Electric vehicle virtual power plant response capability assessment method, medium and electronic equipment

Abstract

The invention relates to the technical field of virtual power plants, in particular to an electric vehicle virtual power plant response capability evaluation method, a medium and electronic equipment, and the method comprises the steps: determining the travel time of an electric vehicle; in combination with the travel time of the electric vehicle, analyzing parameters of the electric vehicle and calculating an initial state of charge (SOC) of the electric vehicle accessed to a power grid; the method comprises the following steps: determining the requirement of an electric vehicle for the SOC during travel, determining a controllable operation area of the electric vehicle based on the initial state of charge SOC and the requirement of the electric vehicle for the SOC during travel, and establishing a calculation formula of the real-time SOC when the electric vehicle is charged at the t moment under the constraint of the controllable operation area of the electric vehicle, based on the calculation formula, calculating the exchange power of the networked electric vehicle and the power grid at the t moment; and determining the virtual power plant response capability of the electric vehicles based on the number of the electric vehicles accessed to the network in real time and the power exchanged between the electric vehicles and the power grid. According to the method, the response capability of the virtual power plant of the electric vehicle can be well determined, so that flexible and efficient adjustment resources are provided for a power grid.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of virtual power plants, in particular to a method for evaluating the response capability of an electric vehicle virtual power plant, a medium and an electronic device. BACKGROUND

[0002] The statements in this section merely provide background information related to the present application and do not necessarily constitute prior art.

[0003] The continuous development of energy utilization technology has promoted the emergence of a large number of load resources with adjustment potential on the distribution network side, such as electric vehicles, distributed energy storage, air conditioners, and data centers and 5G base stations under the background of new infrastructure, etc. This type of load is characterized by a large number of points, a large amount, a small capacity, a low voltage level, and a variety of subjects, and traditional grid dispatching is difficult to achieve direct control over a single load.

[0004] Virtual power plant technology is based on the widespread application of advanced information, network, and control technologies such as "big cloud, mobile intelligence, and chain", and relies on the interconnected sensing of load terminal devices to achieve measurable and controllable end loads. Distributed resources such as distributed power sources, energy storage systems, controllable loads, and electric vehicles are aggregated and optimized, and participate in the power market and grid operation as a special power plant.

[0005] Electric vehicles have developed rapidly in recent years due to their energy-saving and zero-emission advantages. Electric vehicle charging piles have high peak load charging power, and the number of electric vehicle clusters is large. Electric vehicle batteries have flexible power bidirectional regulation characteristics and can accept direct control or trade-off incentive prices to change the charging and discharging state. Electric vehicle clusters participating in virtual power plants can effectively improve the response capability of virtual power plants, but there is little research on the response capability evaluation of electric vehicle virtual power plants in existing technologies, and it is often difficult to effectively evaluate it. Therefore, it is necessary to propose a method for evaluating the response capability of an electric vehicle virtual power plant. SUMMARY

[0006] In order to overcome the shortcomings of the background art, the present application provides a method for evaluating the response capability of an electric vehicle virtual power plant, a medium and an electronic device, which determines the response capability of an electric vehicle virtual power plant based on the number of electric vehicles connected to the grid in real time and the power exchanged between the electric vehicles and the grid. It can be convenient for subsequent estimation of electric vehicle charging load, thereby providing flexible and efficient adjustment resources for the grid.

[0007] The technical solution adopted by the present application is: a method for evaluating the response capability of an electric vehicle virtual power plant, comprising: determining the travel time of the electric vehicle; combining the travel time of the electric vehicle, analyzing the electric vehicle parameters and calculating the initial state of charge (SOC) of the electric vehicle connected to the grid; determining the demand of the SOC of the electric vehicle trip based on the initial state of charge SOC and the demand of the SOC of the electric vehicle trip, determining the controllable operation region of the electric vehicle based on the initial state of charge SOC and the demand of the SOC of the electric vehicle trip, establishing a calculation formula of the real-time SOC of the electric vehicle at the time t when charging under the constraint of the controllable operation region of the electric vehicle, and calculating the power exchanged between the electric vehicle and the power grid at the time t based on the calculation formula; determining the response capability of the electric vehicle virtual power plant based on the real-time number of the electric vehicles and the power exchanged between the electric vehicles and the power grid.

[0008] Further, the trip time of the electric vehicle includes the start trip time of the electric vehicle and the end trip time of the electric vehicle; the start trip time of the electric vehicle is determined by sampling based on the probability distribution of the start trip time of the electric vehicle by using the Monte Carlo method; the end trip time of the electric vehicle is determined by sampling based on the probability distribution of the end trip time of the electric vehicle by using the Monte Carlo method.

[0009] Further, the electric vehicle parameters include the SOC before the trip of the electric vehicle, the battery capacity, the battery energy consumption per kilometer and the daily trip distance of the electric vehicle; the battery capacity of the electric vehicle is determined by sampling the probability distribution of the battery capacity by using the Monte Carlo method; the battery energy consumption per kilometer of the electric vehicle is determined by sampling the probability distribution of the battery energy consumption per kilometer by using the Monte Carlo method; the daily trip distance of the electric vehicle is determined by sampling the probability distribution of the daily trip distance by using the Monte Carlo method.

[0010] Further, the initial SOC value of the electric vehicle j connected to the power grid is calculated The formula used is:

[0011] In the formula, is the battery capacity of the electric vehicle j, is the daily trip distance of the electric vehicle j, is the battery energy consumption per kilometer of the electric vehicle j, is the SOC value before the trip of the electric vehicle j.

[0012] Further, the real-time SOC value of the electric vehicle j at the time t when charging The calculation formula of the real-time SOC value of the electric vehicle j at the time t when charging is:

[0013] In the formula, The controllable operation region of the electric vehicle j is restricted; is the real-time SOC value of the electric vehicle j at the time interval ; is the real-time SOC value of the electric vehicle j at the time interval ; is the power exchanged between the electric vehicle j and the power grid at the time t, and is positive in the direction of the electric vehicle supplying power to the power grid, and is negative when the electric vehicle is charging ; needs to satisfy the limit of the exchanged power;

[0014] Further, the generalized battery capacity is expressed as:

[0015] In the formula, is the power exchanged between the electric vehicle j and the power grid at the time t; and are the charging efficiency and the discharging efficiency of the electric vehicle j, respectively.

[0016] Further, the determination of the response capability of the electric vehicle virtual power plant includes determining the power exchanged between the electric vehicle virtual power plant and the power grid at the time t and determining the total energy consumption of the electric vehicle virtual power plant; Let the number of real-time electric vehicles connected to the grid be , then the power exchanged between the electric vehicle virtual power plant and the power grid at the time t is expressed as:

[0017] In the formula, is the power exchanged between the electric vehicle j and the power grid at the time t; The total energy consumption of the electric vehicle virtual power plant can be expressed as:

[0018] In the formula, is the power exchanged between the electric vehicle virtual power plant and the power grid at the time t.

[0019] Further, the determination of the response capability of the electric vehicle virtual power plant also includes determining the up-regulation capability of the electric vehicle virtual power plant at the time t and determining the down-regulation capability of the electric vehicle virtual power plant at the time t ; The up-regulation capability of the electric vehicle virtual power plant at the time t is expressed as:

[0020] In the formula, is the maximum power exchanged between the grid-connected electric vehicle j and the power grid at time t; is the power exchanged between the grid-connected electric vehicle j and the power grid at time t; is the down-regulation capability of the electric vehicle virtual power plant at time t is expressed as:

[0021] In the formula, ; is the minimum power exchanged between the grid-connected electric vehicle j and the power grid at time t; is the power exchanged between the grid-connected electric vehicle j and the power grid at time t.

[0022] Based on the same inventive concept, the application also provides a computer readable storage medium, which stores one or more programs, and when the one or more programs are executed, the electric vehicle virtual power plant response capability evaluation method as described above can be implemented.

[0023] Based on the same inventive concept, the application also provides an electronic device, which comprises a processor, a communication interface, a computer readable storage medium as described above and a communication bus; wherein the processor, the communication interface and the computer readable storage medium communicate with each other through the communication bus; and the processor is used to execute the program stored in the computer readable storage medium.

[0024] Compared with the prior art, the application has the following beneficial effects: 1. By determining the travel time of the electric vehicle, determining the initial SOC of the electric vehicle connected to the power grid, determining the travel demand of the user (i.e. determining the demand of the electric vehicle travel on the SOC), determining the controllable operation area of the electric vehicle j and calculating and , the response capability of the electric vehicle virtual power plant can be determined based on the number of real-time grid-connected electric vehicles and the power exchanged between the electric vehicle and the power grid, which can facilitate subsequent estimation of the electric vehicle charging load, thereby providing flexible and efficient adjustment resources for the power grid; 2. The Monte Carlo method is used to sample and determine the related parameters of the electric vehicle, which simulates and analyzes the problem through a large number of random sampling, thereby obtaining a numerical solution or an approximate solution, and the advantage is that complex mathematical derivation is not required, and the problem can be solved only by random sampling, and the above-mentioned related parameters include: the starting travel time of the electric vehicle, the ending travel time of the electric vehicle, the battery capacity of the electric vehicle, the battery energy consumption per kilometer of the electric vehicle, the daily travel distance of the electric vehicle, etc. 3. Determining the response capability of the electric vehicle virtual power plant mainly includes determining the power exchanged between the electric vehicle virtual power plant and the grid at time t, determining the total energy consumption of the electric vehicle virtual power plant, and the up-regulation and down-regulation capabilities of the electric vehicle virtual power plant, which can comprehensively evaluate the response capability of the electric vehicle virtual power plant.

[0025] Other features and advantages of the invention will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures pointed out in the description, claims and drawings.

[0026] The invention will now be further described with reference to the accompanying drawings. Attached Figure Description

[0027] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0028] Figure 1 This is a flowchart illustrating an embodiment of the electric vehicle virtual power plant response capability assessment method according to the present invention; Figure 2 This is a schematic diagram of the controllable operating area of ​​an electric vehicle j according to an embodiment of the present invention; Figure 3 This is a schematic diagram of the structure of an electronic device according to an embodiment of the present invention. Detailed Implementation

[0029] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of the embodiments of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this invention, and not all embodiments. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention.

[0030] like Figure 1 As shown in the figure, this embodiment of the invention provides a method for evaluating the response capability of a virtual power plant for electric vehicles. The method includes the following steps: S1. Determine the travel time for the electric vehicle; S2. Based on the travel time of electric vehicles, analyze the parameters of electric vehicles and calculate the initial state of charge (SOC) of electric vehicles when connected to the power grid. S3, determining a demand of the electric vehicle trip on the SOC, determining a controllable operation region of the electric vehicle based on the initial state of charge SOC and the demand of the electric vehicle trip on the SOC, establishing a calculation formula of a real-time SOC of the electric vehicle at the time t when charging under the constraint of the controllable operation region of the electric vehicle, and calculating the power exchanged between the electric vehicle and the power grid at the time t based on the calculation formula; S4, determining the response capability of the electric vehicle virtual power plant based on the number of real-time electric vehicles accessing the power grid and the power exchanged between the electric vehicle and the power grid.

[0031] In the above technical solution, by determining the trip time of the electric vehicle, determining the initial SOC of the electric vehicle accessing the power grid, determining the trip demand of the user (i.e. determining the demand of the electric vehicle trip on the SOC), determining the controllable operation region of the electric vehicle j, and calculating and , the response capability of the electric vehicle virtual power plant can be determined based on the number of real-time electric vehicles accessing the power grid and the power exchanged between the electric vehicle and the power grid, which can facilitate subsequent estimation of the electric vehicle charging load, thereby providing flexible and efficient adjustment resources for the power grid.

[0032] The electric vehicle charging load refers to the total power obtained by the electric vehicle from the power grid during the charging process, which is the direct load contribution of the electric vehicle user to the power grid. The size of the charging load depends on the charging power, charging efficiency and charging mode of the electric vehicle and other factors.

[0033] The steps of the above method will be further described below.

[0034] Taking a virtual power plant of 1000 electric vehicles as an example, the specific implementation steps of the electric vehicle virtual power plant response capability evaluation method are as follows: S1, determining the trip time of the electric vehicle; In this embodiment, the trip time includes the start time and the end trip time, and the trip time of the electric vehicle directly affects the charging demand and load prediction of the electric vehicle. The trip time of the electric vehicle j includes the start trip time of the electric vehicle j and the end trip time of the electric vehicle; according to the probability distribution of the start trip time, the start trip time of the electric vehicle j (the time when the electric vehicle leaves the power grid) is determined by sampling using the Monte Carlo method; and according to the probability distribution of the end trip time, the end trip time of the electric vehicle j (the time when the electric vehicle accesses the power grid) is determined by sampling using the Monte Carlo method.

[0035] The Monte Carlo method is a numerical calculation method based on probability and statistics theory. Its core idea is to simulate and analyze problems through a large number of random samples to obtain numerical or approximate solutions. The Monte Carlo method is a powerful numerical calculation tool, which has the advantage of not requiring complex mathematical derivation, but only needs to solve problems through random sampling.

[0036] The basic principle of the Monte Carlo method is to generate a large number of samples using random numbers, and estimate the value of the target function by analyzing the distribution characteristics of these samples. This method relies on the law of large numbers and the central limit theorem, which means that as the number of samples increases, the estimated value will tend to the true value, and the error will gradually decrease.

[0037] The main steps of the Monte Carlo method are as follows: 1. Determine the random variable: Abstract the actual problem into a probability and statistics model, and clearly define the random variable and its distribution that needs to be simulated.

[0038] 2. Construct the probability distribution model: According to the problem requirements, construct the probability distribution model of the random variable.

[0039] 3. Random sampling: Extract a large number of random numbers from the known probability distribution as input data for the problem.

[0040] 4. Calculation and analysis: Calculate the sampling results, and obtain the estimated value of the target function through statistical analysis.

[0041] The above Monte Carlo method can be further referred to the prior art, which will not be repeated here.

[0042] S2, in combination with the travel time of the electric vehicle, analyze the electric vehicle parameters and calculate the initial state of charge (SOC) of the electric vehicle connected to the power grid; As a preferred technical solution, the electric vehicle parameters include the SOC value of the electric vehicle j before traveling , battery capacity , battery energy consumption per kilometer , and daily travel distance ; the Monte Carlo method is used to sample the probability distribution of the battery capacity to determine the battery capacity of the electric vehicle j ; the Monte Carlo method is used to sample the probability distribution of the battery energy consumption per kilometer to determine the battery energy consumption per kilometer of the electric vehicle j ; the Monte Carlo method is used to sample the probability distribution of the daily travel distance to determine the daily travel distance of the electric vehicle j .

[0043] In the embodiment, to determine the initial SOC of the electric vehicle when accessing the power grid, a series of parameters of the electric vehicle (i.e. the aforementioned electric vehicle parameters) are analyzed, including the SOC before the trip, the basic parameters of the electric vehicle (capacity, energy consumption) and the running parameters (daily trip distance), etc. The initial SOC value calculation formula is as follows:

[0044] In the formula, is the initial SOC value when accessing the power grid, is the battery capacity of the electric vehicle j, is the daily trip distance of the electric vehicle j, is the battery energy consumption per kilometer of the electric vehicle j, is the SOC value before the trip of the electric vehicle j.

[0045] In the embodiment, the Monte Carlo method is used to sample the probability distribution of the battery capacity to determine the battery capacity of the electric vehicle j ; based on the same method, the battery energy consumption per kilometer and the daily trip distance are respectively sampled and determined according to the probability distribution of the battery energy consumption and the daily trip distance. In combination with the trip time of the electric vehicle, the initial state of the electric vehicle when accessing the power grid is determined based on the above data by using the above formula.

[0046] Generally, the trip time will affect the initial SOC value of the electric vehicle j, and the initial SOC value will directly affect whether it can participate in the power grid regulation. The start time of the trip determines the SOC value before the trip of the user ; before the start time of the trip, the electric vehicle may have been charged to a certain SOC value, which is . The end time of the trip determines the daily trip distance of the electric vehicle ; the distance traveled by the electric vehicle from the start of the trip to the end of the trip is the daily trip distance ; this distance multiplied by the battery energy consumption per kilometer , and then divided by the battery capacity , the battery consumption can be calculated in this period of time, so as to obtain the initial SOC value when accessing the power grid.

[0047] In the embodiment, the start time and the end time of the trip of the electric vehicle affect the calculation of the initial SOC value when accessing the power grid by affecting the daily trip distance and the SOC value before the trip of the user . These time parameters are determined by sampling through the Monte Carlo method, which ensures the randomness and reality of the model.

[0048] S3, determining the demand of the SOC of the electric vehicle trip, determining the controllable operation region of the electric vehicle based on the initial state of charge SOC and the demand of the SOC of the electric vehicle trip, establishing the calculation formula of the real-time SOC of the electric vehicle at the time t under the constraint of the controllable operation region of the electric vehicle, and calculating the power exchanged between the electric vehicle and the power grid at the time t based on the calculation formula; The initial SOC value is the basis for the calculation of the real-time SOC value, and the real-time SOC value reflects the dynamic change of the electric vehicle in the charging process. The change of the real-time SOC value determines the power exchange amount between the electric vehicle and the power grid, and further affects the overall response capability of the virtual power plant.

[0049] In the embodiment, the demand of the SOC of the electric vehicle j trip is determined by sampling according to the probability distribution of the energy demand , and the Monte Carlo method can be used to determine the sampling in the specific implementation . The controllable operation region of the electric vehicle j is determined based on the and the , which will be further described below in combination with the accompanying drawings. Figure 2

[0050] Figure 2 The schematic diagram of the controllable operation region of the electric vehicle j in one embodiment of the application is shown in the figure, which shows the response characteristics of the independent electric vehicle in the process of entering the grid. The horizontal axis of the figure is time, and the vertical axis is the SOC of the battery. The shaded area ABCFED (i.e. the controllable operation region of the electric vehicle j) in the figure reflects the response characteristics of the electric vehicle in the process of entering the grid. In the figure, is the time of the electric vehicle j entering the grid (i.e. the end of the trip time of the electric vehicle), is the initial SOC of the electric vehicle entering the grid, is the lower limit of the discharge allowed by the electric vehicle, is the upper limit of the charge allowed by the electric vehicle, is the time of the electric vehicle leaving the grid (i.e. the start of the trip time of the electric vehicle), is the demand of the SOC of the electric vehicle before the trip; and EF is to meet the trip demand of the user, and the SOC of the electric vehicle needs to be kept in the range of , before leaving the grid.

[0051] The controllable operation region of the electric vehicle j is as shown in Figure 2 ​As shown in the shaded area ABCFED, electric vehicles within the controllable operating area participate in the system's demand response, which can be achieved by adjusting the amount of power exchanged with the grid. In this embodiment, the direction of electric vehicle power supply to the grid, i.e., reverse power supply, is taken as the positive direction. The active power of electric vehicle j connected to the grid at time t (i.e., the power exchanged between electric vehicle j and the grid) is... Its maximum value is The minimum value is :

[0052] exist During this period, electric vehicles can adjust the power they exchange with the grid to meet the grid's demand response requirements. and Indicates in During this period, the maximum and minimum power adjustment range of electric vehicles.

[0053] exist During this period, the electric vehicle's power was adjusted to zero. and Indicates in During this period, the electric vehicle's power was adjusted to zero.

[0054] This embodiment adjusts the power exchanged between the electric vehicle and the power grid, which can meet the needs of the electric vehicle itself while participating in the demand response of the power grid, thereby improving the stability and efficiency of the power grid.

[0055] As a preferred technical solution, the real-time SOC value of electric vehicle j at time t during charging is... The calculation formula is as follows:

[0056] The SOC value of electric vehicle j changes continuously during charging, as shown in the formula above. Since this embodiment takes the direction of electric vehicle supplying power to the grid, i.e., reverse power supply, as the positive direction, the charging process... It is a negative value, and at the same time Switching power limitations must be met. Real-time SOC value during the network access period. by Figure 2 The constraints of the shaded region ABCFED (i.e., the controllable operating region of electric vehicle j) are used to ensure travel needs are met.

[0057] In the above formula, For time intervals; For electric vehicle j in ( Real-time SOC value during charging; Let be the power exchanged between the electric vehicle j connected to the grid at time t; This refers to the generalized battery capacity.

[0058] In this embodiment, the generalized battery capacity is expressed as:

[0059] In the formula, is the power exchanged between the electric vehicle j and the power grid at time t (negative for charging and positive for discharging); and are the charging efficiency and discharging efficiency of the electric vehicle j, respectively.

[0060] According to the calculation formula of , the change in SOC value depends on the current power and the time interval . When the electric vehicle is charging ( <0), the SOC value will increase; when the electric vehicle is discharging ( ≥0), the SOC value will decrease. This embodiment can calculate the power exchanged between the electric vehicle j and the power grid at time t based on the calculation formula of , which is convenient for subsequent determination of the response capability of the electric vehicle virtual power plant.

[0061] The generalized battery capacity is different depending on whether the electric vehicle is charging or discharging. When discharging, the generalized battery capacity is multiplied by the discharging efficiency ; when charging, the generalized battery capacity is divided by the charging efficiency . This is because there is energy loss during charging and discharging, which needs to be corrected by the efficiency factor.

[0062] S4, determine the response capability of the electric vehicle virtual power plant based on the number of real-time connected electric vehicles and the power exchanged between the electric vehicle and the power grid.

[0063] The response capability of the virtual power plant needs to be dynamically adjusted according to the actual load demand of the power grid to ensure stable operation of the power grid.

[0064] In this embodiment, determining the response capability of the electric vehicle virtual power plant mainly includes determining the power exchanged between the electric vehicle virtual power plant and the power grid at time t, determining the total energy consumption of the electric vehicle virtual power plant, and determining the up-regulation capability and down-regulation capability of the electric vehicle virtual power plant, which will be described below.

[0065] Let the number of real-time connected electric vehicles be , then the power exchanged between the electric vehicle virtual power plant and the power grid is as follows:

[0066] P (t) = ∑Pj (t), t = 1, 2, 3,..., T P (t) is the total power exchanged between the electric vehicle virtual power plant and the grid at time t; N (t) is the number of electric vehicles connected to the grid at time t; Pj (t) is the power exchanged between the jth electric vehicle and the grid at time t (negative for charging, positive for discharging). This formula represents the total power exchanged between all electric vehicles connected to the grid and the grid at a certain time t, i.e., the power exchanged between the electric vehicle virtual power plant and the grid.

[0067] The total energy consumption (energy consumption indicator) of the electric vehicle virtual power plant can be represented as:

[0068] P (t) = ∑Pj (t), t = 1, 2, 3,..., T P (t) is the total power exchanged between the electric vehicle virtual power plant and the grid at time t. This formula represents the cumulative value of the total power exchanged between the electric vehicle virtual power plant and the grid over a period of time, reflecting the total energy consumption of the electric vehicle virtual power plant.

[0069] The efficiency indicator is similar to that of energy storage and can be represented as: ηc(t) = P (t) / ∑Pj (t), t = 1, 2, 3,..., T ηd(t) = ∑Pj (t) / P (t), t = 1, 2, 3,..., T ηc(t) and ηd(t) represent the charging efficiency and discharging efficiency, respectively, similar to energy storage. These efficiency indicators are used to evaluate the energy conversion efficiency of electric vehicles during charging and discharging processes. The up-regulation capability of the electric vehicle virtual power plant

[0070] is as follows:

[0071] P (t) = ∑Pj (t), t = 1, 2, 3,..., T P (t) is the up-regulation capability of the electric vehicle virtual power plant at time t; Pj,max(t) is the maximum charging power of the jth electric vehicle at time t; Pj (t) is the actual charging power of the jth electric vehicle at time t (i.e., the power exchanged between the electric vehicle j and the grid). This formula represents the total charging power that all electric vehicles connected to the grid can still increase based on the current charging power at a certain time t.

[0072] The down-regulation capability of the electric vehicle virtual power plant (ηd(t) ≤ 0) is as follows:

[0073] P (t) = ∑Pj (t), t = 1, 2, 3,..., T P (t) is the down-regulation capability of the electric vehicle virtual power plant at time t; Pj,min(t) is the minimum charging power of the jth electric vehicle at time t; ​​is the actual charging power of the jth electric vehicle at time t (i.e., the power exchanged between the electric vehicle j and the power grid). This formula indicates that at a certain time t, all the electric vehicles connected to the power grid can reduce the total charging power based on the current charging power.

[0074] By implementing the above steps, the response capability of the electric vehicle virtual power plant can be well evaluated, and flexible and efficient adjustment resources can be provided for the power grid.

[0075] Based on the same inventive concept, the application further provides a computer-readable storage medium storing one or more programs, which, when executed, can implement the electric vehicle virtual power plant response capability evaluation method as described above.

[0076] Based on the same inventive concept, the application further provides an electronic device, such as Figure 3 As shown, the electronic device includes a processor, a communication interface, a computer-readable storage medium as described above, and a communication bus; wherein the processor, the communication interface, and the computer-readable storage medium communicate with each other through the communication bus; and the processor is configured to execute the program stored in the computer-readable storage medium.

[0077] It should be noted that, for the above-mentioned method embodiments, in order to facilitate description, they are all described as a combination of a series of actions, but those skilled in the art should know that the application is not limited by the described action sequence, because according to the application, certain steps can be performed in other sequences or simultaneously. Secondly, those skilled in the art should know that the embodiments described in the specification all belong to preferred embodiments, and the actions or steps involved are not necessarily essential to the application.

[0078] In the above embodiments, the description of each embodiment has its own focus, and the parts not described in detail in a certain embodiment can be referred to the related description of other embodiments.

[0079] The parts not involved in the above embodiments are the same as or can be implemented by the prior art, and will not be described further here.

[0080] Although the application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: it can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacement to part of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the application.

Claims

1. An electric vehicle virtual power plant response capability evaluation method, characterized in that, The method comprises: determining the travel time of the electric vehicle; analyzing the electric vehicle parameters and calculating the initial state of charge (SOC) of the electric vehicle accessing the power grid in combination with the travel time of the electric vehicle; determining the demand of the electric vehicle travel on the SOC, determining the controllable operation region of the electric vehicle based on the initial state of charge (SOC) and the demand of the electric vehicle travel on the SOC, establishing the calculation formula of the real-time SOC of the electric vehicle charging at time t under the constraint of the controllable operation region of the electric vehicle, and calculating the power exchanged between the electric vehicle accessing the grid at time t and the power grid based on the calculation formula; determining the response capability of the electric vehicle virtual power plant based on the number of real-time electric vehicles accessing the grid and the power exchanged between the electric vehicle and the power grid.

2. The electric vehicle virtual power plant response capability evaluation method according to claim 1, wherein: the travel time of the electric vehicle comprises the start travel time of the electric vehicle and the end travel time of the electric vehicle; the start travel time of the electric vehicle is determined by sampling according to the probability distribution of the start travel time using the Monte Carlo method; the end travel time of the electric vehicle is determined by sampling according to the probability distribution of the end travel time using the Monte Carlo method.

3. The electric vehicle virtual power plant response capability evaluation method according to claim 1, wherein: the electric vehicle parameters comprise the SOC before travel, the battery capacity, the battery energy consumption per kilometer, and the daily travel distance of the electric vehicle; the battery capacity of the electric vehicle is determined by sampling the probability distribution of the battery capacity using the Monte Carlo method; the battery energy consumption per kilometer of the electric vehicle is determined by sampling the probability distribution of the battery energy consumption per kilometer using the Monte Carlo method; the daily travel distance of the electric vehicle is determined by sampling the probability distribution of the daily travel distance using the Monte Carlo method.

4. The electric vehicle virtual power plant response capability evaluation method according to claim 1, wherein: Calculating the initial SOC value of the electric vehicle j accessing the power grid The formula used is: In the formula, is the battery capacity of the electric vehicle j, is the daily travel distance of the electric vehicle j, is the battery energy consumption per kilometer of the electric vehicle j, is the SOC value of the electric vehicle j before travel.

5. The electric vehicle virtual power plant response capability evaluation method according to claim 1, wherein: Real-time SOC value of the electric vehicle j when charging at time t The calculation formula is: In the formula, subject to the controllable operation region of the electric vehicle j; is the time interval; is the real-time SOC value of the electric vehicle j at the time of charging; is the real-time SOC value of the electric vehicle j at the time of charging; is the power exchanged between the electric vehicle j and the power grid at time t, with the direction of the electric vehicle supplying power to the power grid being the positive direction, and the direction of the power grid charging the electric vehicle being the negative direction; is the negative value, and at the same time the limit of the exchanged power needs to be met; is the generalized battery capacity.

6. The electric vehicle virtual power plant response capability evaluation method according to claim 5, wherein: The generalized battery capacity is expressed as: In the formula, is the power exchanged between the electric vehicle j and the power grid at time t; and is the charging efficiency and the discharging efficiency of the electric vehicle j, respectively.

7. The electric vehicle virtual power plant response capability evaluation method according to any one of claims 1-6, wherein: The determining the response capability of the electric vehicle virtual power plant comprises determining power exchanged between the electric vehicle virtual power plant and the power grid at time t and determining total energy consumption of the electric vehicle virtual power plant Let the number of real-time networked electric vehicles be The power exchanged between the electric vehicle virtual power plant and the power grid at time t is represented as: In the formula, Pj(t) is the power exchanged between the electric vehicle j and the power grid at time t. the total energy consumption of the electric vehicle virtual power plant can be expressed as: In the formula, P is the power exchanged between the virtual power plant of the electric vehicle and the power grid at time t.

8. The electric vehicle virtual power plant response capability evaluation method according to claim 7, wherein: The determining the response capability of the electric vehicle virtual power plant further comprises determining an up-regulation capability of the electric vehicle virtual power plant at time t and determining a down-regulation capability of the electric vehicle virtual power plant at time t ; The up-regulation ability of the electric vehicle virtual power plant at time t is represented as: In the formula, is the maximum power exchanged between the grid-connected electric vehicle j and the grid at time t; is the power exchanged between the grid-connected electric vehicle j and the grid at time t; Down-regulation capability of electric vehicle virtual power plant at time t is represented as: In the formula, ; is the minimum power exchanged between the grid-connected electric vehicle j and the grid at time t; is the power exchanged between the grid-connected electric vehicle j and the grid at time t.

9. A computer-readable storage medium storing one or more programs, wherein: when the one or more programs are executed, the electric vehicle virtual power plant response capability evaluation method according to any one of claims 1-8 can be implemented. 10.An electronic device comprising a processor, a communication interface, the computer readable storage medium of claim 9, and a communication bus; wherein, The processor, the communication interface, and the computer-readable storage medium communicate with each other through a communication bus; wherein: the processor is configured to execute the program stored in the computer-readable storage medium.

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