A method, system, device, medium and product for planning electric vehicle charging and discharging stations based on game theory

Abstract

This application discloses a game-theory-based planning method, system, device, medium, and product for electric vehicle charging and discharging station planning, relating to the field of electric vehicle charging and discharging station planning and decision-making. The method includes determining the operating objectives of the charging and discharging station; obtaining basic data; constructing a multi-objective optimization model based on the basic data and the relationships between various technical units using an evolutionary game-based approach; and obtaining a charging and discharging station planning scheme based on the operating 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 the amount of interactive power; the power service objects are distribution network loads or distributed power systems. This application can improve the scientific nature and stability of electric vehicle charging and discharging station planning.

Description

Technical Field

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

[0002] In urban distribution networks that integrate vehicle-to-grid (V2G) interaction, electric vehicle aggregators configure charging and discharging facilities and energy storage equipment at charging and discharging stations to enable large-scale grid connection and coordinated charging and discharging of electric vehicle fleets. This not only provides power ancillary services for distributed generation systems, but also offers multi-dimensional services such as capacity support, power balancing, and frequency regulation for the distribution network. This enables orderly charging and discharging management of electric vehicle fleets, effectively facilitating fluctuation suppression and peak-valley load regulation in regional distribution systems. However, the development of V2G interaction presents the challenge of complex investment planning for charging and discharging stations. The operational characteristics of the diverse participants and different technical unit combinations within the system are either complementary or restrictive, significantly increasing the uncertainty of charging and discharging station planning and operation. In this technological context, how to more accurately plan key parameters of electric vehicle charging and discharging stations, such as the power service targets, capacity configuration indicators, and the number and power of charging and discharging equipment, has become a pressing technical challenge. Summary of the Invention

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

[0004] To achieve the above objectives, this application provides the following solutions:

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

[0006] Determine the operating objectives of the charging and discharging stations;

[0007] 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;

[0008] 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 supply system, distribution network load, electric vehicle cluster and charging and discharging station; the relationship between each technical unit is that the distributed power supply system adopts distributed power supply or introduces the flexible capacity of electric vehicle cluster through V2G mode, the distribution network load directly interacts with the distributed power supply 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 dispatches 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 supply, and the electric vehicle cluster issues and does not issue a response to the charging and discharging instruction;

[0009] According to the operational 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 the interactive power; the power service objects are distribution network loads or distributed power systems.

[0010] Optionally, the multi-objective optimization model is constructed based on the basic data and the relationship between the technical units and the evolutionary game model, specifically including:

[0011] Based on basic data and the relationship between various technical units, determine the power output strategy of distributed power generation, the capacity configuration strategy of distribution network load, the power auxiliary service strategy of charging and discharging stations, and the response strategy of electric vehicle clusters;

[0012] Determine the hybrid strategy of the four-party game entities 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;

[0013] According to the mixed strategies of the four game players, the game equations for distributed power supply, distribution network load, charging and discharging stations, and electric vehicles are determined respectively.

[0014] 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;

[0015] Based on the four-party evolutionary game replication dynamic equations, a multi-objective optimization model is constructed.

[0016] Optionally, the distributed power supply game equation, the distribution network load game equation, the charging and discharging station game equation, and the electric vehicle game equation are determined respectively according to the mixed strategy of the four game entities, specifically including:

[0017] Using the formula

[0018]

[0019] 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 generation, The loss of traditional power generation 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 interaction between distributed power generation and distribution network loads. The cost and loss of distributed power generation process, For the distributed power grid connection income, When distributed power sources choose to introduce flexible capacity of electric vehicle clusters, the service benefits of providing capacity support to distribution network loads are is the comprehensive benefit of distributed power generation connected to the distribution network when the distributed power generation chooses to introduce the flexible capacity of electric vehicle clusters, w is the probability that the distributed power generation chooses to introduce the flexible capacity strategy of electric vehicle clusters, x is the probability that the distribution network load chooses to be supplied by traditional distributed power generation, y is the probability that the charging and discharging station chooses to be a supplementary power source for the distributed power generation, 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; The risk loss of distributed power in V2G mode, The benefits brought to the distributed power supply by orderly charging and discharging of electric vehicle owners in response to V2G commands;

[0020] Using the formula Determine the distribution network load game equation F(x); where, and The benefits and losses of distributed power supply to 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, Risk loss of distribution network load in V2G mode;

[0021] Using the formula

[0022]

[0023] Determine the charging and discharging station charging and discharging instruction response game equation F(y); where, and The service income and loss of the charging and discharging station when it is 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 neither the distributed power generation nor the distribution network load requires the charging and discharging station to provide capacity services, the service income and loss of the charging and discharging station connecting the power capacity to the distribution network are calculated. The loss when the charging and discharging station expands the dispatch capacity for the weakly responsive electric vehicle owners in the society, The additional loss caused by the cooperation between the charging and discharging station and the distributed power supply, Additional losses from competing with distributed generation for charging and discharging stations;

[0024] Using the formula Determine the electric vehicle game equation F(z); where, and The service revenue and losses when electric vehicle owners choose to respond to the invitation of charging and discharging stations to perform V2G discharge.

[0025] Optionally, a multi-objective optimization model is constructed based on a four-party evolutionary game replicating dynamic equations, which may further include:

[0026] Determine the local equilibrium point based on the evolutionary game model according to the multi-objective optimization model;

[0027] The local stability of the equilibrium point is determined based on the eigenvalues ​​of the Jacobian matrix.

[0028] Optionally, the step of obtaining basic data further includes:

[0029] Perform data cleaning and early warning processing on basic data.

[0030] Optionally, a charging and discharging station planning scheme is obtained according to the operating objectives of the charging and discharging station and the multi-objective optimization model, and then the following steps are further included:

[0031] 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.

[0032] In a second aspect, the present application provides a device for planning electric vehicle charging and discharging stations based on game theory, the device comprising:

[0033] An operation target determination module, used to determine the operation target of the charging and discharging station;

[0034] 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;

[0035] A multi-objective optimization model determination module is used to construct a multi-objective optimization model based on the evolutionary game model according to basic data and the relationship between each technical unit; the technical units include: distributed power supply system, distribution network load, electric vehicle cluster and charging and discharging station; the relationship between each technical unit is that the distributed power supply system adopts distributed power supply or introduces the flexible capacity of electric vehicle cluster through V2G mode, the distribution network load directly interacts with the distributed power supply 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 dispatches 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 supply, and the electric vehicle cluster issues and does not issue a response to the charging and discharging instruction;

[0036] The planning scheme determination module is used to obtain a charging and discharging station planning scheme based on the operating 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 objects are distribution network loads or distributed power systems.

[0037] In a third aspect, the present application provides 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.

[0038] In a fourth aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements any of the game theory-based electric vehicle charging and discharging station planning methods described above.

[0039] In a fifth aspect, the present application provides a computer program product, including a computer program, which, when executed by a processor, implements the game theory-based electric vehicle charging and discharging station planning method.

[0040] According to the specific embodiments provided in this application, this application has the following technical effects:

[0041] The present application provides a method, system, equipment, medium and product for planning electric vehicle charging and discharging stations based on game theory. According to basic data and the relationship between various technical units, a multi-objective optimization model is constructed based on an evolutionary game model. By establishing an evolutionary game model consisting of four technical units: a distributed power supply system, a distribution network load, an electric vehicle cluster and a charging and discharging station, the interaction relationship between the various technical units involved in the project is clarified, the decision-making behavior of each subject after the introduction of the V2G mode is determined, the evolution process and evolutionary stability strategy of the strategy combination are clarified, and then a charging and discharging station planning scheme for the charging and discharging station cluster is obtained, thereby improving the scientific nature of the electric vehicle charging and discharging station planning. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0043] Figure 1 This is a flow chart of a method for planning electric vehicle charging and discharging stations based on game theory in one embodiment of the present application. DETAILED DESCRIPTION

[0044] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0045] In order to make the above-mentioned purposes, features and advantages of the present application more obvious and easy to understand, the present application is further described in detail below with reference to the accompanying drawings and specific implementation methods.

[0046] In an exemplary embodiment, Figure 1 As shown, a method for planning electric vehicle charging and discharging stations based on game theory is provided, which includes the following S101 to S104.

[0047] S101, determining the operation target of the charging and discharging station;

[0048] Specifically, the charging and discharging station planners input the operating parameters (including target capacity configuration indicators and unit capacity benefits) at the user end and set the operating goals of the charging and discharging station.

[0049] S102, 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; 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 charging power level, energy storage capacity, and dispatch weight coefficient. Parameters such as travel time, travel distance, and charging and discharging habits of electric vehicle owners are also included; the basic data also includes system constraints; system constraints include technical specifications such as grid operation constraints, equipment operation limits, and power quality requirements;

[0050] The basic data is cleaned and warned. When the data shows obvious abnormalities, it is automatically marked and a warning is issued to the user in a timely manner.

[0051] S103, constructing 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: a distributed power system, a distribution network load, an electric vehicle cluster, and a charging and discharging station; the relationship between each technical unit is as follows: the distributed power system adopts distributed power or introduces flexible capacity of the electric vehicle cluster through the V2G mode; the distribution network load directly interacts with the distributed power system as a power demander or operates in coordination with the charging and discharging station and supporting energy storage system; the charging and discharging station centrally dispatches 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 supply; and the electric vehicle cluster issues and does not issue a response to the charging and discharging instruction.

[0052] S103 specifically includes:

[0053] S31, based on the basic data and the relationship between the various technical units, determine the power output strategy of the distributed power generation, 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;

[0054] 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 electric vehicle clusters through the V2G mode. The strategies are recorded as follows: 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. The strategies are recorded as The charging and discharging station centrally dispatches electric vehicles to directly provide capacity, power, and frequency support to the distribution network load or serve as an auxiliary regulation device for distributed power supply. The strategy is recorded as The electric vehicle cluster has two strategies: responding to and not responding to charge and discharge instructions, which are respectively denoted as

[0055] In the strategy of distributed power system service benefits, when distributed power chooses to introduce electric vehicle cluster flexible capacity (strategy ), the service income of capacity support provided by distribution network load is The comprehensive benefits of connecting distributed power sources to the distribution network are: The cost and loss of distributed power generation process are: When distributed power sources choose conventional distributed energy sources such as photovoltaic, energy storage, gas, wind power (strategy ), the grid-connected benefit of distributed generation is Or merged into the distribution network, the comprehensive benefits of distributed power generation choosing to introduce traditional power generation methods to the distribution network are The loss of distributed power generation when introducing traditional power generation is

[0056] Strategy 1 Introducing flexible capacity for electric vehicle clusters:

[0057] Assume that the electric vehicle cluster can provide a capacity of Unit capacity benefit when distributed generation provides capacity support to distribution network load When providing capacity support to distribution network load, the service revenue is Unit capacity income of distributed power grid Integrating into the distribution network can obtain comprehensive benefits of connecting distributed power sources to the distribution network

[0058]

[0059] in, The unit capacity benefit of the distributed power supply providing capacity support to the distribution network load, is the grid-connected unit capacity benefit of distributed power sources.

[0060] When the electric vehicle owner cluster responds to the V2G command, the charging and discharging behavior of the electric vehicle cluster becomes more orderly through large-scale scheduling, which can save the investment in traditional peak-shaving and frequency-regulating equipment, and obtain the benefits of orderly charging and discharging to the distributed power supply.

[0061]

[0062] in, To save the unit service revenue of peak-shaving and frequency-regulating equipment.

[0063] Costs and losses in the distributed power generation process for:

[0064]

[0065] in, is the unit capacity benefit of the agreement between the charging and discharging station and the distributed power source, Indicates the loss caused by equipment modification of distributed power sources.

[0066] The additional loss of energy interaction between distributed power generation and charging and discharging stations is recorded as

[0067]

[0068] in, It is the additional loss per unit of energy interaction between distributed generation and charging and discharging station.

[0069] Strategy 2 Distributed power sources choose conventional power generation methods:

[0070] Distributed power generation The distributed power grid connection income obtained is

[0071]

[0072] in, The unit capacity benefit of distributed power generation providing capacity support to distribution network loads.

[0073] Under this strategy, the additional loss of energy interaction between distributed power generation and distribution network load is

[0074]

[0075] in, It is the additional loss per unit of energy interaction between distributed power supply and charging and discharging station. is the energy interaction between distributed power sources and charging and discharging stations.

[0076] When the strategy of distribution network load service income is used, the strategy of distribution network load obtaining power support is (distributed power source, charging and discharging station), which is recorded as In strategy Under this condition, the income of distributed generation supplying electricity to distribution network load is The loss is In strategy Under this condition, the income of distribution network load when obtaining power auxiliary service from charging and discharging station is The loss is The service benefit when powered by an external power supply is The loss is

[0077] Strategy 1 The distribution network load is powered by distributed power sources:

[0078] The benefit of distributed generation providing power to distribution network load is

[0079]

[0080] in, is the capacity demand of distribution network load, e PR The service revenue obtained per unit capacity of distribution network load.

[0081] Under this strategy, the loss of power provided by distributed generation to distribution network loads for:

[0082]

[0083] in, It is the unit capacity benefit of distributed power source and distribution network load.

[0084] Strategy 2 The distribution network load is powered by the charging and discharging stations:

[0085] The benefits of distribution network loads when they obtain power auxiliary services from charging and discharging stations for:

[0086]

[0087] Among them, e PR Profits from unit services.

[0088] The unit capacity benefit of the distribution network load and the charging and discharging station is Losses when distribution network loads obtain power auxiliary services from charging and discharging stations for:

[0089]

[0090] When the distribution network load needs to be supplied with additional power through the distribution network or other independent power producers, the unit capacity benefit is Service benefits when powered by external power supply and loss They are:

[0091]

[0092] in, The unit cost of purchasing additional electricity from other independent power producers for distribution network load.

[0093] However, there are obvious differences in losses. Generally speaking, the loss of energy interaction with the distribution network will be much higher than the loss of interaction with distributed power sources and charging and discharging stations, that is,

[0094] When the service revenue strategy of the charging and discharging station is used, the strategy set of the charging and discharging station is (distributed power supply, distribution network load), which is recorded as When the charging and discharging station is used as auxiliary capacity supplement for distributed power generation, the service revenue is The loss is When the charging and discharging station provides electricity to the distribution network load, the service income is The loss is In addition, electric vehicles need to pay service fees to participate in V2G services, and the service income of the charging and discharging station is When neither the distributed power generation nor the distribution network load requires the charging and discharging station to provide capacity services, the service income of the charging and discharging station connecting the power capacity to the distribution network is The loss is Typically, among the electric vehicle resources that electric vehicle aggregators can integrate, only a portion are car owners or corporate fleets with strong regional response, while the rest are electric vehicle owners with high social uncertainty. When the group of car owners with strong response does not participate in V2G services or the electric vehicle power is insufficient, the charging and discharging station will expand the dispatch capacity for the electric vehicle owners with weak social response, and the loss is When distributed power sources and distribution network loads choose charging and discharging stations to provide capacity support, the service revenue and loss of the charging and discharging stations are both 0.

[0095] Strategy 1 The charging and discharging station is used as an auxiliary regulating device for the distributed power supply:

[0096] When the strategy of the charging and discharging station is When the charging and discharging station revenue consists of two parts, the service income of the charging and discharging station as the auxiliary capacity supplement of the distributed power supply is As well as electric vehicles need to pay service fees to participate in V2G services, the service income of the charging and discharging station is

[0097]

[0098] in, is the unit capacity benefit of the agreement between the charging and discharging station and the distributed power source, The service capacity of the charging and discharging station to the distributed power supply, that is, the capacity that the electric vehicle cluster can provide, The V2G capacity of electric vehicles aggregated by charging and discharging stations, Unit service fee for electric vehicles.

[0099] Losses when charging and discharging stations are used as auxiliary capacity supplements for distributed power generation To cover the electricity purchase losses, operating losses and software development (purchase) and maintenance costs for electric vehicles:

[0100]

[0101] in, The service capacity of the charging and discharging station to the distributed power supply, Service fees paid to electric vehicles by charging and discharging stations, For the daily operating expenses of the charging and discharging station, Expenditure on software development (purchase) and maintenance.

[0102] The additional loss of the cooperation between the charging and discharging station and the distributed power supply is recorded as

[0103]

[0104] in, It is the loss caused by cooperation between the charging and discharging station and other units of distributed power supply.

[0105] Strategy 2 The charging and discharging stations provide services to the distribution network loads:

[0106] When the strategy of the charging and discharging station is When the charging and discharging station chooses to provide direct capacity support or frequency stability guarantee service for the distribution network load, the service income of the charging and discharging station when providing electricity to the distribution network load is

[0107]

[0108] in, It is the unit capacity benefit of the agreement between the charging and discharging station and the distributed power source.

[0109] Strategy and strategies Under this condition, the losses of charging and discharging stations can be considered to be basically equal, that is,

[0110] When the distributed power supply and the charging and discharging station both provide capacity support to the distribution network load, the distributed power supply and the charging and discharging station are in a competitive relationship. The additional loss of the charging and discharging station during the competition is recorded as

[0111]

[0112] in, Additional losses due to competition between charging and discharging stations and distributed power sources.

[0113] In special scenarios:

[0114] 1. When the distributed power supply and distribution network load do not interact with the charging and discharging station, the charging and discharging station can use the flexible capacity of the electric vehicle cluster to interact with the distribution network through V2G technology. The unit capacity benefit is Service revenue is

[0115]

[0116] in, The V2G capacity of electric vehicles aggregated by charging and discharging stations, is the unit capacity benefit.

[0117] When distributed power sources or distribution network loads and charging and discharging stations provide flexible capacity support to the distribution network, 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 stations need to send additional mobilization instructions to weak-response electric vehicle owners in order to provide service prices. Get capacity indicators To meet the demand, the loss when the charging and discharging station expands the dispatching capacity for weakly responsive electric vehicle owners is

[0118]

[0119] When neither the distributed generation nor the distribution network load interacts with the charging and discharging station, the service revenue of the charging and discharging station is 0.

[0120] When the service revenue strategy of electric vehicle cluster is used, the group strategy set of electric vehicle owners is (respond, do not respond), which is recorded as When the electric vehicle owner chooses to respond to the charging and discharging station's invitation to perform V2G discharge, the service revenue is The loss is When the electric car owner chooses not to respond to the invitation, the service benefit of the electric car owner is 0.

[0121] Strategy 1 response

[0122] When electric vehicle owners choose to respond to the charging and discharging station's invitation to perform V2G discharge service revenue and loss for:

[0123]

[0124] in, Capacity configuration indicators for electric vehicles, is the unit capacity benefit of electric vehicles, n is the number of electric vehicles, is the average daily vehicle discharge power, is the available capacity factor of electric vehicles, is the average capacity of electric vehicles in the region, For electric car owners' wishes, Average loss of response to V2G service for electric vehicle owners.

[0125] In strategy Average loss of electric vehicle owners responding to V2G services Including charging loss V2G service process loss Time loss Battery loss and driving losses

[0126]

[0127]

[0128] in: is the unit capacity gain of the charging process, ω is the time coefficient, is the average discharge coefficient, For the responsive capacity, is the average driving speed, is the average dispatch distance, is the average battery consumption, is the average battery cycle number, is the average charging power, is the average charging efficiency, is the average discharge power, is the average discharge efficiency.

[0129] Strategy 2 No response

[0130] When the EV owner's strategy is not to respond to V2G services, both the service benefits and losses are 0.

[0131] S32, determining the mixed strategy of the four-party game entities based on 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;

[0132] Specifically, the four technical units of distributed power system, distribution network load, electric vehicle cluster and charging and discharging station select strategies with probability (w, x, y, z) (0≤w≤1; 0≤x≤1; 0≤y≤1; 0≤z≤1) respectively. Select a strategy with probability {(1-w),(1-x),(1-y),(1-z)}(0≤w≤1;0≤x≤1;0≤y≤1;0≤z≤1) The mixed strategies of the four game players are (w,1-w), (x,1-x), (y,1-y), and (z,1-z).

[0133] S33, according to the mixed strategies of the four game entities, respectively determining the distributed power supply game equation, the distribution network load game equation, the charging and discharging station game equation, and the electric vehicle game equation;

[0134] When the four technical units of distributed power supply, distribution network load, charging and discharging station, and electric vehicle are given probabilities w, x, y, z, the distributed power supply selection strategy and The expected returns are:

[0135]

[0136] Then the expected benefits of distributed power supply choosing strategies m1 and m2 with probability w and 1-w respectively are:

[0137]

[0138] The distributed power game equation is:

[0139]

[0140] Distribution network load selection strategy and The expected returns are:

[0141]

[0142]

[0143] Then the expected benefits of the distribution network load choosing strategies x1 and x2 with probability x and 1-x respectively are:

[0144]

[0145] The distribution network load game equation is:

[0146]

[0147] Charging and discharging station selection strategy and The expected return is:

[0148]

[0149] Then the expected benefits of the charging and discharging station choosing strategies y1 and y2 with probability y and 1-y respectively are:

[0150]

[0151] Then the game equation of the charging and discharging station is:

[0152]

[0153] Electric vehicle selection strategy and The expected returns are:

[0154]

[0155] Then the expected benefits of electric car owners choosing strategies z1 and z2 with probabilities z and 1-z respectively are:

[0156] U z =zUz1+(1-z);

[0157] Then the electric vehicle game equation is:

[0158]

[0159] S34, determining a four-party evolutionary game replication dynamic equation group based on the distributed power supply game equation, the distribution network load game equation, the charging and discharging station game equation, and the electric vehicle game equation;

[0160] By combining the above equations F(p), F(x), F(y), and F(z), we can obtain the four-party evolutionary game replication dynamic equations.

[0161] S35, based on the four-party evolutionary game replication dynamic equations, a multi-objective optimization model is constructed.

[0162] Assuming dp / dt = 0, dx / dt = 0, dy / dt = 0, and dz / dt = 0 in the differential equations, 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), and 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:

[0163]

[0164] After determining the eigenvalues, the evolutionary game of the distributed power system with vehicle-grid interaction exhibits three stable points: 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 in the evolutionary game, E16(1,1,1,1) shows a cooperative relationship between the charging and discharging station and the distributed power source; E2(0,0,0,1) shows a competitive relationship between the charging and discharging station and the distributed power source; and E11(0,1,1,1) shows neither a competitive nor a cooperative relationship between the charging and discharging station and the distributed power source. E2(0,0,0,1) and E11(0,1,1,1) indicate that the planning of the electric vehicle charging and discharging station is feasible. E11(0,1,1,1) indicates that the planning of the charging and discharging station is not feasible.

[0165] Three stable outcomes are obtained (the charging and discharging station and the distributed power source are in a cooperative relationship, the charging and discharging station and the distributed power source are in a competitive relationship, and the charging and discharging station and the distributed power source are neither competitive nor cooperative). The service revenue and net efficiency of the charging and discharging station under the three stable solutions can be calculated.

[0166] S104: Based on the operational objectives of the charging and discharging station and the multi-objective optimization model, a charging and discharging station planning scheme is obtained. Specifically, the operational objectives are determined, and based on these objectives, the service revenue indicators of the charging and discharging station are quantitatively calculated. This is used to optimize the charging and discharging station planning scheme. Under the optimized charging and discharging station planning scheme, unilateral changes in operating parameters by each technical unit will not improve overall system efficiency, thereby achieving a stable operating state. The charging and discharging station planning scheme includes: power service targets, capacity configuration indicators, and the number and power of charging and discharging equipment; the power service targets are distribution network loads or distributed power systems.

[0167] After S104, the operation objectives, basic data and corresponding planning schemes of the charging and discharging stations are stored to form a strategy set and a case library, which can then form case matching for similar planning projects in the future.

[0168] Based on the same inventive concept, embodiments of the present application also provide a game theory-based electric vehicle charging and discharging station planning device for implementing the aforementioned game theory-based electric vehicle charging and discharging station planning method. The solution provided by this device is similar to the solution described in the aforementioned method. Therefore, the specific limitations of one or more embodiments of the game theory-based electric vehicle charging and discharging station planning device provided below can be found in the limitations of the game theory-based electric vehicle charging and discharging station planning method above, and will not be repeated here.

[0169] In an exemplary embodiment, a game theory-based electric vehicle charging and discharging station planning device is provided, comprising:

[0170] An operation target determination module, used to determine the operation target of the charging and discharging station;

[0171] 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;

[0172] A multi-objective optimization model determination module is used to construct a multi-objective optimization model based on the evolutionary game model according to basic data and the relationship between each technical unit; the technical units include: distributed power supply system, distribution network load, electric vehicle cluster and charging and discharging station; the relationship between each technical unit is that the distributed power supply system adopts distributed power supply or introduces the flexible capacity of the electric vehicle cluster through the V2G mode, the distribution network load directly interacts with the distributed power supply 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 dispatches the electric vehicle cluster to directly provide capacity, power and frequency support to the distribution network load or acts as an auxiliary regulating device for the distributed power supply, and the electric vehicle cluster issues and does not issue a response to the charging and discharging instruction;

[0173] A planning scheme determination module is configured to determine a charging and discharging station planning scheme based on the operating objectives of the charging and discharging station and a multi-objective optimization model. The charging and discharging station planning scheme 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 systems. In an exemplary embodiment, a computer device is provided, which may be a server or a terminal. The computer device includes a processor, a memory, an input / output (I / O) interface, and a communication interface. The processor, memory, and I / O interface are connected via a system bus, and the communication interface is connected to the system bus via the I / O interface. The processor of the computer device is configured 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 computer program in the non-volatile storage medium. The I / O interface of the computer device is configured to exchange information between the processor and external devices. The communication interface of the computer device is configured to communicate with external terminals via a network connection. When the computer program is executed by a processor, a method for planning electric vehicle charging and discharging stations based on game theory is implemented.

[0174] In an exemplary embodiment, a computer-readable storage medium is provided, storing a computer program. When the computer program is executed by a processor, the steps in the above-mentioned method embodiments are implemented.

[0175] In an exemplary embodiment, a computer program product is provided, including a computer program. When the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0176] 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 used 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 must comply with relevant regulations.

[0177] Those skilled in the art will appreciate that all or part of the processes in the above-mentioned embodiment methods can be implemented by instructing the relevant hardware through a computer program, and 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-mentioned methods. Among them, any reference to memory, database or other media used in the embodiments provided in this application may include at least one of non-volatile and volatile memory. Non-volatile memory may include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory may include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM may be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0178] The databases involved in the various embodiments provided herein may include at least one of a relational database and a non-relational database. Non-relational databases may include, but are not limited to, distributed databases based on blockchains. The processors involved in the various embodiments provided herein may include, but are not limited to, general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic units, data processing logic units based on quantum computing, and the like.

[0179] In this application, all actions to obtain signals, information or data are carried out in compliance with the relevant data protection laws and policies of the country where they are located and with the authorization given by the owner of the corresponding device.

[0180] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, 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, they should be considered to be within the scope of this specification.

[0181] This document uses specific examples to illustrate the principles and implementation methods of this application. The description of the above examples is only intended to help understand the method and core concept of this application. At the same time, for those skilled in the art, based on the concept of this application, there may be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting 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 stations; 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 supply system, distribution network load, electric vehicle cluster and charging and discharging station; the relationship between each technical unit is that the distributed power supply system adopts distributed power supply or introduces the flexible capacity of electric vehicle cluster through V2G mode, the distribution network load directly interacts with the distributed power supply 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 dispatches 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 supply, and the electric vehicle cluster issues and does not issue a response to the charging and discharging instruction; Based on the operational 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 the amount of interactive power; the power service objects are distribution network loads or distributed power systems; 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: Based on basic data and the relationship between various technical units, determine the power output strategy of distributed power generation, the capacity configuration strategy of distribution network load, 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 entities 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 for distributed power supply, distribution network load, charging and discharging stations, and electric vehicles 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.

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 mixed strategy of the four game entities, the distributed power supply game equation, the distribution network load game equation, the charging and discharging station game equation and the electric vehicle game equation are determined respectively, specifically 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 generation, The loss of traditional power generation 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 interaction between distributed power generation and distribution network loads. The cost and loss of distributed power generation process, For the distributed power grid connection income, When distributed power sources choose to introduce flexible capacity of electric vehicle clusters, the service benefits of providing capacity support to distribution network loads are is the comprehensive benefit of distributed power generation connected to the distribution network when the distributed power generation chooses to introduce the flexible capacity of electric vehicle clusters, w is the probability that the distributed power generation chooses to introduce the flexible capacity strategy of electric vehicle clusters, x is the probability that the distribution network load chooses to be supplied by traditional distributed power generation, y is the probability that the charging and discharging station chooses to be a supplementary power source for the distributed power generation, 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; The risk loss of distributed power in V2G mode, The benefits brought to the distributed power supply by orderly charging and discharging of electric vehicle owners in response to V2G commands; Using the formula Determine the distribution network load game equation F(x); where, and The benefits and losses of distributed power supply to 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, 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 it is 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 calculated. The loss when the charging and discharging station expands the dispatch capacity for the weakly responsive electric vehicle owners in the society, The additional loss caused by the cooperation between the charging and discharging station and the distributed power supply, Additional losses due to competition between charging and discharging stations and distributed power sources; Using the formula Determine the electric vehicle charging and discharging instruction response game equation F(z); where, and The benefits and losses of electric vehicle owners who choose to respond to the invitation of charging and discharging stations and participate in discharge.

3. The electric vehicle charging and discharging station planning method based on game theory according to claim 2 is characterized in that: Based on 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.

4. 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.

5. The electric vehicle charging and discharging station planning method based on game theory according to claim 1 is characterized in that: Based on 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.

6. A device for planning electric vehicle charging and discharging stations based on game theory, used to implement the method for planning electric vehicle charging and discharging stations based on game theory according to any one of claims 1 to 5, 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 basic data and the relationship between each technical unit; the technical units include: distributed power supply system, distribution network load, electric vehicle cluster and charging and discharging station; the relationship between each technical unit is that the distributed power supply system adopts distributed power supply or introduces the flexible capacity of the electric vehicle cluster through the V2G mode, the distribution network load directly interacts with the distributed power supply 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 dispatches the electric vehicle cluster to provide direct capacity, power and frequency support to the distribution network load or acts as an auxiliary regulation device for the distributed power supply, and the electric vehicle cluster issues and does not issue a response to the charging and discharging instruction; The planning scheme determination module is used to obtain a charging and discharging station planning scheme based on the operating 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 objects are distribution network loads or distributed power systems.

7. 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 according to any one of claims 1 to 5.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the electric vehicle charging and discharging station planning method based on game theory described in any one of claims 1 to 5 is implemented.

9. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the electric vehicle charging and discharging station planning method based on game theory described in any one of claims 1 to 5 is implemented.

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