An electric vehicle orderly charging and discharging method and system

Abstract

The application discloses an orderly charging and discharging method and system for electric vehicles, obtains the number of electric vehicles meeting requirements, the number of agents and parameter information of electric vehicles in a target range, and establishes a V2G-based response capability constraint condition of electric vehicles according to the parameter information; a response capability value acquisition rule of electric vehicles under the V2G-based response capability constraint condition of electric vehicles is established, and an electric vehicle charging and discharging strategy double-layer optimization model containing the response capability value of electric vehicles is established; according to an improved multi-population genetic optimization algorithm, the electric vehicle charging and discharging strategy double-layer optimization model is solved, and the optimal charging power of each period is obtained, so that the final electric vehicle charging and discharging strategy is obtained. Through the optimization and scheduling strategy, the power grid load fluctuation can be effectively reduced, and the stability of power grid operation is improved. Meanwhile, by considering the scheduling willingness and scheduling capability of electric vehicle users, the users can obtain the maximum benefit when participating in the V2G plan, so that the enthusiasm of the users in participation is improved.

Description

TECHNICAL FIELD

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

[0002] With the development of social economy, the number of electric vehicles is increasing year by year. Compared with traditional fuel vehicles, electric vehicles have great advantages in energy saving and emission reduction and realizing green travel. Electric vehicles are an inevitable trend as a choice for future travel. Electric vehicles have energy storage characteristics and can be connected to the power grid as loads or distributed power equipment. At the same time, electric vehicles can improve energy utilization through two-way interaction between vehicles and the grid. Reasonable scheduling of electric vehicle loads can improve the operation state of the power grid, alleviate the power load in the power grid, effectively manage the charging load, realize peak load shifting, and at the same time, cooperate with the connection of new energy such as photovoltaic and wind energy to accelerate the integrated construction of new energy wind, light, and storage. To build a smart power city, orderly charging of electric vehicles is the first step, so it is particularly important to optimize the charging and discharging strategy of electric vehicles. SUMMARY

[0003] In view of the above problems, the present application is proposed.

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

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

[0006] As a preferred scheme of the orderly charging and discharging method for electric vehicles, the parameter information includes the time of connecting to the power grid and leaving the power grid, the load state of the electric vehicle, the battery capacity of the electric vehicle, and the charging and discharging power of the electric vehicle.

[0007] As a preferred scheme of the orderly charging and discharging method for electric vehicles, the V2G-based electric vehicle response capability constraint condition includes:

[0008] The electric vehicle access grid time constraint includes the time limit of the qualified electric vehicle access grid, denoted as electric vehicle access grid time T g,i The electric vehicle leaves grid time T l,i is determined by the randomness of the user's behavior, and the electric vehicle access grid time t satisfies T g,i ≤t≤T l,i , wherein i represents the i-th electric vehicle;

[0009] The electric vehicle itself state of charge constraint includes the state of charge when the electric vehicle is connected to the grid and the minimum state of charge SOC min at the end of the charging state, which satisfies 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 divide the available capacity B ke of the qualified electric vehicle battery into three parts, i.e. the qualified battery capacity B1, the battery capacity B2 for maintaining daily driving, and the battery capacity B3 for standby driving, B1+B2+B3=B ke,i , wherein B ke,i represents the total capacity of the battery of the i-th electric vehicle;

[0011] The charging and discharging power constraint includes that the power of the qualified electric vehicle part shall not exceed the rated charging and discharging power.

[0012] As a preferred scheme of the electric vehicle orderly charging and discharging method described in the application, wherein: the response capability acquisition rule of the electric vehicle under the V2G-based electric vehicle response capability constraint condition includes:

[0013] The qualified electric vehicle battery loss rate, battery capacity and regional response coefficient are obtained to obtain the electric vehicle battery capacity influence factor, and the electric vehicle response capability value is obtained according to the electric vehicle battery capacity influence factor combined with the V2G-based electric vehicle response capability constraint condition, which is specifically represented as:

[0014]

[0015] Q=q n B ke,i η

[0016] , wherein q n represents the regional response coefficient, B ke,i represents the battery capacity participating in the V2G plan, η represents the battery loss rate, T l,i represents the electric vehicle leaves grid time; T g,irepresents the time when the electric vehicle accesses the power grid; T min,i represents the shortest time for the electric vehicle to reach the desired battery state at the maximum power, and Q represents an electric vehicle battery capacity influence factor;

[0017] The electric vehicle charging and discharging strategy double-layer optimization model containing the response capability value of the electric vehicle is established only when k>1.

[0018] As a preferred scheme of the electric vehicle orderly charging and discharging method, the electric vehicle charging and discharging strategy double-layer optimization model containing the response capability value of the electric vehicle includes an upper layer target function and a corresponding upper layer target function constraint condition, a lower layer target function and a corresponding lower layer target function constraint condition.

[0019] The upper layer target function constraint condition at least includes a charging amount constraint, an agent time period scheduling constraint and a load peak constraint, and the lower layer target function constraint condition at least includes an electric vehicle charging and discharging time constraint, a vehicle battery capacity constraint and a load amount constraint.

[0020] As a preferred scheme of the electric vehicle orderly charging and discharging method, the upper layer target function makes the total fluctuation curve of the system in the charging and discharging time period minimum, and the sum of the deviation between the actual scheduling of the agent and the electricity utilization strategy formulated by the scheduling center is minimum, and is expressed as:

[0021]

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

[0023]

[0024] Wherein, represents the scheduling deviation function between the scheduling center and the regional agent, and α represents the violation penalty coefficient, and |PD n,t -P n,t represents the scheduling deviation and the constraint constraint of the regional agent, PD n,t represents the scheduling plan formulated by the nth regional agent at t time.

[0025] As a preferred scheme of the electric vehicle orderly charging and discharging method, the lower layer target function satisfies the lowest charging cost, and is expressed as:

[0026]

[0027] Wherein, T0 represents the starting time of charging and discharging, T1 represents the ending time of charging and discharging; And Respectively represent the average value of charging power and discharging power, C c,i And C d,i Respectively represent the charging price and discharging price, a, b respectively represent the charging parameter and discharging parameter, Represent the average response capability 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 application is to provide an electric vehicle orderly charging and discharging system, comprising: an information acquisition and processing module, a response capability value acquisition module, a double-layer optimization model establishment module, and a strategy acquisition module;

[0030] The information acquisition and processing module is configured to acquire the number of electric vehicles participating in a V2G plan, the number of agents, and parameter information of the electric vehicles participating in the V2G plan within a target range, and establish a V2G-based electric vehicle response capability constraint condition based on the parameter information;

[0031] The response capability value acquisition module is configured to establish a rule for acquiring the response capability value of the electric vehicles under the V2G-based electric vehicle response capability constraint condition;

[0032] The double-layer optimization model establishment module is configured to establish a double-layer optimization model of the electric vehicle charging and discharging strategy containing the response capability value of the electric vehicles;

[0033] The strategy acquisition module is configured to solve the double-layer optimization model of the electric vehicle charging and discharging strategy by using an improved multi-population genetic optimization algorithm, so as to obtain the optimal charging power of each time period and thus acquire the final electric vehicle charging and discharging strategy.

[0034] In a third aspect, the present application provides a computer device, comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the electric vehicle orderly charging and discharging method as described above when executing the computer program.

[0035] In a fourth aspect, the present application provides a computer readable storage medium, which stores a computer program, and the computer program implements the steps of the electric vehicle orderly charging and discharging method as described above when executed by a processor.

[0036] The application provides an orderly charging and discharging method and system for an electric vehicle. BRIEF DESCRIPTION OF DRAWINGS

[0037] In order to more clearly illustrate the technical solutions of the embodiments of the application, the drawings needed to be used in the embodiment description will be briefly introduced. Obviously, the drawings in the following description are only some of the embodiments of the application, and all other drawings obtained by those of ordinary skill in the art without creative work based on these drawings should belong to the protection scope of the application.

[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 application;

[0039] Figure 2 An electric vehicle V2G corresponding optimization curve diagram in an embodiment of an orderly charging and discharging method and system for an electric vehicle provided by an embodiment of the application;

[0040] Figure 3 A different area electric vehicle monthly average charging cost diagram in an embodiment of an orderly charging and discharging method and system for an electric vehicle provided by an embodiment of the application. DETAILED DESCRIPTION

[0041] In order to make the above-mentioned purposes, features and advantages of the application more obvious and easy to understand, the specific embodiments of the application will be described in detail below with reference to the drawings in the specification. Obviously, the described embodiments are only some of the embodiments of the application, rather than all the embodiments. Based on the embodiments in the application, all other embodiments obtained by those of ordinary skill in the art without creative work should belong to the protection scope of the application.

[0042] In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present application. However, it will be apparent to one skilled in the art that the present application can be practiced without the specific details set forth in this description. In other instances, well-known methods, procedures, components, and circuits have not been described in detail so as not to unnecessarily obscure aspects of the present application.

[0043] Embodiment 1

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

[0045] V2G: V2G (Vehicle-to-Grid) refers to a technology of bidirectional energy exchange between electric vehicles (EVs) and the grid. Under this technology framework, electric vehicles can not only be power consumers to charge from the grid, but also be power suppliers to discharge to the grid. Through V2G technology, electric vehicles can be charged during off-peak hours and supply power to the grid during peak hours, helping to balance the supply and demand of the grid, reducing the peak load of the power system, and providing backup power support in emergency situations.

[0046] Bi-level optimization model: Bi-level optimization model is a kind of optimization model structure, which is usually used to solve decision-making problems with hierarchical structure. In this application, this model contains two levels: Upper Level Optimization: usually represents the interests of the central dispatcher, the goal may be to minimize the overall operating cost of the grid or maximize the stability of the system, etc. Lower Level Optimization: represents the interests of individual participants (such as users or agents), the goal may be to minimize the cost of users or maximize the revenue of users, etc. The core of bi-level optimization model is that the results of upper level optimization will affect the decision variables of 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 (Genetic Algorithm, GA), which enhances the search ability and robustness of the algorithm by maintaining multiple independent populations. In traditional GA, there is only one population, while in MPGA, there are multiple populations evolving in parallel, each population has its own parameter settings such as crossover rate, mutation rate, etc. Through gene exchange between populations (such as immigration operation), MPGA can maintain population diversity while avoiding premature convergence, thereby improving global search ability.

[0048] District Agent: A district agent generally refers to an entity or organization responsible for managing and coordinating certain activities or resources within a specific geographic area. In the context of electric vehicle charging and discharging strategies, a district agent may refer to an institution or enterprise 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, negotiating charging and discharging strategies with grid operators, etc., to ensure that user needs are met while optimizing the operational efficiency of the power grid.

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

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

[0051] Reference Figure 1 For one embodiment of the present application, an electric vehicle orderly charging and discharging method is provided, comprising:

[0052] S1: Obtain the number of electric vehicles that meet the requirements, the number of agents, and the parameter information of the electric vehicles within the target range, and establish V2G-based electric vehicle response capability constraints based on the parameter information;

[0053] Further, in the embodiments of the present application, the parameter information includes the time of electric vehicles participating in the V2G program accessing the power grid and leaving the power grid, the load state of the electric vehicles, the battery capacity of the electric vehicles, and the charging and discharging power of the electric vehicles;

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

[0055] Further, the benefits of selecting the parameter information in the present application are that it can provide a comprehensive perspective to evaluate the participation potential of electric vehicles in the V2G program. By considering the access and exit time of electric vehicles, it can ensure that charging is performed when the power grid load is low and discharging is performed when the load is high, thereby effectively relieving the pressure on the power grid. The load state, battery capacity, and charging and discharging power parameters of the electric vehicles help to develop reasonable charging and discharging plans, avoid adverse effects on battery life, and ensure that electric vehicle users have enough power to use when needed.

[0056] It should be noted that the addition of weather conditions, electricity price information, and user preference settings, etc. makes the system more flexible to respond to various situations. For example, encourage users to charge during periods of low electricity prices, and encourage users to discharge during periods of high electricity prices, thereby reducing the user's charging costs and improving the economic benefits of the power grid. User preference settings allow users to set the time window for charging and discharging according to individual needs and habits, improving user satisfaction.

[0057] Further, in the embodiments of the present application, the V2G-based electric vehicle response capability constraint conditions include electric vehicle access to the power grid time constraints, electric vehicle self-state of charge constraints, V2G response capacity constraints, and charging and discharging power constraints.

[0058] The V2G-based electric vehicle response capability constraint conditions include: the electric vehicle access to the power grid time constraint is the time limit for the electric vehicle participating in the V2G plan to access the power grid, denoted as T g,i , which is obtained by the battery management system of the vehicle; the electric vehicle leaves the power grid time T l,i , which is determined by the randomness of the user's behavior, so that the electric vehicle access to the power grid time t satisfies T g,i ≤t≤T l,i , wherein i represents the i-th electric vehicle.

[0059] The electric vehicle self-state of charge constraint includes the state of charge when the electric vehicle is connected to the power grid and the minimum state of charge SOC min at the end of the electric vehicle charging state, denoted as SOC(t)≥SOC min , wherein 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 dividing the available capacity B ke of the battery of the electric vehicle participating in the V2G plan into three parts, i.e. the battery capacity B1 participating in the V2G plan, the battery capacity B2 for daily driving, and the battery capacity B3 for standby driving, B1+B2+B3=B ke,i , wherein B ke,i represents the total capacity of the battery of the i-th electric vehicle.

[0061] The charging and discharging power constraint includes that the power of the electric vehicle participating in the V2G plan part should not exceed its rated charging and discharging power.

[0062] It should be noted that the V2G-based electric vehicle response capability constraint conditions can also include weather condition constraints, considering that severe weather can affect the charging and discharging efficiency and safety of electric vehicles, the system will set specific charging and discharging limits according to weather forecast information. For example, under extreme high or low temperature conditions, battery performance may decrease, at which time the system will reduce the charging and discharging power or limit the charging and discharging time to protect the battery; 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 prioritize charging, while during periods of high electricity prices, it will prioritize discharging to reduce the user's charging costs and improve the economic efficiency of the power grid; user preference setting constraints, considering the individual needs of users for charging and discharging times, the system will optimize the charging and discharging plan according to the user's set preferences. For example, users can set not to charge or discharge during certain time periods, and the system will adjust the charging and discharging plan according to these preferences to improve user satisfaction and participation;

[0063] Furthermore, when obtaining parameter information, if the user or operator does not obtain weather conditions, electricity price information, and user preference settings, the V2G-based electric vehicle response capability constraint conditions do not have weather condition constraints, electricity price information constraints, and user preference setting constraints. Only when the user obtains weather conditions, electricity price information, and user preference settings, can the V2G-based electric vehicle response capability constraint conditions have weather condition constraints, electricity price information constraints, and user preference setting constraints.

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

[0065] S2: Establish the electric vehicle response capability value acquisition rule under the V2G-based electric vehicle response capability constraint condition, and establish an electric vehicle charging and discharging strategy double-layer optimization model containing the response capability value of the electric vehicle;

[0066] Furthermore, establishing the electric vehicle response capability acquisition rule under the V2G-based electric vehicle response capability constraint condition includes: obtaining the battery wear rate of the electric vehicle participating in V2G, the battery capacity participating in the V2G plan, and the regional response coefficient to obtain the electric vehicle battery capacity influence factor;

[0067] According to the electric vehicle battery capacity influence factor, combined with the V2G-based electric vehicle response capability constraint condition, the electric vehicle response capability value is obtained;

[0068] In the embodiments of the application, it is specifically represented as:

[0069]

[0070] Wherein, q n represents the regional response coefficient, B ke,i represents the battery capacity participating in the V2G plan, η represents the battery loss rate, T l,i represents the time when the electric vehicle leaves the power grid; T g,i represents the time when the electric vehicle accesses the power grid; T min,i represents the shortest time for the electric vehicle to reach the desired battery state with maximum power, and Q represents the electric vehicle battery capacity influence factor.

[0071] It should be noted that the condition of establishing the electric vehicle charging and discharging strategy double-layer optimization model containing the response capability value of the electric vehicle is met only when k>1.

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

[0073]

[0074] Wherein, T l,i represents the time when the electric vehicle leaves the power grid; T g,i represents the time when the electric vehicle accesses the power grid; T min,i represents the shortest time for the electric vehicle to reach the desired battery state with maximum power.

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

[0076] Further, the fact that a certain regional agent does not consider the influence of the electric vehicle battery capacity may indicate that the electric vehicles in the region governed by the agent are all new batteries, or that the region governed by the agent is a sales area for a certain type of electric vehicle, such as taxis or buses or electric cars. The battery capacity and performance of these vehicles are relatively uniform, so the influence of the difference in battery capacity can be ignored when formulating the charging and discharging strategy.

[0077] It should be noted that the establishment of the response capability value acquisition rule of the electric vehicle under the response capability constraint based on V2G has the benefit of providing a quantitative electric vehicle response capability value for the development of the electric vehicle charging and discharging strategy. By obtaining key parameters such as the electric vehicle battery wear rate, the battery capacity participating in the V2G plan, and the regional response coefficient, the actual contribution capability of each electric vehicle in the V2G system can be accurately evaluated. Such evaluation is crucial for optimizing the charging and discharging behavior of electric vehicles, balancing the power grid load, and improving the overall system operation efficiency.

[0078] Specifically, the calculation of the electric vehicle battery capacity influence 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 developing a more refined charging and discharging strategy. When k>1, it indicates that the electric vehicle has sufficient response capability when participating in the V2G plan and can meet the demand of the power grid and provide effective support to the system. Conversely, 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 bi-level optimization model is derived from a special case in multi-level programming model planning. In bi-level programming, the implementation results of the upper-level strategy not only affect the initial state of the lower-level strategy, but also change the related conditions of its constraints, while the implementation results of the lower-level strategy will be fed back to the upper level in the form of input. Combined with the characteristics of residential travel, this paper formulates the overall power consumption strategy in each region by the upper dispatch center, and the regional power consumption strategy by the regional agent under the power consumption strategy, so that the total fluctuation curve of the system is minimized during the charging and discharging period, and the sum of the actual dispatch by the agent and the deviation from the power consumption strategy formulated by the dispatch center is minimized. In the lower model, the regional agent manages the reservation charging of electric vehicles in the region, and combines the user's dispatch willingness and dispatch capability to maximize the user's benefit.

[0080] It should be noted that the establishment of the electric vehicle charging and discharging strategy bi-level optimization model including the response capability value of the electric vehicle includes: the electric vehicle charging and discharging strategy bi-level optimization model 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 quantity constraint, an agent time period dispatching constraint, and a load peak constraint.

[0081] The electric vehicle charging and discharging strategy bi-level optimization model includes a lower-level objective function and its corresponding lower-level objective function constraint conditions, and the lower-level objective function constraint conditions at least include an electric vehicle charging and discharging time constraint, a vehicle battery capacity constraint, and a load quantity constraint.

[0082] Further, the upper target function includes: the dispatch center formulates the total power consumption strategy in each region, the regional agent formulates the power consumption strategy in the region under the power consumption strategy, and the system minimizes the total fluctuation curve in the charging and discharging period, and the sum of the actual dispatch of the agent and the deviation of the power consumption strategy formulated by the dispatch center is minimized, which is expressed as:

[0083]

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

[0085]

[0086] Wherein, represents the dispatch deviation function between the dispatch center and the regional agent;

[0087] Wherein, α represents the violation penalty coefficient, |PD n,t -P n,t | represents the dispatch deviation and the constraint of the regional agent; PD n,t represents the dispatch plan formulated by the nth regional agent at t time.

[0088] Further, the charging quantity constraint is expressed as:

[0089]

[0090] It should be noted that in order to ensure the travel demand of users and ensure that the total load in the dispatch period is unchanged, the charging quantity constraint behavior PR n,t represents the disordered charging load in the region under the jurisdiction of the agent n at t time.

[0091] The dispatch constraint of the agent in each period is expressed as:

[0092]

[0093] Wherein, and respectively represent the average charging power and the average discharging power of the electric vehicle in the region under the jurisdiction of the agent; A represents the connection state of the electric vehicle and the charging pile in the region, A=0 or A=1; represents the average response capability of the electric vehicle in the region under the jurisdiction of the agent.

[0094] The load peak constraint is expressed as:

[0095] P n,max ≤P 0,max

[0096] wherein, P n,max represents the optimized power distribution load of the region; P 0,max represents the conventional load of the power distribution network of the region.

[0097] It should be noted that the reason why only these three constraints are considered in the present application is to simplify the model and ensure its operability, while ensuring the practicability and effectiveness of the optimization result. By considering the charging capacity constraint, the agent's scheduling constraint in each time period, and the load peak constraint, it can be ensured that the user's travel demand is met while balancing the power grid load and preventing system overload. In addition, these constraint conditions can provide clear boundaries for the formulation of electric vehicle charging and discharging strategies, making it easier to implement in actual operation. However, it does not mean that other constraints cannot be considered. In the process of designing the present application, other constraint conditions have also been considered.

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

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

[0100]

[0101] wherein, P discharge,n,t is the discharging power of the nth agent at time t, P DE,t is the output of other distributed energy at time t, P grid is the power provided by the power grid, L total,t is the total load at time t, and α is the reliability coefficient, P reserve is the reserved standby power.

[0102] Suppose that in a specific time period t, the following data is available:

[0103] The discharging power P discharge of the nth agent is: P discharge,1,t = 10 kW, P discharge,2,t = 15 kW, and it is assumed that there are two agents;

[0104] The output P DE,t20kW;

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

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

[0107] Reliability coefficient a is set to 1.2;

[0108] Reserved standby 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 this inequality holds, meaning that at this moment, the total power provided by the system (including the power of the electric vehicle discharging, distributed energy output, and power provided by the grid) is sufficient to meet the demand of the total load, and there is also a certain redundancy to deal with unforeseen circumstances, meeting the reliability standard.

[0114] It should be noted that the purpose of designing the upper-level objective function is to coordinate the relationship between the grid dispatching center and the regional agent, ensuring that when electric vehicles participate in the V2G plan, the power demand of the entire grid is smoothly, efficiently and economically met.

[0115] Furthermore, the lower-level objective function includes: the regional agent manages the pre-charge of electric vehicles in the region, while combining user scheduling willingness and scheduling capacity, maximizing user benefits, i.e. minimizing charging cost, expressed as:

[0116]

[0117] Where T0 represents the start time of charging and discharging; T1 represents the end time of charging and discharging; and respectively represent the average charging power and discharging power; C c,i and C d,i respectively represent the charging price and discharging price; a, b represent the charging parameter and discharging parameter respectively;

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

[0119] represents the average response capability value of the electric vehicle in the area under the jurisdiction of the agent, and the average response capability values of different agent jurisdictions are different.

[0120] It should be noted that the electric vehicle charging and discharging time constraint is represented as:

[0121] a x b = 0

[0122] The electric vehicle cannot simultaneously perform the charging and discharging processes.

[0123] Further, the vehicle battery capacity constraint is represented as:

[0124]

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

[0126] Further, the load constraint is represented as:

[0127]

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

[0129] It should be noted that the reason why the lower target function in the present application only considers these three constraints is to simplify the complexity of the problem, while ensuring that the charging and discharging behavior of the electric vehicle can be effectively managed in actual operation. However, in actual application, other factors may also need to be considered, such as the battery health of the electric vehicle, the specific needs of the user, the real-time load of the power grid, etc.

[0130] It should be noted that the lower-level objective function can also include battery health constraints, battery charge-discharge cycle number constraints and depth of discharge (DOD) constraints. In order to prolong the service life of the battery, the charge-discharge cycle number and DOD of the battery need to be limited. When the battery health of a certain electric vehicle is detected to decrease to a certain extent in a certain agent jurisdiction, such as a significant decrease in battery capacity or a significant increase in battery internal resistance, the health constraint needs to be started. This can be achieved by regularly monitoring parameters such as SOC (State of Charge), temperature, voltage, etc. of the battery. Once the battery health is found to be poor, the charge-discharge strategy is adjusted to reduce the use of the battery to prevent further damage.

[0131] Furthermore, when the cumulative charge-discharge cycle number of the battery in a certain agent jurisdiction approaches its designed life, i.e. the battery has approached its expected limit of charge-discharge cycle number, the charge-discharge cycle number constraint should be implemented. This is to avoid premature scrapping due to overuse of the battery. The system can track the charge-discharge history of each battery, and when the remaining cycle number is not enough to support regular use, the system will take measures to reduce the frequent charge-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 time period in a certain agent jurisdiction, such as when the battery is at a low state of charge or in extreme weather conditions (high temperature or low temperature), the system will implement the depth of discharge constraint. This usually means limiting the depth of discharge of the battery, 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 depth of charge will also be limited to prevent overcharging.

[0133] It should be noted that the benefit of establishing an electric vehicle charge-discharge strategy double-layer optimization model containing the response capability value of the electric vehicle is that the flexibility and diversity of the electric vehicle in participating in the interaction with the power grid can be fully considered. Through the optimization model, efficient coordination between the electric vehicle and the power grid can be achieved, thereby improving the operation efficiency and reliability of the entire power system.

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

[0135] Furthermore, according to the improved multi-population genetic optimization algorithm, the electric vehicle charge-discharge strategy double-layer optimization model is solved, which includes calculating the optimal charging power of each period by the improved multi-population genetic optimization algorithm, thereby realizing the optimal scheduling strategy of the entire period.

[0136] It should be noted that there are multiple constraints in the electric vehicle charging and discharging dual-layer optimization model. However, due to the evolutionary characteristics of the genetic algorithm (SGA), the search process does not need to pay too much attention to whether the constraints are linear or nonlinear. 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 electric vehicle charging and discharging dual-layer optimization model 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 individually selected 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 initialized, each representing a different set of electric vehicle charging and discharging strategies. For example, each population can represent a set of different charging power configurations. Each population contains a certain number of individuals (charging power configurations), each representing 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-level objective function and the lower-level objective function. The upper-level objective function can consider minimizing the total cost or fluctuation curve of the power grid, while the lower-level objective function focuses on minimizing user costs or maximizing user benefits.

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

[0140] It should be noted that although roulette wheel selection and tournament selection are also suitable for fitness value selection in this application, they are not a good fit. Roulette wheel selection distributes 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 a local optimal solution. 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 the amount of calculation will increase as the population size increases.

[0141] The tournament selection needs to select the appropriate tournament size, too small tournament size may lead to insufficient selection pressure, while too large tournament size may lead to excessive selection pressure, resulting in the same loss of population diversity. Tournament selection introduces a certain degree of randomness, which helps to maintain the diversity of the population, but may also exclude truly excellent individuals in some cases. Tournament selection relies more on the relative comparison between individuals, rather than directly using the fitness value of the individual, which may lead to some individuals with high fitness value failing to fully demonstrate their advantages.

[0142] Therefore, the selection operation mode is improved, specifically as follows:

[0143] Step 1: First, sort all individuals in the population according to the fitness value, get a high to low order list. Calculate the selection probability p of each individual i , which can be calculated using the following formula:

[0144]

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

[0146] Step 2: According to the ranking of the individual, the higher the ranking, the greater the probability of being selected. Calculate the ranking selection probability q of each individual gt , which can be calculated using the following formula:

[0147]

[0148] Where rank(gt) represents the ranking of individual gt.

[0149] Step 3: Combined with the fitness-based selection and ranking-based selection, the comprehensive selection probability r of each individual can be calculated using the weighted method gt , the weight can be adjusted according to the specific application scenario:

[0150] r gt =λ·p gt +(1-λ)·q gt

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

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

[0153] For example, the top 20% of individuals in terms of comprehensive selection probability can be selected as elite individuals, and the individuals between the top 20% and the top 40% in terms of comprehensive selection probability can be selected as elite individuals, and the selection is completed.

[0154] It should be noted that by combining the selection probability with the fitness value and the ranking, the improved strategy can better preserve the diversity of the population. Traditional roulette wheel selection tends to favor individuals with higher fitness values, which can lead to rapid loss of diversity in the population, and thus premature convergence of the algorithm. By introducing the ranking factor, even individuals with relatively low fitness values but high rankings have a chance of being selected, thereby increasing the diversity of the population. The improved strategy can more flexibly balance the selection pressure by adjusting the weight between the fitness value selection probability and the ranking selection probability. This means that not only individuals with the highest fitness values can be selected, but also individuals that perform well despite not having the highest fitness values can have a chance of being selected. This can avoid a few individuals quickly dominating the population and maintain the dynamic balance of the population. The improved strategy reduces the risk of falling into a local optimum. Since the ranking-based selection probability is introduced, even if some individuals do not perform optimally in the current iteration, if they have the potential to become better in future iterations, they still have a chance of being selected and preserved. This helps the algorithm to escape from a local optimal solution and find a global optimal solution.

[0155] Furthermore, the selected individuals are paired and new offspring are generated through crossover operations. The selection of the crossover point can be random or determined according to certain rules, with the goal of generating new individuals with characteristics of both parents.

[0156] The newly generated offspring are subjected to mutation operations to change the values of certain attributes of some individuals to increase the diversity of the population. Mutation operations can be achieved by randomly changing the value of an attribute of an individual.

[0157] In order to maintain the diversity of the population and avoid premature convergence, a migration operator is introduced to allow individuals to be exchanged between different populations. For example, individuals that perform well in each population can be selected to migrate to other populations, while receiving the same number of individuals that perform well from other populations.

[0158] The new generation of individuals generated after selection, crossover, mutation, and migration operations replaces 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 individuals in the evolution process of each generation, and these individuals no longer participate in the subsequent evolution process to ensure that excellent solutions are not lost.

[0160] Check if the preset termination condition is reached, such as the number of evolution generations, the fitness value change of the population, the size of the elite population reaches the limited value, etc. If the termination condition is met, stop evolution; otherwise, return to re-evaluate the fitness of individuals and continue evolution.

[0161] To sum up, the application provides an orderly charging and discharging method for electric vehicles, obtains the number of electric vehicles participating in a V2G plan in a target range, the number of agents, and parameter information of the electric vehicles participating in the V2G plan, and establishes a V2G-based response capability constraint condition of the electric vehicles according to the parameter information; establishes a response capability value acquisition rule of the electric vehicles under the V2G-based response capability constraint condition; establishes an electric vehicle charging and discharging strategy double-layer optimization model containing the response capability values of the electric vehicles; according to an improved multi-population genetic optimization algorithm, the electric vehicle charging and discharging strategy double-layer optimization model is solved to obtain optimal charging power in each period, so that the final electric vehicle charging and discharging strategy is obtained. Through the optimization of the scheduling strategy, the fluctuation of the power grid load can be effectively reduced, and the stability of the power grid operation is improved. At the same time, by considering the scheduling willingness and scheduling capability of the electric vehicle users, the users can obtain the maximum benefit when participating in the V2G plan, thereby improving the enthusiasm of the users.

[0162] Embodiment 2

[0163] For an embodiment of the application, a county power distribution network typical planning scene digital construction system is provided, comprising:

[0164] An information acquisition and processing module is configured to acquire the number of electric vehicles participating in a V2G plan in a target range, the number of agents, and parameter information of the electric vehicles participating in the V2G plan, and establish a V2G-based response capability constraint condition of the electric vehicles according to the parameter information;

[0165] The parameter information includes the time of the electric vehicles participating in the V2G plan accessing the power grid and leaving the power grid, the load state of the electric vehicles, the battery capacity of the electric vehicles, and the charging and discharging power of the electric vehicles;

[0166] The V2G-based response capability constraint condition of the electric vehicles includes an electric vehicle access time constraint, an electric vehicle state of charge constraint, a V2G response capacity constraint, and a charging and discharging power constraint;

[0167] A response capability value acquisition module is configured to establish a response capability value acquisition rule of the electric vehicles under the V2G-based response capability constraint condition;

[0168] A double-layer optimization model establishment module is configured to establish an electric vehicle charging and discharging strategy double-layer optimization model containing the response capability values of the electric vehicles;

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

[0170] The above-mentioned unit modules can be embedded in or independent of the processor in the computer device in hardware form, or can be stored in the memory in the computer device in software form, so as to be called and executed by the processor to perform the operations corresponding to the above-mentioned modules.

[0171] The computer device can be a terminal. The computer device comprises a processor, a memory, a communication interface, a display screen and an input device connected through a system bus. The processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device comprises 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 operating system and the computer program in the non-volatile storage medium to run. The communication interface of the computer device is configured to perform wired or wireless communication with an external terminal. The wireless communication can be achieved through WIFI, an operator network, NFC (near field communication) or other technologies. The computer program is executed by the processor to implement an ordered charging and discharging method for an electric vehicle. The display screen of the computer device can be a liquid crystal display screen or an electronic ink display screen. The input device of the computer device can be a touch layer overlaid on the display screen, or a key, trackball or touchpad arranged on the shell of the computer device, or an external keyboard, touchpad or mouse, etc.

[0172] The computer readable storage medium stores a computer program. The computer program is executed by the processor to implement the following steps:

[0173] 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 in the target range are obtained, and a V2G-based electric vehicle response capability constraint condition is established according to the parameter information;

[0174] The parameter information includes the time of the electric vehicles participating in the V2G plan to access the power grid and leave the power grid, the load state of the electric vehicles, the battery capacity of the electric vehicles and the charging and discharging power of the electric vehicles;

[0175] The V2G-based electric vehicle response capability constraint condition includes an electric vehicle access time constraint, an electric vehicle state of charge constraint, a V2G response capacity constraint and a charging and discharging power constraint;

[0176] The response capability value acquisition rule of the electric vehicle under the V2G-based electric vehicle response capability constraint condition is established.

[0177] A double-layer optimization model of electric vehicle charging and discharging strategy including response capability value of electric vehicle is established.

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

[0179] Embodiment 3

[0180] One embodiment of the present application is different from the previous two embodiments:

[0181] If the function is realized in the form of a software function unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solutions of the present application or the parts that essentially contribute to the prior art or parts of the technical solutions can be embodied in the form of a software product, which is stored in a storage medium and includes a plurality of instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the method described in the various embodiments of the present application. The aforementioned storage medium includes: a U disk, a mobile hard disk, a read-only memory (ROM, Read-Only Memory), a random access memory (RAM, Random Access Memory), a magnetic disk or an optical disk, and various program code storage media.

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

[0183] More specific examples (a non-exhaustive list) of the computer-readable medium include the following: an electrical connection (electronic) having one or more wires, a portable computer diskette (magnetic), a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, and a portable compact disc read-only memory (CDROM). Additionally, the computer readable medium can be paper or another suitable medium upon which the program is printed, as the program can be electronically captured, for example via optical scanning of the paper or other medium, then compiled, interpreted, or otherwise processed in a suitable manner, if necessary, and stored in a computer memory.

[0184] It should be understood that aspects of the application can be implemented in hardware, software, firmware or combinations thereof. In the above embodiments, various steps or methods can be implemented in software or firmware that is stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, any of the following technique, known in the art, or their combinations can be used: discrete logic circuitry having logic gates for implementing logic functions on data signals, application specific integrated circuits having appropriate combinational logic gates, programmable gate arrays (PGA), field programmable gate arrays (FPGA), etc.

[0185] Embodiment 4

[0186] Reference Figures 2-3 For one embodiment of the present application, an orderly charging and discharging method for electric vehicles is provided. In order to verify the beneficial effects of the present application, economic benefit calculation and simulation experiments are used for scientific demonstration.

[0187] Taking five large communities equipped with five district agents as an example, the agents are responsible for the charging and discharging scheduling of the electric vehicles in their respective districts under the overall scheduling plan of the dispatching center. It is assumed that the electric vehicles in the district are all ordinary household electric vehicles, and the charging of the electric vehicles is generally at night. It is assumed that the response V2G ratio of the regional electric vehicles is 90%. The basic power is selected as 200 kVA, and the dispatching period is set as 0 to 24, with an hour as the time period unit, 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 the electric vehicles and other basic load do not change, and the average monthly charging frequency of the vehicle owner is 10 times. The number of electric vehicles in the jurisdiction of each agent and the average response V2G capacity value of the district are set as shown in Table 1.

[0188] Table 1: Number of electric vehicles in the jurisdiction of each agent and average response capacity

[0189] Region number Number of electric vehicles Average response capability Jurisdiction 1 3500 1.29 Jurisdiction 2 4000 1.38 Jurisdiction 3 4500 1.45 Jurisdiction 4 5000 1.51 Jurisdiction 5 5500 1.63

[0190] For simplifying the model charging and discharging process, the electric vehicle related parameters are shown in the following table.

[0191] Table 2: Electric vehicle parameter table

[0192]

[0193] Community electricity, generally using time-of-use electricity price, time-of-use electricity price and buy-back electricity price, the regional user participating in V2G plan sells electricity to the grid price is the response ability of the jurisdiction multiplied by the basic buy-back electricity price, the application adopts the time-of-use electricity price of a city, as shown in the following table 3.

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

[0195]

[0196]

[0197] The electric vehicle V2G response optimization result is shown in Figure 2 . It can be seen from the figure that the basic load peak value is in 10:00-12:00, 18:00-22:00, and the load peak valley difference is 161.33kW, while in the unordered charging state, the load peak value is in 9:00-10:00, 11:00-12:00, 18:00-21:00, which further aggravates the load fluctuation, and the load peak valley difference reaches 289.62kW. After adopting the optimization scheduling strategy of the application, the load peak value appears in 9:00-11:00, 20:00-21:00. The load peak valley difference is 87.63kW, compared with the unordered charging state, the peak load is successfully filled, and the grid load fluctuation is reduced.

[0198] It can be seen from Figure 2 that the basic load appears peak value in 10:00-12:00, 18:00-22:00, at this time the electric vehicles participating in V2G plan begin to feed the grid, which shifts the load peak value and greatly suppresses the grid load fluctuation. According to the monthly average charging frequency of electric vehicle owners and the response ability of electric vehicles in each region to V2G plan, the monthly average charging cost comparison chart between each region is simulated and drawn, as shown in Figure 3 .

[0199] It can be seen from Figure 3 that the monthly average charging expenditure of electric vehicles participating in V2G plan is significantly lower than the charging cost under unordered charging. According to the simulation results, the charging cost difference generated by unordered charging and implementing V2G control strategy in region 5 is the largest, which is 96 yuan, and the charging cost is reduced by 35.16%. From Figure 3It can be observed that the V2G response capability of different regions directly affects the monthly average charging cost of the electric vehicle, and the greater the V2G response capability of the region, the lower the monthly average charging cost, which just embodies the strategy idea of improving user participation and maximizing user benefits.

[0200] To sum up, the application provides an electric vehicle orderly charging and discharging method and system, 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 in a target range, and establishes a V2G-based electric vehicle response capability constraint condition according to the parameter information; establishes an electric vehicle response capability value acquisition rule under the V2G-based electric vehicle response capability constraint condition; establishes an electric vehicle charging and discharging strategy double-layer optimization model containing the response capability value of the electric vehicle; solves 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 of each period, thereby obtaining the final electric vehicle charging and discharging strategy. Through the optimization and scheduling strategy, the fluctuation of the power grid load 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 the electric vehicle user, the user can obtain the maximum benefit when participating in the V2G plan, thereby improving the enthusiasm of the user participation.

[0201] It should be noted that the above embodiments are only used to illustrate the technical solutions of the application and are not limiting. Although the application has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that the technical solutions of the application can be modified or replaced equivalently without departing from the spirit and scope of the technical solutions of the application, and they should be covered in the scope of the claims of the application.

Claims

1. An electric vehicle orderly charge-discharge method, characterized by, The method comprises the following steps: acquiring the number of qualified electric vehicles, the number of agents and parameter information of the electric vehicles in a target range, and establishing a V2G-based electric vehicle response capability constraint condition according to the parameter information; establishing an electric vehicle response capability value acquisition rule under the V2G-based electric vehicle response capability constraint condition, and establishing an electric vehicle charging and discharging strategy double-layer optimization model containing the response capability value of the electric vehicle; solving the electric vehicle charging and discharging strategy double-layer optimization model according to an improved multi-population genetic optimization algorithm to obtain optimal charging power in each period and thus acquire a final electric vehicle charging and discharging strategy; the parameter information comprises the time when the qualified electric vehicles access the power grid and leave the power grid, the load state of the electric vehicle, the battery capacity of the electric vehicle and the charging and discharging power of the electric vehicle; the V2G-based electric vehicle response capability constraint condition comprises: 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 T when the electric vehicle leaves the grid l,i , determined by the randomness of user behavior, the time t for electric vehicles to connect to the grid satisfies T g,i ≤t≤T l,i , where i represents the i-th electric car; The electric vehicle's state of charge constraints, including the state of charge when the electric vehicle is connected to the grid and the minimum state of charge SOC at the end of the electric vehicle's charging state 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 that the available capacity B of the battery of the electric vehicle that meets the requirements ke is divided into three parts, i.e. the battery capacity B1 that meets the requirements, the battery capacity B2 that maintains daily driving, and the battery capacity B3 that is used for standby driving, B1+B2+B3=B ke,i , wherein B ke,i represents the total capacity of the battery of the i-th electric vehicle; the charging and discharging power constraint comprises that the power of the qualified part of the electric vehicle should not exceed the rated charging and discharging power thereof; the electric vehicle charging and discharging strategy double-layer optimization model containing the response capability value of the electric vehicle comprises an upper-layer objective function and a corresponding upper-layer objective function constraint condition, and a lower-layer objective function and a corresponding lower-layer objective function constraint condition; the upper-layer objective function constraint condition at least comprises a charging amount constraint, an agent scheduling constraint in each period and a load peak constraint, and the lower-layer objective function constraint condition at least comprises an electric vehicle charging and discharging time constraint, a vehicle battery capacity constraint and a load amount constraint.

2. The electric vehicle orderly charge and discharge method according to claim 1, characterized by: the electric vehicle response capability acquisition rule under the V2G-based electric vehicle response capability constraint condition comprises: acquiring the battery loss rate, the battery capacity and the regional response coefficient of the qualified electric vehicle to obtain an electric vehicle battery capacity influence factor, and acquiring the response capability value of the electric vehicle according to the electric vehicle battery capacity influence factor and in combination with the V2G-based electric vehicle response capability constraint condition, which is specifically expressed as: Q = q n B ke,i η where q n represents the area response coefficient, B ke,i represents the battery capacity participating in the V2G plan, η represents the battery loss rate, T l,i represents the time when the electric vehicle leaves the power grid; T g,i represents the time when the electric vehicle accesses the power grid; T min,i represents the shortest time for the electric vehicle to reach the desired battery state at the maximum power, Q represents the electric vehicle battery capacity influence factor; only when k>1, the condition of establishing the electric vehicle charging and discharging strategy double-layer optimization model containing the response capability value of the electric vehicle is met.

3. The electric vehicle orderly charge and discharge method according to claim 2, characterized by: the upper-layer objective function makes the total fluctuation curve in the charging and discharging period minimum, and the sum of the actual scheduling of the agent and the deviation of the power utilization strategy formulated by the scheduling center minimum, which is expressed as: Where T represents 24 time periods in a day, n is the number of agents; P d,t is the non-electric vehicle load at time period t, P n,t is the nth electric vehicle charging and discharging load at time t, N0 represents the total number of agents; P DE represents the output of other distributed energy sources in the region; represents the average load of the system in T time periods, and is represented as: where, represents the dispatching deviation function between the dispatch center and the regional agent, and a represents the violation penalty coefficient, |PD n,t -P n,t represents the dispatching deviation and the constraint of the regional agent, PD n,t represents the dispatching plan made by the nth regional agent at time t.

4. The electric vehicle orderly charge and discharge method according to claim 3, characterized by: the lower-layer objective function satisfies the lowest charging cost, which is expressed as: wherein T0 represents a start time of charging and discharging, and T1 represents an end time of charging and discharging; and respectively represent average values of charging power and discharging power, C c,i and C d,i respectively represent charging price and discharging price, and a, b respectively represent charging parameter and discharging parameter, represents an average response capability value of an electric vehicle in a region under jurisdiction of an agent; when charging, a is 1 and b is 0; when discharging, a is 0 and b is 1.

5. A system employing the orderly charge and discharge method for an electric vehicle according to any one of claims 1 to 4, characterized by The method comprises an information acquisition and processing module, a response capability value acquisition module, a double-layer optimization model establishing module and a strategy acquisition module. The information acquisition and processing module is used to acquire the number of electric vehicles participating in a V2G plan, the number of agents and parameter information of the electric vehicles participating in the V2G plan in a target range, and establish a V2G-based electric vehicle response capability constraint condition according to the parameter information; the response capability value acquisition module is used to establish an electric vehicle response capability value acquisition rule under the V2G-based electric vehicle response capability constraint condition; and the double-layer optimization model establishing module is used to establish an electric vehicle charging and discharging strategy double-layer optimization model containing the response capability value of the electric vehicle. The double-layer optimization model establishing module is configured to establish a double-layer optimization model of electric vehicle charging and discharging strategy containing response capability values of the electric vehicles. The strategy obtaining module is configured to solve the double-layer optimization model of electric vehicle charging and discharging strategy according to an improved multi-population genetic optimization algorithm, to obtain optimal charging power of each time period, and to obtain a final electric vehicle charging and discharging strategy. 6.A computer device, comprising a memory and a processor, wherein the memory stores a computer program, and the computer device is configured to perform the method according to any one of claims 1-5 when the computer program is executed by the processor. The processor executes the computer program to implement the steps of the electric vehicle orderly charging and discharging method in any one of claims 1 to 4.

7. A computer-readable storage medium having stored thereon a computer program, characterized in that The computer program is executed by the processor to implement the steps of the electric vehicle orderly charging and discharging method in any one of claims 1 to 4.

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