Distributed photovoltaic electric vehicle charging station energy transaction method and system

Abstract

The invention relates to a distributed photovoltaic electric vehicle charging station energy transaction method. The method comprises the following steps of 1, establishing an electric vehicle distribution problem model in a traffic network; 2, establishing a single electric vehicle charging station operation model which does not consider distributed energy transaction; step 3, on the basis of the step 2, establishing and considering distributed energy transaction, and establishing an electric vehicle charging station operation model considering the distributed energy transaction; fourthly, on the basis of the first step and the third step, a distributed energy transaction model utilizing the Nash bargaining theory is established; and 5, solving the proposed related model by using a distributed solution algorithm, and carrying out simulation verification on an MATLAB platform. According to the method, the electric vehicles needing to be charged can be reasonably distributed, the profit level of the electric vehicle charging station can be effectively improved, and fair distribution of benefits is achieved.

Description

Technical Field

[0001] The present invention relates to the field of distributed energy trading technology, and in particular to a distributed photovoltaic electric vehicle charging station energy trading method and system. Background Art

[0002] In recent years, distributed renewable energy generation has developed rapidly, and more and more distributed renewable energy such as photovoltaics have been deployed to electric vehicles. Electric vehicle charging stations equipped with distributed renewable energy generation can provide charging services to electric vehicles and sell electricity to the upper power grid, which can reduce the operating costs of charging stations and enhance their profitability. To achieve a higher level of utilization of distributed renewable energy, it is necessary to consider the integration of battery energy storage systems to achieve more benefits. Therefore, it is of greater significance to study the operation and power trading of this new type of electric vehicle. Summary of the invention

[0003] In view of the above problems existing in the prior art, the present invention is proposed.

[0004] Therefore, the present invention provides a distributed photovoltaic electric vehicle charging station energy trading method, which takes into account the operation of the transportation network, deploys distributed renewable energy such as photovoltaics to electric vehicle charging stations, and formulates a distributed energy trading mechanism, which can effectively improve the profitability of electric vehicle charging stations and achieve fair distribution of benefits.

[0005] To solve the above technical problems, the present invention provides the following technical solutions: a distributed photovoltaic electric vehicle charging station energy trading method, comprising: establishing an electric vehicle configuration model in a transportation network; establishing a single electric vehicle charging station operation model without distributed energy trading; considering distributed energy trading based on the electric vehicle charging station operation model, and establishing an electric vehicle charging station operation model considering distributed energy trading; establishing a distributed energy trading model based on the electric vehicle allocation model and the electric vehicle charging station operation model using Nash bargaining theory; solving the distributed energy trading model using a distributed solution algorithm to obtain a distributed energy trading strategy for the electric vehicle charging station.

[0006] As a preferred solution of the distributed photovoltaic electric vehicle charging station energy trading method described in the present invention, wherein: the electric vehicle configuration model in the traffic network includes, considering the physical constraints of the traffic roads, the traffic demand of the vehicles and the charging demand of the electric vehicles, establishing an electric vehicle configuration model with the comprehensive time cost of smooth travel and delayed travel caused by congestion as the objective function;

[0007] The objective function is expressed as:

[0008]

[0009] In the formula, C TN is the comprehensive time cost; and are the traffic flow of electric vehicles that need to be charged and the traffic flow of vehicles without charging requirements on road k at time t; time and λ delay are the time cost coefficients of smooth travel and delayed travel caused by traffic congestion at time t; and t k,t and are the smooth driving time and congested driving time of road k respectively;

[0010] The road traffic flow restriction constraint is expressed as:

[0011]

[0012]

[0013]

[0014]

[0015]

[0016]

[0017]

[0018] In the formula, f k,t is the traffic flow of the road at time t; K is the road set; is the time set; c k is the road capacity; and They are the paths h at time t. se Traffic flow of electric vehicles and vehicles without charging demand; is the correlation coefficient; h se is the path corresponding to the starting point and the end point pair se; H se is the set of paths in the transportation network; κ EVs and κ ch are the proportion of electric vehicles and electric vehicles that need to be charged; d se,t is the flow demand of the starting point and the end point for se; SE TN is the set of start-point and end-point pairs se;

[0019] The charging power of electric vehicles is as follows:

[0020]

[0021] In the formula, is the charging demand allocated to the EV charging stations by the transportation network operator at time t; is the traffic flow of electric vehicles assigned to charging stations at time t; The power demand of each EV planned by the transport network operator at time t; Assemble for electric vehicles;

[0022] The compact mode of the electric vehicle configuration model is expressed as:

[0023]

[0024] As a preferred solution of the distributed photovoltaic electric vehicle charging station energy trading method described in the present invention, wherein: the single electric vehicle charging station operation model without distributed energy trading includes establishing a single electric vehicle station operation model with the goal of providing charging services to electric vehicles and selling electricity to the power grid minus the comprehensive operating costs of purchasing electricity and energy storage system maintenance costs;

[0025] The objective function is expressed as:

[0026]

[0027] In the formula, is the operating income of a single charging station; and They are the electricity purchased and sold from the upstream power grid at time t respectively; and are the electricity purchase price and electricity sales price of the charging station at time t respectively; and are the charging and discharging power of the energy storage system in the charging station at time t respectively; and are the charging and discharging efficiencies of the charging station, respectively; is the operating cost coefficient of the energy storage system; Reserve charging load in advance for time t; is the charging power of the electric vehicle at time t; is the charging power of the charging station in the transportation network at time t; is the charging electricity price of the charging station at time t; Δt is a period of time; α(l) is the set of electric vehicles dispatched by the charging station;

[0028] The operating constraints of the battery energy storage system are expressed as:

[0029]

[0030] In the formula, is the photovoltaic output power at time t; is the maximum photovoltaic output power; is a binary variable, indicating the state of the charging station at time t; M is a large number;

[0031] The constraints of electric vehicles dispatched by charging stations are expressed as:

[0032]

[0033]

[0034]

[0035]

[0036]

[0037]

[0038] Where, SoC l,t is the charge of the charging station at time t; SoC l,t+1 is the charge of the charging station at time t+1; is the initial charge of the charging station; and The minimum and maximum charge of the charging station at time t respectively; is the maximum power of the energy storage system; is a binary variable, indicating the state of the energy storage system at time t; and are the charge amounts of the electric vehicles dispatchable at the charging station when they arrive and leave, respectively; is the charge of the electric vehicle at time t; is the charge of the electric vehicle at time t+1; and is the charge of the electric vehicle when it arrives and leaves; and are the minimum and maximum charges of electric vehicles respectively; η c charging efficiency for electric vehicles; Maximum charging power for electric vehicles;

[0039] The power balance constraint is expressed as:

[0040]

[0041] The compact mode of the electric vehicle charging station operation model without distributed energy trading is expressed as:

[0042]

[0043] As a preferred solution of the distributed photovoltaic electric vehicle charging station energy trading method described in the present invention, the electric vehicle charging station operation model considering distributed energy trading includes considering distributed energy trading, electric vehicle charging stations buying or selling energy from each other and obtaining corresponding rewards, and supplementing the model constraints when considering distributed energy trading:

[0044] Additional consideration of distributed energy electric vehicle charging station operation constraints:

[0045]

[0046]

[0047]

[0048] Where, ν l,t The reward for buying or selling a charging station in distributed energy transactions at time t; is the transaction capacity of charging stations in distributed energy transactions at time t; transaction costs for charging stations; Paying for the cost of charging stations;

[0049] Convert the original problem into the first stage of a two-stage problem:

[0050] The first stage is the social welfare maximization problem, which is expressed as:

[0051]

[0052] As a preferred solution of the distributed photovoltaic electric vehicle charging station energy trading method described in the present invention, wherein: the distributed energy trading model established by using the Nash bargaining theory includes: the distributed energy trading model using the Nash bargaining theory:

[0053]

[0054]

[0055] Transform the original problem into the second stage of the following two-stage problem:

[0056] The second stage is the problem of benefit distribution, which can be expressed as:

[0057]

[0058] In the formula, ψ l is the auxiliary variable of the subproblem.

[0059] As a preferred solution of the distributed photovoltaic electric vehicle charging station energy trading method described in the present invention, wherein: the distributed energy trading model is solved by using a distributed solution algorithm, including a distributed method for solving the first stage problem:

[0060]

[0061]

[0062]

[0063]

[0064] In the formula, is an auxiliary variable; l,t is the Lagrange multiplier; is the Lagrange multiplier of the kth iteration; is the transaction volume of the kth iteration; is the auxiliary variable for the kth iteration.

[0065] As a preferred solution of the distributed photovoltaic electric vehicle charging station energy trading method described in the present invention, the closed form solution of the second stage problem is obtained:

[0066]

[0067] In the formula, ψ l′ It is an auxiliary variable before iteration.

[0068] As a preferred solution of the distributed photovoltaic electric vehicle charging station energy trading system described in the present invention, it includes: a measurement module, a calculation module and a control output module;

[0069] The measuring module measures the parameter value required for controlling the superheated steam temperature through a sensor;

[0070] The calculation module stores a calculation logic of a distributed photovoltaic electric vehicle charging station energy trading method, and calculates the cooling water flow rate according to the parameter value measured by the measurement module;

[0071] The control output module adjusts the cooling water flow regulating valve to make the actual cooling water flow consistent with the calculation result of the calculation module.

[0072] A computer device comprises a memory and a processor, wherein the memory stores a computer program, and wherein when the processor executes the computer program, the processor implements the steps of any one of the methods for energy trading in a distributed photovoltaic electric vehicle charging station.

[0073] A computer-readable storage medium having a computer program stored thereon, characterized in that when the computer program is executed by a processor, the steps of any one of the methods of energy trading in a distributed photovoltaic electric vehicle charging station are implemented.

[0074] Beneficial effects of the invention: The invention proposes a distributed energy trading method for electric vehicle charging stations that deploy distributed photovoltaics and consider the operation of the transportation network, which can effectively improve the profitability of electric vehicle charging stations and achieve fair distribution of benefits. The invention adopts a distributed solution algorithm to solve the relevant model, which can improve the efficiency of problem solving. BRIEF DESCRIPTION OF THE DRAWINGS

[0075] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0076] Figure 1 A schematic diagram of the topological structure of a traffic network (a) and the topological structure of a power network (b) for a distributed photovoltaic electric vehicle charging station energy trading method provided by an embodiment of the present invention.

[0077] Figure 2 A schematic diagram of charging loads allocated to different charging stations by a transportation network operator in a distributed photovoltaic electric vehicle charging station energy trading method provided in an embodiment of the present invention.

[0078] Figure 3 A distributed energy trading result between electric vehicle charging stations of a distributed photovoltaic electric vehicle charging station energy trading method provided in an embodiment of the present invention.

[0079] Figure 4 The iterative process of the algorithm proposed in the distributed photovoltaic electric vehicle charging station energy trading method provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0080] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0081] Example 1, reference Figure 1-Figure 4, which is the first embodiment of the present invention, provides a distributed photovoltaic electric vehicle charging station energy trading method, comprising:

[0082] Step 1: Establish an electric vehicle configuration model in the transportation network;

[0083] Step 2: Establish a single EV charging station operation model without distributed energy trading;

[0084] Step 3: Based on the model established in step 2, considering distributed energy transactions, an electric vehicle charging station operation model considering distributed energy transactions is established;

[0085] Step 4: Based on the electric vehicle allocation model established in step 1 and the electric vehicle charging station operation model established in step 3, a distributed energy transaction model is established using the Nash bargaining theory;

[0086] Step 5: Use the distributed solution algorithm to solve the distributed energy trading model in step 4 and obtain the distributed energy trading strategy of the electric vehicle charging station.

[0087] A distributed photovoltaic electric vehicle charging station energy trading method, the first step is to establish an electric vehicle allocation problem model in a transportation network; the second step is to establish a single electric vehicle charging station operation model without considering distributed energy trading; the third step is to establish an electric vehicle charging station operation model considering distributed energy trading on the basis of the second step; the fourth step is to establish a distributed energy trading model using Nash bargaining theory on the basis of the first and third steps; the fifth step is to solve the proposed related models using a distributed solution algorithm and perform simulation verification on a MATLAB platform.

[0088] The specific process is as follows:

[0089] The first step is to establish a model for the configuration of electric vehicles in the transportation network.

[0090] The objective function is as follows:

[0091]

[0092] The road traffic flow restrictions are as follows:

[0093]

[0094]

[0095]

[0096]

[0097]

[0098]

[0099]

[0100] The charging power of electric vehicles is as follows:

[0101]

[0102] Therefore, the compact model of the electric vehicle assignment problem is as follows:

[0103]

[0104] The second step is to establish an operation model of electric vehicle charging stations without distributed energy trading.

[0105] The objective function is as follows:

[0106]

[0107] The operating constraints of the battery energy storage system are as follows:

[0108]

[0109] The constraints on electric vehicles dispatched by charging stations are as follows:

[0110]

[0111]

[0112]

[0113]

[0114]

[0115]

[0116] The power balance constraints are as follows:

[0117]

[0118] Therefore, the compact mode of the electric vehicle charging station operation model without distributed energy trading is as follows:

[0119] The third step is to establish an electric vehicle charging station operation model considering distributed energy trading.

[0120] Based on step 1, the operating constraints of electric vehicle charging stations with distributed energy are additionally considered:

[0121]

[0122]

[0123]

[0124] Transform the original problem above into the first stage of the following two-stage problem:

[0125] Stage 1: The problem of maximizing social welfare

[0126]

[0127] Transform the original problem above into the first stage of the following two-stage problem:

[0128] Stage 1: The problem of maximizing social welfare

[0129]

[0130] The fourth step is to establish a distributed energy transaction model using Nash bargaining theory.

[0131] Distributed energy transaction model using Nash bargaining theory:

[0132]

[0133]

[0134] Transform the original problem above into the second stage of the following two-stage problem:

[0135] Phase 2: Benefit Distribution Issues

[0136]

[0137] The fifth step is to use the distributed solution algorithm to solve the relevant models and perform simulation verification on the MATLAB platform.

[0138] Distributed approach to solving the first stage problem:

[0139]

[0140]

[0141]

[0142]

[0143] Find a closed-form solution to the second-stage problem:

[0144]

[0145] In the formula, ψ l′ It is an auxiliary variable before iteration.

[0146] All the above models and their solutions are simulated and verified on the MATLAB platform.

[0147] A system consisting of a transportation network and several electric vehicle charging stations is used as the research object to verify the effectiveness of the method. The topological structure of the power and transportation coordination network is as follows: Figure 1 shown.

[0148] Four electric vehicle charging stations are selected on different roads to show their detailed charging loads allocated by the transportation network operator and study their distributed energy transactions. EVCS#12 is equipped with photovoltaic and energy storage systems, EVCS#17 is equipped with energy storage systems only, EVCS#9 is equipped with photovoltaic only, and EVCS#20 is not equipped with any equipment. The detailed charging loads allocated by the transportation network operator are as follows: Figure 2 As shown. It can be concluded that the electric vehicle allocation model proposed in the present invention can realize the traffic flow allocation in the traffic network including electric vehicles that need to be charged.

[0149] Detailed energy transactions between electric vehicle charging stations Figure 3 As shown in the figure, it can be concluded that when distributed renewable energy generation is sufficient during the day, EVCS#12 and EVCS#9 sell their excess power to EVCS#17 and EVCS#20, while EVCS#12 still provides power to locations without distributed energy generation at night.

[0150] The operating profits of all electric vehicle charging stations with and without considering distributed energy are shown in Table 1. As can be seen from the table, the transaction settlement between charging stations is balanced. The overall profit of all charging stations has increased by nearly 16.07%. The operating profits of individual charging stations have increased by 16.42%, 17.69%, 20.04% and 12.26% respectively. When considering distributed energy transactions, EVCS#17 and EVCS#20 purchase energy from other charging stations to increase their own operating benefits, and they need to pay costs but the benefits are still increasing. EVCS#12 and EVCS#9 reduce electricity sales to the upstream power grid, but they can obtain additional energy payments from other charging stations to increase their final benefits.

[0151] Table 1

[0152]

[0153] The performance of the algorithm execution under different convergence precisions is as follows Figure 4As shown. It can be seen from the figure that for different convergence progress values, the number of iterations and the relative error at convergence are different. When it is 0.01, after the 84th iteration, the solution result using the distributed algorithm converges to $1811.71. When it is 0.05, after the 341st iteration, a convergence result of about $1805.44 can be obtained. The time spent is 36s and 82s respectively, which is acceptable. Therefore, the distributed algorithm of the present invention can effectively solve the proposed DET problem.

[0154] This embodiment also provides a computing device applicable to a distributed photovoltaic electric vehicle charging station energy trading method, including:

[0155] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc., which can store program codes.

[0156] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0157] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering or, if necessary, processing in another suitable manner, and then stored in a computer memory.

[0158] Example 2

[0159] The second embodiment of the present invention provides a flow-adaptive power system fault recording data acquisition system, which is characterized by: including an electric vehicle configuration model, a single-station charging operation model, a distributed charging model, a Nash bargaining transaction model, and a distributed solution model;

[0160] Electric vehicle configuration model, to establish an electric vehicle configuration model in the transportation network;

[0161] Single-station charging operation model, establishes a single electric vehicle charging station operation model without distributed energy trading;

[0162] Considering the distributed charging model, based on the electric vehicle charging station operation model, considering distributed energy trading, an electric vehicle charging station operation model considering distributed energy trading is established;

[0163] Nash bargaining transaction model, based on the electric vehicle allocation model and the electric vehicle charging station operation model, uses Nash bargaining theory to establish a distributed energy transaction model;

[0164] Distributed solution model, uses distributed solution algorithm to solve the distributed energy trading model and obtain the distributed energy trading strategy of electric vehicle charging stations.

[0165] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, a plurality of steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

Claims

1. A distributed photovoltaic electric vehicle charging station energy trading method, characterized in that: include, Modeling the deployment of electric vehicles in transportation networks; Establish a single EV charging station operation model without distributed energy trading; According to the operation model of electric vehicle charging station, considering distributed energy trading, an operation model of electric vehicle charging station considering distributed energy trading is established; According to the electric vehicle allocation model and the electric vehicle charging station operation model, the distributed energy transaction model is established using the Nash bargaining theory; The distributed energy trading model is solved using a distributed solution algorithm to obtain the distributed energy trading strategy for electric vehicle charging stations.

2. A distributed photovoltaic electric vehicle charging station energy trading method as claimed in claim 1, characterized in that: The electric vehicle configuration model in the traffic network includes considering the physical constraints of traffic roads, the traffic demand of vehicles and the charging demand of electric vehicles, and establishing an electric vehicle configuration model with the comprehensive time cost of smooth travel and delayed travel caused by congestion as the objective function; The objective function is expressed as: In the formula, C TN is the comprehensive time cost; and are the traffic flow of electric vehicles that need to be charged and the traffic flow of vehicles without charging requirements on road k at time t; time and λ delay are the time cost coefficients of smooth travel and delayed travel caused by traffic congestion at time t; and t k,t and are the smooth driving time and congested driving time of road k respectively; The road traffic flow restriction constraint is expressed as: In the formula, f k,t is the traffic flow of the road at time t; K is the road set; is the time set; c k is the road capacity; and They are the paths h at time t. se Traffic flow of electric vehicles and vehicles without charging demand; is the correlation coefficient; h se is the path corresponding to the starting point and the end point pair se; H se is the set of paths in the transportation network; κ EVs and κ ch are the proportion of electric vehicles and electric vehicles that need to be charged; d se,t is the flow demand of the starting point and the end point for se; SE TN is the set of start-point and end-point pairs se; The charging power of electric vehicles is as follows: In the formula, is the charging demand allocated to the EV charging stations by the transportation network operator at time t; is the traffic flow of electric vehicles assigned to charging stations at time t; The power demand of each EV planned by the transport network operator at time t; Assemble for electric vehicles; The compact mode of the electric vehicle configuration model is expressed as: minimizeC TN (f,p) subjectto:(2)-(9) 3. A distributed photovoltaic electric vehicle charging station energy trading method as claimed in claim 2, characterized in that: The single electric vehicle charging station operation model without distributed energy trading includes establishing a single electric vehicle station operation model with the goal of providing charging services to electric vehicles and selling electricity to the power grid minus the comprehensive operating costs of purchasing electricity and maintaining the energy storage system; The objective function is expressed as: In the formula, is the operating income of a single charging station; and They are the electricity purchased and sold from the upstream power grid at time t respectively; and are the electricity purchase price and electricity sales price of the charging station at time t respectively; and are the charging and discharging power of the energy storage system in the charging station at time t respectively; and are the charging and discharging efficiencies of the charging station, respectively; is the operating cost coefficient of the energy storage system; Reserve charging load in advance for time t; is the charging power of the electric vehicle at time t; is the charging power of the charging station in the transportation network at time t; is the charging electricity price of the charging station at time t; Δt is a period of time; α(l) is the set of electric vehicles dispatched by the charging station; The operating constraints of the battery energy storage system are expressed as: In the formula, is the photovoltaic output power at time t; is the maximum photovoltaic output power; is a binary variable, indicating the state of the charging station at time t; M is a large number; The constraints of electric vehicles dispatched by charging stations are expressed as: Where, SoC l,t is the charge of the charging station at time t; SoC l,t+1 is the charge of the charging station at time t+1; is the initial charge of the charging station; and The minimum and maximum charge of the charging station at time t respectively; is the maximum power of the energy storage system; is a binary variable, indicating the state of the energy storage system at time t; and are the charge amounts of the electric vehicles dispatchable at the charging station when they arrive and leave, respectively; is the charge of the electric vehicle at time t; is the charge of the electric vehicle at time t+1; and is the charge of the electric vehicle when it arrives and leaves; and are the minimum and maximum charge of electric vehicles respectively; η c charging efficiency for electric vehicles; Maximum charging power for electric vehicles; The power balance constraint is expressed as: The compact mode of the electric vehicle charging station operation model without distributed energy trading is expressed as: subjectto:(12)-(19) 4. A distributed photovoltaic electric vehicle charging station energy trading method as claimed in claim 3, characterized in that: The electric vehicle charging station operation model considering distributed energy transactions includes considering distributed energy transactions, electric vehicle charging stations buying or selling energy from each other and obtaining corresponding rewards, and supplementing the model constraints when considering distributed energy transactions: Additional consideration of distributed energy electric vehicle charging station operation constraints: Where, ν l,t The reward for buying or selling a charging station in distributed energy transactions at time t; is the transaction capacity of charging stations in distributed energy transactions at time t; transaction costs for charging stations; Paying the costs for charging stations; The original problem is transformed into the first stage of a two-stage problem, the social welfare maximization problem, which can be expressed as:

5. A distributed photovoltaic electric vehicle charging station energy trading method as claimed in claim 4, characterized in that: The distributed energy transaction model established by using the Nash bargaining theory includes: st(12)-(19),(21)-(23)and(25) (25) The original problem is transformed into the second stage of the following two-stage problem, the benefit distribution problem, which can be expressed as: In the formula, ψ l is the auxiliary variable of the subproblem.

6. A distributed photovoltaic electric vehicle charging station energy trading method as claimed in claim 5, characterized in that: The method of solving the distributed energy trading model using a distributed solution algorithm includes a distributed method for solving the first stage problem: In the formula, is an auxiliary variable; l,t is the Lagrange multiplier; is the Lagrange multiplier of the kth iteration; is the transaction volume of the kth iteration; is the auxiliary variable for the kth iteration.

7. A distributed photovoltaic electric vehicle charging station energy trading method as claimed in claim 6, characterized in that: Find a closed-form solution to the second-stage problem: In the formula, ψ l′ It is an auxiliary variable before iteration.

8. A system based on a distributed photovoltaic electric vehicle charging station energy trading method according to any one of claims 1 to 7, characterized in that: Including electric vehicle configuration model, single station charging operation model, distributed charging model, Nash bargaining transaction model, and distributed solution model; Electric vehicle configuration model, to establish an electric vehicle configuration model in the transportation network; Single-station charging operation model, establishes a single electric vehicle charging station operation model without distributed energy trading; Considering the distributed charging model, based on the electric vehicle charging station operation model, considering distributed energy trading, an electric vehicle charging station operation model considering distributed energy trading is established; Nash bargaining transaction model, based on the electric vehicle allocation model and the electric vehicle charging station operation model, uses Nash bargaining theory to establish a distributed energy transaction model; Distributed solution model, uses distributed solution algorithm to solve the distributed energy trading model and obtain the distributed energy trading strategy of electric vehicle charging stations.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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