V2g pricing method of optical storage charging station considering carbon trading based on evolutionary game

Abstract

This invention provides a V2G pricing method, system, storage medium, and electronic device for photovoltaic-storage-charging stations considering carbon trading, based on evolutionary game theory, and relating to the V2G technology field. This invention addresses the V2G pricing problem for EV users and charging station operators (CSOs). First, Monte Carlo simulation is used to obtain EV user data, simulating their decision-making behavior in real-world scenarios. Then, the Minkowski method is used to aggregate feasible power regions of CSOs, deriving the day-ahead total power and capacity boundaries of CSOs, and comprehensively modeling both EV users and CSOs. Next, under the premise of an initialized pricing strategy, evolutionary game theory is used to select CSOs, charging / discharging power, and price schemes for boundedly rational EV users. Then, considering tiered carbon trading, an improved multi-objective whale optimization algorithm is used to optimize pricing among various CSOs, maximizing economic and social benefits. Finally, the pricing strategy is returned to EV users, and multiple iterations are performed to achieve convergence, ultimately outputting the globally optimal V2G price.

Description

Technical Field

[0001] This invention relates to the field of V2G technology, and specifically to a V2G pricing method, system, storage medium, and electronic device for photovoltaic-storage-charging stations that considers carbon trading based on evolutionary game theory. Background Technology

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

[0003] Among the related technologies, the following have emerged

[0004] For example, patent CN117593027A proposes a V2G demand response pricing method for industrial and commercial power users based on cooperative game theory, maximizing the interests of both EV owners and industrial and commercial power users. Another example is patent CN117952641A, which proposes a pricing method considering a master-slave game between the power grid company and electric vehicle aggregators. This method solves for the optimal real-time pricing strategy of the electric vehicle aggregator and the optimal participation rate of electric vehicles, helping the power grid company to smooth out peak loads and reduce load fluctuations.

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

[0006] (a) Technical problems to be solved

[0007] To address the shortcomings of existing technologies, this invention provides a V2G pricing method, system, storage medium, and electronic device for photovoltaic-storage-charging stations that considers carbon trading based on evolutionary game theory, solving the technical problem that existing technologies do not consider photovoltaic equipment and carbon emissions.

[0008] (II) Technical Solution

[0009] To achieve the above objectives, the present invention provides the following technical solution:

[0010] A V2G pricing method for photovoltaic-storage-charging stations considering carbon trading, based on evolutionary game theory, includes:

[0011] 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. The Minkowski method is used to aggregate feasible regions for CSO to aggregate the behavioral boundaries of each type of EV user, and the total power boundary and capacity boundary of CSO in the day-ahead stage are derived.

[0012] S2. With the goal of maximizing revenue through demand response, an EV user model is constructed; and a photovoltaic power output model and a tiered carbon trading model are introduced, with economic and social benefits as the synergistic optimization goals, and a CSO model is constructed by combining the total power boundary and capacity boundary of the CSO.

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

[0014] S4. The charging and discharging power set and the current V2G price are used as inputs to the corresponding EV user model, and the optimal CSO is selected for each type of EV user through evolutionary game theory, and the optimal charging and discharging efficiency and price scheme are determined.

[0015] S5. Using the optimal charge and discharge efficiency as input, and employing an improved multi-objective whale optimization algorithm to solve the CSO model, the current optimal V2G price is obtained.

[0016] S6. If the current optimal V2G price meets the preset accuracy requirement, output the globally optimal V2G price; otherwise, return to S4.

[0017] Preferably, the EV user model includes:

[0018] Objective function:

[0019]

[0020] Formula (1) represents maximizing revenue through demand response; This represents the total revenue of the nth type of EV user participating in the i-th CSO's V2G activity, consisting of charging and discharging revenue. Government subsidies Service fee expenses And the cycle loss of Cd in a 1Ah electric vehicle battery during V2G activities. Composition; of which:

[0021]

[0022] T energy =L N s n，st η D (7)

[0023]

[0024] in, These represent the charging and discharging power of the nth type of EV user participating in the i-th CSO's V2G activity and the V2G price of the i-th CSO for the nth type of EV user, respectively.

[0025] W awardIndicates the unit price subsidized by the government; This represents the charging and discharging power of the nth type of EV user during peak electricity consumption periods;

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

[0027] C c，n T represents the battery investment of the nth EV user; energy L represents the cumulative capacity across all completed charge / discharge cycles. N Indicates the total number of reference charge / discharge cycles when the depth of discharge is 0.8 within the battery's lifespan; η D is the influence factor of V2G on battery cycle life; x1 and x2 are both constants representing battery loss characteristics;

[0028] Indicates the number of battery cycles;

[0029] and constraints:

[0030]

[0031]

[0032] Wherein, constraint (9) represents the maximum limit q of the V2G price for the i-th CSO for the n-th type of EV user. n，max and minimum limit q n，min Inside;

[0033] Constraint (10) represents the relationship between the percentage change in the remaining battery charge (SOC) in adjacent time periods; η ch η dis These represent charging efficiency and discharging efficiency, respectively; s n Represents the total battery capacity of the nth type of EV user; SOC t SOC t+1 These represent the percentage of remaining battery charge (SOC) in time period t and time period t+1, respectively.

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

[0035] Preferably, the photovoltaic power output model and the tiered carbon trading model are respectively expressed as:

[0036] (1) Photovoltaic power output model

[0037] Assuming that the radiation intensity follows a beta distribution within a specific time window, a probabilistic model of the illumination intensity is constructed as follows:

[0038]

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

[0040]

[0041] Based on the probabilistic model of the aforementioned light intensity, and assuming the total area and efficiency of the photovoltaic system (PVS) are known, the photovoltaic output can be approximated as follows:

[0042]

[0043] in, η represents the output power of the photovoltaic unit of the i-th CSO during time period t; pv Photoelectric conversion efficiency; Let be the total area of ​​the photovoltaic units in the i-th CSO;

[0044] (2) Tiered carbon trading model

[0045] Carbon trading costs are defined as follows:

[0046]

[0047] Where ξ represents the length of the carbon emission range; This indicates that the actual carbon emissions of the i-th CSO exceed the carbon emission quota. The 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] Formula (17) represents economic benefits; This represents the total operating cost of the i-th CSO, subsidized by the government. The sum of the difference between the charging and discharging revenue and service fee expenses paid by all EV users to the CSO. Operation and maintenance costs of photovoltaic units and energy storage systems Grid interaction cost Carbon trading costs Composition; of which:

[0052]

[0053] Where N represents the number of EV user types and I represents the number of CSOs;

[0054] min represents the function to be minimized; F aw_limit W represents the maximum subsidy that the government can provide to the i-th CSO. g This is the policy subsidy unit price of CSO during time period t;

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

[0056] Let represent the operation and maintenance costs of the photovoltaic and energy storage systems for the i-th CSO, respectively. This represents the output power of the photovoltaic device of the i-th CSO within one cycle; C represents the input power of the energy storage device of the i-th CSO within one cycle; E This indicates the unit price of energy storage capacity; c represents the energy storage configuration capacity of the i-th CSO; bs This indicates the unit price for operation and maintenance of a photovoltaic system;

[0057] These represent the prices at which the i-th CSO purchases and sells electricity from the grid, respectively. The power purchased and sold by the i-th CSO from the grid, respectively;

[0058] Formula (18) represents the peak reduction and shifting benefits used to characterize social benefits; These represent the peak electricity prices of the corresponding EVs before and after the nth type of EV user participates in the i-th CSO's V2G activity. The charging and discharging power at the following levels; This indicates the difference in off-peak electricity prices for the nth type of EV user before and after participating in the i-th CSO's V2G activity. The charging and discharging power at the following levels;

[0059] and constraints:

[0060]

[0061] Wherein, constraint (25) represents the power balance constraint; Take the i-th CSO respectively The positive and negative values ​​in the figure represent the charging power and the discharging power. and They are The value during time period t; This represents the charging and discharging power of the energy storage device during time period t. and Take respectively The positive and negative values ​​of q during time period t, i.e., charging power and discharging power; basic,t This represents the power of the station's regular load during time period t;

[0062] Constraint (26) represents the constraints of energy storage devices; These represent the state of charge of the energy storage device at times t and t+1, respectively. These represent the maximum charging and discharging power of the energy storage device; β t This is a Boolean variable; a value of 1 indicates the charging state, and a value of 0 indicates the discharging state. These represent the minimum and maximum states of charge of the energy storage device, respectively.

[0063] Constraint (27) represents the total power boundary and capacity boundary of the CSO; Determine the i-th CSO as the feasible aggregation region for virtual energy storage devices. Positive Let represent the charging and discharging power of the i-th CSO during time period t, and equations (28) and (29) respectively represent their calculation processes. and S represents the maximum charge / discharge power of the i-th CSO during time period t; i，t Let i represent the energy level of the i-th CSO in time period t. and For the upper and lower limits; ΔS i，t η represents the energy change of the i-th CSO caused by EV connection / disconnection during time period t; Δt is the duration of the change; η is the energy change of the EV connection / disconnection during time period t. ch and η dis η represents charging efficiency and discharging efficiency, respectively. ref This is the flow replenishment coefficient, determined by the flow loss.

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

[0065] Assume each CSO provides K strategies for each type of EV user, and the nth type of EV user chooses the kth charge / discharge power and price strategy of the utility model of the i-th CSO, denoted as: The kth strategy The fitness function is:

[0066]

[0067] Where, x k This represents the proportion of individuals in the nth class of EV users who choose the kth strategy out of the total number of users. The initial proportion of strategies is randomly set to x. o , satisfying 0≤x k≤1, and

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

[0069]

[0070] in, This indicates switching from policy θ to policy θ. The conditional switching probability, where φ is the noise level;

[0071] Based on the dynamic evolution equation of the electric vehicle population, the proportion of individual strategies adopted is updated to solve for the evolutionary stable strategy; wherein the dynamic evolution equation of the electric vehicle population is:

[0072]

[0073] A distributed iterative algorithm is used to solve the evolutionary equilibrium. The dynamic evolution equation of the electric vehicle population given in the above equation is discretized to obtain the final dynamic evolution equation of the electric vehicle population:

[0074]

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

[0076] Preferably, in step S4, an evolutionary game is used to select the optimal CSO for each type of EV user and determine the optimal charge / discharge efficiency and pricing scheme; including:

[0077] The selection probability of each type of EV user is initialized based on the set of charging and discharging power and the current V2G price. The benefits are calculated to evaluate the utility of each type of EV user in choosing different CSOs, and the selection status of the EV user group is updated.

[0078] By iterating until the state selection of each type of EV user converges and an evolutionary equilibrium is reached, the policy probabilities of different CSOs for each clustered EV user are obtained. The policy with the highest probability is selected as the equilibrium policy. Finally, the optimal CSO is selected for each type of EV user, and the optimal charging and discharging efficiency and price scheme are determined.

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

[0080] S51. Initialize the population:

[0081] Input basic data, including population size, maximum number of iterations, and inertia weights; the population is randomly generated using a logistic chaotic mapping method; represented as:

[0082]

[0083] Where s represents the number of iterations, ys, ys + 1 represents the population proportion at the s-th iteration and the population proportion at the (s+1)-th iteration, respectively; For branch parameters;

[0084] S52. Calculate fitness:

[0085] Calculate the fitness values ​​of the objective function to form a multi-objective fitness matrix, and record the optimal population.

[0086] S53. Non-dominated sorting and calculation of crowding:

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

[0088]

[0089] Where A[g-1].h is the h-th objective function value in solution set A, sorted according to the h-th objective function value, and second to the ith solution; These are the maximum and minimum values ​​of the h-th objective function, respectively;

[0090] S54. Selection Operation:

[0091] Select the new top A groups;

[0092] S55, Update Location:

[0093] Based on the selected whale, new candidate solutions are generated by updating its position; a variable spiral method is used to update the position; parameter b is set as a logarithmic fall shape constant, and parameter b is dynamically adjusted with the number of iterations to increase the whale's ability to explore unknown areas, 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 current distance between the whale and the prey, l represents a random number between [-1, 1], b0 is the initial parameter value, k is the rate of parameter adjustment, and 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 results, and finally decode to obtain the current optimal V2G price.

[0098] A V2G pricing system for photovoltaic-storage-charging stations that considers carbon trading, based on evolutionary game theory, includes:

[0099] The simulation and aggregation module is used to execute S1, simulate EV user data using the Monte Carlo method to initialize the charging and discharging power set of EV users; and treat the charging station operator CSO as a virtual energy storage device aggregated by EVs, use the Minkowski method to aggregate the feasible region of CSO, aggregate the behavioral boundary of each type of EV user, and derive the total power boundary and capacity boundary of CSO in the day-ahead phase.

[0100] The module is used to execute S2 and build an EV user model with the optimization goal of maximizing revenue through demand response; and to introduce a photovoltaic power output model and a tiered carbon trading model, with economic and social benefits as the synergistic optimization goals, and to build a CSO model by combining the total power boundary and capacity boundary of the CSO.

[0101] The random generation module is used to execute S3 and randomly generate the V2G price for each CSO for each type of EV user;

[0102] The evolutionary game module is used to execute S4, taking the set of charging and discharging power and the current V2G price as inputs to 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.

[0103] The optimization solution module is used to execute s5, take the optimal charge and discharge efficiency as input, and use an improved multi-objective whale optimization algorithm to solve the CSO model 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, it returns to S4.

[0105] A storage medium storing a computer program for V2G pricing of photovoltaic-storage-charging stations considering carbon trading based on evolutionary game theory, wherein the computer program causes a computer to execute the V2G pricing method for photovoltaic-storage-charging stations 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 configured to be executed by the one or more processors, the programs including methods for executing the V2G pricing method for photovoltaic-storage-charging stations as described above.

[0108] (III) Beneficial Effects

[0109] This invention provides a V2G pricing method, system, storage medium, and electronic device for photovoltaic-storage-charging stations that considers carbon trading, based on evolutionary game theory. Compared with existing technologies, it has the following advantages:

[0110] This invention first uses Monte Carlo simulation to obtain EV user data and simulate the decision-making behavior of EV users in real-world scenarios. Then, it employs the Minkowski method to aggregate feasible power regions of Charge Station Operators (CSOs), deriving the day-ahead total power and capacity boundaries of CSOs, and comprehensively models EV users and CSOs. Next, under the premise of an initialized pricing strategy, it uses evolutionary game theory to select CSOs, charging / discharging power, and pricing schemes for boundedly rational EV users. Then, considering tiered carbon trading, it uses an improved multi-objective whale optimization algorithm to optimize pricing among various CSOs, maximizing economic and social benefits. Finally, it returns the pricing strategy to EV users, iterates multiple times to achieve convergence, and ultimately outputs the globally optimal V2G price. Addressing the V2G pricing problem for EV users and charging station operators (CSOs), this invention focuses on pricing optimization as its core objective, with the ultimate goal of achieving CSO operation and management, and assists in peak shaving, valley filling, and load fluctuation mitigation. Attached Figure Description

[0111] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0112] Figure 1 This is a block diagram of a V2G pricing method for photovoltaic-storage-charging stations that considers carbon trading, based on evolutionary game theory, provided as an embodiment of the present invention. Detailed Implementation

[0113] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention are described clearly and completely. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0114] This application provides a V2G pricing method, system, storage medium, and electronic device for photovoltaic-storage charging stations that considers carbon trading, based on evolutionary game theory, thereby solving the technical problem that the prior art does not consider photovoltaic equipment and carbon emissions.

[0115] The technical solution in this application is to solve the above-mentioned technical problems, and the general idea is as follows:

[0116] Unlike existing technologies that do not consider photovoltaic equipment and carbon emissions in the construction of vehicle-to-grid (V2G) charging station infrastructure, the embodiments of this invention address the V2G pricing issue for electric vehicle (EV) users and charging station operators (CSOs) to improve environmental benefits.

[0117] This invention, based on evolutionary game theory grounded in bounded rationality and limited information, more realistically simulates the decision-making behavior of EV users regarding electricity prices and charging / discharging power. CSOs utilize an improved Non-Dominated Sorting Whale Optimization Algorithm (NSWOA) to achieve multi-objective optimization of economic and social benefits. This intelligent optimization algorithm boasts advantages such as a simple optimization mechanism, strong search capability, and few parameters, achieving synergistic optimization of economic and social benefits and improving the reliability and economy of V2G charging / discharging scheduling. A game equilibrium is reached between CSOs and EV users, consistent with real-world scenarios.

[0118] To better understand the above technical solutions, the following will provide a detailed explanation of the technical solutions in conjunction with the accompanying drawings and specific implementation methods.

[0119] Example 1:

[0120] like Figure 1 As shown, this embodiment of the invention provides a V2G pricing method for photovoltaic-storage-charging stations that considers carbon trading, based on evolutionary game theory, including:

[0121] 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. The Minkowski method is used to aggregate feasible regions for CSO to aggregate the behavioral boundaries of each type of EV user, and the total power boundary and capacity boundary of CSO in the day-ahead stage are derived.

[0122] S2. With the goal of maximizing revenue through demand response, an EV user model is constructed; and a photovoltaic power output model and a tiered carbon trading model are introduced, with economic and social benefits as the synergistic optimization goals, and a CSO model is constructed by combining the total power boundary and capacity boundary of the CSO.

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

[0124] S4. The charging and discharging power set and the current V2G price are used as inputs to the corresponding EV user model, and the optimal CSO is selected for each type of EV user through evolutionary game theory, and the optimal charging and discharging efficiency and price scheme are determined.

[0125] S5. Using the optimal charge and discharge efficiency as input, and employing an improved multi-objective whale optimization algorithm to solve the CSO model, the current optimal V2G price is obtained.

[0126] S6. If the current optimal V2G price meets the preset accuracy requirement, output the globally optimal V2G price; otherwise, return to S4.

[0127] This invention takes into account photovoltaic equipment and carbon emissions, and aims to guide CSOs to actively participate in the V2G market and carbon market, thereby achieving energy conservation and emission reduction, while also considering the economic and social benefits of charging station operation. It has practical application value.

[0128] The following will detail each step of the above solution:

[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 feasible regions for CSO to aggregate the behavioral boundaries 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 this embodiment of the invention is applicable to large parking lots, such as those in office buildings or supermarkets. Electric vehicle users of this business model typically have fixed charging and discharging patterns, including charging and discharging start and end times, daily mileage, etc.

[0131] Data from a National Household Travel Survey (NHTS) of a certain country shows that the departure and return times of EV users each day follow a log-normal distribution, with probability density functions shown in equations (1) and (2), respectively. For any EV user j:

[0132]

[0133] in, For EV user j The probability density function for departure at time t; For EV users The probability density function for the return time; μ1 and σ1 are the mean and standard deviation of the departure time of EV user j each day; μ2 and σ2 are the mean and standard deviation of the return time of EV user j each day. In other words, V2G in Time's up It operates between moments.

[0134] As mentioned above, EV user charging and discharging behavior is mainly affected by factors such as daily mileage, grid connection and disconnection times, initial SOC, expected SOC, and battery characteristics. Furthermore, based on NHTS data, daily mileage follows a log-normal distribution, with the following probability density function:

[0135]

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

[0137] Based on the above analysis of the probability distribution of daily driving mileage and network access time for EV users, we can determine the initial battery remaining charge (SOC) of each EV user at the start of charging.

[0138]

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

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

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

[0142] Cluster all EV user data into N classes. The data records of EV users in the nth class are as follows:

[0143]

[0144] The invention cycle is defined by a 24-hour period throughout the day. These are the departure and return times for the nth type of EV user each day; s n It is the total battery capacity of the nth type of EV user; s n，st s n，en These represent the battery capacity of the nth type of EV user when they join the network and when they leave the network. These are the minimum and maximum battery levels for the nth type of EV user; These represent the maximum charging and discharging power of the nth type of EV user in each time period; It is the initial SOC of the nth type of EV user at the start of charging.

[0145] The overall behavioral boundaries of various types of electric vehicles are aggregated using Minkowski summation, and the total power and energy boundaries for the previous day and real-time phases are derived based on Equation (7). In the day-ahead phase, based on EV information from Equation (5), the parameters of each CSO acting as a virtual energy storage device are calculated using Equation (6). Finally, these calculated parameters are used to obtain the a posteriori day-ahead aggregated feasible region of the CSO, as shown below:

[0146]

[0147] in, The i-th CSO was identified as a feasible aggregation region for virtual energy storage devices. and Let represent the charging and discharging power of the i-th CSO during time period t, respectively. and S represents the maximum charge / discharge power of the i-th CSO during time period t; i，t Let i represent the energy level of the i-th CSO in time period t. and For the upper and lower limits; ΔS i,t η represents the energy change of the i-th CSO caused by the connection / disconnection of the electric vehicle at time t, where Δt is the duration of the change; ch and η dis η represents charging efficiency and discharging efficiency, respectively. ref This is the flow replenishment coefficient, determined by the flow loss.

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

[0149] In this embodiment of the invention, the EV user's final decision is based on the net revenue brought by the V2G service, that is, the V2G revenue minus the total cost caused by battery wear, service fees, parking fees and driving costs, etc. The electric vehicle can only perform one activity, charging or discharging, during the time period t.

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

[0151] Objective function (EV user scheduling plans are only reflected in the day-ahead phase, and their goal is to maximize revenue through demand response):

[0152]

[0153] Formula (8) represents maximizing revenue through demand response; This represents the total revenue of the nth type of EV user participating in the i-th CSO's V2G activity, consisting of charging and discharging revenue. Government subsidies Service fee expenses And the cycle loss of Cd in a 1Ah electric vehicle battery during V2G activities. Composition; of which:

[0154]

[0155]

[0156] in, These represent the charging and discharging power of the nth type of EV user participating in the i-th CSO's V2G activity and the V2G price of the i-th CSO for the nth type of EV user, respectively.

[0157] W award Indicates the unit price subsidized by the government; This represents the charging and discharging power of the nth type of EV user during peak electricity consumption periods;

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

[0159] C c，n T represents the battery investment of the nth EV user; energy L represents the cumulative capacity across all completed charge / discharge cycles. N Indicates the total number of reference charge / discharge cycles when the depth of discharge is 0.8 within the battery's lifespan; η D is the influence factor of V2G on battery cycle life; x1 and x2 are both constants representing battery loss characteristics;

[0160] This indicates the number of battery cycles.

[0161] and constraints:

[0162]

[0163] Wherein, constraint (16) means that the V2G price of the i-th CSO for the n-th type of EV user is within the specified maximum limit q. n，max and minimum limit q n，min Inside;

[0164] Constraint (17) represents the relationship between the percentage change in the remaining battery charge (SOC) in adjacent time periods; η ch η dis These represent charging efficiency and discharging efficiency, respectively; sn Represents the total battery capacity of the nth type of EV user; SOC t SOC t+1 These represent the percentage of remaining battery charge (SOC) in time period t and time period t+1, respectively.

[0165] Constraint (18) indicates that the remaining battery charge (SOC) of the nth type of 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 phase, CSO will optimize energy planning, including charging and discharging of battery energy storage systems (BESS), renewable energy generation, V2G charging stations, and purchasing and selling electricity in the real-time electricity market based on EV user demand response, in order to minimize operating costs. Prior to this, for the CSO model, this embodiment of the invention considers tiered carbon trading, taking into account both the economic and social benefits of CSO, where the minimum peak-to-valley difference is used as the social benefit.

[0167] Specifically, this step first introduces a photovoltaic power output model and a tiered carbon trading model, which are respectively expressed as follows:

[0168] (1) Photovoltaic power output model

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

[0170]

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

[0172]

[0173] Based on the probabilistic model of light intensity, and assuming the total area and efficiency of the photovoltaic system (PVS) are known, the photovoltaic output can be approximated as follows:

[0174]

[0175] in, η represents the output power of the photovoltaic unit of the i-th CSO during time period t; pv Photoelectric conversion efficiency; Let be the total area of ​​the photovoltaic unit of the i-th CSO.

[0176] (2) Tiered carbon trading model

[0177] This invention constructs a tiered carbon trading model to further address the carbon emission problem, defining the carbon trading cost as follows:

[0178]

[0179] Where ξ represents the length of the carbon emission range; This indicates that the actual carbon emissions of the i-th CSO exceed the carbon emission quota. The part is λ, where λ represents the reward coefficient, τ is the penalty coefficient, and pc is the carbon price.

[0180] Building upon this foundation, this step further constructs the CSO model, specifically including:

[0181] The objective function (focusing on both economic benefits and social benefits – peak reduction and shifting benefits, i.e., considering that without an orderly charging strategy, the charging behavior of electric vehicles is entirely determined by the owners, exhibiting off-peak electricity demand during the day and peak electricity demand at night):

[0182]

[0183] Wherein, formula (24) represents economic benefits; This represents the total operating cost of the i-th CSO, subsidized by the government. The sum of the difference between the charging and discharging revenue and service fee expenses paid by all EV users to the CSO. Operation and maintenance costs of photovoltaic units and energy storage systems Grid interaction cost Carbon trading costs Composition; of which:

[0184]

[0185] Where N represents the number of EV user types and I represents the number of CSOs;

[0186] min represents the function to be minimized; F aw_limit W represents the maximum subsidy that the government can provide to the i-th CSO. g This is the policy subsidy unit price of CSO during time period t;

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

[0188] Let represent the operation and maintenance costs of the photovoltaic and energy storage systems for the i-th CSO, respectively. This represents the output power of the photovoltaic device of the i-th CSO within one cycle; C represents the input power of the energy storage device of the i-th CSO within one cycle; E This indicates the unit price of energy storage capacity; c represents the energy storage configuration capacity of the i-th CSO; bs This indicates the unit price for operation and maintenance of a photovoltaic system;

[0189] These represent the prices at which the i-th CSO purchases and sells electricity from the grid, respectively. The power purchased and sold by the i-th CSO from the grid, respectively;

[0190] Formula (25) represents the peak reduction and shifting benefits used to characterize social benefits; These represent the peak electricity prices of the corresponding EVs before and after the nth type of EV user participates in the i-th CSO's V2G activity. The charging and discharging power at the following levels; This indicates the difference in off-peak electricity prices for the nth type of EV user before and after participating in the i-th CSO's V2G activity. The charging and discharging power at that point.

[0191] and constraints:

[0192]

[0193] Wherein, constraint (32) represents the power balance constraint; Take the i-th CSO respectively The positive and negative values ​​in the figure represent the charging power and the discharging power. and They are The value during time period t; This represents the charging and discharging power of the energy storage device during time period t. and Take respectively The positive and negative values ​​in time period t represent charging power and discharging power; qbasic,t represents the power of the station's conventional load in time period t.

[0194] Constraint (33) represents the constraints of energy storage devices; These represent the state of charge of the energy storage device at times t and t+1, respectively. These represent the maximum charging and discharging power of the energy storage device; βt is a Boolean variable, with 1 indicating the charging state and 0 indicating the discharging state. These represent the minimum and maximum states of charge of the energy storage device, respectively.

[0195] Constraint (34), also known as the above formula (7), represents the total power boundary and capacity boundary of the CSO; Determine the i-th CSO as the feasible aggregation region for virtual energy storage devices. and Let represent the charging and discharging power of the i-th CSO during time period t, and equations (35) and (36) respectively represent their calculation processes. and S represents the maximum charge / discharge power of the i-th CSO during time period t; i，t Let i represent the energy level of the i-th CSO in time period t. and For the upper and lower limits; ΔS i，t η represents the energy change of the i-th CSO caused by EV connection / disconnection during time period t; Δt is the duration of the change; η is the energy change of the EV connection / disconnection during time period t. ch and η dis η represents charging efficiency and discharging efficiency, respectively. ref This is the flow replenishment coefficient, determined by the flow loss.

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

[0197] This step performs pricing strategy initialization to facilitate subsequent evolutionary game operations.

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

[0199] In the evolutionary game process of EV users choosing charging stations, EV users obtain economic benefits based on electricity prices and charging / discharging power simulation results to evaluate the utility of the charging station optimization (CSO), and select the optimal CSO using evolutionary game theory. After the users make stable choices, an evolutionary game equilibrium state is formed.

[0200] Assume each CSO provides K strategies for each type of EV user, and the nth type of EV user chooses the kth charge / discharge power and price strategy of the utility model of the i-th CSO, denoted as: The kth strategy The fitness function is:

[0201]

[0202] Where xk represents the proportion of individuals in the nth class of EV users who choose the kth strategy out of the total number of users, and the initial proportion of strategies is randomly set to x. o , satisfying 0≤x k≤1, and

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

[0204]

[0205] in, This indicates switching from policy θ to policy θ. The conditional switching probability, where φ is the noise level;

[0206] Based on the dynamic evolution equation of the electric vehicle population, the proportion of individual strategies adopted is updated to solve for the evolutionary stable strategy; wherein the dynamic evolution equation of the electric vehicle population is:

[0207]

[0208] A distributed iterative algorithm is used to solve the evolutionary equilibrium. The dynamic evolution equation of the electric vehicle population given in the above equation is discretized to obtain the final dynamic evolution equation of the electric vehicle population:

[0209]

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

[0211] Based on this, this step uses evolutionary game theory to select the optimal CSO for each type of EV user and determines the optimal charge / discharge efficiency and pricing scheme; including:

[0212] The selection probability of each type of EV user is initialized based on the set of charging and discharging power and the current V2G price. The revenue is calculated to evaluate the utility of each type of EV user in choosing different CSOs, and the selection status of the EV user group is updated. It can be understood that in the first run, the current V2G price is the V2G price randomly generated in step S1. In each subsequent run, the current V2G price is the current optimal V2G price obtained in the previous iteration.

[0213] By iterating until the state selection of each type of EV user converges and an evolutionary equilibrium is reached, the policy probabilities of different CSOs for each clustered EV user are obtained. The policy with the highest probability is selected as the equilibrium policy. Finally, the optimal CSO is selected for each type of EV user, and the optimal charging and discharging efficiency and price scheme are determined.

[0214] In step S5, the optimal charge-discharge efficiency is used as input, and the improved multi-objective whale optimization algorithm is used to solve the CSO model to obtain the current optimal V2G price.

[0215] This step employs the whale optimization algorithm (non-dominated sorting) to solve the multi-objective optimization problem of the economic and social benefits of charging stations, specifically including:

[0216] S51. Initialize the population:

[0217] Input basic data, including population size, maximum number of iterations, and inertia weights; the population is randomly generated using a logistic chaotic mapping method; represented as:

[0218]

[0219] Where s represents the number of iterations, ys, ys + 1 represents the population proportion at the s-th iteration and the population proportion at the (s+1)-th iteration, respectively; For branch parameters;

[0220] S52. Calculate fitness:

[0221] Calculate the fitness values ​​of the objective function to form a multi-objective fitness matrix, and record the optimal population.

[0222] S53. Non-dominated sorting and calculation of crowding:

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

[0224]

[0225] Where A[g-1].h is the h-th objective function value in solution set A, sorted according to the h-th objective function value, and second to the ith solution; These are the maximum and minimum values ​​of the h-th objective function, respectively;

[0226] S54. Selection Operation:

[0227] Select the new top A groups;

[0228] S55, Update Location:

[0229] Based on the selected whale, new candidate solutions are generated by updating its position; a variable spiral method is used to update the position; parameter b is set as a logarithmic fall shape constant, and parameter b is dynamically adjusted with the number of iterations to increase the whale's ability to explore unknown areas, 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 current distance between the whale and the prey, l represents a random number between [-1, 1], b0 is the initial parameter value, k is the rate of parameter adjustment, and 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 results, 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. After multiple iterations and convergence, the globally optimal V2G price is finally output. The preset precision requirement means that convergence is considered to occur when the current optimal V2G price meets a certain value.

[0236] Thus, this embodiment of the invention has realized the entire process of V2G pricing for photovoltaic-storage-charging stations that considers carbon trading based on evolutionary game theory.

[0237] Example 2:

[0238] This invention provides a V2G pricing system for photovoltaic-storage-charging stations that considers carbon trading, based on evolutionary game theory, comprising:

[0239] The simulation and aggregation module is used to execute S1, simulate EV user data using the Monte Carlo method to initialize the charging and discharging power set of EV users; and treat the charging station operator CSO as a virtual energy storage device aggregated by EVs, use the Minkowski method to aggregate the feasible region of CSO, aggregate the behavioral boundary of each type of EV user, and derive the total power boundary and capacity boundary of CSO in the day-ahead phase.

[0240] The module is used to execute S2 and build an EV user model with the optimization goal of maximizing revenue through demand response; and to introduce a photovoltaic power output model and a tiered carbon trading model, with economic and social benefits as the synergistic optimization goals, and to build a CSO model by combining the total power boundary and capacity boundary of the CSO.

[0241] The random generation module is used to execute S3 and randomly generate the V2G price for each CSO for each type of EV user;

[0242] The evolutionary game module is used to execute S4, taking the set of charging and discharging power and the current V2G price as inputs to 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.

[0243] The optimization solution module is used to execute S5, take the optimal charge and discharge efficiency as input, and use the improved multi-objective whale optimization algorithm to solve the CSO model 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, it returns to S4.

[0245] Example 3:

[0246] This invention provides a storage medium storing a computer program for V2G pricing of photovoltaic-storage-charging stations considering carbon trading based on evolutionary game theory, wherein the computer program causes a computer to execute the V2G pricing method for photovoltaic-storage-charging stations as described in Embodiment 1.

[0247] Example 4:

[0248] This invention provides an electronic device, comprising:

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

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

[0251] In summary, compared with existing technologies, it has the following beneficial effects:

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

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

[0254] 3. The embodiments of the present invention aggregate the feasible power region of the charging station based on the Minkowski method, constrain variables such as the total charging and discharging power of the charging station, and perform comprehensive modeling of CSO and EV based on the aggregated feasible power region of CSO.

[0255] 4. From an economic and environmental perspective, this invention proposes a CSO operation optimization model that considers photovoltaic uncertainty, multi-energy coupling, and V2G demand response. This model can address the risks and instabilities that exist during system operation and make the optimization results robust.

[0256] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0257] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A V2G pricing method for photovoltaic-storage-charging stations considering carbon trading based on evolutionary game theory, characterized in that, include: S1. Use the Monte Carlo method to simulate EV user data to initialize the charging and discharging power set of EV users; Furthermore, the charging station operator (CSO) is regarded as a virtual energy storage device aggregated by EVs. The Minkowski method is used to aggregate feasible regions for CSOs, to aggregate the behavioral boundaries of each type of EV user, and to derive the total power boundary and capacity boundary of CSOs in the day-ahead phase. S2. Construct an EV user model with the optimization goal of maximizing revenue through demand response; In addition, a photovoltaic power output model and a tiered carbon trading model are introduced, with economic and social benefits as the synergistic optimization objectives. The CSO model is constructed by combining 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. The charging and discharging power set and the current V2G price are used as inputs to the corresponding EV user model, and the optimal CSO is selected for each type of EV user through evolutionary game theory, and the optimal charging and discharging efficiency and price scheme are determined. S5. Using the optimal charge and discharge efficiency as input, and employing an improved multi-objective whale optimization algorithm to solve the CSO model, the current optimal V2G price is obtained. S6. If the current optimal V2G price meets the preset accuracy requirement, output the globally optimal V2G price; otherwise, return to S4. The EV user model includes: Objective function: Formula (1) represents maximizing revenue through demand response; This represents the total revenue of the nth type of EV user participating in the i-th CSO's V2G activity, consisting of charging and discharging revenue. Government subsidies Service fee expenses And the cycle loss of Cd in a 1Ah electric vehicle battery during V2G activities. Composition; of which: T energy =L N s n，st or D (7) in, These represent the charging and discharging power of the nth type of EV user participating in the i-th CSO's V2G activity and the V2G price of the i-th CSO for the nth type of EV user, respectively. W award Indicates the unit price subsidized by the government; This represents the charging and discharging power of the nth type of EV user during peak electricity consumption periods; C d,n This represents the cycle loss of the 1Ah battery for the nth EV user; C c，n T represents the battery investment of the nth EV user; energy L represents the cumulative capacity across all completed charge / discharge cycles. N Indicates the total number of reference charge / discharge cycles when the depth of discharge is 0.8 within the battery's lifespan; η D , where x1 and x2 are the factors affecting the battery cycle life; x1 and x2 are both constants representing the battery loss characteristics. Indicates the number of battery cycles; and constraints: Wherein, constraint (9) represents the maximum limit q of the V2G price for the i-th CSO for the n-th type of EV user. n,max and minimum limit q n,min Inside; Constraint (10) represents the relationship between the percentage change in the remaining battery charge (SOC) in adjacent time periods; η ch η dis These represent charging efficiency and discharging efficiency, respectively; s n Represents the total battery capacity of the nth type of EV user; SOC t SOC t+1 These represent the percentage of remaining battery charge (SOC) in time period t and time period t+1, respectively. Constraint (11) indicates that the remaining battery charge (SOC) of the nth type of EV user at different time periods is within the specified maximum limit. and minimum limit Inside; The photovoltaic power output model and the tiered carbon trading model are respectively expressed as: (1) Photovoltaic power output model Assuming that the radiation intensity follows a beta distribution within a specific time window, a probabilistic model of the illumination intensity is constructed as follows: in, Let be the light intensity received by the i-th CSO during time period t; Γ(·) is the gamma function. r max α represents the maximum light intensity over a certain time period; α and p are the expected value μ and variance σ of the light intensity over the selected time period. 2 The result is shown in the following formula: Based on the probabilistic model of light intensity, and assuming the total area and efficiency of the photovoltaic system (PVS) are known, the photovoltaic output can be approximated as: in, η represents the output power of the photovoltaic unit of the i-th CSO during time period t; pv Photoelectric conversion efficiency; Let be the total area of ​​the photovoltaic unit of the i-th CSO; (2) Tiered carbon trading model Carbon trading costs are defined as follows: Where ξ represents the length of the carbon emission range; This indicates that the actual carbon emissions of the i-th CSO exceed the carbon emission quota. The part; λ represents the reward coefficient, τ is the penalty coefficient; p c It is the carbon price; The CSO model includes: Objective function: Formula (17) represents economic benefits; This represents the total operating cost of the i-th CSO, subsidized by the government. The sum of the difference between the charging and discharging revenue and service fee expenses paid by all EV users to the CSO. Operation and maintenance costs of photovoltaic units and energy storage systems Grid interaction cost Carbon trading costs Composition; of which: Where N represents the number of EV user types and I represents the number of CSOs; min represents the function to be minimized; F aw_limit W represents the maximum subsidy that the government can provide to the i-th CSO. g This is the policy subsidy unit price of CSO during time period t; This represents the total charging and discharging power of EV users in the i-th CSO; Let represent the operation and maintenance costs of the photovoltaic and energy storage systems for the i-th CSO, respectively. This represents the output power of the photovoltaic device of the i-th CSO within one cycle; C represents the input power of the energy storage device of the i-th CSO within one cycle; E This indicates the unit price of energy storage capacity; c represents the energy storage configuration capacity of the i-th CSO; bs This indicates the unit price for operation and maintenance of a photovoltaic system; These represent the prices at which the i-th CSO purchases and sells electricity from the grid, respectively. The power purchased and sold by the i-th CSO from the grid, respectively; Formula (18) represents the peak reduction and shifting benefits used to characterize social benefits; These represent the peak electricity prices of the corresponding EVs before and after the nth type of EV user participates in the i-th CSO's V2G activity. The charging and discharging power at the following levels; This indicates the difference in off-peak electricity prices for the nth type of EV user before and after participating in the i-th CSO's V2G activity. The charging and discharging power at the following levels; and constraints: Wherein, constraint (25) represents the power balance constraint; Take the i-th CSO respectively The positive and negative values ​​in the figure represent the charging power and the discharging power. and They are The value during time period t; This represents the charging and discharging power of the energy storage device during time period t. and Take respectively The positive and negative values ​​of q during time period t, i.e., charging power and discharging power; basic，t This represents the power of the station's regular load during time period t; Constraint (26) represents the constraints of energy storage devices; These represent the state of charge of the energy storage device at times t and t+1, respectively. These represent the maximum charging and discharging power of the energy storage device; β t This is a Boolean variable; a value of 1 indicates the charging state, and a value of 0 indicates the discharging state. These represent the minimum and maximum states of charge of the energy storage device, respectively. Constraint (27) represents the total power boundary and capacity boundary of the CSO; Determine the i-th CSO as the feasible aggregation region for virtual energy storage devices. and Let represent the charging and discharging power of the i-th CSO during time period t, and equations (28) and (29) respectively represent their calculation processes. and S represents the maximum charge / discharge power of the i-th CSO during time period t; i,t Let i represent the energy level of the i-th CSO in time period t. and For the upper and lower limits; ΔS i,t η represents the energy change of the i-th CSO caused by EV connection / disconnection during time period t; Δt is the duration of the change; η is the energy change of the EV connection / disconnection during time period t. ch and η dis η represents charging efficiency and discharging efficiency, respectively. ref The flow compensation factor is determined by the flow loss. The evolutionary game in S4 refers to: Assume each CSO provides K strategies for each type of EV user, and the nth type of EV user chooses the kth charge / discharge power and price strategy of the utility model of the i-th CSO, denoted as: The kth strategy The fitness function is: Where xk represents the proportion of individuals in the nth class of EV users who choose the kth strategy out of the total number of users, and the initial proportion of strategies is randomly set to x. o , satisfying 0≤x k ≤1, and Calculate the conditional switching rate for each strategy: in, This indicates switching from policy θ to policy θ. The conditional switching probability, where φ is the noise level; Based on the dynamic evolution equation of the electric vehicle population, the proportion of individual strategies adopted is updated to solve for the evolutionary stable strategy; wherein the dynamic evolution equation of the electric vehicle population is: A distributed iterative algorithm is used to solve the evolutionary equilibrium. The dynamic evolution equation of the electric vehicle population given in the above equation is discretized to obtain the final dynamic evolution equation of the electric vehicle population: in, λ represents the number of iterations in the evolutionary game; λ represents the iteration step size in the evolutionary game. In step S4, an evolutionary game is used to select the optimal CSO for each type of EV user and determine the optimal charge / discharge efficiency and pricing scheme; including: Initialize the selection probability for each type of EV user, calculate the revenue based on the charging and discharging power set and the current V2G price to evaluate the utility of each type of EV user choosing different CSOs, and update the selection status of the EV user group. By iterating until the state selection of each type of EV user converges and an evolutionary equilibrium is reached, the strategy probability of different CSOs for each clustered EV user is obtained. The strategy with the highest 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 scheme are determined. In step S5, the optimal charge / discharge efficiency is used as input, and an improved multi-objective whale optimization algorithm is employed to solve the CSO model to obtain the current optimal V2G price; including: S51. Initialize the population: Input basic data, including population size, maximum number of iterations, and inertia weights; the population is randomly generated using a logistic chaotic mapping method; represented as: Where s represents the number of iterations, ys, ys + 1 represents the population proportion at the s-th iteration and the population proportion at the (s+1)-th iteration, respectively; For branch parameters; S52. Calculate fitness: Calculate the fitness values ​​of the objective function to form a multi-objective fitness matrix, and record the optimal population. S53. Non-dominated sorting and calculation of crowding: The population is divided into different Pareto levels based on the multi-objective fitness matrix, and the crowding degree is calculated; the crowding degree formula is: Where A[g-1].h is the h-th objective function value in solution set A, sorted according to the h-th objective function value, and second to the ith solution; These are the maximum and minimum values ​​of the h-th objective function, respectively; S54. Selection Operation: Select the new top A groups; S55, Update Location: Based on the selected whale, new candidate solutions are generated by updating its position; a variable spiral method is used to update the position; parameter b is set as a logarithmic fall shape constant, and parameter b is dynamically adjusted with the number of iterations to increase the whale's ability to explore unknown areas, 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 process; D * The distance between the whale and its prey is represented by ; l represents a random number between [-1, 1]; b0 is the initial parameter value; k is the rate at which the parameters are adjusted; and 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 results, and finally decode to obtain the current optimal V2G price.

2. A V2G pricing system for photovoltaic-storage-charging stations considering carbon trading based on evolutionary game theory, characterized in that, The method for implementing the V2G pricing method for photovoltaic-storage charging stations as described in claim 1 includes: The simulation and aggregation module is used to execute S1, simulate EV user data using the Monte Carlo method to initialize the charging and discharging power set of EV users; and treat the charging station operator CSO as a virtual energy storage device aggregated by EVs, use the Minkowski method to aggregate the feasible region of CSO, aggregate the behavioral boundary of each type of EV user, and derive the total power boundary and capacity boundary of CSO in the day-ahead phase. The module is used to execute S2 and build an EV user model with the optimization goal of maximizing revenue through demand response; and to introduce a photovoltaic power output model and a tiered carbon trading model, with economic and social benefits as the synergistic optimization goals, and to build a CSO model by combining the total power boundary and capacity boundary of the CSO. The random generation module is used to execute S3 and randomly generate the V2G price for each CSO for each type of EV user; The evolutionary game module is used to execute S4, taking the set of charging and discharging power and the current V2G price as inputs to 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. The optimization solution module is used to execute S5, take the optimal charge and discharge efficiency as input, and use the improved multi-objective whale optimization algorithm to solve the CSO model 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, it returns to S4.

3. A storage medium, characterized in that, It stores a computer program for V2G pricing of photovoltaic-storage-charging stations that takes carbon trading into account, based on evolutionary game theory, wherein the computer program causes the computer to execute the V2G pricing method for photovoltaic-storage-charging stations as described in claim 1.

4. 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 configured to be executed by the one or more processors, the programs including methods for performing the V2G pricing method for photovoltaic-storage charging stations as described in claim 1.

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