarithmic drop line shape constant, and the parameter b is dynamically adjusted with the number of iterations to increase the whale's ability to explore the unknown area, which is expressed as:

[0230]

[0231] Where X(u+1) represents the position of the whale in the next iteration, X*(u) represents the optimal position of the whale in the current iteration; D* represents the distance between the current whale and the prey; l represents a random number between [-1, 1]; b0 is the initial parameter value; k is the rate of adjusting the parameters; u is the current iteration number;

[0232] S56, update the external archive to store the Pareto frontier solution set;

[0233] S57, repeat the iteration and output the result, and finally decode to obtain the current optimal V2G price.

[0234] In step S6, if the current optimal V2G price meets the preset accuracy requirement, the global optimal V2G price is output; otherwise, the process returns to S4.

[0235] In this step, the current optimal V2G price is returned to the corresponding EV user model, and after multiple iterations and convergence, the global optimal V2G price is finally output. The preset accuracy requirement means that convergence is considered when the current optimal V2G price meets a certain value.

[0236] So far, the embodiment of the present invention has realized the entire process of V2G pricing of photovoltaic charging stations taking carbon trading into consideration based on evolutionary game.

[0237] Embodiment 2:

[0238] The embodiment of the present invention provides a V2G pricing system for a photovoltaic charging station taking carbon trading into consideration based on evolutionary game, including:

[0239] A simulation and aggregation module, for executing S1, simulating EV user data by Monte Carlo method to initialize the charging and discharging power set of EV users; and considering the charging station operator CSO as a virtual energy storage device aggregated by EVs, and aggregating the feasible area for CSO by Minkowski method to aggregate the behavior boundary of each type of EV user, and deriving the total power boundary and capacity boundary of CSO in the day-ahead stage;

[0240] A construction module is used to execute S2, construct an EV user model with the optimization goal of maximizing revenue through demand response; and introduce a photovoltaic output model and a ladder-type carbon trading model, with economic and social benefits as the collaborative optimization goals, and construct a CSO model in combination with the CSO total power boundary and capacity boundary;

[0241] A random generation module, used to execute S3 and randomly generate a V2G price for each CSO for each type of EV user;

[0242] An evolutionary game module, used to execute S4, use the charging and discharging power set and the current V2G price as inputs of the corresponding EV user model, select the optimal CSO for each type of EV user through evolutionary game, and determine the optimal charging and discharging efficiency and price plan;

[0243] An optimization solution module, used to execute S5, take the optimal charging and discharging efficiency as input, and solve the CSO model using an improved multi-objective whale optimization algorithm to obtain the current optimal V2G price;

[0244] The judgment module is used to execute S6. If the current optimal V2G price reaches the preset accuracy requirement, the global optimal V2G price is output; otherwise, the process returns to S4.

[0245] Embodiment 3:

[0246] An embodiment of the present invention provides a storage medium storing a computer program for V2G pricing of photovoltaic charging stations taking into account carbon trading based on evolutionary game, wherein the computer program enables a computer to execute the V2G pricing method of photovoltaic charging stations as described in Example 1.

[0247] Embodiment 4:

[0248] An embodiment of the present invention provides an electronic device, including:

[0249] One or more processors; a memory; and one or more programs, wherein the one or more programs are stored in the memory and are configured to be executed by the one or more processors, and the programs include methods for executing the V2G pricing method for a photovoltaic charging station as described in Example 1.

[0250] It can be understood that the V2G pricing system, storage medium and electronic device for photovoltaic charging stations considering carbon trading based on evolutionary game provided in the embodiments of the present invention correspond to the V2G pricing method for photovoltaic charging stations considering carbon trading based on evolutionary game provided in the embodiments of the present invention. The explanations, examples and beneficial effects of the relevant contents can refer to the corresponding parts in the V2G pricing method for photovoltaic charging stations, and will not be repeated here.

[0251] In summary, compared with the prior art, the present invention has the following beneficial effects:

[0252] 1. The embodiments of the present invention take into account photovoltaic equipment and carbon emissions, guide CSOs to actively participate in the V2G market and the carbon market, achieve energy conservation and emission reduction, and take into account the economic and social benefits of the operation of charging stations, which has practical application value.

[0253] 2. The embodiment of the present invention obtains the travel time and daily driving distance of EV users based on Monte Carlo simulation, and establishes an evolutionary game based on bounded rationality and limited information to simulate the decision-making behavior of EV users in real scenarios.

[0254] 3. The embodiment of the present invention aggregates the feasible power area of ​​the charging station based on the Minkowski method, constrains variables such as the total charging and discharging power of the charging station, and comprehensively models the CSO and EV according to the aggregated feasible power area of ​​the CSO.

[0255] 4. From the perspective of economy and environment, the embodiment of the present invention proposes a CSO operation optimization model that considers photovoltaic uncertainty, multi-energy coupling and V2G demand response, which can cope with the risks and instability in the system operation process and make the optimization result robust.

[0256] 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 statement "comprise a ..." do not exclude the presence of other identical elements in the process, method, article or device including the elements.

[0257] 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 V2G pricing method for photovoltaic charging stations considering carbon trading based on evolutionary game, characterized in that: include: S1. Using the Monte Carlo method to simulate EV user data to initialize the charging and discharging power set of EV users; As well as considering the charging station operator CSO as a virtual energy storage device aggregated by EVs, the Minkowski method is used to aggregate the feasible area for the CSO to aggregate the behavior boundary of each type of EV user, and the total power boundary and capacity boundary of the CSO in the day-ahead stage are derived; S2, building an EV user model with the optimization goal of maximizing revenue through demand response; And introduce the photovoltaic output model and the ladder-type carbon trading model, take economic and social benefits as the coordinated optimization goals, and build a CSO model in combination with the total power boundary and capacity boundary of the CSO; S3, randomly generate the V2G price for each CSO for each type of EV user; S4, using the charging and discharging power set and the current V2G price as inputs of the corresponding EV user model, and selecting the optimal CSO for each type of EV user through evolutionary game, and determining the optimal charging and discharging efficiency and price scheme; S5. Taking the optimal charge and discharge efficiency as input, and using the improved multi-objective whale optimization algorithm to solve the CSO model, to obtain the current optimal V2G price; S6. If the current optimal V2G price meets the preset accuracy requirement, the global optimal V2G price is output; otherwise, the process returns to S4.

2. The V2G pricing method for a photovoltaic charging station as claimed in claim 1, characterized in that: The EV user model includes: Objective function: Among them, formula (1) represents the maximization of revenue through demand response; represents the total benefit of the nth EV user participating in the V2G activity of the i-th CSO, which is composed of the charging and discharging benefits Government subsidies Service fee expenditure And the cycle loss of 1Ah electric vehicle battery Cd in V2G activities Composition; of which: in, They represent the charging and discharging power of the nth EV user participating in the V2G activity of the ith CSO and the V2G price of the ith CSO for the nth EV user respectively; W award represents the unit price of government subsidies; represents the charging and discharging power of the nth type EV user during the peak period of electricity consumption; C d，n Represents the cycle loss of the 1Ah battery of the nth EV user; C c，n represents the battery investment of the nth EV user; T enetgy Indicates the cumulative capacity of all completed charge / discharge cycles; L N Indicates the total number of reference charge / discharge cycles when the depth of discharge is 0.8 within the battery life; η D is the impact factor of V2G on battery cycle life; x1 and x2 are constants representing battery loss characteristics; Indicates the number of battery cycles; And the constraints: Among them, constraint (9) indicates that the V2G price of the i-th CSO for the n-th EV user is within the prescribed maximum limit q n，max and the minimum limit q n.min Inside; Constraint (10) represents the changing relationship of the battery remaining power SOC ratio in adjacent time periods; η ch , η dis Respectively represent the charging efficiency and discharging efficiency; s n Indicates the total battery power of the nth EV user; SOC t , SOC t+1 Respectively represent the battery remaining power SOC ratio in period t and period t+1; Constraint (11) indicates that the remaining battery capacity SOC of the nth EV user at different time periods is within the specified maximum limit and minimum limit Inside.

3. The V2G pricing method for a photovoltaic charging station as claimed in claim 2, characterized in that: The photovoltaic output model and the ladder-type carbon trading model are respectively expressed as: (1) Photovoltaic output model Assuming that the radiation intensity follows the Beta distribution within a specific time window, a probability model of light intensity is constructed, which is expressed as follows: in, is the light intensity received by the ith CSO during period t; Γ(·) is the gamma function, r max is the maximum value of the light intensity in a certain period of time; α and β are determined by the expected μ and variance σ of the light intensity in the selected time period. 2 It is obtained as shown in the following formula: Based on the probability model of light intensity, when the total area and efficiency of the photovoltaic system PVS are known, the photovoltaic output is approximately expressed as: in, represents the output power of the PV unit of the i-th CSO during period t; η pv is the photoelectric conversion efficiency; is the total area of ​​the PV array of the i-th CSO; (2) Ladder-type carbon trading model The carbon trading cost is defined as follows: Among them, ξ represents the length of the carbon emission interval; Indicates that the actual carbon emissions of the i-th CSO exceed the carbon emission quota part; λ represents the reward coefficient, τ is the penalty coefficient; p c It’s the carbon price.

4. The V2G pricing method for a photovoltaic charging station as claimed in claim 3, characterized in that: The CSO model includes: Objective function 2 Among them, formula (17) represents the economic benefit; represents the total operating cost of the ith CSO, subsidized by the government The sum of the difference between the charging and discharging income and service fee expenditure paid by all EV users to CSO Operation and maintenance costs of photovoltaic units and energy storage systems Grid interaction costs Carbon trading costs Composition; of which: Among them, N represents the number of types of EV users, and I represents the number of CSOs; min means minimization function; F aw_limit represents the upper limit of government subsidies to the i-th CSO, W g is the unit price of the policy subsidy of CSO in period t; represents the total charging and discharging power of EV users in the i-th CSO; They represent the operation and maintenance costs of photovoltaic and energy storage of the i-th CSO respectively; represents the output power of the photovoltaic device of the i-th CSO within one cycle; represents the input power of the energy storage device of the i-th CSO within one cycle; C E Indicates the unit price of energy storage capacity; represents the energy storage configuration capacity of the ith CSO; c bs Indicates the unit price of operation and maintenance of the photovoltaic system; They represent the prices of electricity purchased and sold by the ith CSO to the grid; The power of electricity purchased and sold by the ith CSO to the grid respectively; Formula (18) represents the peak reduction and shifting benefits used to characterize social benefits; They represent the peak electricity price of the corresponding EV before and after the nth EV user participates in the V2G activity of the i-th CSO. The charge and discharge power under It indicates that the corresponding EV has low electricity price before and after the nth EV user participates in the V2G activity of the i-th CSO. The charge and discharge power under And the constraints: Among them, constraint (25) represents the power balance constraint; Take the i-th CSO The positive and negative values ​​in, i.e., charging power and discharging power; and They are The value at time period t; represents the charging and discharging power of the energy storage device during period t, and Take separately The positive and negative values ​​in the t period, i.e., charging power and discharging power; qbasic,t represents the power of the conventional load in the station during the t period; Constraint (26) represents the energy storage device constraint; Respectively represent the charge state of the energy storage device at time t and t+1; Respectively represent the maximum charging and discharging power of the energy storage device; β t It is a Boolean variable. When it is 1, it indicates the charging state, and when it is 0, it indicates the discharging state. Respectively represent the minimum and maximum charge states of the energy storage device; Constraint (27) represents the total power boundary and capacity boundary of CSO; Determine the aggregate feasible area of ​​the ith CSO as a virtual energy storage device, and They represent the charge and discharge power of the ith CSO in period t, respectively. Equations (28) and (29) represent their calculation processes, respectively. and They represent the maximum charge and discharge power of the i-th CSO in period t; S i，t represents the energy level of the ith CSO at time t, and is the upper and lower limits; ΔS i，t represents the energy change of the i-th CSO caused by EV connection / disconnection during period t; Δt is the duration of the change; η ch and η dis Respectively represent the charging efficiency and discharging efficiency; η ref is the flow replenishment coefficient, which is determined by the flow loss.

5. The V2G pricing method for a photovoltaic charging station as claimed in claim 4, characterized in that: The evolutionary game in S4 refers to: Assume that each CSO provides K strategies for each type of EV user, and the nth type of EV user selects the kth charging and discharging power and price strategy of the utility model of the i-th CSO, expressed as The kth strategy The fitness function is: Among them, x k represents the proportion of individuals who choose the kth strategy among the nth type of EV users. The proportion of the initial strategy is randomly set to x o , satisfying 0≤x k ≤1, and Calculate the conditional switching rate of each strategy: in, represents a switch from policy θ to policy The conditional switching probability is , φ is the noise level; According to the dynamic evolution equation of the electric vehicle group, the proportion of individual strategies is updated to solve the evolutionary stable strategy; wherein the dynamic evolution equation of the electric vehicle group is: The distributed iterative algorithm is used to solve the evolutionary equilibrium, and the dynamic evolution equation of the electric vehicle group given by the above formula is discretized to obtain the final dynamic evolution equation of the electric vehicle group: in, represents the number of iterations of the evolutionary game; λ represents the iteration step of the evolutionary game.

6. The V2G pricing method for a photovoltaic charging station as claimed in claim 5, characterized in that: In S4, the optimal CSO is selected for each type of EV user through evolutionary game, and the optimal charging and discharging efficiency and price scheme are determined; including: Initialize the selection probability of each type of EV user based on the charging and discharging power set and the current V2G price, calculate the benefits to evaluate the utility of each type of EV user selecting different CSOs, and update the selection status of the EV user group; Iterate until the selection state of each type of EV user converges and reaches evolutionary equilibrium, and obtain the strategy probability of different CSOs for each type of EV user after clustering. Select the strategy with the highest probability as the equilibrium strategy, and finally select the optimal CSO for each type of EV user, and determine the optimal charging and discharging efficiency and price plan.

7. The V2G pricing method for a photovoltaic charging station as claimed in claim 6, characterized in that: In S5, the optimal charging and discharging efficiency is used as input, and the CSO model is solved by using the improved multi-objective whale optimization algorithm to obtain the current optimal V2G price; including: S51. Initialize the group: Input basic data, including population size, maximum number of iterations, inertia weight, and randomly generate the population using the logistic chaos mapping method; expressed as: Among them, s represents the number of iterations, ys, ys + 1 represents the population proportion at the sth iteration and the population proportion at the s+1th iteration respectively; is the branch parameter; S52, calculate fitness: Calculate the fitness value of the objective function, form a multi-objective fitness matrix, and record the optimal population; S53, non-dominated sorting and calculation of congestion: According to the multi-objective fitness matrix, the population is divided into different Pareto levels and the crowding degree is calculated; the crowding degree formula is: Among them, A[g-1].h is the hth objective function value in the solution set A, sorted by the hth objective function value, which is lower than the hth objective function value of the i-th solution; are the maximum and minimum values ​​of the h-th objective function respectively; S54. Select operation: Select the new top A groups; S55, Update location: According to the selected whale, a new candidate solution is generated by updating the position; the variable spiral method is used to update the position; the parameter b is set as the logarithmic drop line shape constant, and the parameter b is dynamically adjusted with the number of iterations to increase the whale's ability to explore the unknown area, which is expressed as: Where X(u+1) represents the position of the whale in the next iteration, X*(u) represents the optimal position of the whale in the current iteration; D* represents the distance between the current whale and the prey; l represents a random number between [-1, 1]; b0 is the initial parameter value; k is the rate of adjusting the parameters; u is the current iteration number; S56, update the external archive to store the Pareto frontier solution set; S57, repeat the iteration and output the result, and finally decode to obtain the current optimal V2G price.

8. A V2G pricing system for photovoltaic charging stations considering carbon trading based on evolutionary game, characterized in that: include: A simulation and aggregation module, for executing S1, simulating EV user data using a Monte Carlo method to initialize a charging and discharging power set of EV users; As well as considering the charging station operator CSO as a virtual energy storage device aggregated by EVs, the Minkowski method is used to aggregate the feasible area for the CSO to aggregate the behavior boundary of each type of EV user, and the total power boundary and capacity boundary of the CSO in the day-ahead stage are derived; A building module for executing S2, building an EV user model with the optimization goal of maximizing revenue through demand response; And introduce the photovoltaic output model and the ladder-type carbon trading model, take economic and social benefits as the coordinated optimization goals, and build a CSO model in combination with the total power boundary and capacity boundary of the CSO; A random generation module, used to execute S3 and randomly generate a V2G price for each CSO for each type of EV user; An evolutionary game module, used to execute S4, use the charging and discharging power set and the current V2G price as inputs of the corresponding EV user model, select the optimal CSO for each type of EV user through evolutionary game, and determine the optimal charging and discharging efficiency and price plan; An optimization solution module, used to execute S5, take the optimal charging and discharging efficiency as input, and solve the CSO model using an improved multi-objective whale optimization algorithm to obtain the current optimal V2G price; The judgment module is used to execute S6. If the current optimal V2G price reaches the preset accuracy requirement, the global optimal V2G price is output; otherwise, the process returns to S4.

9. A storage medium, characterized in that: It stores a computer program for V2G pricing of photovoltaic and energy storage charging stations taking into account carbon trading based on evolutionary game, wherein the computer program enables the computer to execute the V2G pricing method of photovoltaic and energy storage charging stations as described in any one of claims 1 to 7.

10. An electronic device, characterized in that: include: one or more processors; Memory; and one or more programs, wherein the one or more programs are stored in the memory and are configured to be executed by the one or more processors, and the programs include programs for executing the V2G pricing method for a photovoltaic charging station as described in any one of claims 1 to 7.

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