Ordered charging and discharging method and system for electric vehicle

Abstract

The invention discloses an electric vehicle ordered charging and discharging method and system, and the method comprises the steps: obtaining the number of electric vehicles meeting the requirements in a target range, the number of agents, and the parameter information of the electric vehicles, and building a V2G-based electric vehicle response capability constraint condition according to the parameter information; establishing a response capability value acquisition rule of the electric vehicle under an electric vehicle response capability constraint condition based on V2G, and establishing an electric vehicle charging and discharging strategy double-layer optimization model containing the response capability value of the electric vehicle; and according to an improved multi-population genetic optimization algorithm, solving the electric vehicle charging and discharging strategy double-layer optimization model to obtain the optimal charging power of each time period so as to obtain a final electric vehicle charging and discharging strategy. By optimizing the scheduling strategy, the power grid load fluctuation can be effectively reduced, and the power grid operation stability is improved. Meanwhile, by considering the scheduling willingness and scheduling capability of the electric vehicle user, the user can obtain the maximum income when participating in the V2G plan, so that the participation enthusiasm of the user is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of orderly charging and discharging of electric vehicles, and in particular to an orderly charging and discharging method and system for electric vehicles. Background Art

[0002] With the development of social economy, the growth of electric vehicles has been increasing year by year. Compared with traditional fuel vehicles, electric vehicles have great advantages in energy conservation and emission reduction and green travel. Electric vehicles are an inevitable trend as the choice of future travel mode. Electric vehicles have energy storage characteristics and can be connected to the power grid as loads or distributed power supply equipment. At the same time, they can improve energy utilization through two-way interaction between vehicles and the grid. Reasonable scheduling of electric vehicle loads can improve the operation status of the power grid, alleviate the power load in the power grid, effectively manage the charging load, and realize peak load reduction and valley filling. At the same time, with the access of new energy such as photovoltaic and wind energy, the construction of new energy wind, light and storage integration can be accelerated. To build a smart power city, electric vehicles must be charged in an orderly manner. Therefore, the optimization of electric vehicle charging and discharging strategies is particularly important. Summary of the invention

[0003] In view of the above-mentioned problems, the present invention is proposed.

[0004] Therefore, the technical problem solved by the present invention is: how to optimize the charging and discharging strategy of electric vehicles.

[0005] To solve the above technical problems, the present invention provides the following technical solutions: a method for orderly charging and discharging of electric vehicles, comprising: obtaining the number of electric vehicles that meet the requirements, the number of agents and parameter information of electric vehicles within a target range, and establishing a V2G-based electric vehicle response capability constraint condition according to the parameter information; establishing a response capability value acquisition rule for electric vehicles under the V2G-based electric vehicle response capability constraint condition, and establishing a two-layer optimization model for electric vehicle charging and discharging strategy including the response capability value of the electric vehicle; solving the two-layer optimization model for electric vehicle charging and discharging strategy according to an improved multi-population genetic optimization algorithm to obtain the optimal charging power in each time period, thereby obtaining the final electric vehicle charging and discharging strategy.

[0006] As a preferred solution of the orderly charging and discharging method of electric vehicles described in the present invention, the parameter information includes the time when the electric vehicles that meet the requirements connect to and leave the power grid, the load status of the electric vehicles, the battery capacity of the electric vehicles, and the charging and discharging power of the electric vehicles.

[0007] As a preferred solution of the orderly charging and discharging method of electric vehicles described in the present invention, the V2G-based electric vehicle response capability constraint conditions include:

[0008] The time constraint for electric vehicles to access the grid includes the time limit for electric vehicles that meet the requirements to access the grid, which is recorded as the time for electric vehicles to access the grid, T g,i , obtained by the vehicle's battery management system; the time when the electric vehicle leaves the grid is T l,i , determined by the randomness of user behavior, the time t for electric vehicles to access the grid satisfies T g,i ≤t≤T l,i , where i represents the i-th electric car;

[0009] The state of charge constraints of the electric vehicle itself, including the state of charge when the electric vehicle is connected to the grid and the minimum state of charge SOC when the electric vehicle is finished charging min , satisfying SOC(t)≥SOC min , SOC(t) represents the state of charge of the electric vehicle at the end of the charging state;

[0010] The V2G response capacity constraint is to convert the available capacity B of the electric vehicle battery that meets the requirements into ke It is divided into three parts, namely the battery capacity B1 that meets the requirements, the battery capacity B2 for daily driving, and the battery capacity B3 for standby driving, B1+B2+B3=B ke,i , where B ke,i represents the total battery capacity of the i-th electric vehicle;

[0011] The charging and discharging power constraints include that the power of the required part of the electric vehicle shall not exceed its rated charging and discharging power.

[0012] As a preferred solution of the orderly charging and discharging method of electric vehicles described in the present invention, the responsiveness acquisition rules of electric vehicles under the responsiveness constraint conditions of electric vehicles based on V2G include:

[0013] Obtain the electric vehicle battery loss rate, battery capacity and regional response coefficient that meet the requirements, and obtain the electric vehicle battery capacity impact factor. According to the electric vehicle battery capacity impact factor, combined with the electric vehicle response capacity constraint condition based on V2G, obtain the electric vehicle response capacity value, which is specifically expressed as:

[0014]

[0015] Q=q n B ke,i η

[0016] Among them, q n represents the regional response coefficient, B ke,i represents the battery capacity of the V2G plan, η represents the battery loss rate, T l,i Indicates the time when the electric vehicle leaves the power grid; T g,iIndicates the time when the electric vehicle is connected to the power grid; T min,i It represents the shortest time for an electric vehicle to reach the desired battery state at maximum power, and Q represents the influencing factor of the electric vehicle battery capacity;

[0017] If and only if k>1, the conditions for establishing a two-level optimization model for electric vehicle charging and discharging strategies including the response capability value of the electric vehicle are met.

[0018] As a preferred solution of the orderly charging and discharging method of an electric vehicle according to the present invention, wherein: the two-level optimization model of the charging and discharging strategy of an electric vehicle including the response capability value of the electric vehicle comprises an upper-level objective function and its corresponding upper-level objective function constraint condition, a lower-level objective function and its corresponding lower-level objective function constraint condition;

[0019] The upper objective function constraint conditions include at least charging amount constraint, agent scheduling constraint in each time period and load peak constraint, and the lower objective function constraint conditions include at least electric vehicle charging and discharging time constraint, vehicle battery capacity constraint and load amount constraint.

[0020] As a preferred solution of the orderly charging and discharging method of electric vehicles described in the present invention, the upper objective function minimizes the total fluctuation curve of the system during the charging and discharging period, and at the same time minimizes the sum of the deviations between the actual dispatch of the agent and the power consumption strategy formulated by the dispatch center, which is expressed as:

[0021]

[0022] Where T represents 24 time periods in a day, n represents the number of agents; P d,t is the non-electric vehicle load during period t, P n,t represents the charging and discharging load of the nth electric vehicle at time t, N0 represents the total number of agents; P DE Indicates the output of other distributed energy resources in the region; It represents the average system load in T time, expressed as:

[0023]

[0024] in, represents the scheduling deviation function between the scheduling center and the regional agent, α represents the violation penalty coefficient, |PD n,t -P n,t | represents the scheduling deviation and the constraints of regional agents, PD n,t Represents the scheduling plan made by the nth regional agent at time t.

[0025] As a preferred solution of the orderly charging and discharging method of an electric vehicle according to the present invention, the lower objective function satisfies the minimum charging cost, which is expressed as:

[0026]

[0027] Among them, T0 represents the start time of charging and discharging, and T1 represents the end time of charging and discharging; and Represent the average value of charging power and discharging power, C c,i and C d,i They represent charging price and discharging price respectively, a and b represent charging parameters and discharging parameters respectively, It represents the average responsiveness value of electric vehicles in the area under the jurisdiction of the agent;

[0028] When charging, a is 1 and b is 0; when discharging, a is 0 and b is 1.

[0029] In a second aspect, another object of the present invention is to provide an orderly charging and discharging system for electric vehicles, comprising: an information acquisition and processing module, a response capability value acquisition module, a two-layer optimization model establishment module, and a strategy acquisition module;

[0030] The information acquisition and processing module is used to obtain the number of electric vehicles participating in the V2G plan, the number of agents and parameter information of electric vehicles participating in the V2G plan within the target range, and establish V2G-based electric vehicle response capability constraint conditions according to the parameter information;

[0031] The response capability value acquisition module is used to establish a response capability value acquisition rule for an electric vehicle under a V2G-based electric vehicle response capability constraint condition;

[0032] The two-layer optimization model establishment module is used to establish a two-layer optimization model of the electric vehicle charging and discharging strategy including the response capability value of the electric vehicle;

[0033] The strategy acquisition module is used to solve the electric vehicle charging and discharging strategy double-layer optimization model according to the improved multi-population genetic optimization algorithm to obtain the optimal charging power in each time period, thereby obtaining the final electric vehicle charging and discharging strategy.

[0034] In a third aspect, the present invention provides a computer device including a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the above-mentioned method for orderly charging and discharging of an electric vehicle when executing the computer program.

[0035] In a fourth aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the steps of the method for orderly charging and discharging of an electric vehicle as described above are implemented.

[0036] Beneficial effects of the present invention: The method and system for orderly charging and discharging of electric vehicles provided by the present invention obtain the number of electric vehicles participating in the V2G plan, the number of agents and the parameter information of electric vehicles participating in the V2G plan within the target range, and establish the V2G-based electric vehicle response capability constraint conditions according to the parameter information; establish the response capability value acquisition rules of electric vehicles under the V2G-based electric vehicle response capability constraint conditions; establish a double-layer optimization model of electric vehicle charging and discharging strategy including the response capability value of electric vehicles; solve the double-layer optimization model of electric vehicle charging and discharging strategy according to the improved multi-population genetic optimization algorithm to obtain the optimal charging power in each time period, thereby obtaining the final electric vehicle charging and discharging strategy. By optimizing the scheduling strategy, the load fluctuation of the power grid can be effectively reduced and the stability of the power grid operation can be improved. At the same time, by considering the scheduling willingness and scheduling capability of electric vehicle users, users can obtain the maximum benefits when participating in the V2G plan, thereby improving the enthusiasm of users to participate. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0038] Figure 1 A method flow chart of an orderly charging and discharging method and system for an electric vehicle provided by an embodiment of the present invention;

[0039] Figure 2 A schematic diagram of an electric vehicle V2G corresponding optimization curve in an embodiment of an electric vehicle orderly charging and discharging method and system provided by an embodiment of the present invention;

[0040] Figure 3 A schematic diagram of average monthly charging costs for electric vehicles in different regions in an embodiment of a method and system for orderly charging and discharging of electric vehicles provided by one embodiment of the present invention. DETAILED DESCRIPTION

[0041] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0042] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0043] Example 1

[0044] Before describing the embodiments of the present application in detail, some related concepts are first explained for the sake of clarity.

[0045] V2G: V2G (Vehicle-to-Grid) refers to the two-way energy exchange technology between electric vehicles (EVs) and the grid. Under this technical framework, electric vehicles can not only charge from the grid as electricity consumers, but also discharge electricity to the grid as electricity suppliers. Through V2G technology, electric vehicles can charge during off-peak hours and supply electricity to the grid during peak hours, helping to balance the supply and demand of the grid, reduce the peak load of the power system, and provide backup power support in emergencies.

[0046] Two-layer optimization model: The two-layer optimization model is an optimization model structure, which is usually used to solve decision-making problems with a hierarchical structure. In this application, this model contains two levels: Upper Level Optimization: It usually represents the interests of the central dispatcher, and the goal may be to minimize the overall operating cost of the power grid or maximize the stability of the system. Lower Level Optimization: It represents the interests of individual participants (such as users or agents), and the goal may be to minimize the user's costs or maximize the user's benefits. The core of the two-layer optimization model is that the results of the upper-level optimization will affect the decision variables of the lower-level optimization, and vice versa. This interaction makes the model more complex but also closer to the multi-agent decision-making environment in the real world.

[0047] Multi-population genetic optimization algorithm: Multi-population genetic optimization algorithm is a variant of genetic algorithm (GA), which enhances the algorithm's search ability and robustness by maintaining multiple independent populations. In traditional GA, there is only one population, while in MPGA, there are multiple populations evolving in parallel, each with its own parameter settings, such as crossover rate, mutation rate, etc. Through gene exchange between populations (such as immigration operations), MPGA can avoid premature convergence while maintaining population diversity, thereby improving global search capabilities.

[0048] Jurisdiction agent: Jurisdiction agents are usually entities or organizations responsible for managing and coordinating certain activities or resources within a specific geographic area. In the context of electric vehicle charging and discharging strategies, jurisdictional agents may refer to institutions or companies responsible for managing electric vehicle charging and discharging activities within a certain area. The tasks of these agents may include collecting electric vehicle usage data, developing charging and discharging plans, and negotiating charging and discharging strategies with grid operators to ensure that the grid's operating efficiency is optimized while meeting user needs.

[0049] There are some challenges in related technologies, such as how to effectively manage the charging and discharging behaviors of a large number of electric vehicles to avoid excessive pressure on the power grid, and how to motivate electric vehicle users to actively participate in V2G programs. Existing methods often lack comprehensive consideration of electric vehicle user needs and grid load changes, resulting in the inability to achieve the optimal charging and discharging strategy.

[0050] The present application provides a method that can effectively solve the above-mentioned problems. Next, a method for realizing the orderly charging and discharging method and system of an electric vehicle will be described in detail in combination with multiple embodiments.

[0051] Reference Figure 1 , as an embodiment of the present invention, provides an orderly charging and discharging method for an electric vehicle, comprising:

[0052] S1: obtaining the number of electric vehicles that meet the requirements, the number of dealers, and parameter information of electric vehicles within the target range, and establishing V2G-based electric vehicle response capability constraint conditions according to the parameter information;

[0053] Furthermore, in an embodiment of the present application, the parameter information includes the time when the electric vehicles participating in the V2G plan connect to and leave the grid, the load status of the electric vehicle, the battery capacity of the electric vehicle, and the charging and discharging power of the electric vehicle;

[0054] It should be noted that parameter information may also include weather conditions, electricity price information, and user preference settings, etc. Such information helps to more accurately predict and optimize the charging and discharging behavior of electric vehicles, thereby better balancing the grid load and meeting user needs.

[0055] Furthermore, the benefit of the parameter information selected in this application is that it can provide a comprehensive perspective to evaluate the potential participation of electric vehicles in V2G programs. By considering the time when electric vehicles are connected and leave, it can ensure that charging is carried out when the grid load is low and discharging is carried out when the load is high, thereby effectively alleviating the pressure on the grid. The load status, battery capacity and charge and discharge power parameters of electric vehicles help to formulate reasonable charge and discharge plans to avoid adverse effects on battery life, while ensuring that electric vehicle users have enough power to use when needed.

[0056] It should be noted that the addition of parameters such as weather conditions, electricity price information, and user preference settings enables the system to respond to various situations more flexibly. For example, users are encouraged to charge during periods of low electricity prices and to discharge during periods of high electricity prices, thereby reducing users' charging costs and improving the economic benefits of the power grid. User preference settings allow users to set charging and discharging time windows according to their personal needs and habits, thereby improving user satisfaction.

[0057] Furthermore, in the embodiment of the present application, the V2G-based electric vehicle response capability constraint conditions include the electric vehicle access grid time constraint, the electric vehicle's own charge state constraint, the V2G response capacity constraint, and the charging and discharging power constraint;

[0058] The V2G-based electric vehicle response capacity constraints include: the electric vehicle access grid time constraint is the time limit for electric vehicles participating in the V2G plan to access the grid, and the electric vehicle access grid time T is g,i , obtained by the vehicle's battery management system; the time when the electric vehicle leaves the grid is T l,i , which is determined by the randomness of the user's behavior, then the time t for the electric vehicle to access the grid satisfies T g,i ≤t≤T l,i , where i represents the i-th electric car;

[0059] The state of charge constraints of the electric vehicle itself include the state of charge when the electric vehicle is connected to the grid and the minimum state of charge SOC when the electric vehicle is finished charging. min , denoted as SOC(t)≥SOC min , where SOC(t) represents the state of charge of the electric vehicle at time t at the end of the charging state;

[0060] The V2G response capacity constraint includes the available battery capacity B of the electric vehicles that will participate in the V2G program. ke It is divided into three parts, namely the battery capacity B1 for participating in the V2G plan, the battery capacity B2 for maintaining daily driving, and the battery capacity B3 for backup driving. B1+B2+B3=B ke,i , where B ke,i represents the total battery capacity of the i-th electric vehicle;

[0061] The charging and discharging power constraints include that the power of electric vehicles participating in the V2G plan must not exceed their rated charging and discharging power.

[0062] It should be noted that V2G-based electric vehicle response capability constraints can also include weather condition constraints. Considering that severe weather may affect the charging and discharging efficiency and safety of electric vehicles, the system will set specific charging and discharging restrictions based on weather forecast information. For example, under extremely high or low temperature conditions, battery performance may decline. At this time, the system will reduce the charging and discharging power or limit the charging and discharging time to protect the battery; electricity price information constraints. The system will adjust the charging and discharging strategy according to real-time electricity price information to maximize cost-effectiveness. During periods of low electricity prices, the system will give priority to charging, and during periods of high electricity prices, it will give priority to discharging to reduce users' charging costs and improve the economic benefits of the power grid; user preference setting constraints. Considering the user's personalized needs for charging and discharging time, the system will optimize the charging and discharging plan according to the preferences set by the user. For example, users can set not to charge or discharge within a specific time period. The system will adjust the charging and discharging plan according to these preferences to improve user satisfaction and participation;

[0063] Furthermore, when acquiring parameter information, if the user or operator does not obtain information such as weather conditions, electricity price information, and user preference settings, then there will be no weather condition constraints, electricity price information constraints, and user preference setting constraints in the V2G-based electric vehicle responsiveness constraint conditions. Only when the user obtains information such as weather conditions, electricity price information, and user preference settings, will there be weather condition constraints, electricity price information constraints, and user preference setting constraints in the V2G-based electric vehicle responsiveness constraint conditions.

[0064] It should be noted that the benefit of the above steps is that it can provide a comprehensive parameter information basis for the orderly charging and discharging system of electric vehicles, thereby ensuring that the system can formulate a more reasonable and efficient charging and discharging strategy based on real-time data and user needs. By comprehensively considering parameters such as the access and departure time of electric vehicles, load status, battery capacity, charging and discharging power, weather conditions, electricity price information and user preference settings, the system can better balance the grid load, optimize the charging and discharging behavior of electric vehicles, extend battery life, and improve user satisfaction.

[0065] S2: Establishing the rules for obtaining the responsiveness value of electric vehicles under the responsiveness constraint conditions of electric vehicles based on V2G, and establishing a two-level optimization model for the charging and discharging strategy of electric vehicles including the responsiveness value of electric vehicles;

[0066] Furthermore, establishing the rules for obtaining the response capability of electric vehicles under the V2G-based electric vehicle response capability constraint condition includes: obtaining the battery loss rate of electric vehicles participating in V2G, the battery capacity of the participating V2G plan, and the regional response coefficient, and obtaining the electric vehicle battery capacity influencing factor;

[0067] According to the influencing factor of the electric vehicle battery capacity and the constraint condition of the electric vehicle response capability based on V2G, the electric vehicle response capability value is obtained;

[0068] In the embodiment of the present application, it is specifically expressed as:

[0069]

[0070] Among them, q n represents the regional response coefficient, B ke,i represents the battery capacity of the V2G plan, η represents the battery loss rate, T l,i Indicates the time when the electric vehicle leaves the grid; T g,i Indicates the time when the electric vehicle is connected to the power grid; T min,i It represents the shortest time for an electric vehicle to reach the desired battery state at maximum power, and Q represents the influencing factor of the electric vehicle battery capacity;

[0071] It should be noted that the conditions for establishing a two-level optimization model for electric vehicle charging and discharging strategies including the response capability value of electric vehicles are met when and only when k>1.

[0072] Furthermore, if a regional agent does not consider the impact of electric vehicle battery capacity, the electric vehicle response capability value can be simplified as follows:

[0073]

[0074] Among them, T l,i Indicates the time when the electric vehicle leaves the grid; T g,i Indicates the time when the electric vehicle is connected to the power grid; T min,i It indicates the shortest time for an electric vehicle to reach the desired battery state at maximum power;

[0075] It should be noted that the calculation of the response capability value without considering the influence of the battery capacity of electric vehicles provides convenience for certain specific scenarios. For example, in a group of vehicles with relatively uniform battery performance, simplified calculations can reduce the computational complexity and speed up the strategy formulation process while ensuring the effectiveness of the overall strategy. This simplified method can be used as a rapid response solution for special situations in practical applications, providing agents or grid operators with flexible strategy options.

[0076] Furthermore, if a regional agent does not consider the impact of electric vehicle battery capacity, it may mean that the electric vehicles in the area under the jurisdiction of the agent may all have new batteries, or that the electric vehicles in the area under the jurisdiction are all of the same type, such as taxis or buses or electric vehicle sales areas. The battery capacity and performance of these vehicles are relatively uniform, so the impact of battery capacity differences can be ignored when formulating charging and discharging strategies.

[0077] It should be noted that the benefit of establishing a rule for obtaining the EV response capability value under the EV response capability constraint condition based on V2G is that it provides a quantified EV response capability value for the formulation of EV charging and discharging strategies. By obtaining key parameters such as EV battery loss rate, battery capacity participating in the V2G plan, and regional response coefficient, the actual contribution capacity of each EV in the V2G system can be accurately evaluated. Such an evaluation is crucial for optimizing the charging and discharging behavior of EVs, balancing the grid load, and improving the overall system operation efficiency.

[0078] Specifically, the calculation of the electric vehicle battery capacity influencing factor Q can reflect the battery performance and available capacity of each electric vehicle when participating in the V2G plan, thereby providing a basis for formulating a more refined charging and discharging strategy. When k>1, it indicates that the electric vehicle has sufficient responsiveness when participating in the V2G plan, can meet the needs of the power grid and provide effective support for the system. On the contrary, if k≤1, it indicates that the battery performance of the electric vehicle may not be sufficient to support the V2G plan, and needs to be adjusted accordingly or excluded from the plan.

[0079] Furthermore, the two-level optimization model originates from a special case in the multi-level planning model. In the two-level planning, the implementation results of the upper-level strategy not only affect the initial state of the lower-level strategy, but also change the relevant conditions of its constraints, and the implementation results of the lower-level strategy will be fed back to the upper level in the form of input. In this paper, combined with the travel characteristics of residential areas, the upper-level dispatching center formulates the overall electricity consumption strategy in each area, and the regional agents formulate regional electricity consumption strategies under the electricity consumption strategy to minimize the total fluctuation curve of the system during the charging and discharging period, and at the same time, the sum of the deviations between the actual dispatch of the agents and the electricity consumption strategy formulated by the dispatching center is minimized. In the lower-level model, the regional agents make appointments for charging management of electric vehicles in the region, and at the same time combine the user's dispatching willingness and dispatching ability to maximize user benefits.

[0080] It should be noted that establishing a two-level optimization model for electric vehicle charging and discharging strategy including the response capability value of electric vehicles includes: the two-level optimization model for electric vehicle charging and discharging strategy includes an upper-level objective function and its corresponding upper-level objective function constraint conditions, and the upper-level objective function constraint conditions at least include a charging amount constraint, an agent's scheduling constraint for each time period, and a load peak constraint;

[0081] The two-level optimization model of electric vehicle charging and discharging strategy includes a lower-level objective function and its corresponding lower-level objective function constraints. The lower-level objective function constraints at least include electric vehicle charging and discharging time constraints, vehicle battery capacity constraints, and load constraints.

[0082] Furthermore, the upper objective function includes: the dispatch center formulates the overall power consumption strategy in each region, and the regional agent formulates the power consumption strategy in the region under the power consumption strategy, so that the total fluctuation curve of the system during the charging and discharging period is minimized, and at the same time, the sum of the deviations between the actual dispatch of the agent and the power consumption strategy formulated by the dispatch center is minimized, which is expressed as:

[0083]

[0084] Where T represents 24 time periods in a day, n represents the number of agents; P d,t is the non-electric vehicle load during period t, P n,t represents the charging and discharging load of the nth electric vehicle at time t, N0 represents the total number of agents; P DE Indicates the output of other distributed energy resources in the region; Indicates the average system load in T time;

[0085]

[0086] in, represents the scheduling deviation function between the scheduling center and the regional agents;

[0087] Among them, α represents the violation penalty coefficient, |PD n,t -P n,t | represents the scheduling deviation and the constraints of regional agents; PD n,t Represents the scheduling plan made by the nth regional agent at time t.

[0088] Furthermore, the charge capacity constraint is expressed as:

[0089]

[0090] It should be noted that in order to ensure the travel needs of users and ensure that the total load remains unchanged during the scheduling period, it is necessary to formulate charging capacity constraints, PR n,t It represents the disordered charging load in the area under the jurisdiction of agent n at time t.

[0091] The dispatch constraints of agents in each period are expressed as:

[0092]

[0093] in, and They represent the average charging power and average discharging power of electric vehicles in the area under the jurisdiction of the agent respectively; A represents the connection status between electric vehicles and charging piles in the area, A=0 or A=1; Represents the average responsiveness of electric vehicles within the agency’s jurisdiction.

[0094] The load peak constraint is expressed as:

[0095] P n,max ≤P 0,max

[0096] Among them, P n,max represents the optimized distribution load in the region; P 0,max Indicates the conventional load of the regional distribution network.

[0097] It should be noted that the reason why only these three constraints are considered in this application is to simplify the model and ensure its operability, while ensuring the practicality and effectiveness of the optimization results. By focusing on the charging capacity constraints, the dispatching constraints of the agents in each period, and the load peak constraints, it can be ensured that the grid load is balanced and the system is prevented from overloading while meeting the travel needs of users. In addition, these constraints can provide clear boundaries for the formulation of electric vehicle charging and discharging strategies, making it easier to implement in actual operations. However, it does not mean that other constraints cannot be considered. In the process of designing this application, other constraints are actually taken into consideration.

[0098] It should be noted that the upper-level objective function constraints may also include other constraints. For example, when the agent needs to ensure the reliability of the power grid within its jurisdiction, a power supply reliability constraint can be added. When it is necessary to ensure that electric vehicles do not cause excessive load fluctuations on the power grid when participating in the V2G plan, a load fluctuation constraint can be added.

[0099] It should be noted that when an agent needs to ensure the reliability of the power grid within its jurisdiction, a power supply reliability constraint is added. The power supply reliability constraint can be:

[0100]

[0101] Among them, P discharge,n,t is the discharge power of the nth agent at time t, P DE,t is the output of other distributed energy resources at time t, P grid is the power provided by the grid, L total,t is the total load in time t, α is the reliability coefficient, P reserve It is the reserved backup power.

[0102] Assume that in a specific time period t, there are the following data:

[0103] The discharge power P of the nth agent discharge ,n,t are: P discharge,1,t =10kW, P discharge,2,t =15kW, assuming there are two agents;

[0104] The output P of other distributed energy sources (such as solar energy or wind energy) within time t DE,t20kW;

[0105] Power provided by the grid P grid 50kW;

[0106] Total load L in time t total ,t is 70kW;

[0107] The reliability coefficient α was set to 1.2;

[0108] Reserved backup power P reserve 10kW;

[0109] According to the power supply reliability constraint formula, substitute the specific values:

[0110] P discharge,1,t +P discharge,2,t +P DE,t +P grid ≥L total,t +α·P reserve

[0111] 10+15+20+50≥70+1.2·10

[0112] 95≥82

[0113] It should be noted that the establishment of this inequality means that at this moment, the total power provided by the system (including the discharge power of electric vehicles, distributed energy output and the power provided by the power grid) is sufficient to meet the total load demand, and there is also a certain redundancy to deal with unforeseen situations and meet the reliability standards.

[0114] It should be noted that the upper objective function is designed to coordinate the relationship between the grid dispatch center and the regional agents to ensure that the power demand of the entire grid is met smoothly, efficiently and economically when electric vehicles participate in the V2G program.

[0115] Furthermore, the lower-level objective function includes: the regional agent manages the reservation charging of electric vehicles in the region, and at the same time combines the user's scheduling willingness and scheduling ability to maximize the user's benefits, that is, the lowest charging cost, which is expressed as:

[0116]

[0117] Among them, T0 represents the start time of charging and discharging; T1 represents the end time of charging and discharging; and Represent the average value of charging power and discharging power respectively; C c,i and C d,i They represent charging electricity price and discharging electricity price respectively; a and b represent charging parameters and discharging parameters respectively;

[0118] When charging, a is 1 and b is 0, and when discharging, a is 0 and b is 1;

[0119] It represents the average responsiveness value of electric vehicles in the area under the jurisdiction of the agent. The average responsiveness value of the areas under the jurisdiction of different agents is different.

[0120] It should be noted that the charging and discharging time constraints of electric vehicles are expressed as:

[0121] a×b=0

[0122] Electric vehicles cannot be charged and discharged at the same time.

[0123] Furthermore, the vehicle battery capacity constraint is expressed as:

[0124]

[0125] Among them, η c,i and η d,i They represent the charging and discharging efficiency respectively; S represents the full load of the battery; S2 represents the load required for daily driving; and S3 represents the standby load.

[0126] Furthermore, the load constraint is expressed as:

[0127]

[0128] Among them, P M is the maximum load of the system, P c,t , P d,t Represent the charging power and discharging power respectively.

[0129] It should be noted that the reason why the lower-level objective function in this application only considers these three constraints is to simplify the complexity of the problem and ensure that the charging and discharging behavior of electric vehicles can be effectively managed in actual operation. However, in actual applications, many other factors may need to be considered, such as the battery health of electric vehicles, the specific needs of users, and the real-time load of the power grid.

[0130] It should be noted that the lower objective function can also include battery health constraints, battery charge and discharge cycle constraints and charge and discharge depth constraints (Depth of Discharge, DOD). In order to extend the service life of the battery, it is necessary to limit the number of charge and discharge cycles and DOD of the battery. When the battery health of an electric vehicle is detected to have dropped to a certain level within the jurisdiction of an agent, such as a significant decrease in battery capacity or a significant increase in battery internal resistance, the health constraint needs to be activated. This can be achieved by regularly monitoring the battery's SOC (State of Charge), temperature, voltage and other parameters. Once the battery is found to be in poor health, the charge and discharge strategy is adjusted to reduce the intensity of battery use to prevent further damage.

[0131] Furthermore, when the cumulative number of charge and discharge cycles of batteries in an agent's jurisdiction is close to their design life, that is, when the battery is close to its expected charge and discharge cycle limit, a charge and discharge cycle limit should be implemented. This is to avoid premature scrapping of batteries due to excessive use. The system can track the charge and discharge history of each battery, and when the estimated remaining number of cycles is insufficient to support regular use, the system will take measures to reduce the frequent charge and discharge of the battery.

[0132] It should be noted that when there is a need to maximize the protection of the battery within a certain period of time within the jurisdiction of an agency, such as when the battery is at a lower state of charge or in extreme weather conditions (high or low temperature), the system will implement charge and discharge depth constraints. This usually means limiting the discharge depth of the battery and not allowing the battery to be fully discharged below its minimum recommended state of charge, thereby avoiding damage to the battery. In addition, when the battery is close to full charge, the charge depth will also be limited to prevent overcharging.

[0133] It should be noted that the benefit of establishing a two-level optimization model for electric vehicle charging and discharging strategies that includes the response capability value of electric vehicles is that it can fully consider the flexibility and diversity of electric vehicles when participating in grid interaction. Through the optimization model, efficient coordination between electric vehicles and the grid can be achieved, thereby improving the operating efficiency and reliability of the entire power system.

[0134] S3: According to the improved multi-population genetic optimization algorithm, the double-layer optimization model of the electric vehicle charging and discharging strategy is solved to obtain the optimal charging power in each time period, thereby obtaining the final electric vehicle charging and discharging strategy.

[0135] Furthermore, according to the improved multi-population genetic optimization algorithm, the two-level optimization model of electric vehicle charging and discharging strategy is solved, including: calculating the optimal charging power of each time period through the improved multi-population genetic optimization algorithm, thereby realizing the optimized scheduling strategy of the entire time period.

[0136] It should be noted that there are multiple constraints in the two-level optimization model of electric vehicle charging and discharging. However, due to the evolutionary characteristics of the genetic algorithm (SGA), there is no need to pay too much attention to whether the constraints are linear or nonlinear during the search process. Instead, the objective function is directly used as the search information, which has very high flexibility. When super-strong individuals appear in the group or the group size is small, the convergence boundary will move forward. Therefore, a multi-population genetic optimization algorithm (MPGA) is adopted, and different parameter settings are adopted for different populations. At the same time, the immigration operator is introduced to achieve the simultaneous evolution of different populations, and finally the elite population is selected. The two-level optimization model of electric vehicle charging and discharging can calculate the optimal charging power for each time period through the multi-population genetic optimization algorithm, thereby realizing the optimal scheduling strategy for the entire time period. The implementation process of the multi-population genetic optimization algorithm is to select the best individuals and put them into the elite population every time the population evolves, and the elite population is selected separately and no longer participates in the evolution until the elite population reaches the limit value and the program is terminated;

[0137] Furthermore, when using a multi-population genetic optimization algorithm, multiple populations are first initialized, each population representing a different set of electric vehicle charging and discharging strategies. For example, each population can represent a set of different charging power configuration schemes. Each population contains a certain number of individuals (charging power configuration schemes), and each individual represents a possible solution.

[0138] It should be noted that the fitness value of each individual is calculated. The fitness function should be defined based on the upper objective function and the lower objective function. The upper objective function can consider the total cost of the power grid or the minimization of the fluctuation curve, while the lower objective function focuses on minimizing the user cost or maximizing the user benefit.

[0139] Furthermore, excellent individuals are selected according to their fitness values ​​to enter the next generation population. The selection operation can use methods such as roulette selection and tournament selection.

[0140] It should be noted that although roulette selection and tournament selection are also suitable for fitness value selection in this application, they are not a good fit. Roulette selection allocates selection probabilities based on the relative size of individual fitness values. Individuals with high fitness have a higher probability of being selected, while individuals with low fitness have a very low probability of being selected. This selection method may cause individuals with high fitness to quickly occupy the population, resulting in a rapid loss of population diversity and easy to fall into local optimal solutions. Small changes in fitness values ​​may lead to huge differences in selection probabilities, which may cause some potentially good individuals to be eliminated prematurely due to small fitness differences. Each selection requires recalculation of the cumulative probability distribution, and as the population size increases, the amount of calculation will also increase.

[0141] Tournament selection requires choosing an appropriate tournament size. A tournament size that is too small may lead to insufficient selection pressure, while a tournament size that is too large may lead to excessive selection pressure, resulting in the same problem of loss of population diversity. Tournament selection introduces a certain degree of randomness, which helps maintain the diversity of the population, but may also cause truly excellent individuals to be excluded in some cases. Tournament selection relies more on relative comparisons between individuals rather than directly using individual fitness values, which may cause some individuals with higher fitness values ​​to fail to fully demonstrate their advantages.

[0142] Therefore, a selection operation method is improved as follows:

[0143] Step 1: Sort all individuals in the population by fitness value to get a list in descending order. Calculate the selection probability p of each individual i , the following formula can be used:

[0144]

[0145] Among them, F(gt) is the fitness value of individual gt, and N1 is the population size.

[0146] Step 2: Assign selection probabilities based on individual rankings. The higher the ranking, the greater the probability of being selected. Calculate the ranking selection probability q for each individual gt , the following formula can be used:

[0147]

[0148] Among them, rank(gt) represents the ranking of individual gt.

[0149] Step 3: Combining fitness-based selection and ranking-based selection, the comprehensive selection probability r of each individual can be calculated in a weighted manner gt , the weights can be adjusted according to the specific application scenario:

[0150] r gt =λ·p gt +(1-λ)·q gt

[0151] Among them, λ is a weight value between 0 and 1, which is used to balance the selection probability of fitness value and ranking.

[0152] Step 4: Randomly select individuals based on the above comprehensive selection probability to form the next generation population, and select individuals with the highest fitness value from a group of randomly selected individuals to ensure that excellent individuals have the opportunity to be selected, and then merge the results of roulette selection and tournament selection, and the selected individuals enter the next generation population.

[0153] Exemplarily, the individuals ranking in the top 20% of the comprehensive selection probability can be regarded as superior individuals, and the individuals ranking between the top 20% and 40% of the comprehensive selection probability can be regarded as superior individuals to complete the selection.

[0154] It should be noted that by combining the selection probabilities of fitness value and ranking, the improved strategy can better preserve the diversity of the population. Traditional roulette selection tends to prefer individuals with higher fitness values, which may cause the population to lose diversity quickly and lead to premature convergence of the algorithm. By introducing the ranking factor, even individuals with relatively low fitness values ​​but high rankings have the opportunity to be selected, thereby increasing the diversity of the population. The improved strategy can balance the selection pressure more flexibly by adjusting the weights between the fitness value selection probability and the ranking selection probability. This means that not only those individuals with the highest fitness values ​​can be selected, but also those individuals who do not have the highest fitness values ​​but still perform well can have the opportunity to be selected. This can prevent a few individuals from quickly dominating and maintain the dynamic balance of the population. The improved strategy reduces the risk of falling into a local optimum. Due to the introduction of the ranking-based selection probability, even if some individuals do not perform best in the current iteration, if they have the potential to become better in future iterations, they still have the opportunity to be selected and retained. This helps the algorithm to jump out of the local optimal solution and find the global optimal solution.

[0155] Furthermore, the selected individuals are paired and new offspring are produced through a crossover operation. The crossover point can be selected randomly or according to certain rules, with the goal of producing new individuals with the characteristics of both parents.

[0156] Perform mutation operations on the newly generated offspring to change the attribute values ​​of some individuals to increase the diversity of the population. The mutation operation can be achieved by randomly changing the attribute value of a certain individual.

[0157] In order to maintain the diversity of the population and avoid premature convergence, the immigration operator is introduced so that different populations can exchange individuals with each other. For example, the individuals with better performance in each population can be selected to migrate to other populations, while receiving the same number of individuals with better performance from other populations.

[0158] The new generation of individuals produced after selection, crossover, mutation and immigration operations will replace the individuals in the old population.

[0159] Each time the population evolves, the best individuals are selected from the current population and placed in the elite population. The elite population is a population specifically used to store the best performing individuals in each generation of evolution. These individuals no longer participate in the subsequent evolution process to ensure that excellent solutions are not lost.

[0160] Check whether the preset termination conditions are met, such as the number of evolutionary generations, the change in the fitness value of the population, the size of the elite population reaching the limit, etc. If the termination conditions are met, the evolution stops; otherwise, return to re-evaluate the individual fitness and continue the evolution.

[0161] In summary, the present invention proposes an orderly charging and discharging method for electric vehicles, obtains the number of electric vehicles participating in the V2G plan, the number of agents and the parameter information of the electric vehicles participating in the V2G plan within the target range, and establishes the V2G-based electric vehicle response capability constraint condition according to the parameter information; establishes the response capability value acquisition rule of the electric vehicle under the V2G-based electric vehicle response capability constraint condition; establishes a double-layer optimization model of the electric vehicle charging and discharging strategy including the response capability value of the electric vehicle; solves the double-layer optimization model of the electric vehicle charging and discharging strategy according to the improved multi-population genetic optimization algorithm, obtains the optimal charging power in each time period, and thus obtains the final electric vehicle charging and discharging strategy. By optimizing the scheduling strategy, the load fluctuation of the power grid can be effectively reduced and the stability of the power grid operation can be improved. At the same time, by considering the scheduling willingness and scheduling capability of electric vehicle users, users can obtain the maximum benefits when participating in the V2G plan, thereby improving the enthusiasm of users to participate.

[0162] Example 2

[0163] As an embodiment of the present invention, a digital construction system for typical planning scenarios of county distribution networks is provided, including:

[0164] An information acquisition and processing module, used to obtain the number of electric vehicles participating in the V2G plan, the number of agents and parameter information of electric vehicles participating in the V2G plan within the target range, and establish V2G-based electric vehicle response capability constraint conditions according to the parameter information;

[0165] The parameter information includes the time when the electric vehicles participating in the V2G program connect to and leave the grid, the load status of the electric vehicle, the battery capacity of the electric vehicle, and the charging and discharging power of the electric vehicle;

[0166] The V2G-based electric vehicle response capability constraints include the electric vehicle access grid time constraint, the electric vehicle's own charge state constraint, the V2G response capacity constraint, and the charging and discharging power constraint;

[0167] A response capability value acquisition module, used to establish a response capability value acquisition rule for an electric vehicle under a V2G-based electric vehicle response capability constraint condition;

[0168] A two-layer optimization model building module, used to build a two-layer optimization model for electric vehicle charging and discharging strategy including a response capability value of the electric vehicle;

[0169] The strategy acquisition module is used to solve the double-layer optimization model of electric vehicle charging and discharging strategy according to the improved multi-population genetic optimization algorithm, obtain the optimal charging power in each time period, and thus obtain the final electric vehicle charging and discharging strategy.

[0170] The above-mentioned unit modules may be embedded in or independent of a processor in a computer device in the form of hardware, or may be stored in a memory in a computer device in the form of software, so that the processor can call and execute operations corresponding to the above-mentioned modules.

[0171] This embodiment also provides a computer device, which can be a terminal. The computer device includes a processor, a memory, a communication interface, a display screen and an input device connected through a system bus. 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 and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, an operator network, NFC (near field communication) or other technologies. When the computer program is executed by the processor, an orderly charging and discharging method of an electric vehicle is implemented. The display screen of the computer device can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covered on the display screen, or a key, trackball or touchpad set on the housing of the computer device, or an external keyboard, touchpad or mouse.

[0172] This embodiment further provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the following steps are implemented:

[0173] Obtaining the number of electric vehicles participating in the V2G plan, the number of agents, and parameter information of electric vehicles participating in the V2G plan within the target range, and establishing V2G-based electric vehicle response capability constraint conditions based on the parameter information;

[0174] The parameter information includes the time when the electric vehicles participating in the V2G program connect to and leave the grid, the load status of the electric vehicle, the battery capacity of the electric vehicle, and the charging and discharging power of the electric vehicle;

[0175] The V2G-based electric vehicle response capability constraints include the electric vehicle access grid time constraint, the electric vehicle's own charge state constraint, the V2G response capacity constraint, and the charging and discharging power constraint;

[0176] Establishing the rules for obtaining the responsiveness value of electric vehicles under the responsiveness constraint conditions of electric vehicles based on V2G;

[0177] Establish a two-level optimization model for electric vehicle charging and discharging strategy that includes the EV’s response capability value;

[0178] According to the improved multi-population genetic optimization algorithm, the double-layer optimization model of electric vehicle charging and discharging strategy is solved to obtain the optimal charging power in each time period, thereby obtaining the final electric vehicle charging and discharging strategy.

[0179] Example 3

[0180] An embodiment of the present invention is different from the first two embodiments in that:

[0181] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc., which can store program code.

[0182] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0183] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering or, if necessary, processing in another suitable manner, and then stored in a computer memory.

[0184] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, a plurality of steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0185] Example 4

[0186] See also Figure 2-Figure 3 , which is an embodiment of the present invention, provides an orderly charging and discharging method for an electric vehicle. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through economic benefit calculation and simulation experiments.

[0187] Taking 5 large communities equipped with 5 district agents as an example, the agents are responsible for the charging and discharging dispatching in their respective jurisdictions under the overall dispatching plan of the dispatching center. It is assumed that all electric vehicles in the jurisdiction are ordinary household electric vehicles, and electric vehicles are generally charged at night. Assume that the proportion of regional electric vehicles responding to V2G is 90%. Select the basic power as 200kVA, set the dispatching cycle from 0 to 24 o'clock, and use hours as the time unit, for a total of 24 time periods. A total of 24h. In the current time period, it is assumed that the charging and discharging load of electric vehicles and other basic loads have not changed, and the average monthly charging frequency of car owners is 10 times. The number of electric vehicles in the jurisdiction of each agent and the average response V2G capability value of the jurisdiction are set as shown in Table 1 below.

[0188] Table 1: Number of electric vehicles and average responsiveness in each agency’s jurisdiction

[0189] Area Code Number of electric vehicles Average responsiveness District 1 3500 1.29 Jurisdiction 2 4000 1.38 Jurisdiction 3 4500 1.45 District 4 5000 1.51 District 5 5500 1.63

[0190] In order to simplify the charging and discharging process of the model, the relevant parameters of electric vehicles are shown in the table below.

[0191] Table 2: Electric vehicle parameters

[0192]

[0193] For community electricity use, time-of-use electricity prices, time-of-use electricity prices and repurchase electricity prices are generally used. The price at which users in the region participating in the V2G plan sell electricity to the grid is the response capacity assessed in the jurisdiction multiplied by the basic repurchase electricity price. This application uses a certain city's time-of-use electricity price, see Table 3 below.

[0194] Table 3: Time-of-use electricity price table

[0195]

[0196]

[0197] The results of the electric vehicle V2G response optimization are as follows: Figure 2 As shown. It can be seen from the figure that the basic load peak is at 10:00-12:00 and 18:00-22:00, and the load peak-to-valley difference is 161.33kW, while the load peak in the disordered charging state is at 9:00-10:00, 11:00-12:00, and 18:00-21:00, which further aggravates the load fluctuation, and the load peak-to-valley difference reaches 289.62kW. After the optimized scheduling strategy adopted in this application, the load peak occurs at 9:00-11:00 and 20:00-21:00. The load peak-to-valley difference is 87.63kW. Compared with the disordered charging state, peak shaving and valley filling are successfully achieved, reducing the load fluctuation of the power grid.

[0198] Depend on Figure 2 It can be seen that the basic load peaks at 10:00-12:00 and 18:00-22:00. At this time, electric vehicles participating in the V2G plan begin to feed power to the grid, shifting the load peak and greatly smoothing the grid load fluctuation. According to the average monthly charging times of electric vehicle owners and the ability of electric vehicles in various regions to respond to the V2G plan, the simulation draws a comparison chart of the average monthly charging costs of electric vehicles in various regions, as shown in the figure below: Figure 3 shown.

[0199] from Figure 3 It can be seen that the average monthly charging cost of electric vehicles participating in the V2G plan is significantly lower than the charging cost under disordered charging. According to the simulation results, the difference in charging costs between disordered charging and implementing the V2G control strategy in area 5 is the largest, at 96 yuan, and the charging cost is reduced by 35.16%. Figure 3It can be observed that the V2G responsiveness of different regions directly affects the average monthly charging cost of electric vehicles, and the greater the regional V2G responsiveness, the lower the average monthly charging cost, which just reflects the strategic idea of ​​increasing user participation and maximizing user benefits.

[0200] In summary, the present invention proposes an orderly charging and discharging method and system for electric vehicles, obtains the number of electric vehicles participating in the V2G plan, the number of agents and the parameter information of electric vehicles participating in the V2G plan within the target range, and establishes the V2G-based electric vehicle response capability constraint conditions according to the parameter information; establishes the response capability value acquisition rules of electric vehicles under the V2G-based electric vehicle response capability constraint conditions; establishes a double-layer optimization model of electric vehicle charging and discharging strategy including the response capability value of electric vehicles; solves the double-layer optimization model of electric vehicle charging and discharging strategy according to the improved multi-population genetic optimization algorithm, obtains the optimal charging power in each time period, and thus obtains the final electric vehicle charging and discharging strategy. By optimizing the scheduling strategy, the load fluctuation of the power grid can be effectively reduced and the stability of the power grid operation can be improved. At the same time, by considering the scheduling willingness and scheduling capability of electric vehicle users, users can obtain the maximum benefits when participating in the V2G plan, thereby improving the enthusiasm of users to participate.

[0201] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A method for orderly charging and discharging of an electric vehicle, characterized in that: include: Obtaining the number of electric vehicles, the number of dealers and parameter information of electric vehicles that meet the requirements within the target range, and establishing V2G-based electric vehicle response capability constraint conditions according to the parameter information; Establish the rules for obtaining the responsiveness value of electric vehicles under the responsiveness constraint conditions of electric vehicles based on V2G, and establish a two-level optimization model for the charging and discharging strategy of electric vehicles including the responsiveness value of electric vehicles; According to the improved multi-population genetic optimization algorithm, the double-layer optimization model of the electric vehicle charging and discharging strategy is solved to obtain the optimal charging power in each time period, thereby obtaining the final electric vehicle charging and discharging strategy.

2. The orderly charging and discharging method of an electric vehicle as claimed in claim 1, characterized in that: The parameter information includes the time when the electric vehicle that meets the requirements is connected to the power grid and leaves the power grid, the load status of the electric vehicle, the battery capacity of the electric vehicle, and the charging and discharging power of the electric vehicle.

3. The orderly charging and discharging method of an electric vehicle as claimed in claim 2, characterized in that: The V2G-based electric vehicle response capability constraints include: The time constraint for electric vehicles to access the grid includes the time limit for electric vehicles that meet the requirements to access the grid, which is recorded as the time for electric vehicles to access the grid, T g,i , obtained by the vehicle's battery management system; the time when the electric vehicle leaves the grid is T l,i , determined by the randomness of user behavior, the time t for electric vehicles to access the grid satisfies T g,i ≤t≤T l,i , where i represents the i-th electric car; The state of charge constraints of the electric vehicle itself, including the state of charge when the electric vehicle is connected to the grid and the minimum state of charge SOC when the electric vehicle is finished charging min , satisfying SOC(t)≥SOC min , SOC(t) represents the state of charge of the electric vehicle at the end of the charging state; The V2G response capacity constraint is to convert the available capacity B of the electric vehicle battery that meets the requirements into ke It is divided into three parts, namely the battery capacity B1 that meets the requirements, the battery capacity B2 for daily driving, and the battery capacity B3 for standby driving, B1+B2+B3=B ke,i , where B ke,i represents the total battery capacity of the i-th electric vehicle; The charging and discharging power constraints include that the power of the required part of the electric vehicle shall not exceed its rated charging and discharging power.

4. The orderly charging and discharging method of an electric vehicle as claimed in claim 3, characterized in that: The responsiveness acquisition rules of the electric vehicle under the responsiveness constraint conditions of the V2G electric vehicle include: Obtain the electric vehicle battery loss rate, battery capacity and regional response coefficient that meet the requirements, and obtain the electric vehicle battery capacity impact factor. According to the electric vehicle battery capacity impact factor, combined with the electric vehicle response capacity constraint condition based on V2G, obtain the electric vehicle response capacity value, which is specifically expressed as: Q=q n B ke,i η Among them, q n represents the regional response coefficient, B ke,i represents the battery capacity of the V2G plan, η represents the battery loss rate, T l,i Indicates the time when the electric vehicle leaves the power grid; T g,i Indicates the time when the electric vehicle is connected to the power grid; T min,i It represents the shortest time for an electric vehicle to reach the desired battery state at maximum power, and Q represents the influencing factor of the electric vehicle battery capacity; If and only if k>1, the conditions for establishing a two-level optimization model for electric vehicle charging and discharging strategies including the response capability value of the electric vehicle are met.

5. The orderly charging and discharging method of an electric vehicle as claimed in claim 4, characterized in that: The electric vehicle charging and discharging strategy two-level optimization model including the electric vehicle response capability value includes an upper-level objective function and its corresponding upper-level objective function constraint condition, a lower-level objective function and its corresponding lower-level objective function constraint condition; The upper objective function constraint conditions include at least charging amount constraint, agent scheduling constraint in each time period and load peak constraint, and the lower objective function constraint conditions include at least electric vehicle charging and discharging time constraint, vehicle battery capacity constraint and load amount constraint.

6. The orderly charging and discharging method of an electric vehicle as claimed in claim 5, characterized in that: The upper objective function minimizes the total fluctuation curve of the system during the charging and discharging period, and at the same time minimizes the sum of the deviations between the actual dispatch of the agent and the power consumption strategy formulated by the dispatch center, which can be expressed as: Where T represents 24 time periods in a day, n represents the number of agents; P d,t is the non-electric vehicle load during period t, P n,t represents the charging and discharging load of the nth electric vehicle at time t, N0 represents the total number of agents; P DE Indicates the output of other distributed energy resources in the region; It represents the average system load in T time, expressed as: in, represents the scheduling deviation function between the scheduling center and the regional agent, α represents the violation penalty coefficient, |PD n,t -P n,t | represents the scheduling deviation and the constraints of regional agents, PD n,t Represents the scheduling plan made by the nth regional agent at time t.

7. The orderly charging and discharging method of an electric vehicle as claimed in claim 6, characterized in that: The lower objective function satisfies the minimum charging cost and is expressed as: Among them, T0 represents the start time of charging and discharging, and T1 represents the end time of charging and discharging; and Represent the average value of charging power and discharging power, C c,i and C d,i They represent charging price and discharging price respectively, a and b represent charging parameters and discharging parameters respectively, It represents the average responsiveness value of electric vehicles in the area under the jurisdiction of the agent; When charging, a is 1 and b is 0; when discharging, a is 0 and b is 1.

8. A system using the orderly charging and discharging method of an electric vehicle as claimed in any one of claims 1 to 7, characterized in that: It includes information acquisition and processing module, response capability value acquisition module, double-layer optimization model building module, and strategy acquisition module; The information acquisition and processing module is used to obtain the number of electric vehicles participating in the V2G plan, the number of agents and parameter information of electric vehicles participating in the V2G plan within the target range, and establish V2G-based electric vehicle response capability constraint conditions according to the parameter information; The response capability value acquisition module is used to establish a response capability value acquisition rule for an electric vehicle under a V2G-based electric vehicle response capability constraint condition; The two-layer optimization model establishment module is used to establish a two-layer optimization model of the electric vehicle charging and discharging strategy including the response capability value of the electric vehicle; The strategy acquisition module is used to solve the electric vehicle charging and discharging strategy double-layer optimization model according to the improved multi-population genetic optimization algorithm to obtain the optimal charging power in each time period, thereby obtaining the final electric vehicle charging and discharging strategy.

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

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

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