Electric vehicle charging planning method, device and equipment and storage medium

Abstract

The application discloses an electric vehicle charging planning method, device and equipment and a storage medium, and belongs to the technical field of electric vehicles. The method comprises the following steps: the greater the load pressure, the higher the electricity price, the higher the economic sensitivity, and the higher the vehicle-grid interaction compensation; a benefit model processes the electricity price and the compensation to obtain the electricity price and the compensation with the maximum benefit; expenditure differences based on charging station selection are used to divide user pools; user planning strategies are planned according to charging requests and load matching degrees, and charging is carried out in different stages or reverse charging is carried out; if the charging request conflicts with the load, small-power charging or reverse charging is carried out, and if the charging request does not conflict with the load, large-power charging is carried out. In the application, the electricity price and the compensation are formulated according to different regional economic characteristics and load characteristics, charging behaviors are accurately regulated, charging station selection is affected, the endogenously divided user pool is clear, the user pool truly reflects market dynamics, market uncertainty and regional characteristics are combined to maximize benefits, and the application improves the charging profit of the electric vehicle.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of electric vehicles, in particular to an electric vehicle charging planning method, device, equipment and storage medium. BACKGROUND

[0002] At present, electric vehicles in the distribution network not only accept charging, but also can release electric energy to the distribution network, and the electric vehicles play a role of distributed energy main body in the power grid. This new mode of two-way charging and discharging of electric vehicles and power grid is called vehicle-to-grid interaction mode. The vehicle-to-grid interaction technology can not only realize peak shaving and valley filling, smooth load fluctuation, but also provide frequency modulation, spinning reserve and other auxiliary services for the power grid, enhances the integration capability of renewable energy of the power grid, and improves the overall flexibility and economic efficiency of the power grid.

[0003] However, as an investor of the vehicle-to-grid interaction project, the fixed cost of installing electric vehicle two-way chargers and other infrastructure is high, in order to encourage users to participate in the vehicle-to-grid interaction project, the additional expenditure of the electricity price incentive and compensation mechanism increases the additional cost, and these expenditures are not completely clear. If the vehicle-to-grid interaction service is deployed in the whole region, there is a problem of high initial investment cost, and there is another problem of unclear later income. The current business model of vehicle-to-grid interaction mainly focuses on the optimization of the technical level, or carries out economic analysis of a single scene. The economic analysis of a single scene cannot plan comprehensively and is divorced from the actual market. The electricity price incentive and compensation mechanism will affect the decision of users whether to participate in the vehicle-to-grid interaction project, or affect the actual charging decision of users who have participated in the vehicle-to-grid interaction project. Therefore, the variable user decision behavior and the not completely clear expenditure make the income of the vehicle-to-grid interaction project not clear, and it is difficult to guarantee high profit based on the not clear income. Therefore, there is a problem of low profit of electric vehicle charging in the prior art.

[0004] The above content is only used to assist in understanding the technical solutions of the present application, and does not represent the acknowledgement of the above content as prior art. SUMMARY

[0005] The main purpose of the present application is to provide an electric vehicle charging planning method, which aims to solve the problem of low profit of electric vehicle charging in the prior art.

[0006] In order to achieve the above purpose, the present application provides an electric vehicle charging planning method, which is applied to an electric vehicle charging planning system, the electric vehicle charging planning system includes a conventional charging station and a V2G charging station, the V2G charging station is divided into V2G sub-areas based on different power grid load conditions in space, and the method comprises the following steps:

[0007] if it is detected that the charging request of the vehicle comes from the V2G charging area, obtaining load information of fluctuation of load with time in the V2G charging area, fixed cost of setting the V2G charging station in the V2G charging area, and spatiotemporal distribution information of the charging request;

[0008] initializing a charging price based on load pressure of the load information and user economic sensitivity to obtain an initial charging price, and initializing a vehicle-grid interaction compensation to obtain an initial vehicle-grid interaction compensation, wherein the vehicle-grid interaction compensation is used to encourage users to select the V2G charging station for charging, the greater the load pressure is, the higher the initial charging price is, and the higher the user economic sensitivity is, the higher the initial vehicle-grid interaction compensation is;

[0009] inputting the initial charging price and the initial vehicle-grid interaction compensation as input information into a preset charging revenue model, processing the input information based on the charging revenue model to obtain a charging price and a vehicle-grid interaction compensation at which expected charging revenue is maximum, wherein the expected charging revenue is composed of the fixed cost, vehicle-grid interaction compensation cost, and charging income;

[0010] dividing a preset charging user pool based on a difference between charging expenditures of the charging station selected by the charging price and the vehicle-grid interaction compensation to obtain a regular charging user pool and a V2G charging user pool, wherein the difference between the charging expenditures is a difference between a charging expenditure of the user selecting the V2G charging station for charging and a charging expenditure of the user selecting a regular charging station for charging;

[0011] planning a first charging planning strategy for users of the V2G charging user pool according to a matching degree of the spatiotemporal distribution information of the charging request and the load information, and feeding back the vehicle-grid interaction compensation of the first charging planning strategy to the users of the V2G charging user pool for confirmation by the users of the V2G charging user pool, wherein the first charging planning strategy includes charging the vehicle at different charging powers in stages or controlling the vehicle to reversely charge the grid in stages;

[0012] in response to a confirmation instruction of the vehicle-grid interaction compensation by the users of the V2G charging user pool, if the spatiotemporal distribution information and the load information conflict when the users of the V2G charging user pool start charging the vehicle at the V2G charging station, controlling a power supply of the charging station to charge the vehicle at a first power or controlling the vehicle to reversely charge, and if the spatiotemporal distribution information and the load information do not conflict, controlling the power supply of the charging station to charge the vehicle at a second power, wherein the first power is less than the second power.

[0013] In a possible implementation of the present application, the step of inputting the initial charging price and the initial vehicle-to-grid interaction compensation as input information into a preset charging revenue model, processing the input information based on the charging revenue model, and obtaining the charging price and the vehicle-to-grid interaction compensation when the expected charging revenue is maximum includes:

[0014] The initial charging price and the initial vehicle-to-grid interaction compensation are input as input information into a preset charging revenue model, and the charging revenue model includes an expected charging revenue function.

[0015] Based on the initial charging price and the number of vehicles in the charging request, the charging income under the charging request is obtained.

[0016] The product of the initial vehicle-to-grid interaction compensation and the number of vehicles in the charging request is calculated to obtain the vehicle-to-grid interaction compensation cost for encouraging and subsidizing the vehicles.

[0017] Based on the expected charging revenue function, the charging income is subtracted from the fixed cost and then subtracted from the vehicle-to-grid interaction compensation cost to obtain the expected charging revenue.

[0018] A preset capacity balance constraint condition and a preset economic constraint condition are obtained, wherein the capacity balance constraint condition is that if there is a vehicle migration from an original V2G charging subarea to other V2G charging subareas, the benefit obtained based on the vehicle-to-grid interaction compensation in the other V2G charging subareas is still greater than the benefit obtained in the original V2G charging subarea after the migration cost is subtracted; and the economic constraint condition is that the charging price is greater than the power cost and the vehicle-to-grid interaction compensation is less than a preset vehicle-to-grid interaction compensation limit, and the vehicle-to-grid interaction compensation is a non-negative number.

[0019] Under the constraints of the capacity balance constraint condition and the economic constraint condition, the expected charging revenue is optimized based on the variable charging price and the variable vehicle-to-grid interaction compensation to obtain the charging price and the vehicle-to-grid interaction compensation when the expected charging revenue is maximum.

[0020] In a possible implementation of the present application, the charging revenue model is a mixed integer nonlinear model, and the step of optimizing the expected charging revenue based on the variable charging price and the variable vehicle-to-grid interaction compensation under the constraints of the capacity balance constraint condition and the economic constraint condition to obtain the charging price and the vehicle-to-grid interaction compensation when the expected charging revenue is maximum includes:

[0021] The expected charging revenue function is taken as an objective function, and the charging price and the vehicle-to-grid interaction compensation are taken as continuous variables.

[0022] acquire preset path complementary constraint conditions and capacity complementary constraint conditions, wherein the path complementary constraint conditions and the capacity complementary constraint conditions are non-convex;

[0023] linearize the path complementary constraint conditions and the capacity complementary constraint conditions through binary auxiliary variables to obtain linearized path complementary constraint conditions and linearized capacity complementary constraint conditions;

[0024] establish the mixed integer nonlinear model based on the objective function, the continuous variables, the capacity balance constraint conditions, the economic constraint conditions, the linearized path complementary constraint conditions and the linearized capacity complementary constraint conditions;

[0025] perform McCormick envelope convexification processing on the objective function in the mixed integer nonlinear model;

[0026] relax the remaining binary terms in the objective function after the McCormick envelope convexification processing to a continuous interval [0, 1];

[0027] sharpen all future convex points, enumerate the lower bounds of each descendant node in the tree, and when no interval is shortened by more than a threshold value, the loop terminates;

[0028] at each node of the branch and bound tree, solve a relaxed linear programming problem combined with McCormick envelope relaxation and cutting plane construction to obtain an effective lower bound of the maximum objective function value;

[0029] reconstruct a locally feasible solution according to the solution of the relaxed linear programming problem to obtain an effective upper bound;

[0030] obtain the charging price when the expected charging revenue is maximum and the vehicle-to-grid interaction compensation based on the effective lower bound and the effective upper bound.

[0031] In a possible implementation of the application, different V2G charging sub-areas have different load peak periods in time, and the first charging planning strategy is to charge the vehicles at different charging powers in stages or to control the vehicles to reversely charge the power grid in stages;

[0032] The step of planning a first charging planning strategy for the V2G charging user pool based on the spatio-temporal distribution information of the charging requests and the load information includes:

[0033] compare the spatio-temporal distribution information and the load information in time and space respectively to obtain a comparison result;

[0034] If the comparison result shows that the load peak period of the same V2G charging sub-area coincides with the charging time, the first power is determined for small power charging in the charging start stage;

[0035] If the comparison result shows that the load peak period of the same V2G charging sub-area does not coincide with the charging time, the second power is determined for large power charging in the charging start stage, wherein the first power is less than the second power;

[0036] After the end of the charging start stage, if the charging intermediate stage coincides with the load peak period, the vehicle determined for small power charging in the charging start stage with the first power continues charging with the first power in the charging intermediate stage, and the vehicle determined for large power charging in the charging start stage with the second power reversely charges the power grid in the charging intermediate stage;

[0037] After the end of the charging start stage, if the charging intermediate stage does not coincide with the load peak period, the vehicle determined for small power charging in the charging start stage with the first power charges with the second power in the charging intermediate stage, and the vehicle determined for large power charging in the charging start stage with the second power continues large power charging with the second power;

[0038] After the end of the charging intermediate stage, for the vehicle determined for small power charging with the first power in the charging start stage and the intermediate stage, the remaining charging amount gap is calculated to obtain the remaining charging amount, and the third power is determined for charging in the charging final stage to match the remaining charging amount.

[0039] In a possible implementation of the present application, after the step of planning a first charging planning strategy for the users of the V2G charging user pool according to the matching degree of the space-time distribution information of the charging request and the load information, and feeding back the vehicle-grid interaction compensation of the first charging planning strategy to the users of the V2G charging user pool, the step comprises:

[0040] In response to the confirmation instruction of the vehicle-grid interaction compensation of the users of the V2G charging user pool, the V2G charging user queue arriving within a preset time interval is queued;

[0041] The charging power curve of each vehicle corresponding to the V2G charging user is planned based on the first charging planning strategy in turn based on the queuing order, and the charging power curve comprises the first power, the second power and the third power;

[0042] When a user in the V2G charging user pool starts charging a vehicle at the V2G charging station, the power supply of the charging station is controlled to charge at a corresponding power based on the pre-planned charging power curve at a corresponding time.

[0043] In a possible implementation of the application, the step of dividing the preset charging user pool based on the difference between the charging expenditures of the charging station selection obtained from the charging price and the vehicle-network interaction compensation to obtain the regular charging user pool and the V2G charging user pool comprises:

[0044] The first charging expenditure of the user selecting the V2G charging station for charging is calculated based on the charging price and the vehicle-network interaction compensation.

[0045] The second charging expenditure of the user selecting the regular charging station for charging is calculated based on the charging price.

[0046] The difference between the first charging expenditure and the second charging expenditure is taken as the difference between the charging expenditures of the charging station selection, wherein the users affected by the incentive effect of the vehicle-network interaction compensation gradually select the V2G charging station for charging based on the difference between the charging expenditures, the users in the regular charging user pool decrease, and the users in the V2G charging user pool increase.

[0047] The preset charging user pool is divided based on the difference between the charging expenditures and the marginal diminishing effect of the incentive of the vehicle-network interaction compensation to obtain the regular charging user pool and the V2G charging user pool in a preset time interval.

[0048] In a possible implementation of the application, after the step of dividing the preset charging user pool based on the difference between the charging expenditures of the charging station selection obtained from the charging price and the vehicle-network interaction compensation to obtain the regular charging user pool and the V2G charging user pool, the method further comprises:

[0049] A second charging planning strategy is planned for the users in the regular charging user pool, wherein the second charging planning strategy is to charge the vehicle based on a fixed fourth power.

[0050] When the user in the regular charging user pool starts charging a vehicle at the regular charging station, the power supply of the charging station is controlled to unidirectionally charge the vehicle at the fourth power.

[0051] Furthermore, to achieve the above object, the application also provides an electric vehicle charging planning device, which is arranged in an electric vehicle charging planning system, wherein the electric vehicle charging planning system comprises conventional charging stations and V2G charging stations, the V2G charging stations are divided into V2G charging sub-areas based on different power grid load conditions in space, and the electric vehicle charging planning device comprises:

[0052] an acquisition module, configured to acquire load information of load fluctuation with time in a V2G charging sub-area, fixed cost of setting the V2G charging station in the V2G charging sub-area, and spatio-temporal distribution information of the charging request if it is detected that the charging request of the vehicle comes from the V2G charging sub-area;

[0053] an initialization module, configured to initialize a charging price based on load pressure of the load information and user economic sensitivity to obtain an initial charging price, and initialize vehicle-grid interaction compensation to obtain an initial vehicle-grid interaction compensation, wherein the vehicle-grid interaction compensation is used to encourage users to select the V2G charging station for charging, the greater the load pressure is, the higher the initial charging price is, and the higher the user economic sensitivity is, the higher the initial vehicle-grid interaction compensation is;

[0054] a processing module, configured to input the initial charging price and the initial vehicle-grid interaction compensation as input information into a preset charging income model, process the input information based on the charging income model, and obtain a charging price and vehicle-grid interaction compensation at which expected charging income is maximum, wherein the expected charging income is composed of the fixed cost, vehicle-grid interaction compensation cost and charging income;

[0055] a division module, configured to divide a preset charging user pool based on a difference between charging expenditures of charging stations selected by the charging price and the vehicle-grid interaction compensation to obtain a conventional charging user pool and a V2G charging user pool, wherein the difference between the charging expenditures is a difference between a charging expenditure of the user selecting the V2G charging station for charging and a charging expenditure of the user selecting the conventional charging station for charging;

[0056] a planning module, configured to plan a first charging planning strategy for users in the V2G charging user pool according to a matching degree of spatio-temporal distribution information of the charging request and the load information, and feed back the vehicle-grid interaction compensation of the first charging planning strategy to the users in the V2G charging user pool for confirmation, wherein the first charging planning strategy comprises charging the vehicle at different charging powers in stages or controlling the vehicle to reversely charge the power grid in stages.

[0057] A control module is configured to, in response to a confirmation instruction of the vehicle network interaction compensation from a user in the V2G charging user pool, control a power supply of the charging station to charge the vehicle at a first power or control the vehicle to reverse charge, if the spatio-temporal distribution information conflicts with the load information, or control the power supply of the charging station to charge the vehicle at a second power, if the spatio-temporal distribution information does not conflict with the load information, when the user in the V2G charging user pool starts charging the vehicle at the V2G charging station, wherein the first power is less than the second power.

[0058] In addition, the application also provides an electric vehicle charging planning device, which is an entity node device. The electric vehicle charging planning device comprises a memory, a processor, and an electric vehicle charging planning program stored in the memory and executable on the processor. The processor executes the electric vehicle charging planning program to realize the steps of the electric vehicle charging planning method.

[0059] In addition, the application also provides a storage medium having an electric vehicle charging planning program stored thereon. The electric vehicle charging planning program is executable by a processor to realize the steps of the electric vehicle charging planning method.

[0060] The application provides an electric vehicle charging planning method, device, equipment and storage medium. Compared with the prior art, in the application, if it is detected that the charging request of the vehicle comes from a V2G charging area, the load information of the load fluctuation with time in the V2G charging area is obtained, the fixed cost of the V2G charging station in the V2G charging area is set, and the spatio-temporal distribution information of the charging request is obtained. The initial charging price is initialized based on the load pressure of the load information and the user economic sensitivity, and the initial vehicle-to-grid interaction compensation is initialized. The vehicle-to-grid interaction compensation is used to encourage users to select the V2G charging station for charging. The greater the load pressure is, the higher the initial charging price is. The higher the user economic sensitivity is, the higher the initial vehicle-to-grid interaction compensation is. The initial charging price and the initial vehicle-to-grid interaction compensation are input into a preset charging income model as input information. The input information is processed based on the charging income model to obtain the charging price and the vehicle-to-grid interaction compensation when the expected charging income is maximum. The expected charging income is composed of the fixed cost, the vehicle-to-grid interaction compensation cost and the charging income. Based on the difference between the charging expenditure of the charging station selected by the charging price and the vehicle-to-grid interaction compensation, the preset charging user pool is divided to obtain a conventional charging user pool and a V2G charging user pool. The difference between the charging expenditure is the difference between the charging expenditure of the user selecting the V2G charging station for charging and the charging expenditure of the user selecting the conventional charging station for charging. According to the matching degree of the spatio-temporal distribution information of the charging request and the load information, a first charging planning strategy is planned for the users in the V2G charging user pool, and the vehicle-to-grid interaction compensation of the first charging planning strategy is fed back to the users in the V2G charging user pool for confirmation. The first charging planning strategy includes charging the vehicle at different charging powers in stages or controlling the vehicle to reversely charge the power grid in stages. In response to the confirmation instruction of the vehicle-to-grid interaction compensation of the users in the V2G charging user pool, when the users in the V2G charging user pool start charging the vehicle at the V2G charging station, if the spatio-temporal distribution information and the load information conflict, the power supply of the charging station is controlled to charge the vehicle at a first power or control the vehicle to reversely charge, and if the spatio-temporal distribution information and the load information do not conflict, the power supply of the charging station is controlled to charge the vehicle at a second power. The first power is less than the second power. In the application, the price and compensation formulated according to the economic characteristics and load characteristics of different V2G charging areas are used to regulate the charging behavior. The pre-formulated price and compensation act on the charging station selection, and the endogenously divided user pool determines the influence. The user pool truly reflects the market charging dynamics, combines market uncertainty with regional characteristics, and strives for maximum benefit. Therefore, the application improves the charging profit of the electric vehicle. BRIEF DESCRIPTION OF DRAWINGS

[0061] Figure 1 Flowchart of an embodiment of the electric vehicle charging planning method of the present application;

[0062] Figure 2 V2G charging station space map in an embodiment of the electric vehicle charging planning method of the present application;

[0063] Figure 3 Space-time distribution map of charging requests in an embodiment of the electric vehicle charging planning method of the present application;

[0064] Figure 4 Schematic diagram of an electric vehicle charging planning device in an embodiment of the electric vehicle charging planning method of the present application;

[0065] Figure 5 Schematic diagram of the device structure of the hardware operating environment involved in an embodiment of the electric vehicle charging planning method of the present application. DETAILED DESCRIPTION

[0066] In order to make the above objectives, features and advantages of the present application more apparent, clear and easy to understand, the technical solutions in the embodiments of the present application will be described in detail below with reference to the drawings of the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those of ordinary skill in the art without creative work fall within the scope of protection of the present application.

[0067] Embodiment one

[0068] The embodiment of the present application provides an electric vehicle charging planning method, which is applied to an electric vehicle charging planning system. The electric vehicle charging planning system includes a conventional charging station and a V2G charging station. The V2G charging station is divided into V2G sub-areas in space based on different power grid load conditions, such as Figure 1 The method includes steps S110-S160:

[0069] The core concept of vehicle-to-grid interaction is to utilize the bidirectional charging and discharging capability of electric vehicle batteries. This enables them to absorb excess power from the power grid during off-peak hours and inject it into the power grid during peak hours, thereby serving as mobile energy storage units. Vehicle-to-grid interaction can not only achieve peak shaving and load smoothing, but also provide auxiliary services such as frequency regulation and spinning reserve for the power grid. This enhances the integration capability of renewable energy for the power grid and improves the overall flexibility and economic efficiency of the power grid.

[0070] In this implementation, as an investor of vehicle-to-grid projects, firstly, the high hardware cost is the main obstacle. Secondly, the concern about battery health degradation is the core problem affecting the willingness of users to participate. Finally, the business model is not mature, and there is a lack of stable and attractive electricity price incentives and compensation mechanisms, which makes the profitability of vehicle-to-grid projects uncertain. It is unrealistic to fully launch a vehicle-to-grid network. It should be studied how to gradually deploy a vehicle-to-grid business network under the conditions of limited resources and market uncertainty.

[0071] V2G is the abbreviation of Vehicle to grid, which means vehicle to grid.

[0072] As an example, as shown in Figure 2 , the V2G charging station is divided into 10 V2G charging sub-areas based on different grid load conditions in space, which are sub-area 1 to sub-area 10 in turn.

[0073] The city is composed of a core built-up area (sub-area 1 to sub-area 9) and external transport hubs. The core area is modeled as a compact 3x3 grid, with a distance of 4 kilometers between the centers of adjacent areas. Sub-area 10 is an independent transport hub, such as an airport or a large industrial park, connected to the core grid through sub-area 6, with a center-to-center distance of 6 kilometers. In order to capture the heterogeneous characteristics of modern cities, these 10 areas are divided into four different functional prototypes, each with unique geographical, economic and strategic attributes. The absolute core area (sub-area 5) is located at the geometric center of the grid, and this area represents the central business district (CBD) with high-quality accessibility. The sub-core areas (sub-areas 2, 4, 6 and 8) These areas form an inner ring around the absolute core area, serving as the sub-core areas of the city. They include high-end residential areas, administrative centers and commercial facilities. Peripheral / mixed-use (sub-areas 1, 3, 7 and 9) are located at the four corners of the grid, and these areas are modeled as mixed-use areas with large residential communities and emerging business centers. Specialized functional area (sub-area 10) This area is modeled as an independent specialized hub, such as an airport, a high-speed rail station or a large industrial park. The grid load conditions of these 10 V2G charging sub-areas are different.

[0074] Sub-area 5 is located at the geometric center of the grid, and this area represents the central business district with high-quality accessibility. Its characteristics are high daytime charging demand for commuters and business activities, and users show low price sensitivity. High land value and construction cost bring huge investment barriers, but also bring high potential returns. Its large amount of night basic load makes it a major candidate for V2G activation to perform grid peak shaving.

[0075] Regions 2, 4, 6, and 8 form an inner ring around the absolute core region, serving as the secondary core region of the city. They include high-end residential areas, administrative centers, and commercial facilities. They experience high charging demand from both residents and commuters. Region 6, as the gateway to the transportation hub (Region 10), has special strategic importance. Although the real estate value is high, their prices are relatively low compared to the absolute core region, making these regions crucial for capturing a wide range of demand and the logical next target for V2G network expansion.

[0076] Regions 1, 3, 7, and 9 are located at the four corners of the grid, and these regions are modeled as mixed-use areas with large residential communities and emerging commercial centers. Their nodes are mainly composed of residential loads and supporting commercial / industrial facilities. They exhibit strong demand for night-time residential charging, making them ideal locations for implementing V2G valley filling strategies to absorb off-peak power. The lower activation cost of these peripheral regions enables them to serve as reservoirs for energy temporal arbitrage.

[0077] Region 10 is modeled as an independent professional hub, such as an airport, high-speed rail station, or large industrial park. Its charging demand is tidal and has a strong correlation with external schedules (such as flight arrivals, work shifts), and its user base is largely transient. It is dominated by industrial and commercial loads and features strategic high-capacity charging stations. The V2G potential here is enormous, albeit latent. Its unique demand profile makes it a suitable candidate for developing independent microgrids, where V2G services can be activated to absorb significant load fluctuations of its own, thus justifying investment.

[0078] In step S110, if it is detected that the charging request of the vehicle comes from the V2G charging sub-region, the load information of the load fluctuation with time in the V2G charging sub-region, the fixed cost of setting the V2G charging station in the V2G charging sub-region, and the spatio-temporal distribution information of the charging request are obtained.

[0079] If the vehicle user makes an appointment for charging in the V2G charging sub-region through the back-end platform, the electric vehicle charging planning system receives this appointment information and detects that there is a vehicle requesting V2G charging.

[0080] The load information of the load fluctuation with time in the V2G charging sub-region includes load pressure and user economic sensitivity. As an example, the daytime charging demand of the 5th sub-region is high, the charging demand peak is in the daytime, and the user's price sensitivity is low.

[0081] The fixed cost hi of setting the V2G charging station in the V2G charging sub-region is obtained. As an example, the fixed cost h5 of the V2G charging station in the 5th sub-region is obtained.

[0082] The user charging demand presents a non-uniform distribution in the time and space dimensions. The spatiotemporal distribution information includes charging time request and charging space geographical position request. As shown in Figure 3 (a) electricity price profile, (b) time distribution of user demand, (c) spatial distribution of user demand, and (d) standardized load profile by sector. Standardized daily load profiles for five representative sectors, residential, commercial, industrial, shopping, and charging stations, are depicted. Each profile shows a unique pattern. Residential load peaks in the evening, industrial and commercial loads dominate the daytime work hours, shopping loads persist from afternoon to evening, and the baseline load for charging stations peaks at midnight. First, the economic signal from the grid is represented by the time-of-use electricity price. This pricing structure aims to guide the charging and discharging behavior of users through the price lever, thereby promoting the economic and efficient operation of the grid. Specifically, the off-peak period price from 0:00 to 7:00 is the lowest, at 0.3 yuan / kWh, to encourage users to charge. The price during the period from 10:00 to 12:00 and from 19:00 to 22:00 is the highest, at 1.1 yuan / kWh, reflecting the high load condition of the system. The remaining period is the medium peak period, with a price of 0.7 yuan / kWh. This significant price difference creates a clear economic incentive for electric vehicle (EV) users to participate in vehicle-to-grid (V2G) spatiotemporal energy arbitrage.

[0083] In step S120, the charging price is initialized based on the load pressure of the load information and the user economic sensitivity to obtain an initial charging price, and the V2G interaction compensation is initialized to obtain an initial V2G interaction compensation, wherein the V2G interaction compensation is used to encourage users to choose the V2G charging station for charging, the greater the load pressure, the higher the initial charging price, and the higher the user economic sensitivity, the higher the initial V2G interaction compensation;

[0084] The load information presents a non-uniform distribution in the spatial dimension, and the load information of each V2G charging sub-area in the 10 V2G charging sub-areas is different, and the load pressure and user economic sensitivity are different. As shown in the 5th V2G charging sub-area, the load pressure is mainly concentrated in the daytime, and the user's price sensitivity is low. For this high load pressure and low user economic sensitivity, a higher initial charging price can be initialized to obtain higher charging income, and the V2G interaction compensation can be initialized to be lower. The V2G interaction compensation is used to encourage users to choose the V2G charging station for charging when facing the conventional charging station or the V2G charging station. The load pressure of the 5th V2G charging sub-area is the highest, so the initial charging price is the highest, and the lower the user economic sensitivity of the 5th V2G charging sub-area, the lower the initial V2G interaction compensation.

[0085] The V2G charging sub-area of the first, third, seventh and ninth areas is mainly composed of residential loads, the load pressure is mainly concentrated at night, and the user's price sensitivity is relatively high. Therefore, the initial charging price is slightly lower than that of the fifth area V2G charging sub-area, and the vehicle-to-grid interaction compensation can be initialized to be slightly higher than that of the fifth area V2G charging sub-area.

[0086] The greater the load pressure of the V2G charging sub-area, the higher the initial charging price. The smaller the load pressure of the V2G charging sub-area, the lower the initial charging price. The higher the user's economic sensitivity of the V2G charging sub-area, the higher the initial vehicle-to-grid interaction compensation. The lower the user's economic sensitivity of the V2G charging sub-area, the lower the initial vehicle-to-grid interaction compensation.

[0087] In step S130, the initial charging price and the initial vehicle-to-grid interaction compensation are input as input information into a preset charging revenue model, and the input information is processed based on the charging revenue model to obtain a charging price and a vehicle-to-grid interaction compensation when the expected charging revenue is maximum, wherein the expected charging revenue is composed of the fixed cost, the vehicle-to-grid interaction compensation cost and the charging income.

[0088] For the initial charging price and the initial vehicle-to-grid interaction compensation after the initialization of the above V2G charging sub-area, the initial charging price and the initial vehicle-to-grid interaction compensation are input as input information into a preset charging revenue model, and the input information is processed based on the charging revenue model to obtain a charging price and a vehicle-to-grid interaction compensation when the expected charging revenue is maximum.

[0089] The steps of step S130 include steps S1301-S1306:

[0090] In step S1301, the initial charging price and the initial vehicle-to-grid interaction compensation are input as input information into a preset charging revenue model, and the charging revenue model includes an expected charging revenue function.

[0091] The expected charging revenue function refers to the charging revenue left after the construction cost and the incentive cost are comprehensively considered. The expected charging revenue function W is composed of the total charging service income, the vehicle-to-grid interaction incentive and the vehicle-to-grid interaction service fixed cost. Wherein, V1 is the user pool participating in the vehicle-to-grid interaction project, i is any one of the areas, T is a preset time period, rit represents the charging price charged when the vehicle is charging, zit is the wholesale price of the platform wholesale power, represents the average charging amount of each vehicle, N1it represents the number of users participating in the vehicle-to-grid interaction project, wit represents the unit compensation amount, that is, the vehicle-to-grid interaction compensation when the vehicle releases electric energy to the grid side, hi represents the fixed cost of each area i, and yi represents whether the area i is set as a V2G service area, 1 if set, and 0 if not set;

[0092] ;

[0093] The expected charging revenue function W is the main source of income of the platform. Increasing the price r it Increases the profit margin of each service, but may reduce the total number of vehicles for the service by reducing its appeal to users, thereby affecting the total revenue.

[0094] Step S1302, based on the initial charging price and the number of vehicles of the charging request, the charging income under the charging request is obtained;

[0095] The difference between the initial charging price rit and the wholesale power price zit for the platform is calculated, and the difference is multiplied by the average charging amount of each vehicle, and then multiplied by the number of vehicles N1it of the charging request to obtain the charging income under the charging request .

[0096] Step S1303, calculate the product of the initial vehicle-to-grid interaction compensation and the number of vehicles of the charging request, to obtain the vehicle-to-grid interaction compensation cost of the incentive subsidy to the vehicles;

[0097] The product of the initial vehicle-to-grid interaction compensation wit and the number of vehicles N1it of the charging request is calculated to obtain the vehicle-to-grid interaction compensation cost wit*N1it of the incentive subsidy to the vehicles.

[0098] Step S1304, based on the expected charging revenue function, the charging income is subtracted from the fixed cost and then subtracted from the vehicle-to-grid interaction compensation cost to obtain the expected charging revenue;

[0099] The charging income is subtracted from the fixed cost hi and then subtracted from the vehicle-to-grid interaction compensation cost wit*N1it to obtain the expected charging revenue W.

[0100] In step S1305, preset capacity balance constraint conditions and preset economic constraint conditions are obtained. The capacity balance constraint condition is that if there is a vehicle migrating from an original V2G charging subarea to another V2G charging subarea, the benefit obtained based on the vehicle-to-grid interaction compensation in the other V2G charging subarea minus the migration cost is still greater than the benefit obtained in the original V2G charging subarea. The economic constraint condition is that the charging price is greater than the power cost and the vehicle-to-grid interaction compensation is less than a preset vehicle-to-grid interaction compensation limit, and the vehicle-to-grid interaction compensation is a non-negative number.

[0101] The capacity balance constraint condition is that if there is a vehicle migrating from an original V2G charging subarea to another V2G charging subarea, the benefit obtained based on the vehicle-to-grid interaction compensation in the other V2G charging subarea minus the migration cost is still greater than the benefit obtained in the original V2G charging subarea.

[0102] Let Nji≥0 represent the number of vehicles migrating from the jth subarea to the ith subarea in the equilibrium state,

[0103] wherein, represents the equilibrium average benefit of migrating from the jth subarea to any non-congestion subarea, represents the congestion shadow price of the ith subarea. From the perspective of the jth subarea, when the market reaches equilibrium, if a driver is willing to go to the ith subarea for vehicle-to-grid interaction service, the expected benefit satisfies . Conversely, if no driver migrates from the jth subarea to the ith subarea for charging, the expected benefit must satisfy . This equilibrium condition is economically intuitive. The physical meaning is that if the expected income of going to a specific ith subarea exceeds that of other subareas, and the ith subarea has no capacity, then the drivers currently engaged in vehicle-to-grid interaction service in other subareas will be redeployed to the ith subarea to obtain higher income. This migration will continue until the expected income uji of the ith subarea decreases to be equal to that of other non-congestion subareas. A vehicle can choose to charge in the current service subarea or in other service subareas, and it will take time cost to migrate from the current subarea to other subareas. Therefore, the vehicle will migrate to other subareas for charging only when the income of other subareas is greater than that of the current service subarea. If the vehicle chooses not to charge in the current service subarea, it means that the income of the vehicle in the current service subarea is less than that of other charging service subareas.

[0104] The economic constraint condition is that the charging price rit is greater than the power cost zit and the vehicle-to-grid interaction compensation wit is less than a preset vehicle-to-grid interaction compensation limit, and the vehicle-to-grid interaction compensation is a non-negative number. Although a higher vehicle-to-grid interaction compensation can attract more users to participate in V2G charging service, the vehicle-to-grid interaction compensation is not limited by an upper limit in order to obtain final charging income.

[0105] In addition to the capacity balance constraint and the economic constraint, there is a grid side constraint, and the increase of the charging load cannot violate the safe operation limit of the grid.

[0106] Step S1306, under the constraint of the capacity balance constraint and the economic constraint, the expected charging revenue is optimized based on the variable charging price and the variable vehicle-to-grid interaction compensation, to obtain the charging price and the vehicle-to-grid interaction compensation when the expected charging revenue is maximum.

[0107] The expected charging revenue function has been introduced above:

[0108] ;

[0109] Under the constraint of the capacity balance constraint and the economic constraint, the charging price and the vehicle-to-grid interaction compensation are adjusted as variable variables, and the expected charging revenue W is iteratively optimized to find the charging price and the vehicle-to-grid interaction compensation when the expected charging revenue W is maximum.

[0110] The charging revenue model is a mixed integer nonlinear model, and the step S1306 includes steps A1-A10:

[0111] The charging revenue model includes the expected charging revenue function, the capacity balance constraint and the economic constraint. The charging revenue model belongs to a non-convex mixed integer nonlinear programming (MINLP) model. Due to the existence of complex interactions between integer variables, complementary constraints and endogenous variables, its solution is particularly challenging.

[0112] Step A1, taking the expected charging revenue function as the objective function, and taking the charging price and the vehicle-to-grid interaction compensation as continuous variables.

[0113] Taking the expected charging revenue function W as the objective function, and taking the charging price and the vehicle-to-grid interaction compensation as continuous variables.

[0114] Step A2, obtaining a preset path complementary constraint and a capacity complementary constraint, wherein the path complementary constraint and the capacity complementary constraint are non-convex.

[0115] path complementarity conditions, which require that at least one of Nji and m^ - (u^ - λ^) must be equal to zero. When Nji = 0, several cases can occur: region i can provide lower benefits, can be at full capacity, or can provide benefits equal to the equilibrium level, but the equilibrium occurs at the special point where Nji = 0. In all these cases, no specific constraints need to be satisfied. When Nji > 0, we must have m^ = u^ - λ^, which contains two scenarios: either region i is not running at full capacity, providing benefits equal to the equilibrium level, or region i is running at full capacity, although it provides benefits higher than the equilibrium level, but can only accommodate a fraction of the demand, the rest of the drivers being directed to other regions, where they can obtain the equilibrium benefits.

[0116] capacity complementarity constraints, which require that at least one of Si, which represents the number of free stations in region i at equilibrium, and λ^ must be equal to zero. When region i is not at full capacity, the shadow price λ^ must be zero. Conversely, when region i is at full capacity, λ^ is unrestricted and can take any non-negative value.

[0117] The path complementarity constraints and the capacity complementarity constraints are non-convex.

[0118] Step A3, linearizing the path complementarity constraints and the capacity complementarity constraints by binary auxiliary variables;

[0119] In this embodiment, the bilinear terms within the objective function are non-linear, as are the constraints. By introducing binary auxiliary variables to eliminate each non-linear source within the constraints, the model is processed into a mixed integer programming model, the constraints become purely linear, obtaining linearized path complementarity constraints and linearized capacity complementarity constraints.

[0120] Step A4, establishing the mixed integer nonlinear model based on the objective function, the continuous variables, the capacity balance constraints, the economic constraints, the linearized path complementarity constraints and the linearized capacity complementarity constraints;

[0121] Each nonlinear source inside the constraints is eliminated by introducing a binary auxiliary variable. The resulting model is a mixed integer program whose constraints are purely linear, while the objective retains a set of bounded, non-convex bilinear terms. Such problems cannot be handled by ordinary MIP techniques because the objective destroys convexity, nor by standard nonlinear solvers because of the binary domain. Therefore, a custom global algorithm is designed that interweaves ideas from discrete optimization and deterministic global NLPs. The space branch and bound (SBB) framework is adopted. A mixed integer nonlinear model is built based on the objective function, the continuous variables, the capacity balance constraints, the economic constraints, the linearized path complementarity constraints, and the linearized capacity complementarity constraints.

[0122] Step A5, applying McCormick envelope convexification to the objective function in the mixed integer nonlinear model;

[0123] For each bilinear product is four-faced McCormick envelope convexified.

[0124] Step A6, relaxing the remaining binary terms in the McCormick envelope convexified objective function to the continuous interval [0, 1];

[0125] From the classical solution propagation, the dominant constraints are deleted, singleton binaries are repaired, and trivial bounds are propagated. However, the most profitable is the optimization-based constraint tightening loop. At this stage, the bilinear terms in the objective function are linearized by auxiliary variables and McCormick envelopes, and the remaining binary terms are temporarily relaxed to the continuous interval [0, 1].

[0126] Step A7, sharpening all future convex points, enumerating the lower bounds of each descendant node in the tree, and terminating the loop when no interval is shortened beyond a threshold;

[0127] For each continuous variable, the charging price rit and the vehicle-to-grid compensation wit, the minimum and maximum values are solved. Because the McCormick envelope depends on these bounds, each improvement implemented in the optimization-based constraint tightening loop sharpens all future convex points, thus enumerating the lower bounds of each descendant node in the tree. The loop terminates when no interval is shortened beyond a threshold. After the optimization-based constraint tightening loop, a polytope domain is obtained, which is usually much smaller than the original one.

[0128] Step A8, at each node of the branch-and-bound tree, solving a relaxed linear programming problem combined with McCormick envelope relaxation and cut plane construction, obtaining an effective lower bound of the maximum objective function value;

[0129] At each branching node, the McCormick LP is already a valid lower bound, but it rarely comes within a fraction of the optimality gap. To further squeeze the slack, RLT cuts are generated. Only level-1 candidate pairs, either row x row or row x variable binding, are examined to limit memory growth. During node processing, the current LP is solved, and all accepted RLT cuts are stored in a global pool and automatically inherited by each descendant node, so separation cost is incurred at most once. At each node of the branch-and-bound tree, a relaxed LP constructed by combining McCormick envelope relaxations and RLT cut planes is solved to obtain a valid lower bound on the optimal objective of the original problem.

[0130] Step A9, according to the solution of the relaxed linear programming problem, reconstructing a locally feasible solution to obtain an effective upper bound;

[0131] At each node of the branch-and-bound tree, a relaxed LP constructed by combining McCormick envelope relaxations and RLT cut planes is solved to obtain a valid lower bound on the optimal objective of the original problem. Based on the solution of the relaxed LP, a set of heuristics can be applied to reconstruct a locally feasible solution of the original MINLP, thus obtaining an effective upper bound.

[0132] Step A10, based on the effective lower bound and the effective upper bound, obtaining the charging price and the vehicle-to-grid interaction compensation when the expected charging revenue is maximum.

[0133] After obtaining the effective lower bound and the upper bound of the objective function, the algorithm enters the branch binding cycle, and based on the effective lower bound and the effective upper bound, the charging price and the vehicle-to-grid interaction compensation when the expected charging revenue is maximum are obtained. Each node is marked as a set of fixed binaries, still free continuous variables tightening intervals, and a global cut pool accumulated so far.

[0134] Step S140, based on the charging expenditure difference of the charging station selection obtained by the charging price and the vehicle-to-grid interaction compensation, dividing the preset charging user pool to obtain a conventional charging user pool and a V2G charging user pool, wherein the charging expenditure difference is the difference between the charging expenditure of the user selecting the V2G charging station for charging and the charging expenditure of the user selecting the conventional charging station for charging;

[0135] The charging station selection can be a conventional charging station or a V2G charging station. The charging expenditure of selecting the conventional charging station for charging is compared with the charging expenditure of selecting the V2G charging station for charging to obtain the charging expenditure difference of the charging station selection.

[0136] The steps of step S140 include steps S1401-S1404:

[0137] Step S1401, based on the charging price and the vehicle-to-grid interaction compensation, calculate the first charging expenditure if the user selects the V2G charging station for charging;

[0138] If the user selects the V2G charging station for charging, calculate the first charging expenditure corresponding to the charging price and the vehicle-to-grid interaction compensation.

[0139] Step S1402, based on the charging price, calculate the second charging expenditure if the user selects the conventional charging station for charging;

[0140] Step S1403, the difference between the first charging expenditure and the second charging expenditure is taken as the difference between the charging expenditures of the charging stations, wherein the user who is stimulated by the vehicle-to-grid interaction compensation gradually selects the V2G charging station for charging based on the difference between the charging expenditures, the number of users in the conventional charging user pool decreases, and the number of users in the V2G charging user pool increases;

[0141] The difference between the first charging expenditure and the second charging expenditure, that is, the difference between the charging expenditures of the charging stations: ;

[0142] where r is the average charging price, is the average travel time required to obtain the service, is the average vehicle-to-grid interaction compensation, t0 is a constant representing the additional time required for the vehicle-to-grid interaction period relative to the completion of charging, and η is a parameter representing the monetary value of time.

[0143] The number of users of different types is usually considered as an exogenous parameter. However, in reality, the proportion of users in a region who are willing to consider vehicle-to-grid interaction services is significantly affected by platform incentive measures. It is assumed that in each origin region j and time period t, there is a total potential EV charging user pool with a size of Mj total , which is an exogenous parameter.

[0144] Step S1404, based on the difference between the charging expenditures and the marginal decreasing effect of the vehicle-to-grid interaction compensation incentive, divide the preset charging user pool to obtain the conventional charging user pool and the V2G charging user pool within the preset time interval. Given Δv, the share of the V2G charging user pool is approximated by a quadratic expression: ;

[0145] where k2<0 to capture the marginal effect of the incentive decreasing, and the number of V2G charging users within time interval j is . The preset charging user pool Mj total is divided to obtain the conventional charging user pool M j1 and the V2G charging user pool M j 2 The user pool is divided into a regular charging user pool M j 1 and a V2G charging user pool M j 2 The sum of the two user pools is equal to the total number of potential users in the area. M j 1 and M j 2 is no longer a fixed parameter, but an endogenous decision variable of platform decision-making, making the entire bi-level model more dynamic and realistic. From the fundamental reaction to market dynamics, in this embodiment, the division of regular users and V2G users is not so strict, considering the impact of vehicle-to-grid interaction compensation incentives on the transition between user types, considering its marginal effect, making the updated user set more in line with the actual market situation, and improving the profit of electric vehicles.

[0146] After the step S140, steps B1-B2 are included:

[0147] Step B1, planning a second charging planning strategy for users in the regular charging user pool, wherein the second charging planning strategy is to charge the vehicle based on a fixed fourth power;

[0148] For charging stations in areas that do not choose to provide vehicle-to-grid services, only one-way charging is provided. All connected electric vehicles are charged at a fixed standard power of fourth power 40kW. The impact on the power grid is purely load aggregation.

[0149] Step B2, when a user in the regular charging user pool starts charging a vehicle at the regular charging station, controlling the power supply of the charging station to charge the vehicle at the fourth power in one direction.

[0150] When a user in the regular charging user pool starts charging a vehicle at the regular charging station, control the power supply of the charging station to charge the vehicle at 40kW in one direction.

[0151] Step S150, planning a first charging planning strategy for users in the V2G charging user pool according to the matching degree of the spatio-temporal distribution information of the charging request and the load information, and feeding back the vehicle-to-grid interaction compensation of the first charging planning strategy to the users in the V2G charging user pool for confirmation, wherein the first charging planning strategy includes charging the vehicle at different charging powers in stages or controlling the vehicle to charge the power grid in stages in reverse;

[0152] The matching degree of the space-time distribution information and the load information of the charging request is verified in time and space. The spatial positions of different V2G charging sub-areas are different, and the load peak periods of different V2G charging sub-areas in time are also different. The first charging planning strategy is to charge the vehicle at different charging powers in different stages or to control the vehicle to reversely charge the power grid in different stages. The steps of step S150 include steps S1501-S1506:

[0153] In step S1501, the space-time distribution information and the load information are compared in time and space respectively to obtain a comparison result.

[0154] In the same V2G charging sub-area, whether the charging time of the internal charging request coincides with the peak period of the internal load is compared to obtain a comparison result of coincidence or non-coincidence.

[0155] In step S1502, if it is determined according to the comparison result that the load peak period in the same V2G charging sub-area coincides with the charging time, small-power charging is determined to be performed at the first power in the charging start stage.

[0156] As an example, if the load peak period in the fifth V2G charging sub-area is from 8:00 to 18:00 in a day, and the charging request is from 8:00 to 12:00 in a day, the fifth V2G charging sub-area is determined to perform small-power charging at the first power in the charging start stage.

[0157] The load peak period coincides with the charging time in the V2G charging sub-area. It should be avoided to absorb too much power from the power grid during the load peak period. Therefore, small-power charging is performed at the first power in the charging start stage.

[0158] As an example, the user attracted by the vehicle-grid interaction compensation agrees to leave the vehicle at the charging station for three time intervals (from t to t+1 and then to t+2) in succession because of accepting the vehicle-grid interaction compensation. t is the charging start stage, t+1 is the charging intermediate stage, and t+2 is the charging last stage.

[0159] For the group of vehicles arriving at time t, if time t is a peak load period of the power grid, the platform performs a cost postponing strategy in the charging start stage. The charging power of the vehicle queue is controlled to be zero or a first power close to zero for small-power charging. In this way, the power grid load reduction service can be realized, and the platform actively avoids increasing new load at a critical moment.

[0160] In step S1503, if it is determined according to the comparison result that the load peak period in the same V2G charging sub-area does not coincide with the charging time, large-power charging is determined to be performed at a second power in the charging start stage, and the first power is less than the second power.

[0161] If the time t is a non-peak period or valley load period of the power grid, the platform executes the opportunity charging policy. If the load peak period on the V2G charging sub-area is from 8:00 to 18:00 of a day, the charging request has a to-be-charged time from 18:00 to 22:00 of a day, and the load peak period does not overlap with the to-be-charged time, it is determined to perform large-power charging at the second power in the charging start stage, where the first power is less than the second power. The vehicle is charged by using lower power cost and sufficient power grid capacity, thereby establishing energy reserves for subsequent regulatory services.

[0162] In step S1504, after the end of the charging start stage, if the charging intermediate stage overlaps with the load peak period, it is determined that the vehicle performing small-power charging at the first power in the charging start stage continues to charge at the first power in the charging intermediate stage, and the vehicle performing large-power charging at the second power in the charging start stage reversely charges the power grid in the charging intermediate stage.

[0163] The scheduling potential of the t+1 charging intermediate stage depends entirely on the decision of the first stage t charging start stage.

[0164] If charging is performed in the first stage, the vehicle now has energy reserves. If the time t+1 is a power grid peak, the platform can schedule the queue to discharge, thereby realizing true peak shaving. If it is a low valley period, charging can continue to be performed, and valley filling can be performed. After the end of the charging start stage, if the charging intermediate stage overlaps with the load peak period, small-power charging at the first power in the charging intermediate stage continues. If the vehicle performing large-power charging at the second power in the charging start stage reversely charges the power grid in the charging intermediate stage.

[0165] In step S1505, after the end of the charging start stage, if the charging intermediate stage does not overlap with the load peak period, it is determined that the vehicle performing small-power charging at the first power in the charging start stage performs large-power charging at the second power in the charging intermediate stage, and the vehicle performing large-power charging at the second power in the charging start stage continues to perform large-power charging at the second power.

[0166] If no charging is performed in the first stage, the vehicle lacks discharging capability. The platform can only decide whether to start charging according to the power grid condition of the second stage t+1 stage. If t+1 is still a peak period, the charging can be continued to be delayed. If it has transitioned to a non-peak period, charging at the second power is started. If the charging intermediate stage does not overlap with the load peak period, the vehicle performing small-power charging at the first power in the charging start stage does not have sufficient energy to perform large-power charging at the second power in the charging intermediate stage.

[0167] At step S1506, after the end of the intermediate charging stage, for the vehicle which is charged at the first power in the charging start stage and the intermediate stage, the remaining charging amount gap is calculated to obtain the remaining charging amount to be charged, and it is determined to charge at the third power matching the remaining charging amount to be charged in the final charging stage.

[0168] For the vehicle which is charged at the first power in the charging start stage and the intermediate stage, the final charging stage is the last scheduling window, and the primary goal of this stage is to ensure user satisfaction. Regardless of the scheduling actions in the previous two stages, the charging demand of the vehicle must be met before the end of this stage. The remaining charging amount gap is directly calculated to obtain the remaining charging amount to be charged, and it is determined to charge at the third power matching the remaining charging amount to be charged in the final charging stage. This stage must be in a net charging state to make up for the difference between the total energy demand of the user and the net charging energy in the previous two stages, which ensures that the vehicle reaches the expected charging state when it departs.

[0169] After the steps of step S150, steps C1-C3 are included:

[0170] At step C1, in response to the confirmation instruction of the vehicle-to-grid charging user pool user on the vehicle-to-grid interaction compensation, the vehicle-to-grid charging user queue arriving within a preset time interval is queued.

[0171] After the user arrives at the vehicle-to-grid charging station, the platform is based on the pre-announced vehicle-to-grid interaction compensation and the user confirmation compensation standard, and the user agrees to extend the stay from t to t+2. The user confirms the vehicle-to-grid interaction compensation and issues a confirmation instruction, and the electric vehicle charging planning system queues the vehicle-to-grid charging user queue arriving within a preset time interval in response to the confirmation instruction of the vehicle-to-grid charging user pool user on the vehicle-to-grid interaction compensation.

[0172] At step C2, based on the queuing order, the first charging planning strategy is used to plan a charging power curve for each vehicle corresponding to the vehicle-to-grid charging user, and the charging power curve includes the first power, the second power, and the third power.

[0173] Instead of planning for a single electric vehicle, an aggregated average power curve is planned for the entire vehicle-to-grid interaction vehicle queue arriving within a given time interval. The average power curve is drawn based on the first power, the second power, and the third power based on the first charging planning strategy described above.

[0174] At step C3, when the vehicle-to-grid charging user pool user starts charging the vehicle at the vehicle-to-grid charging station, the power supply of the charging station is controlled based on the pre-planned charging power curve to charge at the corresponding power at the corresponding time.

[0175] When the user of the V2G charging user pool arrives at the V2G charging station, the vehicle accesses the charging port to start, and the power supply of the charging station is controlled to charge the vehicle at a corresponding power (including the first power, the second power and the third power) based on the pre-planned charging power curve in three stages at corresponding times.

[0176] In step S160, in response to the confirmation instruction of the vehicle-to-grid interaction compensation of the user of the V2G charging user pool, when the user of the V2G charging user pool starts to charge the vehicle at the V2G charging station, if the space-time distribution information and the load information conflict, the power supply of the charging station is controlled to charge the vehicle at the first power or control the vehicle to reverse charge, and if the space-time distribution information and the load information do not conflict, the power supply of the charging station is controlled to charge the vehicle at the second power, wherein the first power is less than the second power.

[0177] The conflict between the space-time distribution information and the load information means that the load peak period of the same V2G sub-area conflicts with the time of the charging request or coincides with the time of the charging request.

[0178] The vehicle accesses the charging port to start. If the conflict exists, the power supply of the charging station is controlled to charge at the first power or the vehicle is controlled to reverse charge to feed back power to the power grid according to the first charging planning strategy planned in step S150. If the conflict does not exist, the vehicle is controlled to charge at the second power, wherein the first power is less than the second power.

[0179] The detailed steps of the first charging planning strategy corresponding to the conflict or non-conflict between the space-time distribution information and the load information have been introduced in step S150, and will not be introduced in detail here. In step S160, the vehicle is charged according to the first charging planning strategy corresponding to the three-stage charging planning strategy in step S150.

[0180] The application provides an electric vehicle charging planning method, device, equipment and storage medium. Compared with the prior art, in the application, if it is detected that the charging request of the vehicle comes from a V2G charging area, the load information of the load fluctuation with time in the V2G charging area is obtained, the fixed cost of the V2G charging station in the V2G charging area is set, and the spatio-temporal distribution information of the charging request is obtained. The initial charging price is initialized based on the load pressure of the load information and the user economic sensitivity, and the initial vehicle-to-grid interaction compensation is initialized. The vehicle-to-grid interaction compensation is used to encourage users to select the V2G charging station for charging. The greater the load pressure is, the higher the initial charging price is. The higher the user economic sensitivity is, the higher the initial vehicle-to-grid interaction compensation is. The initial charging price and the initial vehicle-to-grid interaction compensation are input into a preset charging income model as input information. The input information is processed based on the charging income model to obtain the charging price and the vehicle-to-grid interaction compensation when the expected charging income is maximum. The expected charging income is composed of the fixed cost, the vehicle-to-grid interaction compensation cost and the charging income. Based on the difference between the charging expenditure of the charging station selected by the charging price and the vehicle-to-grid interaction compensation, the preset charging user pool is divided to obtain a conventional charging user pool and a V2G charging user pool. The difference between the charging expenditure is the difference between the charging expenditure of the user selecting the V2G charging station for charging and the charging expenditure of the user selecting the conventional charging station for charging. According to the matching degree of the spatio-temporal distribution information of the charging request and the load information, a first charging planning strategy is planned for the users in the V2G charging user pool, and the vehicle-to-grid interaction compensation of the first charging planning strategy is fed back to the users in the V2G charging user pool for confirmation. The first charging planning strategy includes charging the vehicle at different charging powers in stages or controlling the vehicle to reversely charge the power grid in stages. In response to the confirmation instruction of the vehicle-to-grid interaction compensation of the users in the V2G charging user pool, when the users in the V2G charging user pool start to charge the vehicle at the V2G charging station, if the spatio-temporal distribution information and the load information conflict, the power supply of the charging station is controlled to charge the vehicle at a first power or control the vehicle to reversely charge, and if the spatio-temporal distribution information and the load information do not conflict, the power supply of the charging station is controlled to charge the vehicle at a second power. The first power is less than the second power. In the application, the price and compensation formulated according to the economic characteristics and load characteristics of different V2G charging areas are used to regulate the charging behavior. The pre-formulated price and compensation act on the charging station selection, and the endogenously divided user pool determines the influence. The user pool truly reflects the market charging dynamics, combines market uncertainty with regional characteristics, and strives for maximum benefit. Therefore, the application improves the charging profit of the electric vehicle.

[0181] Embodiment two

[0182] Further, based on all the above embodiments, another embodiment of the present application is provided, in which the method is applied to an electric vehicle charging planning system, the electric vehicle charging planning system comprising conventional charging stations and V2G charging stations, the V2G charging stations being divided into V2G charging sub-areas in space based on different power grid load conditions, such as Figure 4 , an electric vehicle charging planning device is provided, the device comprising:

[0183] an acquisition module, configured to acquire, if it is detected that the charging request of the vehicle comes from a V2G charging sub-area, load information of load fluctuation with time of the V2G charging sub-area, fixed cost of setting V2G charging stations in the V2G charging sub-area, and spatio-temporal distribution information of the charging request;

[0184] an initialization module, configured to initialize a charging price based on load pressure of the load information and user economic sensitivity to obtain an initial charging price, and initialize a vehicle-grid interaction compensation to obtain an initial vehicle-grid interaction compensation, wherein the vehicle-grid interaction compensation is used to encourage users to select V2G charging stations for charging, the greater the load pressure is, the higher the initial charging price is, and the higher the user economic sensitivity is, the higher the initial vehicle-grid interaction compensation is;

[0185] a processing module, configured to input the initial charging price and the initial vehicle-grid interaction compensation as input information into a preset charging revenue model, process the input information based on the charging revenue model, and obtain a charging price and a vehicle-grid interaction compensation at which expected charging revenue is maximum, wherein the expected charging revenue is composed of the fixed cost, a vehicle-grid interaction compensation cost, and charging income;

[0186] a division module, configured to divide a preset charging user pool based on a difference between charging expenditures of charging stations selected by the charging price and the vehicle-grid interaction compensation to obtain a conventional charging user pool and a V2G charging user pool, wherein the difference between the charging expenditures is a difference between a charging expenditure of the user selecting the V2G charging station for charging and a charging expenditure of the user selecting the conventional charging station for charging;

[0187] a planning module, configured to plan a first charging planning strategy for users of the V2G charging user pool according to a matching degree of the spatio-temporal distribution information of the charging request and the load information, and feed back the vehicle-grid interaction compensation of the first charging planning strategy to the users of the V2G charging user pool for confirmation by the users of the V2G charging user pool, wherein the first charging planning strategy comprises charging the vehicle at different charging powers in stages or controlling the vehicle to reversely charge the power grid in stages;

[0188] The control module is configured to, in response to a confirmation instruction of a user of a V2G charging user pool on vehicle-network interaction compensation, control a power supply of a charging station to charge a vehicle at a first power or control the vehicle to reverse charge when the user of the V2G charging user pool starts charging the vehicle at the V2G charging station, and control the power supply of the charging station to charge the vehicle at a second power when the space-time distribution information does not conflict with the load information, wherein the first power is less than the second power.

[0189] In a possible implementation of the present application, the initial charging price and the initial vehicle-network interaction compensation are input as input information into a preset charging revenue model, and the input information is processed based on the charging revenue model to obtain the charging price and the vehicle-network interaction compensation when the expected charging revenue is maximum. The device comprises:

[0190] The input module is configured to input the initial charging price and the initial vehicle-network interaction compensation as input information into a preset charging revenue model, and the charging revenue model comprises an expected charging revenue function.

[0191] The first calculation module is configured to obtain charging revenue under the charging request based on the initial charging price and the number of vehicles of the charging request.

[0192] The second calculation module is configured to calculate the product of the initial vehicle-network interaction compensation and the number of vehicles of the charging request to obtain the vehicle-network interaction compensation cost for encouraging subsidies.

[0193] The third calculation module is configured to subtract the fixed cost and the vehicle-network interaction compensation cost from the charging revenue based on the expected charging revenue function to obtain the expected charging revenue.

[0194] The first acquisition module is configured to acquire a preset capacity balance constraint condition and a preset economic constraint condition. The capacity balance constraint condition is that if there is migration of a vehicle from an original V2G charging subarea to another V2G charging subarea, the benefit obtained based on the vehicle-network interaction compensation in the other V2G charging subarea is greater than the benefit obtained in the original V2G charging subarea after the migration cost is subtracted. The economic constraint condition is that the charging price is greater than the power cost and the vehicle-network interaction compensation is less than a preset vehicle-network interaction compensation limit, and the vehicle-network interaction compensation is a non-negative number.

[0195] The optimization module is configured to optimize the expected charging revenue based on a variable charging price and a variable vehicle-network interaction compensation under the constraint of the capacity balance constraint condition and the economic constraint condition to obtain the charging price and the vehicle-network interaction compensation when the expected charging revenue is maximum.

[0196] In a possible implementation of the present application, the charging revenue model is a mixed integer nonlinear model, under the constraints of the capacity balance constraint and the economic constraint, the expected charging revenue is optimized based on the variable charging price and the variable vehicle-to-grid interaction compensation, and the charging price and the vehicle-to-grid interaction compensation at which the expected charging revenue is maximum are obtained, and the device comprises:

[0197] A construction function module is configured to take the expected charging revenue function as a target function and take the charging price and the vehicle-to-grid interaction compensation as continuous variables.

[0198] A second acquisition module is configured to acquire preset path complementary constraint conditions and capacity complementary constraint conditions, wherein the path complementary constraint conditions and the capacity complementary constraint conditions are non-convex.

[0199] A first processing module is configured to linearize the path complementary constraint conditions and the capacity complementary constraint conditions through binary auxiliary variables to obtain linearized path complementary constraint conditions and linearized capacity complementary constraint conditions.

[0200] A model establishment module is configured to establish a mixed integer nonlinear model based on the target function, the continuous variables, the capacity balance constraint, the economic constraint, the linearized path complementary constraint conditions, and the linearized capacity complementary constraint conditions.

[0201] A second processing module is configured to perform McCormick envelope convexification processing on the target function in the mixed integer nonlinear model.

[0202] A third processing module is configured to relax the remaining binary terms in the target function after the McCormick envelope convexification processing to the continuous interval [0, 1].

[0203] A fourth processing module is configured to sharpen all future convex points, enumerate the lower bounds of each descendant node in the tree, and terminate the loop when no interval is shortened by more than a threshold value.

[0204] A first solving module is configured to solve a relaxed linear programming problem combined with McCormick envelope relaxation and cutting plane construction at each node of a branch and bound tree to obtain an effective lower bound of the maximum target function value.

[0205] A second solving module is configured to reconstruct a local feasible solution based on the solution of the relaxed linear programming problem to obtain an effective upper bound.

[0206] An acquisition module is configured to obtain the charging price and the vehicle-to-grid interaction compensation at which the expected charging revenue is maximum based on the effective lower bound and the effective upper bound.

[0207] In a possible implementation of the present application, different V2G charging sub-areas have different load peaks in time, and the first charging planning strategy is to charge the vehicles at different charging powers in stages or to control the vehicles to reversely charge the power grid in stages.

[0208] The device comprises:

[0209] A comparison module is configured to compare the space-time distribution information and the load information in time and space respectively to obtain a comparison result.

[0210] A first determination module is configured to determine, if it is determined according to the comparison result that the load peak in the same V2G charging sub-area coincides with the charging time, that small-power charging is performed at a first power in the charging start stage.

[0211] A second determination module is configured to determine, if it is determined according to the comparison result that the load peak in the same V2G charging sub-area does not coincide with the charging time, that large-power charging is performed at a second power in the charging start stage, wherein the first power is less than the second power.

[0212] A third determination module is configured to, after the charging start stage ends, determine that the vehicle performing small-power charging at the first power in the charging start stage continues to charge at the first power in the charging intermediate stage if the charging intermediate stage coincides with the load peak, and determine that the vehicle performing large-power charging at the second power in the charging start stage reversely charges the power grid in the charging intermediate stage.

[0213] A fourth determination module is configured to, after the charging start stage ends, determine that the vehicle performing small-power charging at the first power in the charging start stage charges at the second power in the charging intermediate stage if the charging intermediate stage does not coincide with the load peak, and determine that the vehicle performing large-power charging at the second power in the charging start stage continues to charge at the second power.

[0214] A fourth calculation module is configured to, after the charging intermediate stage ends, calculate a remaining charging amount gap to obtain a remaining charging amount for the vehicle performing small-power charging at the first power in the charging start stage and the intermediate stage, and determine that the vehicle charges at a third power matching the remaining charging amount in the charging final stage.

[0215] In a possible implementation of the present application, after the step of planning a first charging planning strategy for the users in the V2G charging user pool according to the matching degree of the space-time distribution information of the charging request and the load information, and feeding back the vehicle-network interaction compensation of the first charging planning strategy to the users in the V2G charging user pool, the device comprises:

[0216] a queuing module configured to queue the V2G charging user queue arrived within a preset time interval in response to the confirmation instruction of the vehicle-network interaction compensation of the users in the V2G charging user pool;

[0217] a first planning module configured to plan a charging power curve for each vehicle of the V2G charging user based on the first charging planning strategy based on the queuing order in sequence;

[0218] a first control module configured to control the power supply of the charging station to charge at a corresponding power based on the pre-planned charging power curve at a corresponding time when the user in the V2G charging user pool starts to charge the vehicle at the V2G charging station.

[0219] In a possible implementation of the present application, the device comprises:

[0220] a fifth calculation module configured to calculate a first charging expenditure of the user if the user selects the V2G charging station to charge based on the charging price and the vehicle-network interaction compensation;

[0221] a sixth calculation module configured to calculate a second charging expenditure of the user if the user selects the conventional charging station to charge based on the charging price;

[0222] a seventh calculation module configured to subtract the second charging expenditure from the first charging expenditure to obtain a difference of the charging expenditure of the charging station selection, wherein the users affected by the vehicle-network interaction compensation incentive gradually select the V2G charging station to charge based on the difference of the charging expenditure, the users in the conventional charging user pool decrease, and the users in the V2G charging user pool increase.

[0223] a division module configured to divide the preset charging user pool based on the difference of the charging expenditure and the marginal diminishing effect of the vehicle-network interaction compensation incentive to obtain the conventional charging user pool and the V2G charging user pool within a preset time interval.

[0224] In a possible implementation of the present application, after the step of dividing the preset charging user pool into a regular charging user pool and a V2G charging user pool based on the difference between the charging expenditure of the charging station selected by the charging price and the vehicle-to-grid interaction compensation, the device comprises:

[0225] a second planning module, configured to plan a second charging planning strategy for the users in the regular charging user pool, wherein the second charging planning strategy is to charge the vehicles based on a fixed fourth power;

[0226] a second control module, configured to control the power supply of the charging station to unidirectionally charge the vehicles at the fourth power when the users in the regular charging user pool start charging the vehicles at the regular charging station.

[0227] The specific implementation of the electric vehicle charging planning device of the present application is basically the same as that of the above-mentioned electric vehicle charging planning method, and will not be repeated here.

[0228] Embodiment Three

[0229] Further, based on all the above-mentioned embodiments, another embodiment of the present application is provided, in which an electric vehicle charging planning device is provided, which is an entity node device, and comprises a memory, a processor and a program stored in the memory for implementing the electric vehicle charging planning method. The memory is used to store the program for implementing the electric vehicle charging planning method. The processor is used to execute the program for implementing the electric vehicle charging planning method to implement the steps of the electric vehicle charging planning method in the above-mentioned embodiments.

[0230] Reference Figure 5 , Figure 5 is a device structure diagram of a hardware running environment involved in the embodiment scheme of the present application.

[0231] As Figure 5 shown, the electric vehicle charging planning device can comprise a processor 1001 such as a CPU, a memory 1005 and a communication bus 1002. The communication bus 1002 is used to realize the connection and communication between the processor 1001 and the memory 1005. The memory 1005 can be a high-speed RAM memory or a stable memory (non-volatile memory) such as a disk memory. The memory 1005 can optionally be a storage device independent of the aforementioned processor 1001.

[0232] In a possible implementation of the present application, the electric vehicle charging planning device can further comprise a network interface, an audio circuit, a display, a connecting line, a sensor, an input module, etc. The network interface can optionally comprise a standard wired interface, a wireless interface (such as a WI-FI interface, a Bluetooth interface), and the input module can optionally comprise a keyboard, a system soft keyboard, a voice input, a wireless receiving input, etc.

[0233] Those skilled in the art can understand that the electric vehicle charging planning device structure does not constitute a limitation on the electric vehicle charging planning device, and can comprise more or fewer components than shown, or combine certain components, or different component arrangements.

[0234] The memory of the electric vehicle charging planning device can comprise an operating system, an information exchange module, and an electric vehicle charging planning program. The operating system is a program that manages and controls the hardware and software resources of the electric vehicle charging planning device, supports the running of the electric vehicle charging planning program and other software and / or programs. The information exchange module is used to realize communication between the components in the memory, and communication with other hardware and software in the management system.

[0235] In the electric vehicle charging planning device, the processor is used to execute the electric vehicle charging planning program stored in the memory, and realize the steps of the electric vehicle charging planning method described above.

[0236] The specific implementation of the electric vehicle charging planning device of the present application is basically the same as that of the above-mentioned electric vehicle charging planning method, and will not be repeated here.

[0237] Embodiment Four

[0238] The present application provides a storage medium, and the storage medium stores one or more programs, and the one or more programs can also be executed by one or more processors to realize the steps of the electric vehicle charging planning method in the above-mentioned embodiments.

[0239] The specific implementation of the storage medium of the present application is basically the same as that of the above-mentioned electric vehicle charging planning method, and will not be repeated here.

[0240] It should be noted that in this paper, the term "comprise", "include" or any other variant thereof is intended to cover non-exclusive inclusion, so that the process, method, article or system including a series of elements not only includes those elements, but also includes other elements not explicitly listed or inherent to such process, method, article or system. Without more limitations, the element defined by the statement "comprises a" does not exclude the presence of another identical element in the process, method, article or system comprising the element.

[0241] The above-mentioned sequence numbers of the embodiments of the present application are only for description, and do not represent the advantages and disadvantages of the embodiments.

[0242] Through the above description of the embodiments, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be realized by means of software and the necessary general hardware platform, of course, they can also be realized by hardware, but in many cases the former is a better embodiment. Based on such understanding, the technical solutions of the present application can be embodied in the form of a software product, which is stored in a storage medium (such as a ROM or a RAM, a magnetic disk, an optical disk) as described above, and includes a plurality of instructions for causing a terminal device (which can be a mobile phone, a server, an air conditioner, or a network device, etc.) to execute the methods described in the various embodiments of the present application.

[0243] The above is only a preferred embodiment of the present application, and does not limit the patent scope of the present application, and any equivalent structure or equivalent process transformation, or direct or indirect application in other related technical fields, which is made by using the content of the specification and drawings of the present application, is also included in the patent protection scope of the present application.

Claims

1. An electric vehicle charging planning method, characterized by, The method is applied to an electric vehicle charging planning system, the electric vehicle charging planning system comprising conventional charging stations and V2G charging stations, the V2G charging stations being divided into V2G charging sub-areas in space based on different power grid load conditions, and the method comprising: if it is detected that a charging request of a vehicle comes from the V2G charging sub-area, obtaining load information of load fluctuation with time in the V2G charging sub-area, a fixed cost of setting the V2G charging station in the V2G charging sub-area, and spatiotemporal distribution information of the charging request; initializing a charging price based on load pressure of the load information and user economic sensitivity to obtain an initial charging price, and initializing a vehicle-grid interaction compensation to obtain an initial vehicle-grid interaction compensation, wherein the vehicle-grid interaction compensation is used to encourage users to select the V2G charging station for charging, the greater the load pressure is, the higher the initial charging price is, and the higher the user economic sensitivity is, the higher the initial vehicle-grid interaction compensation is; inputting the initial charging price and the initial vehicle-grid interaction compensation as input information into a preset charging revenue model, processing the input information based on the charging revenue model to obtain a charging price and a vehicle-grid interaction compensation at which expected charging revenue is maximum, wherein the expected charging revenue is composed of the fixed cost, a vehicle-grid interaction compensation cost and charging income; dividing a preset charging user pool based on a difference between charging expenditures of charging station selection obtained by the charging price and the vehicle-grid interaction compensation to obtain a conventional charging user pool and a V2G charging user pool, wherein the difference between the charging expenditures is a difference between a charging expenditure of the user selecting the V2G charging station for charging and a charging expenditure of the user selecting the conventional charging station for charging; planning a first charging planning strategy for users of the V2G charging user pool according to a matching degree of the spatiotemporal distribution information of the charging request and the load information, and feeding back the vehicle-grid interaction compensation of the first charging planning strategy to the users of the V2G charging user pool for confirmation by the users of the V2G charging user pool, wherein the first charging planning strategy comprises charging the vehicle at different charging powers in stages or controlling the vehicle to reversely charge the power grid in stages; in response to a confirmation instruction of the vehicle-grid interaction compensation by the users of the V2G charging user pool, if the spatiotemporal distribution information and the load information conflict when the users of the V2G charging user pool start charging the vehicle at the V2G charging station, controlling a power source of the charging station to charge the vehicle at a first power or controlling the vehicle to reversely charge, and if the spatiotemporal distribution information and the load information do not conflict, controlling the power source of the charging station to charge the vehicle at a second power, wherein the first power is less than the second power.

2. The electric vehicle charging planning method of claim 1, wherein, The initial charging price and the initial vehicle-to-grid interaction compensation are input as input information into a preset charging revenue model, the input information is processed based on the charging revenue model, and a charging price and a vehicle-to-grid interaction compensation at which expected charging revenue is maximum are obtained. The initial charging price and the initial vehicle-to-grid interaction compensation are input as input information into a preset charging revenue model, the input information is processed based on the charging revenue model, and a charging price and a vehicle-to-grid interaction compensation at which expected charging revenue is maximum are obtained. The charging income under the charging request is obtained based on the initial charging price and the number of vehicles of the charging request. The product of the initial vehicle-to-grid interaction compensation and the number of vehicles of the charging request is calculated to obtain a vehicle-to-grid interaction compensation cost for encouraging subsidies for the vehicles. The expected charging revenue is obtained by subtracting the fixed cost and the vehicle-to-grid interaction compensation cost from the charging income based on the expected charging revenue function. A preset capacity balance constraint condition and a preset economic constraint condition are obtained, wherein the capacity balance constraint condition is that if there is migration of vehicles from an original V2G charging subarea to other V2G charging subareas, the benefit obtained based on the vehicle-to-grid interaction compensation of the other V2G charging subareas after the migration cost is subtracted is still greater than the benefit obtained in the original V2G charging subarea; and the economic constraint condition is that the charging price is greater than the power cost and the vehicle-to-grid interaction compensation is less than a preset vehicle-to-grid interaction compensation limit, and the vehicle-to-grid interaction compensation is a non-negative number. Under the constraint of the capacity balance constraint condition and the economic constraint condition, the expected charging revenue is optimized based on a variable charging price and a variable vehicle-to-grid interaction compensation to obtain a charging price and a vehicle-to-grid interaction compensation at which the expected charging revenue is maximum.

3. The electric vehicle charging planning method of claim 2, wherein, The charging revenue model is a mixed integer nonlinear model, and the step of optimizing the expected charging revenue based on the variable charging price and the variable vehicle-to-grid interaction compensation under the constraint of the capacity balance constraint condition and the economic constraint condition to obtain the charging price and the vehicle-to-grid interaction compensation at which the expected charging revenue is maximum includes: The expected charging revenue function is taken as an objective function, and the charging price and the vehicle-to-grid interaction compensation are taken as continuous variables. A preset path complement constraint condition and a capacity complement constraint condition are obtained, wherein the path complement constraint condition and the capacity complement constraint condition are non-convex. The path complement constraint condition and the capacity complement constraint condition are linearized by binary auxiliary variables to obtain a linearized path complement constraint condition and a linearized capacity complement constraint condition. The mixed integer nonlinear model is established based on the objective function, the continuous variables, the capacity balance constraint condition, the economic constraint condition, the linearized path complement constraint condition, and the linearized capacity complement constraint condition. The objective function in the mixed integer nonlinear model is subjected to McCormick envelope convexification processing. The remaining binary terms in the objective function after the McCormick envelope convexification processing are relaxed to the continuous interval [0, 1]. sharpening all future convex points, enumerating lower bounds for each descendant node in the branch-and-bound tree, and terminating the loop when no gap is shortened beyond a threshold value; at each node of the branch-and-bound tree, solving a relaxed linear programming problem combined with McCormick envelope relaxation and cutting plane construction to obtain an effective lower bound of the maximum objective function value; reconstructing a locally feasible solution according to the solution of the relaxed linear programming problem to obtain an effective upper bound; obtaining the charging price at which the expected charging revenue is maximum based on the effective lower bound and the effective upper bound.

4. The electric vehicle charging planning method of claim 1, wherein, different V2G charging sub-areas have different load peak periods in time, and the first charging planning strategy is to charge the vehicles at different charging powers in different stages or to control the vehicles to reversely charge the power grid in different stages; the step of planning a first charging planning strategy for the users of the V2G charging user pool according to the spatio-temporal distribution information of the charging requests and the load information, comprises: comparing the spatio-temporal distribution information and the load information in time and space respectively to obtain a comparison result; if it is determined according to the comparison result that the load peak period in the same V2G charging sub-area coincides with the charging time, it is determined to charge at a first power in a small power charging mode in the charging start stage; if it is determined according to the comparison result that the load peak period in the same V2G charging sub-area does not coincide with the charging time, it is determined to charge at a second power in a large power charging mode in the charging start stage, wherein the first power is smaller than the second power; after the end of the charging start stage, if the charging intermediate stage coincides with the load peak period, it is determined that the vehicles charged at the first power in the small power charging mode in the charging start stage continue to be charged at the first power in the charging intermediate stage, and the vehicles charged at the second power in the large power charging mode in the charging start stage reversely charge the power grid in the charging intermediate stage; after the end of the charging start stage, if the charging intermediate stage does not coincide with the load peak period, it is determined that the vehicles charged at the first power in the small power charging mode in the charging start stage are charged at the second power in the large power charging mode in the charging intermediate stage, and the vehicles charged at the second power in the large power charging mode in the charging start stage continue to be charged at the second power in the large power charging mode; after the end of the charging intermediate stage, for the vehicles charged at the first power in the small power charging mode in the charging start stage and the intermediate stage, the remaining charging amount gap is calculated to obtain the remaining charging amount, and it is determined to charge at a third power matching the remaining charging amount in the charging final stage.

5. The electric vehicle charging planning method of claim 4, wherein, after the step of planning a first charging planning strategy for the users of the V2G charging user pool according to the matching degree of the spatio-temporal distribution information of the charging requests and the load information, and feeding back the vehicle-grid interaction compensation of the first charging planning strategy to the users of the V2G charging user pool, comprises: responding to the confirmation instruction of the user of the V2G charging user pool to the vehicle-network interaction compensation, queue the V2G charging user queue arriving within a preset time interval; based on the queuing order, plan a charging power curve for each V2G charging user based on the first charging planning strategy, wherein the charging power curve comprises the first power, the second power and the third power; when the user of the V2G charging user pool starts charging the vehicle at the V2G charging station, control the power supply of the charging station to charge at the corresponding power based on the pre-planned charging power curve.

6. The electric vehicle charging planning method of claim 1, wherein, the step of dividing the preset charging user pool based on the difference between the charging station selection charging expenditure obtained from the charging price and the vehicle-network interaction compensation to obtain the regular charging user pool and the V2G charging user pool, comprises: based on the charging price and the vehicle-network interaction compensation, calculate the first charging expenditure if the user selects the V2G charging station for charging; based on the charging price, calculate the second charging expenditure if the user selects the regular charging station for charging; the difference between the first charging expenditure and the second charging expenditure is taken as the difference between the charging station selection charging expenditure, wherein the user affected by the vehicle-network interaction compensation incentive gradually selects the V2G charging station for charging based on the difference between the charging expenditure, the number of users in the regular charging user pool decreases, and the number of users in the V2G charging user pool increases; based on the difference between the charging expenditure and the marginal diminishing effect of the vehicle-network interaction compensation incentive, divide the preset charging user pool to obtain the regular charging user pool and the V2G charging user pool within a preset time interval.

7. The electric vehicle charging planning method of claim 1, wherein, after the step of dividing the preset charging user pool based on the difference between the charging station selection charging expenditure obtained from the charging price and the vehicle-network interaction compensation to obtain the regular charging user pool and the V2G charging user pool, comprises: plan a second charging planning strategy for the user of the regular charging user pool, wherein the second charging planning strategy is to charge the vehicle based on a fixed fourth power; when the user of the regular charging user pool starts charging the vehicle at the regular charging station, control the power supply of the charging station to unidirectionally charge the vehicle at the fourth power.

8. An electric vehicle charging planning apparatus characterized by comprising: The electric vehicle charging planning device is deployed in an electric vehicle charging planning system, the electric vehicle charging planning system comprises a regular charging station and a V2G charging station, the V2G charging station is divided into V2G charging sub-areas based on different power grid load conditions in space, and the electric vehicle charging planning device comprises: an acquisition module, configured to, if it is detected that the charging request of the vehicle comes from a V2G charging sub-area, acquire load information of load fluctuation with time in the V2G charging sub-area, a fixed cost of setting the V2G charging station in the V2G charging sub-area, and spatiotemporal distribution information of the charging request; An initialization module is configured to initialize a charging price based on a load pressure of the load information and a user economic sensitivity to obtain an initial charging price, and to initialize a vehicle-to-grid (V2G) compensation to obtain an initial V2G compensation, wherein the V2G compensation is used to encourage users to select the V2G charging station for charging, the greater the load pressure is, the higher the initial charging price is, and the higher the user economic sensitivity is, the higher the initial V2G compensation is; A processing module is configured to input the initial charging price and the initial V2G compensation as input information into a preset charging revenue model, and to process the input information based on the charging revenue model to obtain a charging price and a V2G compensation when an expected charging revenue is maximum, wherein the expected charging revenue is composed of the fixed cost, a V2G compensation cost, and a charging income; A division module is configured to divide a preset charging user pool based on a difference between charging expenditures of the charging price and the V2G compensation to obtain a regular charging user pool and a V2G charging user pool, wherein the difference between the charging expenditures is a difference between a charging expenditure of the user selecting the V2G charging station for charging and a charging expenditure of the user selecting a regular charging station for charging; A planning module is configured to plan a first charging planning strategy for users of the V2G charging user pool according to a matching degree of spatiotemporal distribution information of the charging request and the load information, and to feed back the V2G compensation of the first charging planning strategy to the users of the V2G charging user pool for confirmation, wherein the first charging planning strategy includes charging the vehicle at different charging powers in stages or controlling the vehicle to reversely charge the power grid in stages; A control module is configured to, in response to a confirmation instruction of the V2G compensation of the users of the V2G charging user pool, control a power supply of a charging station to charge the vehicle at a first power or control the vehicle to reversely charge when the users of the V2G charging user pool start charging the vehicle at the V2G charging station, or control the power supply of the charging station to charge the vehicle at a second power if the spatiotemporal distribution information and the load information are not in conflict, wherein the first power is less than the second power.

9. An electric vehicle charging planning apparatus characterized by comprising: An electric vehicle charging planning program is stored in a memory and executable on a processor, and the processor executes the electric vehicle charging planning program to implement the steps of the electric vehicle charging planning method in any one of claims 1 to 7.

10. A storage medium, characterized by A storage medium stores a program for implementing an electric vehicle charging planning method, and the program for implementing the electric vehicle charging planning method is executed by a processor to implement the steps of the electric vehicle charging planning method in any one of claims 1 to 7.

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