Electric vehicle charging and discharging station planning method, system and device based on game theory, medium and product

Abstract

The invention discloses an electric vehicle charging and discharging station planning method, system and device based on the game theory, a medium and a product, and relates to the field of electric vehicle charging and discharging station planning decision, and the method comprises the steps: determining an operation target of a charging and discharging station; obtaining basic data; according to the basic data and the relationship among the technical units, constructing a multi-objective optimization model by adopting an evolutionary game-based method; obtaining a charging and discharging station planning scheme according to the operation target of the charging and discharging station and the multi-target optimization model; the charging and discharging station planning scheme comprises an electric power service object, a capacity configuration index, the number of charging and discharging equipment and interaction electric quantity; the electric power service object is a distribution network load or a distributed power supply system. According to the invention, the scientificity and stability of electric vehicle charging and discharging station planning can be improved.

Description

Technical Field

[0001] The present application relates to the field of electric vehicle charging and discharging station planning and decision-making, and particularly to a method, system, device, medium and product for planning an electric vehicle charging and discharging station based on game theory. Background Technique

[0002] In an urban distribution network system integrating vehicle-to-grid (V2G), electric vehicle aggregators configure the charging and discharging facilities and energy storage devices of charging and discharging stations to conduct large-scale grid connection and coordinated charging and discharging scheduling for electric vehicle groups. This can not only provide power auxiliary services for distributed generation systems, but also provide multi-dimensional services such as capacity support, power balance and frequency regulation for the distribution network system, so as to realize the orderly charging and discharging management of electric vehicle groups and effectively promote the fluctuation suppression and peak-valley load regulation of regional distribution systems. However, in the process of the development of vehicle-to-grid interaction, there are challenges in the complexity of the investment planning of charging and discharging stations. There are complementary or restrictive relationships among the multiple participating entities in the system and the operating characteristics of different combinations of technical units, which significantly increases the uncertainty of the planning and operation of charging and discharging stations. Under this technical background, how to more accurately plan key parameters such as the power service objects, capacity configuration indicators, the number and power of charging and discharging devices of electric vehicle charging and discharging stations has become a technical problem to be solved urgently. Summary of the Invention

[0003] The purpose of the present application is to provide a method, system, device, medium and product for planning an electric vehicle charging and discharging station based on game theory, which can improve the scientificity and stability of the planning of electric vehicle charging and discharging stations.

[0004] To achieve the above purpose, the present application provides the following solutions:

[0005] In the first aspect, the present application provides a method for planning an electric vehicle charging and discharging station based on game theory, and the method for planning an electric vehicle charging and discharging station based on game theory includes:

[0006] Determine the operation objective of the charging and discharging station;

[0007] Obtain basic data; the basic data includes: vehicle charging and discharging characteristics, travel behavior data, distribution network load characteristics, distributed generation output characteristics;

[0008] Based on the basic data and the relationships among various technical units, a multi-objective optimization model is constructed based on the evolutionary game model; the technical units include: distributed power systems, distribution network loads, electric vehicle clusters, and charging and discharging stations; the relationships among the technical units are that the distributed power system adopts distributed power or introduces the flexible capacity of the electric vehicle cluster through the V2G mode, the distribution network load interacts directly with the distributed power system as the electricity demand side or operates in coordination with the charging and discharging stations and the supporting energy storage system, the charging and discharging station centrally schedules the electric vehicle cluster to directly provide capacity, electricity, and frequency support to the distribution network load or serves as an auxiliary regulation device for the distributed power source, and the electric vehicle cluster issues and does not issue response charging and discharging instructions;

[0009] According to the operation objectives of the charging and discharging station, a multi-objective optimization model is constructed to obtain the charging and discharging station planning scheme; the charging and discharging station planning scheme includes: power service objects, capacity configuration indicators, the number of charging and discharging devices, and interactive electricity; the power service objects are the distribution network load or the distributed power system.

[0010] Optionally, the constructing of the multi-objective optimization model based on the evolutionary game model according to the basic data and the relationships among the technical units specifically includes:

[0011] According to the basic data and the relationships among the technical units, determine the power output strategy of the distributed power source, the capacity configuration strategy of the distribution network load, the power auxiliary service strategy of the charging and discharging station, and the response strategy of the electric vehicle cluster;

[0012] Determine the mixed strategies of the four-party game players according to the power output strategy of the distributed power source, the capacity configuration strategy of the distribution network load, the power auxiliary service strategy of the charging and discharging station, and the response strategy of the electric vehicle cluster;

[0013] According to the mixed strategies of the four-party game players, respectively determine the distributed power source game equation, the distribution network load game equation, the charging and discharging station game equation, and the electric vehicle game equation;

[0014] Determine the four-party evolutionary game replication dynamic equations according to the distributed power source game equation, the distribution network load game equation, the charging and discharging station game equation, and the electric vehicle game equation;

[0015] Construct a multi-objective optimization model according to the four-party evolutionary game replication dynamic equations.

[0016] Optionally, the respectively determining the distributed power source game equation, the distribution network load game equation, the charging and discharging station game equation, and the electric vehicle game equation according to the mixed strategies of the four-party game players specifically includes:

[0017] Using the formula

[0018] Determine the game equation \(F(w)\) of distributed power sources; where, is the comprehensive benefit of distributed power sources choosing to introduce the traditional power generation method to connect to the distribution network, is the loss of distributed power sources choosing to introduce the traditional power generation method, is the additional loss of energy interaction between distributed power sources and charging and discharging stations, is the additional loss of energy interaction between distributed power sources and distribution network loads, is the cost and loss during the power generation process of distributed power sources, is the grid connection benefit of distributed power sources, is the service benefit of the distribution network load providing capacity support when the distributed power source chooses to introduce the flexible capacity of the electric vehicle cluster, is the comprehensive benefit of distributed power sources connecting to the distribution network when the distributed power source chooses to introduce the flexible capacity of the electric vehicle cluster. \(w\) is the probability of the distributed power source choosing to introduce the flexible capacity strategy of the electric vehicle cluster, \(x\) is the probability of the distribution network load choosing to be powered by the traditional distributed power source, \(y\) is the probability of the charging and discharging station choosing to be the supplementary power source of the distributed power source, and \(z\) is the probability of the electric vehicle owner choosing to respond to the invitation of the charging and discharging station and participate in discharging; is the risk loss of distributed power sources in the V2G mode, is the benefit brought to distributed power sources by the orderly charging and discharging after the electric vehicle owner cluster responds to the V2G instruction;

[0019] Use the formula Determine the game equation \(F(x)\) of the distribution network load; where, and are the benefit and loss of the distributed power source providing electricity to the distribution network load, and are the benefit and loss when the distribution network load obtains the power auxiliary service of the charging and discharging station, and are the service benefit and loss when powered by the external power source, is the risk loss of the distribution network load in the V2G mode;

[0020] Use the formula

[0021] Determine the game equation \(F(y)\) of the charging and discharging station's response to the charging and discharging instruction; where, and are the service benefit and loss when the charging and discharging station serves as the auxiliary capacity supplement of the distributed power source, and are the service benefit and loss when the charging and discharging station provides electricity to the distribution network load, and When neither the distributed power source nor the distribution network load requires the charging and discharging station to provide capacity services, the service revenue and losses of the charging and discharging station for grid connection of power capacity to the distribution network The losses when the charging and discharging station expands the dispatching capacity for weak-response electric vehicle owners in society The additional losses of the charging and discharging station in cooperation with the distributed power source The additional losses of the charging and discharging station in competition with the distributed power source;

[0022] Using the formula To determine the game equation F(z) of electric vehicles; where and Are the service revenue and losses when electric vehicle owners choose to respond to the invitation of the charging and discharging station for V2G discharging.

[0023] Optionally, according to the four-party evolutionary game replication dynamic equation set, a multi-objective optimization model is constructed, and then it further includes:

[0024] Determine the local equilibrium point based on the evolutionary game model according to the multi-objective optimization model;

[0025] Judge the local stability of the equilibrium point according to the eigenvalues of the Jacobian matrix.

[0026] Optionally, after obtaining the basic data, it further includes:

[0027] Perform data cleaning processing and early warning processing on the basic data.

[0028] Optionally, according to the operation objectives of the charging and discharging station and the multi-objective optimization model, a charging and discharging station planning scheme is obtained, and then it further includes:

[0029] Store the operation objectives of the charging and discharging station, the basic data, and the corresponding charging and discharging station planning scheme to form a strategy set and a case library.

[0030] In a second aspect, the present application provides a game theory-based electric vehicle charging and discharging station planning device, and the game theory-based electric vehicle charging and discharging station planning device includes:

[0031] An operation objective determination module, configured to determine the operation objectives of the charging and discharging station;

[0032] A basic data acquisition module, configured to acquire basic data; the basic data includes: vehicle charging and discharging characteristics, travel behavior data, distribution network load characteristics, distributed generation output characteristics;

[0033] A multi-objective optimization model determination module, configured to construct a multi-objective optimization model based on the basic data and the relationships between various technical units, based on an evolutionary game model; the technical units include: a distributed power system, a distribution network load, an electric vehicle cluster, and a charging and discharging station; the relationships between the various technical units are that the distributed power system adopts distributed power or introduces the flexible capacity of the electric vehicle cluster through the V2G mode, the distribution network load interacts directly with the distributed power system as the power consumption side or operates in coordination with the charging and discharging station and the supporting energy storage system, the charging and discharging station centrally schedules the electric vehicle cluster to directly provide capacity, power, and frequency support to the distribution network load or serves as an auxiliary regulating device for the distributed power source, and the electric vehicle cluster issues and does not issue response charging and discharging instructions.

[0034] A planning scheme determination module, configured to obtain a charging and discharging station planning scheme according to the operation objectives of the charging and discharging station and the multi-objective optimization model; the charging and discharging station planning scheme includes: power service objects, capacity configuration indicators, the number of charging and discharging devices, and interactive power; the power service objects are the distribution network load or the distributed power system.

[0035] In a third aspect, the present application provides a computer device, including: a memory, a processor, and a computer program stored on the memory and executable on the processor, where the processor executes the computer program to implement the above-mentioned method for planning an electric vehicle charging and discharging station based on game theory.

[0036] In a fourth aspect, the present application provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, it implements any one of the above-mentioned methods for planning an electric vehicle charging and discharging station based on game theory.

[0037] In a fifth aspect, the present application provides a computer program product, including a computer program, and when the computer program is executed by a processor, it implements the above-mentioned method for planning an electric vehicle charging and discharging station based on game theory.

[0038] According to the specific embodiments provided by the present application, the present application has the following technical effects:

[0039] The present application provides a method, system, device, medium, and product for planning an electric vehicle charging and discharging station based on game theory. According to the basic data and the relationships between various technical units, a multi-objective optimization model is constructed based on an evolutionary game model; by establishing an evolutionary game model composed of four technical units: a distributed power system, a distribution network load, an electric vehicle cluster, and a charging and discharging station, the interaction relationships between the various technical units involved in the project are clarified, the decision-making behaviors of each subject after introducing the V2G mode are determined, the evolutionary process of the strategy combination and the evolutionary stable strategy are clarified, and then the charging and discharging station planning scheme of the charging and discharging station group is obtained, thereby improving the scientificity of the planning of the electric vehicle charging and discharging station. Brief Description of the Drawings

[0040] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required for use in the embodiments. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0041] Figure 1 It is a schematic flowchart of a method for planning an electric vehicle charging and discharging station based on game theory in an embodiment of the present application. Detailed Embodiments

[0042] The following will clearly and completely describe the technical solutions in the embodiments of the present application with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only some embodiments of the present application, rather than all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present application.

[0043] To make the above objects, features, and advantages of the present application more obvious and understandable, the present application will be further described in detail below with reference to the drawings and specific embodiments.

[0044] In an exemplary embodiment, as Figure 1 shown, a method for planning an electric vehicle charging and discharging station based on game theory is provided, which includes the following S101 to S104. Among them:

[0045] S101, determine the operation target of the charging and discharging station;

[0046] Specifically, the planners of the charging and discharging station input operation parameters (including the target capacity configuration index and the income per unit capacity) at the user end and set the operation target of the charging and discharging station.

[0047] S102, obtain basic data; the basic data includes: vehicle charging and discharging characteristics, travel behavior data, distribution network load characteristics, distributed generation output characteristics; the capacity demand index reflects the load characteristics and regulation requirements of the distribution network system, and the capacity configuration index includes technical parameters such as the charging power level, energy storage capacity, and scheduling weight coefficient. Parameters such as the travel time, travel distance, and charging and discharging habits of the electric vehicle owner group; among them, the basic data also includes system constraint conditions; the system constraint conditions include technical specifications such as power grid operation constraints, equipment operation limits, and power quality requirements;

[0048] Perform data cleaning processing and early warning processing on the basic data. When obvious anomalies occur in the data, they are automatically marked and a warning is sent to the user in a timely manner.

[0049] S103. Based on the basic data and the relationships among various technical units, construct a multi-objective optimization model based on the evolutionary game model; the technical units include: distributed power system, distribution network load, electric vehicle cluster, and charging and discharging station; the relationships among the technical units are that the distributed power system adopts distributed power or introduces the flexible capacity of the electric vehicle cluster through the V2G mode, the distribution network load directly interacts with the distributed power system as the electricity demand side or operates in coordination with the charging and discharging station and the supporting energy storage system, the charging and discharging station centrally schedules the electric vehicle cluster to directly provide capacity, electricity, and frequency support to the distribution network load or serves as an auxiliary regulation device for the distributed power source, and the electric vehicle cluster issues and does not issue response charging and discharging instructions.

[0050] S103 specifically includes:

[0051] S31. Based on the basic data and the relationships among various technical units, determine the power output strategy of the distributed power source, the capacity configuration strategy of the distribution network load, the power auxiliary service strategy of the charging and discharging station, and the response strategy of the electric vehicle cluster.

[0052] Among them, the distributed power system can adopt conventional distributed energy such as photovoltaic, energy storage, gas, and wind power, or introduce the flexible capacity of the electric vehicle cluster through the V2G mode, and its strategies are respectively denoted as The distribution network load directly interacts with the distributed power source as the electricity demand side or operates in coordination with the charging and discharging station and the supporting energy storage system, and the strategies are respectively denoted as The charging and discharging station centrally schedules the electric vehicles to directly provide capacity, electricity, and frequency support to the distribution network load or serves as an auxiliary regulation device for the distributed power source, and the strategy is denoted as The electric vehicle cluster has two strategies of responding and not responding to the charging and discharging instructions, which are respectively denoted as

[0053] In the strategy of the service revenue of the distributed power system, when the distributed power source chooses to introduce the flexible capacity of the electric vehicle cluster (strategy ), the service revenue of the distribution network load providing capacity support is The comprehensive revenue of the distributed power source connecting to the distribution network is The cost and loss in the power generation process of the distributed power source are When the distributed power source chooses conventional distributed energy such as photovoltaic, energy storage, gas, and wind power (strategy ), the grid connection revenue of the distributed power source is Or connecting to the distribution network, the comprehensive revenue of the distributed power source choosing to introduce the traditional power generation method to connect to the distribution network is The loss of the distributed power source choosing to introduce the traditional power generation method is

[0054] Strategy 1 Introduce the flexible capacity of the electric vehicle cluster:

[0055] Assume that the electric vehicle cluster can provide a capacity of When the unit capacity revenue of distributed power generation providing capacity support to the distribution network load When providing capacity support to the distribution network load, the service revenue is Based on the grid-connected unit capacity revenue of distributed power generation Connecting to the distribution network can obtain the comprehensive revenue of distributed power generation connecting to the distribution network

[0056]

[0057] Among them, is the unit capacity revenue of distributed power generation providing capacity support to the distribution network load, is the grid-connected unit capacity revenue of distributed power generation. Usually,

[0058] When the electric vehicle owner cluster responds to the V2G command, through large-scale scheduling, the charging and discharging behavior of the electric vehicle cluster becomes more orderly, and the investment in traditional peak shaving and frequency modulation equipment can be saved. The revenue brought by the orderly charging and discharging to distributed power generation is

[0059]

[0060] Among them, is the unit service revenue for saving peak shaving and frequency modulation equipment.

[0061] The cost and loss in the distributed power generation process are:

[0062]

[0063] Among them, is the protocol unit capacity revenue between the charging and discharging station and the distributed power generation, represents the loss generated by the distributed power generation for equipment transformation, etc.

[0064] The additional loss of energy interaction between the distributed power generation and the charging and discharging station is denoted as

[0065]

[0066] Among them, is the unit additional loss of energy interaction between the distributed power generation and the charging and discharging station.

[0067] Strategy 2 Distributed power selects the conventional power generation method:

[0068] Distributed power generation The grid connection benefits obtained from distributed power are respectively

[0069] Among them, is the unit capacity benefit of distributed power providing capacity support to the distribution network load.

[0070] Under this strategy, the additional loss of energy interaction between distributed power and the distribution network load is

[0071] Among them, is the unit additional loss of energy interaction between distributed power and the charging and discharging station, is the energy interaction volume between distributed power and the charging and discharging station.

[0072] When formulating the strategy for the service benefit of the distribution network load, the strategy for the distribution network load to obtain power support is (distributed power, charging and discharging station), denoted as Under strategy the benefit of distributed power providing electricity to the distribution network load is The loss is Under strategy when the distribution network load obtains the power auxiliary service of the charging and discharging station, the benefit is The loss is When powered by the external power supply, the service benefit is The loss is

[0073] Strategy 1 The distribution network load is powered by distributed power:

[0074] The benefit of distributed power providing electricity to the distribution network load is

[0075]

[0076] Among them, is the capacity demand of the distribution network load, and e PR is the service benefit obtained per unit capacity of the distribution network load.

[0077] The loss of distributed power providing electricity to the distribution network load under this strategy is:

[0078]

[0079] Among them, It is the unit capacity revenue of distributed power sources and distribution network loads.

[0080] Strategy 2 The power of the distribution network load is provided by the charging and discharging station:

[0081] The revenue when the distribution network load obtains the power auxiliary service of the charging and discharging station is:

[0082]

[0083] Among them, e PR is the unit service revenue.

[0084] The unit capacity revenue of the distribution network load and the charging and discharging station is The loss when the distribution network load obtains the power auxiliary service of the charging and discharging station is:

[0085]

[0086] When the distribution network load needs to obtain electric energy from the distribution network or other independent power producers additionally, the unit capacity revenue is The service revenue when powered by external power and the loss are respectively:

[0087]

[0088] Among them, is the unit cost for the distribution network load to purchase electricity from other independent power producers additionally.

[0089] However, there are obvious differences in the losses. Generally speaking, the losses of energy interaction with the distribution network will be much higher than those of interaction with distributed power sources and charging and discharging stations, that is

[0090] When considering the service revenue strategy of the charging and discharging station, the strategy set of the charging and discharging station is (distributed power source, distribution network load), denoted as When the charging and discharging station serves as the auxiliary capacity supplement of the distributed power source, the service revenue is The loss is When the charging and discharging station provides electricity for the distribution network load, the service revenue is The loss is In addition, electric vehicles need to pay service fees for participating in V2G services, and the service revenue of this part of the charging and discharging station is When neither the distributed power source nor the distribution network load requires the charging and discharging station to provide capacity services, the service revenue of the charging and discharging station for grid connection of the power capacity to the distribution network is The loss is Under normal circumstances, among the electric vehicle resources that can be integrated by an electric vehicle aggregator, only some are vehicle owners or enterprise fleets with strong response in the region, and the remaining are electric vehicle owners facing high uncertainty in society. When the vehicle owner group with strong response does not participate in V2G services or the electric vehicle has insufficient power, the loss of the charging and discharging station to expand the dispatching capacity for electric vehicle owners with weak response in society is When distributed power sources and distribution network loads select the charging and discharging station to provide capacity support, both the service revenue and loss of the charging and discharging station are 0.

[0091] Strategy 1 The charging and discharging station serves as an auxiliary regulation device for distributed power sources:

[0092] When the strategy of the charging and discharging station is The revenue of the charging and discharging station consists of two parts. The service revenue when the charging and discharging station serves as an auxiliary capacity supplement for distributed power sources is And the service fee that needs to be paid for electric vehicles to participate in V2G services. The service revenue of this part of the charging and discharging station is

[0093]

[0094] Among them, is the revenue per unit capacity of the agreement between the charging and discharging station and the distributed power source, is the service capacity of the charging and discharging station to the distributed power source, that is, the capacity that the electric vehicle cluster can provide, is the V2G capacity of the electric vehicles aggregated by the charging and discharging station, is the unit service fee of the electric vehicle.

[0095] The loss when the charging and discharging station serves as an auxiliary capacity supplement for distributed power sources is the loss of purchasing electricity from electric vehicles, operation loss, and software development (purchase) and maintenance costs:

[0096]

[0097] Among them, is the service capacity of the charging and discharging station to the distributed power source, is the service fee paid by the charging and discharging station to the electric vehicle, is the daily operation expenditure of the charging and discharging station, is the software development (purchase) and maintenance expenditure.

[0098] The additional loss of the cooperation between the charging and discharging station and the distributed power source is recorded as

[0099]

[0100] Among them, It is the cooperation loss between the charging and discharging station and other units of distributed power sources.

[0101] Strategy Two The charging and discharging station provides services to the distribution network load:

[0102] When the strategy of the charging and discharging station is , the charging and discharging station chooses to provide direct capacity support or frequency stability guarantee services for the distribution network load. The service income when the charging and discharging station provides electricity to the distribution network load is

[0103] Among them, It is the protocol unit capacity income between the charging and discharging station and the distributed power source.

[0104] Strategy and Strategy Under this condition, the losses of the charging and discharging station can be considered basically equal, that is

[0105] When both the distributed power source and the charging and discharging station have strategies to provide capacity support to the distribution network load, they are in a competitive relationship. The additional loss of the charging and discharging station during the competition is recorded as

[0106] Among them, It is the additional loss of the competition between the charging and discharging station and the distributed power source.

[0107] In special scenarios:

[0108] 1. When neither the distributed power source nor the distribution network load interacts with the charging and discharging station in terms of energy, the charging and discharging station can interact the flexible capacity of the electric vehicle cluster with the distribution network through V2G technology. The unit capacity income is The service income is

[0109]

[0110] Among them, It is the V2G capacity of electric vehicles aggregated by the charging and discharging station, It is the unit capacity income.

[0111] When the distributed power source or the distribution network load provides flexible capacity support to the distribution network with the charging and discharging station, due to the different behavioral characteristics of electric vehicle owners, when the capacity provided by strong-response electric vehicle owners is insufficient, the charging and discharging station needs to send additional dispatch instructions to weak-response electric vehicle owners to obtain capacity indicators at the service price obtain capacity indicators To meet the demand, the loss when the charging and discharging station expands the dispatching capacity for weak-response electric vehicle owners in the society is

[0112]

[0113] When neither the distributed power source nor the distribution network load interacts with the charging and discharging station in terms of energy, the service revenue of the charging and discharging station is 0.

[0114] When considering the service revenue strategy of the electric vehicle cluster, the set of group strategies of electric vehicle owners is (respond, not respond), denoted as When electric vehicle owners choose to respond to the invitation of the charging and discharging station for V2G discharging, the service revenue is The loss is When electric vehicle owners choose not to respond to the invitation, the service revenue of electric vehicle owners is 0.

[0115] Strategy 1 Respond

[0116] When electric vehicle owners choose to respond to the invitation of the charging and discharging station for V2G discharging, the service revenue and the loss are:

[0117]

[0118]

[0119] Among them, is the capacity configuration index of the electric vehicle, is the unit capacity revenue of the electric vehicle, n is the number of electric vehicles, is the daily average vehicle discharging power, is the available capacity coefficient of the electric vehicle, is the average capacity of regional electric vehicles, is the willingness of electric vehicle owners, is the average loss of electric vehicle owners responding to V2G services.

[0120] Under strategy the average loss of electric vehicle owners responding to V2G services includes charging loss V2G service process loss time loss battery loss and driving loss

[0121] Among them: is the unit capacity revenue during the charging process, ω is the time coefficient, is the average discharge coefficient, is the responsive capacity, is the average driving speed, is the average dispatching distance, is the average battery loss, is the average number of battery cycles, is the average charging power, is the average charging efficiency, is the average discharge power, is the average discharge efficiency.

[0122] Strategy Two Do not respond

[0123] When the strategy of electric vehicle owners is not to respond to V2G services, both the service revenue and losses are 0.

[0124] S32. Determine the mixed strategies of the four-party game players according to the power output strategy of distributed power sources, the capacity configuration strategy of distribution network loads, the power auxiliary service strategy of charging and discharging stations, and the response strategy of electric vehicle clusters;

[0125] Specifically, the four technical units of the distributed power system, distribution network load, electric vehicle cluster, and charging and discharging station respectively select strategies with probabilities (w, x, y, z) (0 ≤ w ≤ 1; 0 ≤ x ≤ 1; 0 ≤ y ≤ 1; 0 ≤ z ≤ 1) select strategies with probabilities {(1 - w), (1 - x), (1 - y), (1 - z)} (0 ≤ w ≤ 1; 0 ≤ x ≤ 1; 0 ≤ y ≤ 1; 0 ≤ z ≤ 1) The obtained mixed strategies of the four-party game players are (w, 1 - w), (x, 1 - x), (y, 1 - y), (z, 1 - z).

[0126] S33. Determine the game equations of distributed power sources, distribution network loads, charging and discharging stations, and electric vehicles respectively according to the mixed strategies of the four-party game players;

[0127] When the probabilities w, x, y, z are given for the four technical units of distributed power sources, distribution network loads, charging and discharging stations, and electric vehicles, the expected revenues of the distributed power source for selecting strategies and are respectively:

[0128]

[0129] Then the expected revenue of the distributed power source for selecting strategies m 1 and m 2 with probabilities w and 1 - w is:

[0130]

[0131] The game equation of distributed power sources is as follows:

[0132]

[0133] Distribution network load selection strategy and The expected revenues are respectively:

[0134]

[0135] Then the distribution network load selects strategies x and 1 - x with probabilities x and 1 - x respectively 1 and x 2 The expected revenue is:

[0136] Its distribution network load game equation is:

[0137]

[0138] Charge and discharge station selection strategy and The expected revenues are:

[0139]

[0140] Then the charge and discharge station selects strategies y and 1 - y with probabilities y and 1 - y respectively 1 and y 2 The expected revenue is:

[0141] Then the charge and discharge station game equation is:

[0142]

[0143] Electric vehicle selection strategy and The expected revenues are respectively:

[0144]

[0145] Then the electric vehicle owners select strategies z and 1 - z with probabilities z and 1 - z respectively 1 and z 2 The expected revenue is:

[0146] U z = zUz 1 +(1 - z);

[0147] Then the electric vehicle game equation is:

[0148]

[0149] S34, determining a four-party evolutionary game replication dynamic equation group according to a distributed power source game equation, a distribution network load game equation, a charging and discharging station game equation, and an electric vehicle game equation;

[0150] Combining the above equations F(p), F(x), F(y), and F(z), we get the four-party evolutionary game replication dynamic equations;

[0151] S35, construct a multi-objective optimization model based on the four-party evolutionary game replication dynamic equations.

[0152] Let dp / dt=0、dx / dt=0、dy / dt=0、dz / dt=0 in the differential equations, and the local equilibrium points of the game system are E1(0,0,0,0), E2(0,0,0,1), E3(0,0,1,0), E4(0,0,1,1), E5(0,1,0,0), E6(1,0,0,0), E7(1,0,0,1), E8(0,1,0,1), E9(0,1,1,0), E10(1,0,1,0), E11(0,1,1,1), E12(1,0,1,1), E13(1,1,0,1), E14(1,1,0,0), E15(1,1,1,0), E16(1,1,1,1). The local stability of the equilibrium points is determined by establishing the eigenvalues ​​of the Jacobian matrix. The Jacobian matrix is:

[0153]

[0154] After determining the eigenvalues, there are three stable points in the evolutionary game of the distributed power system considering the interaction between the vehicle and the grid, namely E2 (0, 0, 0, 1), E11 (0, 1, 1, 1) and E16 (1, 1, 1, 1). Among the three stable points of the pure equilibrium strategy of the evolutionary game, the charging and discharging station and the distributed power source are in a cooperative relationship in E16 (1, 1, 1, 1), the charging and discharging station and the distributed power source are in a competitive relationship in E2 (0, 0, 0, 1), and the charging and discharging station and the distributed power source are neither in a competitive nor cooperative relationship in E11 (0, 1, 1, 1). E2 (0, 0, 0, 1) and E11 (0, 1, 1, 1) represent that the planning of the electric vehicle charging and discharging station is feasible. E11 (0, 1, 1, 1) represents that the charging and discharging station is not feasible to plan.

[0155] Three stable outcomes are obtained (the charging and discharging station and the distributed power sources are in a cooperative relationship, the charging and discharging station and the distributed power sources are in a competitive relationship, and the charging and discharging station and the distributed power sources are neither competitive nor cooperative). The service revenue and net efficiency of the charging and discharging stations under the three stable solutions can be calculated.

[0156] S104. According to the operation objectives of the charging and discharging power station and the multi-objective optimization model, obtain the planning scheme of the charging and discharging power station; that is, determine the operation objectives, quantitatively calculate the service revenue indicators of the charging and discharging power station on this basis, and optimize the planning scheme of the charging and discharging power station. Under the optimized planning scheme of the charging and discharging power station, if each technical unit unilaterally changes the operation parameters, the overall system efficiency will not be improved, thus achieving a stable operation state. The planning scheme of the charging and discharging power station includes: power service objects, capacity configuration indicators, the number and power of charging and discharging equipment; the power service objects are distribution network loads or distributed power source systems.

[0157] After S104, store the operation objectives, basic data, and the corresponding planning scheme of the charging and discharging power station to form a strategy set and a case library, and then case matching can be formed for future similar planning projects.

[0158] Based on the same inventive concept, the embodiment of the present application also provides a game theory-based electric vehicle charging and discharging power station planning device for implementing the above-mentioned game theory-based electric vehicle charging and discharging power station planning method. The implementation solutions provided by this device to solve problems are similar to the implementation solutions described in the above method. Therefore, the specific limitations in one or more embodiments of the game theory-based electric vehicle charging and discharging power station planning device provided below can refer to the limitations on the game theory-based electric vehicle charging and discharging power station planning method in the above text, and will not be repeated here.

[0159] In an exemplary embodiment, a game theory-based electric vehicle charging and discharging power station planning device is provided, including:

[0160] An operation objective determination module, configured to determine the operation objectives of the charging and discharging power station;

[0161] A basic data acquisition module, configured to acquire basic data; the basic data includes: vehicle charging and discharging characteristics, travel behavior data, distribution network load characteristics, distributed generation output characteristics;

[0162] A multi-objective optimization model determination module, configured to construct a multi-objective optimization model based on the basic data and the relationships between each technical unit, based on the evolutionary game model; the technical units include: distributed power source systems, distribution network loads, electric vehicle clusters, and charging and discharging power stations; the relationships between each technical unit are that the distributed power source system adopts distributed power or introduces the flexible capacity of the electric vehicle cluster through the V2G mode, the distribution network load interacts directly with the distributed power source system as the power consumption side or operates in coordination with the charging and discharging power station and the supporting energy storage system, the charging and discharging power station centrally schedules the electric vehicle cluster to directly provide capacity, power, and frequency support to the distribution network load or serves as an auxiliary regulation device for the distributed power source, and the electric vehicle cluster issues and does not issue response charging and discharging instructions;

[0163] A planning scheme determination module is configured to obtain a charging and discharging power station planning scheme according to the operation objectives of the charging and discharging power station and a multi-objective optimization model; the charging and discharging power station planning scheme includes: power service objects, capacity configuration indicators, the number and power of charging and discharging devices; the power service objects are distribution network loads or distributed power generation systems. In an exemplary embodiment, a computer device is provided, which can be a server or a terminal. The computer device includes a processor, a memory, an input / output interface (Input / Output, abbreviated as I / O), and a communication interface. Among them, the processor, the memory, and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The input / output interface of the computer device is used to exchange information between the processor and external devices. The communication interface of the computer device is used to communicate with external terminals through a network connection. When the computer program is executed by the processor, it implements a method for planning an electric vehicle charging and discharging power station based on game theory.

[0164] In an exemplary embodiment, a computer-readable storage medium is provided, storing a computer program, which when executed by a processor implements the steps in the above method embodiments.

[0165] In an exemplary embodiment, a computer program product is provided, including a computer program, which when executed by a processor implements the steps in the above method embodiments.

[0166] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use, and processing of relevant data need to comply with relevant regulations.

[0167] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above methods. Among them, any reference to a memory, database, or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memories. Non-volatile memories can include read-only memory (ROM), magnetic tapes, floppy disks, flash memories, optical memories, high-density embedded non-volatile memories, resistive random access memories (ReRAM), magnetoresistive random access memories (MRAM), ferroelectric random access memories (FRAM), phase change memories (PCM), graphene memories, etc. Volatile memories can include random access memories (RAM) or external cache memories, etc. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc.

[0168] The databases involved in the embodiments provided in the present application can include at least one of relational databases and non-relational databases. Non-relational databases can include distributed databases based on blockchain, etc., without limitation. The processors involved in the embodiments provided in the present application can be general-purpose processors, central processors, graphics processors, digital signal processors, programmable logics, data processing logics based on quantum computing, etc., without limitation.

[0169] In the present application, all actions of obtaining signals, information, or data are carried out on the premise of complying with the corresponding data protection regulations and policies of the country where the location is located and obtaining authorization from the owner of the corresponding device.

[0170] The technical features of the above embodiments can be combined arbitrarily. For the sake of brevity of description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered as the scope recorded in this specification.

[0171] In this article, specific examples are used to illustrate the principle and implementation of this application. The description of the above embodiments is only used to help understand the method and its core idea of this application; at the same time, for those of ordinary skill in the art, according to the idea of this application, there will be changes in the specific implementation and application scope. To sum up, the content of this specification should not be construed as a limitation to this application.

Claims

1. A method for planning electric vehicle charging and discharging stations based on game theory, characterized in that: The electric vehicle charging and discharging station planning method based on game theory includes: Determine the operating objectives of the charging and discharging station; Obtaining basic data; the basic data includes: vehicle charging and discharging characteristics, travel behavior data, distribution network load characteristics, and distributed power generation output characteristics; According to the basic data and the relationship between each technical unit, a multi-objective optimization model is constructed based on the evolutionary game model; the technical units include: distributed power system, distribution network load, electric vehicle cluster and charging and discharging station; the relationship between each technical unit is that the distributed power system adopts distributed power or introduces the flexible capacity of the electric vehicle cluster through the V2G mode, the distribution network load directly interacts with the distributed power system as the power demand side or cooperates with the charging and discharging station and the supporting energy storage system, the charging and discharging station centrally dispatches the electric vehicle cluster to directly provide capacity, power, frequency support to the distribution network load or serves as an auxiliary regulating device for the distributed power supply, and the electric vehicle cluster issues a response to the charging and discharging instruction and does not issue a response to the charging and discharging instruction; According to the operation objectives of the charging and discharging station, a multi-objective optimization model is constructed to obtain a charging and discharging station planning scheme; the charging and discharging station planning scheme includes: power service objects, capacity configuration indicators, the number of charging and discharging equipment and interactive power; the power service object is the distribution network load or the distributed power system.

2. The electric vehicle charging and discharging station planning method based on game theory according to claim 1 is characterized in that: According to the basic data and the relationship between each technical unit, a multi-objective optimization model is constructed based on the evolutionary game model, which specifically includes: According to the basic data and the relationship between each technical unit, determine the power output strategy of distributed power sources, the capacity configuration strategy of distribution network loads, the power auxiliary service strategy of charging and discharging stations, and the response strategy of electric vehicle clusters; Determine the hybrid strategy of the four-party game players based on the power output strategy of distributed power sources, the capacity configuration strategy of distribution network loads, the power auxiliary service strategy of charging and discharging stations, and the response strategy of electric vehicle clusters; According to the mixed strategies of the four game players, the game equations of distributed power supply, distribution network load, charging and discharging station and electric vehicle are determined respectively; According to the distributed power supply game equation, distribution network load game equation, charging and discharging station game equation and electric vehicle game equation, a four-party evolutionary game replication dynamic equation group is determined; Based on the four-party evolutionary game replication dynamic equations, a multi-objective optimization model is constructed.

3. The electric vehicle charging and discharging station planning method based on game theory according to claim 2 is characterized in that: The game equations of distributed power supply, distribution network load, charging and discharging station and electric vehicle are determined according to the mixed strategies of the four game players, including: Using the formula Determine the distributed power game equation F(w); where, The comprehensive benefits of introducing traditional power generation methods into the distribution network for distributed power sources, The loss of traditional power generation methods is introduced for distributed power generation. The additional loss of energy interaction between distributed power sources and charging and discharging stations. The additional loss caused by the energy interaction between distributed power sources and distribution network loads. The cost and loss of distributed power generation process, The benefits of distributed power grid connection are: When the distributed power source chooses to introduce flexible capacity of electric vehicle clusters, the service revenue provided by the distribution network load for capacity support is is the comprehensive benefit of distributed power supply connected to the distribution network when the distributed power supply chooses to introduce the flexible capacity of electric vehicle clusters, w is the probability that the distributed power supply chooses to introduce the flexible capacity strategy of electric vehicle clusters, x is the probability that the distribution network load chooses to be powered by traditional distributed power supply, y is the probability that the charging and discharging station chooses to be the supplementary power supply of the distributed power supply, and z is the probability that the electric vehicle owner chooses to respond to the invitation of the charging and discharging station and participate in the discharge; is the risk loss of distributed power in V2G mode, The benefits brought to the distributed power source by orderly charging and discharging when the electric vehicle owner cluster responds to the V2G command; Using the formula Determine the distribution network load game equation F(x); where, and The benefits and losses of distributed power supply for distribution network loads, and The benefits and losses of obtaining power auxiliary services from charging and discharging stations for distribution network loads, and Service gains and losses when powering an external source, The risk loss of distribution network load in V2G mode; Using the formula Determine the charging and discharging station game equation F(y); where, and The service income and loss of the charging and discharging station when used as auxiliary capacity supplement for distributed power generation. and The service income and loss when the charging and discharging station provides electricity to the distribution network load, and When the charging and discharging station provides capacity services for distributed power sources and distribution network loads, the service income and loss of the charging and discharging station connecting the power capacity to the distribution network is The loss when the charging and discharging station expands the dispatching capacity for the weakly responsive electric vehicle owners in the society, The additional loss of the charging and discharging station in cooperation with the distributed power generation, Additional losses for charging and discharging stations competing with distributed generation; Using the formula Determine the electric vehicle charging and discharging command response game equation F(z); where, and The benefits and losses when electric vehicle owners choose to respond to the invitation of charging and discharging stations and participate in discharging.

4. The electric vehicle charging and discharging station planning method based on game theory according to claim 3 is characterized in that: According to the four-party evolutionary game replication dynamic equations, a multi-objective optimization model is constructed, which also includes: Determine the local equilibrium point based on the evolutionary game model according to the multi-objective optimization model; The local stability of the equilibrium point is determined based on the eigenvalues ​​of the Jacobian matrix.

5. The electric vehicle charging and discharging station planning method based on game theory according to claim 1 is characterized in that: The acquisition of basic data further includes: Perform data cleaning and early warning processing on basic data.

6. The electric vehicle charging and discharging station planning method based on game theory according to claim 1 is characterized in that: According to the operation objectives of the charging and discharging station and the multi-objective optimization model, the charging and discharging station planning scheme is obtained, which also includes: The operation objectives, basic data and corresponding planning schemes of the charging and discharging stations are stored to form a strategy set and case library.

7. An electric vehicle charging and discharging station planning device based on game theory, characterized in that: The electric vehicle charging and discharging station planning device based on game theory includes: An operation target determination module, used to determine the operation target of the charging and discharging station; A basic data acquisition module is used to acquire basic data; the basic data includes: vehicle charging and discharging characteristics, travel behavior data, distribution network load characteristics, and distributed power generation output characteristics; A multi-objective optimization model determination module is used to construct a multi-objective optimization model based on the evolutionary game model according to the basic data and the relationship between each technical unit; the technical units include: distributed power system, distribution network load, electric vehicle cluster and charging and discharging station; the relationship between each technical unit is that the distributed power system adopts distributed power or introduces the flexible capacity of the electric vehicle cluster through the V2G mode, the distribution network load directly interacts with the distributed power system as the power demand side or cooperates with the charging and discharging station and the supporting energy storage system, the charging and discharging station centrally dispatches the electric vehicle cluster to provide direct capacity, power, frequency support to the distribution network load or serves as an auxiliary regulating device for the distributed power supply, and the electric vehicle cluster issues a response to the charging and discharging instruction and does not issue a response to the charging and discharging instruction; The planning scheme determination module is used to obtain the charging and discharging station planning scheme according to the operation objectives of the charging and discharging station and the multi-objective optimization model; the charging and discharging station planning scheme includes: power service objects, capacity configuration indicators, the number of charging and discharging equipment and the interactive power; the power service object is the distribution network load or the distributed power system.

8. A computer device comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the game theory-based electric vehicle charging and discharging station planning method described in any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the game theory-based electric vehicle charging and discharging station planning method described in any one of claims 1 to 6 is implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the game theory-based electric vehicle charging and discharging station planning method described in any one of claims 1 to 6 is implemented.

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