Electric vehicle charging work scheduling method and device

Abstract

The invention discloses an electric vehicle charging work scheduling method and device, and relates to the technical field of electric power scheduling, and the method comprises the steps: calculating and obtaining a battery charge state at a trip end moment based on a probability distribution condition of daily mileage; according to the probability distribution condition of the initial charging moment, the initial charging moment is obtained through random extraction; calculating to obtain a charging load corresponding to each electric vehicle, and accumulating to obtain a charging load curve; based on the peak value and the valley value of the charging load curve, setting an electric vehicle charging load; and setting a wind-light power generation output value of the corresponding wind-light power generation equipment based on the electric vehicle charging load. Based on the charging time and the charging parameters of the electric vehicle, statistical analysis is performed to obtain the charging load curve, so that the charging load is predicted and set, the power generation condition is adjusted in advance, and the actual charging requirement is met.

Description

Technical Field

[0001] The present application relates to the technical field of power dispatching, and specifically relates to a method and device for scheduling the charging operation of electric vehicles. Background Art

[0002] Currently, with the development of electric vehicles, the charging demand for electric vehicles is increasing day by day. In order to meet the charging demand of electric vehicles, services such as charging stations are gradually put into use in urban construction, which also brings new challenges to the power load and power dispatching of urban power grids. There is a lack of effective means at present for predicting and setting the charging load and pre-adjusting the power generation situation.

[0003] Therefore, to meet the actual needs, a technology for scheduling the charging operation of electric vehicles is provided. Summary of the Invention

[0004] Aiming at the defects existing in the prior art, the purpose of the present application is to provide a method and device for scheduling the charging operation of electric vehicles. Based on the charging time and charging parameters of electric vehicles, statistical analysis is carried out to obtain a charging load curve, so as to predict and set the charging load and pre-adjust the power generation situation to meet the actual charging needs.

[0005] To achieve the above purpose, the technical solution adopted by the present application is as follows:

[0006] In the first aspect, the present application provides a method for scheduling the charging operation of electric vehicles, and the method includes the following steps:

[0007] Based on the probability distribution of the daily driving mileage, calculate the state of charge of the battery at the end of the trip.

[0008] According to the probability distribution of the starting charging time, randomly select the starting charging time.

[0009] Based on the charging power, starting charging time, and state of charge of the battery at the end of the trip corresponding to each electric vehicle, calculate the charging load corresponding to each electric vehicle.

[0010] Based on the charging loads corresponding to each of the electric vehicles, accumulate to obtain a charging load curve, where the abscissa of the charging load curve is time and the ordinate is the charging load.

[0011] Based on the peak and valley values of the charging load curve, set the charging load of the electric vehicle.

[0012] Based on the charging load of the electric vehicle, set the wind-solar power generation output value of the corresponding wind-solar power generation equipment.

[0013] Based on the above technical solution, the method is configured with an electric vehicle charging load setting formula, and the electric vehicle charging load setting formula is:

[0014] SOC min ≤SOC≤SOC max ; where

[0015] SOC is the electric vehicle charging load, SOC min is the valley value of the electric vehicle charging load, and SOC max is the peak value of the electric vehicle charging load.

[0016] Based on the above technical solution, the method is configured with a wind-solar power generation output setting formula, and the wind-solar power generation output setting formula is:

[0017] P n min ≤P n ≤P n max ; where

[0018] P n is the wind-solar power generation output value, which is the same as the value of the electric vehicle charging load, is the lower limit of the wind-solar power generation output value, is the upper limit of the wind-solar power generation output value.

[0019] Second, the present application provides an electric vehicle charging work scheduling device, and the device includes:

[0020] A state of charge acquisition module, which is used to calculate and obtain the state of charge of the battery at the end of the trip based on the probability distribution of the daily driving mileage;

[0021] A starting charging time acquisition module, which is used to randomly extract the starting charging time according to the probability distribution of the starting charging time;

[0022] A charging load calculation module, which is used to calculate and obtain the charging load corresponding to each electric vehicle based on the charging power, the starting charging time, and the state of charge of the battery at the end of the trip corresponding to each electric vehicle;

[0023] A charging load curve acquisition module, which is used to accumulate and obtain a charging load curve based on the charging load corresponding to each electric vehicle, where the abscissa of the charging load curve is time and the ordinate is the charging load;

[0024] A charging load setting module, which is used to set the electric vehicle charging load based on the peak and valley values of the charging load curve;

[0025] A wind-solar power generation output setting module, which is used to set the wind-solar power generation output value of the corresponding wind-solar power generation equipment based on the electric vehicle charging load.

[0026] On the basis of the above technical solution, the method is configured with an electric vehicle charging load setting formula, and the electric vehicle charging load setting formula is:

[0027] SOC min ≤SOC≤SOC max ; where

[0028] SOC is the electric vehicle charging load, SOC min is the valley value of the electric vehicle charging load, and SOC max is the peak value of the electric vehicle charging load.

[0029] On the basis of the above technical solution, the method is configured with a wind-solar power generation output setting formula, and the wind-solar power generation output setting formula is:

[0030] P n min ≤Pn≤P n max ; where

[0031] P n is the wind-solar power generation output value, which is the same as the value of the electric vehicle charging load, is the lower limit of the wind-solar power generation output value, is the upper limit of the wind-solar power generation output value.

[0032] Compared with the prior art, the advantages of the present application are:

[0033] Based on the charging time and charging parameters of the electric vehicle, the present application conducts statistical analysis to obtain the charging load curve, thereby predicting and setting the charging load and pre-adjusting the power generation situation to meet the actual charging demand. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0035] Figure 1 is the step flow chart of the electric vehicle charging work scheduling method in the embodiment of the present application;

[0036] Figure 2 is the technical principle flow chart of the electric vehicle charging work scheduling method in the embodiment of the present application;

[0037] Figure 3 Schematic diagram of the charging load prediction curve of 2000 electric vehicles in the electric vehicle charging work scheduling method according to the embodiment of the present application;

[0038] Figure 4 Flow chart of the parallel selection genetic algorithm in the electric vehicle charging work scheduling method according to the embodiment of the present application;

[0039] Figure 5 Ordered charging load curve in the electric vehicle charging work scheduling method according to the embodiment of the present application;

[0040] Figure 6 Structural block diagram of the electric vehicle charging work scheduling device according to the embodiment of the present application. Detailed implementation manners

[0041] To make the objectives, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application. Apparently, the described embodiments are some, but not all, of the embodiments of the present application. All other embodiments obtained by those of ordinary skill in the art based on the embodiments in the present application without creative efforts shall fall within the protection scope of the present application.

[0042] The embodiments of the present application will be further described in detail below with reference to the accompanying drawings.

[0043] The embodiment of the present application provides an electric vehicle charging work scheduling method and device. Based on the charging time and charging parameters of the electric vehicle, statistical analysis is performed to obtain a charging load curve, so as to predict and set the charging load and pre-adjust the power generation situation to meet the actual charging demand.

[0044] To achieve the above technical effects, the general idea of the present application is as follows:

[0045] An electric vehicle charging work scheduling method, the method includes the following steps:

[0046] S1. Calculate and obtain the state of charge of the battery at the end of the trip based on the probability distribution of the daily driving mileage;

[0047] S2. Randomly extract the starting charging time according to the probability distribution of the starting charging time;

[0048] S3. Calculate and obtain the charging load corresponding to each electric vehicle based on the charging power, starting charging time and state of charge of the battery at the end of the trip corresponding to each electric vehicle;

[0049] S4. Based on the charging loads corresponding to each electric vehicle, accumulate to obtain a charging load curve, where the abscissa of the charging load curve is time and the ordinate is the charging load;

[0050] S5. Based on the peak and valley values of the charging load curve, set the charging load of the electric vehicle;

[0051] S6. Based on the charging load of the electric vehicle, set the corresponding wind and solar power generation output values of the wind and solar power generation equipment.

[0052] The following further elaborates on the embodiments of this application in conjunction with the accompanying drawings.

[0053] In the first aspect, as shown in Figures 1 to 5 the embodiments of this application provide an electric vehicle charging work scheduling method, and the method includes the following steps:

[0054] S1. Based on the probability distribution of the daily driving mileage, calculate to obtain the state of charge of the battery at the end of the trip;

[0055] S2. According to the probability distribution of the starting charging time, randomly extract to obtain the starting charging time;

[0056] S3. Based on the charging power, starting charging time, and the state of charge of the battery at the end of the trip corresponding to each electric vehicle, calculate to obtain the charging load corresponding to each electric vehicle;

[0057] S4. Based on the charging loads corresponding to each electric vehicle, accumulate to obtain a charging load curve, where the abscissa of the charging load curve is time and the ordinate is the charging load;

[0058] S5. Based on the peak and valley values of the charging load curve, set the charging load of the electric vehicle;

[0059] S6. Based on the charging load of the electric vehicle, set the corresponding wind and solar power generation output values of the wind and solar power generation equipment.

[0060] It should be noted that the technical solutions of the embodiments of this application can be applied to a charging station equipped with multiple charging piles, a charging station management platform, a charging parking lot of a building, or a power grid power supply management system of a certain area, for power dispatching according to the situation of the electric vehicles under its charge responsibility, so as to ensure that the stability of the power grid operation in the affiliated area is not affected.

[0061] In the embodiments of this application, based on the charging time and charging parameters of the electric vehicle, statistical analysis is carried out to obtain a charging load curve, so as to predict and set the charging load and pre-adjust the power generation situation to meet the actual charging demand.

[0062] Further, the method is configured with an electric vehicle charging load setting formula, and the electric vehicle charging load setting formula is:

[0063] SOC min ≤SOC≤SOC max ; where

[0064] SOC is the electric vehicle charging load, SOC min is the valley value of the electric vehicle charging load, and SOC max is the peak value of the electric vehicle charging load.

[0065] Further, the method is configured with a wind-solar power generation output setting formula, and the wind-solar power generation output setting formula is:

[0066] P n min ≤Pn≤P n max ; where

[0067] P n is the wind-solar power generation output value, which is the same as the value of the electric vehicle charging load, is the lower limit of the wind-solar power generation output value, is the upper limit of the wind-solar power generation output value.

[0068] As shown in the accompanying drawings of the specification, Figure 2 it is the technical principle flow chart of the embodiment of the present application. The technical solution of the embodiment of the present application is mainly based on the Monte Carlo method for charging load prediction. The Monte Carlo method is also called the statistical simulation method, which obtains an approximate answer to the problem by random sampling, associates the problem to be solved with a probability model, and uses the computer as a tool for realizing random sampling or statistical simulation. The electric vehicle charging load prediction based on Monte Carlo sampling is based on Monte Carlo random sampling. According to the real traffic travel behavior data, by imitating the driving habits of electric vehicle owners, a model with random characteristics is established to predict the charging load demand of electric vehicles.

[0069] Specifically, the embodiment of the present application is also configured with an electric vehicle charging load prediction simulation process, which is as follows:

[0070] Select 2000 household pure electric vehicles as samples for charging power prediction. In the embodiment of the present application, the slow charging power of the electric vehicle is selected as 3kW, the charging efficiency is 0.9, the power consumption per kilometer of the electric vehicle is 0.5kWh, and the electric vehicle starts charging immediately after completing the last return trip and charges at a constant power until it reaches the full charge state. The disordered charging load prediction results of 2000 electric vehicles are obtained through simulation by MATLAB software. As shown in the accompanying drawings of the specification Figure 3As shown, it is the charging load prediction curve of 2,000 electric vehicles.

[0071] From the charging load prediction curve of electric vehicles, it can be seen that the daily charging load of electric vehicles shows obvious peak-valley characteristics. During the period from 16:00 to 24:00, a large number of electric vehicles are charging, but during the period from 6:00 to 10:00, the charging load demand of electric vehicles is relatively low, which is extremely closely related to the living and driving habits of vehicle owners. The peak-valley difference of the disordered charging load curve of electric vehicles is very large, which poses a hazard to the stable operation of the power system. If the charging of electric vehicles is not optimized and scheduled, it will cause large power fluctuations in the power grid, affecting the normal operation of other electrical equipment in the power system. Therefore, it is necessary to optimize and schedule the charging of electric vehicles to reduce the impact on the power grid caused by their disordered charging.

[0072] Specifically, the embodiment of the present application is also configured with an analysis process for the charging method guided by the electricity price of electric vehicles, which is as follows:

[0073] There are many factors that can affect the charging load demand of electric vehicle owners, such as weather, production mode, electricity price, etc. To optimize the charging load of electric vehicles, it is necessary to start from these factors that can affect the charging load demand of vehicle owners. Among them, the most operable factor is the electricity price.

[0074] The peak-valley time-of-use electricity price strategy is an effective means to affect user demand response. Different electricity prices are divided by analyzing the pressure of the stable operation of the power system. A day is divided into three periods: low valley, normal, and peak. The change of electricity price can directly affect the change of the charging load of electric vehicle owners. Different electricity prices are set for different periods, giving full play to the role of the electricity price factor in the user load demand, enabling electric vehicle users to participate in peak shaving and valley filling, so that the power grid can maintain stable operation, and at the same time, the charging cost of electric vehicles can be reduced.

[0075] To simplify the calculation, the embodiment of the present application only considers the influence of the electricity price factor on the charging load demand of vehicle owners for electric vehicles, and does not consider the influence of other factors. To represent the influence of the change of electricity price on the change of user charging load demand, the concept of price elasticity is introduced in the embodiment of the present application. Price elasticity refers to the phenomenon that under the condition that other influencing factors remain unchanged, only the price factor changes, which causes the user demand to change. Price elasticity is a sensitivity index to measure the change of demand caused by the change of price. Under the action of price elasticity, users change their previous electric vehicle charging habits, so that more electric vehicles are connected to the power grid during the price valley period for charging, and fewer electric vehicles are connected during the price peak period for charging, achieving the effect of peak shaving and valley filling, and ensuring the stability of the power grid operation.

[0076] Taking into account the output curve of new energy power generation and the charging load requirements of electric vehicle users, the 24-hour period is divided into three periods: valley, flat, and peak. The division of peak and valley periods is shown in Table 1 below:

[0077]

[0078] Table 1

[0079] Specifically, the embodiment of the present application is also configured with a configuration process for optimizing the scheduling constraints of electric vehicle electricity prices, which is as follows:

[0080] First, the state of charge level constraint of electric vehicles:

[0081] The state of charge level of electric vehicles at any moment has a certain limit and cannot exceed its upper and lower limits to ensure the normal driving of electric vehicles. The specific formula is as follows:

[0082] SOC min ≤SOC≤SOC max ; where

[0083] SOC is the state of charge level of electric vehicles, SOC min is the lower limit of the state of charge level of electric vehicles, SOC max is the upper limit of the state of charge level of electric vehicles.

[0084] Second, the charging electricity price constraint of electric vehicles:

[0085] Taking into account both the power grid and the charging costs of electric vehicle users, the time-of-use electricity price established cannot be lower than its comprehensive cost electricity price, and at the same time, a ceiling value for the electricity price should also be set. The specific formula is as follows:

[0086] π min ≤π t ≤π max ; where

[0087] π t is the charging electricity price of electric vehicles at time t, π min is the cost electricity price at time t, π max is the ceiling value of the charging electricity price of electric vehicles at time t.

[0088] Third, the output constraint of new energy power generation:

[0089] The charging load of a large number of electric vehicles must be within the range of new energy output and cannot exceed the upper and lower limits of wind and solar power generation output. The specific formula is as follows:

[0090] P n min ≤Pn≤P n max ; where

[0091] P n is the charging load demand of electric vehicles, is the lower limit of wind and solar power generation output, is the upper limit of wind and solar power generation output.

[0092] Specifically, the embodiment of the present application is also configured with an optimization scheduling process for electric vehicles based on the genetic algorithm, which is specifically as follows:

[0093] In the 1970s, the genetic algorithm was first proposed by John Holland in the United States. Influenced by Darwin's theory of biological evolution, the genetic algorithm realizes random search and optimization by imitating the evolution of biological populations. The genetic algorithm adopts the principle of survival of the fittest, converts the solution of the problem into phenomena such as gene crossover and mutation in the process of biological evolution through coding, and makes the evolution of individuals appear in the population through gene mutation, so that the population can better adapt to the environment. The genetic algorithm finds the optimal solution according to the principle of survival of the fittest, and only retains the relatively optimal solution as the basis for genetic operations to find a better solution, and so on in a cycle until the optimal solution is found.

[0094] The embodiment of the present application solves the optimization problem with multiple objectives and multiple constraints, and there may be a phenomenon of conflicting objectives during the operation. In order to achieve the purpose of simultaneously achieving the optimal solution for multiple objectives, the embodiment of the present application adopts the parallel selection method as the method for solving multiple objectives.

[0095] The basic idea of the parallel selection genetic algorithm is to divide all individuals in the population into different groups equally according to the number of objective functions. Different groups correspond to different objective functions respectively. Select the individuals with high fitness for the objective function from the groups, merge these selected individuals into a new group, and perform operations such as crossover and mutation in the new group to make it more adaptable to the objective function, and so on in a cycle until the optimal solution that satisfies all objective functions is generated. As shown in the Figure 4 accompanying drawings of the specification, which is the flowchart of the parallel selection genetic algorithm.

[0096] Specifically, the embodiment of the present application is also configured with an optimization scheduling modeling process for electric vehicle charging, which is specifically as follows:

[0097] First, price analysis of time-of-use electricity price strategy:

[0098] The embodiment of the present application takes the actual daily load data of a certain regional power grid as the condition, assuming that a total of 2,000 electric vehicles participate in the optimized charging. Before optimization, the unordered charging electricity price of electric vehicles is 1 yuan / kWh, and the lowest charging electricity price cannot be lower than the comprehensive power generation cost electricity price of 0.25 yuan / kWh, and at the same time cannot be higher than 2 yuan / kWh. By referring to the reference literature, the price elasticity matrix of the peak-valley flat periods of electric vehicles can be obtained, which is specifically as follows:

[0099]

[0100] Second, the modeling of the orderly charging of electric vehicles:

[0101] Using the parallel selection method to solve, an optimized scheduling model of the charging load of electric vehicles with multiple objectives and multiple constraints is obtained. The result of the orderly charging load curve guided by the electricity price of electric vehicles is as shown in the Figure 5 drawing of the specification;

[0102] At the same time, the electricity prices for each period of valley, flat, and peak after iterative optimization are obtained, as shown in Table 2:

[0103]

[0104] Table 2

[0105] Comparing the results before and after optimization, as shown in Table 3 below:

[0106]

[0107]

[0108] Table 3

[0109] Analyzing the optimized results of the load obtained from the simulation, it can be known that due to the implementation of the time-of-use electricity price strategy, more electric vehicles are connected to the grid for charging during the valley period and the flat period, resulting in an increase in the load of the grid during the valley and flat periods; fewer electric vehicles are connected during the peak period, and the grid load decreases. This optimized scheduling strategy for electric vehicles, on the one hand, guides electric vehicle owners to change their original charging habits and choose the orderly charging method, which plays a great role in peak shaving and valley filling. Moreover, the optimized grid load curve fits better with the wind and light output curves, reducing the occurrence of curtailment of electricity. At the same time, the grid maintains its stable operation; on the other hand, users spend less on charging when they choose to charge their electric vehicles during the valley period and the flat period. Using the time-of-use electricity price strategy to guide electric vehicle owners to participate in the charging optimization scheduling safeguards the interests of multiple parties.

[0110] On the second aspect, as shown in Figure 6 the embodiments of the present application provide an electric vehicle charging work scheduling device, which includes:

[0111] A state of charge acquisition module for calculating and obtaining the state of charge of the battery at the end of the trip based on the probability distribution of the daily driving mileage;

[0112] A starting charging time acquisition module for randomly extracting the starting charging time according to the probability distribution of the starting charging time;

[0113] A charging load calculation module, which is used to calculate the charging load corresponding to each electric vehicle based on the charging power, the starting charging time, and the state of charge of the battery at the end of the trip of each electric vehicle;

[0114] A charging load curve acquisition module, which is used to accumulate and obtain a charging load curve based on the charging loads corresponding to each of the electric vehicles, where the abscissa of the charging load curve is time and the ordinate is the charging load;

[0115] A charging load setting module, which is used to set the charging load of the electric vehicle based on the peak and valley values of the charging load curve;

[0116] A wind-solar power generation output setting module, which is used to set the wind-solar power generation output value of the corresponding wind-solar power generation equipment based on the charging load of the electric vehicle.

[0117] It should be noted that the technical solution of the embodiment of the present application can be applied to a charging station equipped with multiple charging piles, a charging station management platform, a charging parking lot of a building, or a power grid power supply management system of a certain area, and is used to perform power dispatching according to the situation of the electric vehicles it is responsible for charging, so as to ensure the stability of the power grid operation in the area where it belongs is not affected.

[0118] In the embodiment of the present application, based on the charging time and charging parameters of the electric vehicle, statistical analysis is performed to obtain a charging load curve, so as to predict and set the charging load and pre-adjust the power generation situation to meet the actual charging demand.

[0119] Further, the method is configured with an electric vehicle charging load setting formula, and the electric vehicle charging load setting formula is:

[0120] SOC min ≤SOC≤SOC max ; where

[0121] SOC is the charging load of the electric vehicle, SOC min is the valley value of the charging load of the electric vehicle, and SOC max is the peak value of the charging load of the electric vehicle.

[0122] Further, the method is configured with a wind-solar power generation output setting formula, and the wind-solar power generation output setting formula is:

[0123] P n min ≤P n ≤P n max ; where

[0124] P n is the wind-solar power generation output value, which is the same as the value of the charging load of the electric vehicle, is the lower limit of the output value of wind and solar power generation, is the upper limit of the output value of wind and solar power generation.

[0125] It should be noted that the electric vehicle charging work scheduling device mentioned in the embodiments of the present application is similar to the technical principle of the electric vehicle charging work scheduling method mentioned in the first aspect in terms of technical problems, technical solutions, and technical effects, and will not be elaborated here.

[0126] In the description of the present application, it should be noted that the orientation or positional relationship indicated by terms such as "upper" and "lower" is based on the orientation or positional relationship shown in the drawings. It is only for the convenience of describing the present application and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, so it cannot be understood as a limitation to the present application. Unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be directly connected or indirectly connected through an intermediate medium, and it can be the communication inside two elements. For those of ordinary skill in the art, the specific meanings of the above terms in the present application can be understood according to specific situations.

[0127] It should be noted that in the present application, relational terms such as "first" and "second" are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variant thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements not only includes those elements, but also includes other elements not expressly listed, or also includes elements inherent to such process, method, article or device. Without further limitation, the element defined by the statement "including one..." does not exclude the existence of additional identical elements in the process, method, article or device including the said element.

[0128] The above are only specific embodiments of the present application, enabling those skilled in the art to understand or implement the present application. Various modifications to these embodiments will be obvious to those skilled in the art. The general principles defined in the embodiments of the present application can be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to these embodiments shown in the embodiments of the present application, but will conform to the widest scope consistent with the principles and novel features claimed in the embodiments of the present application.

Claims

1. A method for scheduling the charging operation of an electric vehicle, characterized in that, The method includes the following steps: Based on the probability distribution of the daily driving mileage, calculate and obtain the state of charge of the battery at the end of the trip; According to the probability distribution of the starting charging time, randomly extract and obtain the starting charging time; Based on the charging power corresponding to each electric vehicle, the starting charging time, and the state of charge of the battery at the end of the trip, calculate and obtain the charging load corresponding to each electric vehicle; Based on the charging loads corresponding to each of the electric vehicles, accumulate to obtain a charging load curve, where the abscissa of the charging load curve is time and the ordinate is the charging load; Based on the peak and valley values of the charging load curve, set the charging load of the electric vehicle; Based on the charging load of the electric vehicle, set the wind-solar power generation output value of the corresponding wind-solar power generation equipment.

2. The electric vehicle charging work scheduling method according to claim 1, wherein The method is configured with an electric vehicle charging load setting formula, and the electric vehicle charging load setting formula is: SOC min ≤SOC≤SOC max ; wherein, The SOC is the charging load of the electric vehicle, and the SOC min is the valley value of the charging load of the electric vehicle, and the SOC max is the peak value of the charging load of the electric vehicle.

3. The electric vehicle charging work scheduling method according to claim 1, wherein The method is configured with a wind-solar power generation output setting formula, and the wind-solar power generation output setting formula is: Among them, P n is the output value of wind and solar power generation, which is the same as the value of the electric vehicle charging load. is the lower limit of the output value of wind and solar power generation. is the upper limit of the output value of wind and solar power generation.

4. An electric vehicle charging work scheduling device, characterized in that The device includes: A state of charge acquisition module, which is used to calculate and obtain the state of charge of the battery at the end of the trip based on the probability distribution of the daily driving mileage; A starting charging time acquisition module, which is used to randomly extract and obtain the starting charging time according to the probability distribution of the starting charging time; A charging load calculation module, which is used to calculate and obtain the charging load corresponding to each electric vehicle based on the charging power corresponding to each electric vehicle, the starting charging time, and the state of charge of the battery at the end of the trip; A charging load curve acquisition module, which is used to accumulate to obtain a charging load curve based on the charging loads corresponding to each of the electric vehicles, where the abscissa of the charging load curve is time and the ordinate is the charging load; A charging load setting module, which is used to set the charging load of the electric vehicle based on the peak and valley values of the charging load curve; A wind-solar power generation output setting module, which is used to set the wind-solar power generation output value of the corresponding wind-solar power generation equipment based on the charging load of the electric vehicle.

5. The electric vehicle charging work scheduling method according to claim 4, wherein, The method is configured with an electric vehicle charging load setting formula, and the electric vehicle charging load setting formula is: SOC min ≤SOC≤SOC max ; where The SOC is the charging load of the electric vehicle, and the SOC min is the valley value of the charging load of the electric vehicle, and the SOC max is the peak value of the charging load of the electric vehicle.

6. The electric vehicle charging work scheduling method according to claim 4, characterized in that The method is configured with a wind-solar power generation output setting formula, and the wind-solar power generation output setting formula is: Among them, P n is the output value of wind and solar power generation, which is the same as the value of the electric vehicle charging load. is the lower limit of the output value of wind and solar power generation. is the upper limit of the output value of wind and solar power generation.

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