Cell electric automobile charging strategy calculation method and system based on nash equilibrium

Abstract

The application is based on a cell electric vehicle charging strategy calculation method and system based on Nash equilibrium, which comprises: simulating electric vehicle arrival charging station data to obtain electric vehicle power consumption data; based on the electric vehicle power consumption data, combining the actual situation of the charging station to describe the electric vehicle charging process to obtain the electric vehicle charging boundary condition; combining the electric vehicle user charging cost objective function and the electric vehicle charging boundary condition to obtain the electric vehicle charging optimization problem; using gradient projection parallel algorithm to solve the electric vehicle charging optimization problem to obtain the load curve under Nash equilibrium, and then obtaining the current dynamic electricity price parameter and the electric vehicle load curve under the power consumption condition. The system comprises a data acquisition module, a boundary condition determination module, an optimization problem determination module and a solving module. The application enables each user to pursue individual interests while smoothing the entire cell night load curve.

Description

TECHNICAL FIELD

[0001] The application belongs to the field of power systems, and particularly relates to a cell electric vehicle charging strategy calculation method and system based on Nash equilibrium. BACKGROUND

[0002] With the integration of renewable energy and the development of smart grid technology, demand response management has become a key to improving the efficiency and reliability of the power grid. In recent years, with the maturity of electric vehicle technology and the popularity of intelligent electricity technology, the scale of electric vehicles has been increasing. However, the large-scale and disorderly charging of a large number of electric vehicles not only leads to an increase in residential area load fluctuation, causing the power grid peak-valley difference to intensify, but also leads to an increase in charging cost while affecting the user experience of electric vehicles. Therefore, it is necessary to design a reasonable charging strategy to achieve electric vehicle charging control within the cell range.

[0003] The centralized charging optimization method has the following difficulties. The controllable load of electric vehicles is small in single capacity, large in quantity and various in types, which makes it difficult for aggregators to centrally obtain the power consumption size and arrival and departure times of electric vehicles; there is a risk of data leakage when aggregators collect data from all users, which cannot protect user privacy; the efficiency of centralized optimization is generally low when calculating a large number of electric vehicles; the centralized system is difficult to adapt flexibly and has poor scalability when facing an increase in the number of users or environmental changes.

[0004] Unlike traditional centralized optimization, each electric vehicle is an independent individual in the algorithm based on distributed optimization, which solves its own small-scale problem, and these small-scale problems coordinate with each other to achieve approximate optimization as a whole, which solves the above-mentioned difficulties faced by centralized methods. In recent years, research on regulating electric vehicle charging by using dynamic electricity prices has received widespread attention. This research assumes that aggregators have the power to set electricity prices within their jurisdiction. Dynamic electricity price refers to the fact that electricity price is a function of load. If the load is high at a certain time, the electricity price will also be high, thereby guiding the user load to shift from this time period to other time periods with lower electricity prices, achieving the purpose of flattening the load curve. The dynamic electricity price framework considers the competitive relationship between users in non-cooperative games, and Nash equilibrium well describes the final balance reached by this non-cooperative game competition, which refers to a state in which no participant can obtain better benefits by changing their own strategy unilaterally. SUMMARY

[0005] The application aims to provide a cell electric vehicle charging strategy calculation method and system based on Nash equilibrium, so that each user pursues individual interests while smoothing the entire cell night load curve and reducing the operation pressure of the power grid. The user does not need to disclose his charging habits or travel arrangement to the central organization, thereby protecting the user's privacy, and the central organization does not need to bear a large amount of data processing and optimization tasks, thereby reducing the operation cost.

[0006] In order to achieve the above-mentioned purpose, the application adopts the following technical solutions:

[0007] The cell electric vehicle charging strategy calculation method based on Nash equilibrium comprises the following steps:

[0008] Step 1: simulate the data of electric vehicles arriving at the charging station to obtain electric vehicle power consumption data, including the number of electric vehicles, arrival and departure times, and the distribution of initial battery capacity and target charging capacity;

[0009] Step 2: describe the electric vehicle charging process based on the electric vehicle power consumption data and the actual situation of the charging station, including the constraints of electric vehicle power demand and charging power upper and lower limits, to obtain the boundary conditions of electric vehicle charging;

[0010] Step 3: calculate the charging cost objective function of the electric vehicle user based on the dynamic price and the aggregation game framework, and combine the charging cost objective function of the electric vehicle user and the boundary conditions of electric vehicle charging to obtain the electric vehicle charging optimization problem;

[0011] Step 4: adopt the gradient projection parallel algorithm to solve the electric vehicle charging optimization problem with the goal of reducing the charging cost of the electric vehicle user, to obtain the load curve under Nash equilibrium, and obtain the current dynamic price parameter and the electric vehicle load curve under the power consumption condition based on the load curve under Nash equilibrium.

[0012] The further improvement of the application is that in step 1, the data of electric vehicles arriving at the charging station is simulated to obtain electric vehicle power consumption data, which comprises:

[0013] First, the arrival and departure times and the required charging capacity of all electric vehicles are generated; in the formula, 、 and respectively represent the arrival and departure times and the required charging capacity of the Nth electric vehicle EV n . and both obey normal distribution, the standard deviation of the arrival time is , the expectation is , the standard deviation of the departure time is , and the expectation is The expected arrival time is 18:00, and the departure time is 9:00; the battery power of the electric vehicle when arriving at the community also approximately obeys normal distribution, and the standard deviation of the power is , the expected value , that is, the expected arrival power is 51.37%, and the variable probability density function formula is as shown in (1)-(3);

[0014] (1)

[0015] (2)

[0016] (3).

[0017] Further improvement of the application is that step two: based on the electric vehicle power consumption data, the charging process of the electric vehicle is described in combination with the actual situation of the charging station, including the power demand and the upper and lower limits of the charging power of the electric vehicle, so as to obtain the boundary conditions of the electric vehicle charging, including:

[0018] The user arrival and departure time, the charging power upper and lower limit and the power consumption demand constraint are considered in the electric vehicle charging model, including:

[0019] (4)

[0020] Wherein, The EV n feasible charging set, the electric vehicle set ; the n th electric vehicle EV n charging period set , wherein the charging power of the electric vehicle outside the arrival and departure time range is 0; is the charging power of EV n in period t , and and respectively represent the minimum and maximum charging power, is the charging efficiency of the electric vehicle, is the initial power of EV n when arriving, is the upper limit of the battery power set to prevent overcharging of the electric vehicle.

[0021] Further improvement of the application is that step three: based on the dynamic price and the aggregation game framework, the charging cost objective function of the electric vehicle user is calculated, the charging cost objective function of the electric vehicle user and the boundary conditions of the electric vehicle charging are combined to obtain the electric vehicle charging optimization problem, including:

[0022] (5)

[0023] (6)

[0024] wherein, is the charging cost function of EV n , is the total load of the cell, t is the load of all electric vehicles, is the fixed load of the cell, is the charging power of all electric vehicles except EV n , ; is the dynamic electricity price of the time period, t is the dynamic electricity price coefficient, is the dynamic electricity price coefficient;

[0025] Each electric vehicle adjusts its charging strategy to minimize the cost function, and the optimization problem is expressed as:

[0026] (7).

[0027] The further improvement of the present application is that step four: the gradient projection parallel algorithm is used to solve the electric vehicle charging optimization problem with the goal of reducing the charging cost of the electric vehicle user, and the load curve under the Nash equilibrium is obtained, comprising:

[0028] (1) First, set the initial iteration strategy is the required charging amount evenly distributed to all charging time periods, and the algorithm convergence criterion is defined according to the required convergence accuracy and the number of electric vehicles;

[0029] (8)

[0030] wherein is the two-norm of the difference between the adjacent iteration strategies of EV n , and the value of is determined according to the required convergence accuracy and the number of electric vehicles;

[0031] (2) The negative gradient direction of the cost function is used to update the current strategy, so that the charging cost is iterated in the descending direction. In order to prevent the new strategy from not meeting the charging constraints of EV n , the vector closest to the new strategy in the feasible region is found as the strategy result of this iteration

[0032] (9)

[0033] (10)

[0034] wherein ​​For EV n The gradient of the cost function with respect to the electricity consumption in different time periods. The iteration step size, For the first k EV in the next iteration n Charging strategy ， To project the new strategy onto the feasible region Operation;

[0035] (3) After each iteration, check whether the convergence criterion (8) is met; if it is met, the algorithm ends and the obtained load curve is the approximate solution of Nash equilibrium; if it is not met, return (2) and repeatedly update the charging strategy until the convergence criterion is met.

[0036] A Nash equilibrium-based system for calculating electric vehicle charging strategies in residential communities includes:

[0037] The data acquisition module simulates and generates data on electric vehicles arriving at charging stations, obtaining electric vehicle electricity consumption data, including the number of electric vehicles, arrival and departure times, and the distribution of initial battery charge and target charging charge.

[0038] The boundary condition determination module describes the electric vehicle charging process based on electric vehicle electricity consumption data and the actual situation of the charging station, including constraints such as electric vehicle electricity demand and upper and lower limits of charging power, and obtains the electric vehicle charging boundary conditions.

[0039] The optimization problem determination module calculates the charging cost objective function of electric vehicle users based on dynamic electricity price and aggregation game framework, and combines the charging cost objective function of electric vehicle users with the electric vehicle charging boundary conditions to obtain the electric vehicle charging optimization problem.

[0040] The solution module uses a gradient projection parallel algorithm to solve the electric vehicle charging optimization problem with the goal of reducing the charging cost for electric vehicle users. It obtains the load curve under Nash equilibrium and, based on the load curve under Nash equilibrium, obtains the current dynamic electricity price parameters and the electric vehicle load curve under the electricity consumption conditions.

[0041] A further improvement of this invention is that, in the data acquisition module, data on the arrival of an electric vehicle at a charging station is simulated to obtain electric vehicle electricity consumption data, including:

[0042] First, generate the arrival and departure times and required charging power for all electric vehicles; in the formula... , and Let EV represent the Nth electric vehicle. n Arrival and departure times and required charging power; and All follow a normal distribution, and the standard deviation of arrival time is... , expected , departure time standard deviation , expected , that is, the expected arrival time is 18 o'clock, and the departure time is 9 o'clock; the battery power of the electric vehicle when arriving at the community also approximately obeys the normal distribution, and the power standard deviation is , expected value , that is, the expected arrival power is 51.37%, and the variable probability density function formula is shown as (1)-(3);

[0043] (1)

[0044] (2)

[0045] (3).

[0046] Further improvement of the application is that in the boundary condition determination module, based on the electric vehicle power consumption data, the electric vehicle charging process is described in combination with the actual situation of the charging station, including the electric vehicle power demand and the upper and lower limits of the charging power and other constraints, to obtain the electric vehicle charging boundary conditions, including:

[0047] The electric vehicle charging model considers the user arrival and departure time, the upper and lower limits of the charging power and the power consumption demand constraints, including:

[0048] (4)

[0049] wherein, EV n The feasible charging set, the electric vehicle set ; the n th electric vehicle EV n The charging period set , wherein the electric vehicle charging power is 0 outside the arrival and departure time range; is the charging power of EV n in period t , and respectively represent the minimum and maximum charging power, is the electric vehicle charging efficiency, is the initial power of EV n when arriving, is the upper limit of the battery power set to prevent overcharging of the electric vehicle.

[0050] The further improvement of the application is that in the optimization problem determining module, the charging cost objective function of the electric vehicle user is calculated based on the dynamic electricity price and the aggregate game framework, the charging cost objective function of the electric vehicle user and the charging boundary condition of the electric vehicle are combined to obtain the electric vehicle charging optimization problem, including:

[0051] (5)

[0052] (6)

[0053] In the formula, is the charging cost function of EV n , is the total load of the cell in the period, t is the load of all electric vehicles, is the fixed load of the cell, is the charging power of other electric vehicles except EV n : is the dynamic electricity price in the period, t and are dynamic electricity price coefficients;

[0054] Each electric vehicle adjusts its own charging strategy to minimize the cost function, and the optimization problem is expressed as:

[0055] (7).

[0056] A computer readable storage medium stores a computer program, and the computer program realizes the steps of the Nash equilibrium-based cell electric vehicle charging strategy calculation method when executed by a processor.

[0057] Compared with the prior art, the application has at least the following beneficial technical effects:

[0058] The Nash equilibrium-based cell electric vehicle charging strategy calculation method and system provided by the application have the following four significant advantages. First, the distributed method can protect the privacy of users. Users do not need to upload their sensitive information such as travel data to the central server, reducing the risk of data leakage. Second, the distributed method supports large-scale electric vehicle calculation. Since each user EV n ​​​The charging strategy calculation is independently performed and there is no coupling between each other, so that parallel calculation of the algorithm can be supported. This method can process optimization problems of a large number of users, and the parallel algorithm can quickly calculate the balancing result. In addition, the balancing method reduces the calculation burden of the central institution. In the traditional centralized optimization, the central institution needs to perform complex calculation to determine the optimal strategy after collecting data of all users, which puts high requirements on the calculation capacity of the central institution. Finally, compared with the centralized and unified optimization method, the distributed optimization method allows the algorithm to be expanded to more users and more complex power grid environment. Each user can adjust the optimization strategy according to own demand and preference, without relying on the unified scheduling of the central institution. BRIEF DESCRIPTION OF DRAWINGS

[0059] Figure 1 Flow chart of the cell electric vehicle charging strategy calculation method based on Nash equilibrium of the present application;

[0060] Figure 2 Schematic diagram of the valley filling effect changing with the number of electric vehicles participating in balancing;

[0061] Figure 3 Schematic diagram of the iteration time and number changing with the number of electric vehicles;

[0062] Figure 4 Structure block diagram of the cell electric vehicle charging strategy calculation system based on Nash equilibrium of the present application. DETAILED DESCRIPTION

[0063] Hereinafter, only certain exemplary embodiments are simply described. As can be appreciated by those skilled in the art, the described embodiments can be modified in various different ways without departing from the spirit or scope of the present application. Therefore, the drawings and the description are considered to be essentially exemplary rather than limiting.

[0064] It should be understood that the terms "comprise" and "include" as used in the specification and the appended claims indicate the presence of the described features, integers, steps, operations, elements, and / or components but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups thereof.

[0065] It should also be understood that the terms used in the present application specification are only for the purpose of describing specific embodiments and are not intended to limit the present application. As used in the present application specification and the appended claims, the singular forms "a", "an" and "the" are intended to include the plural forms unless the context clearly indicates otherwise.

[0066] It should be further understood that the term "and / or" as used in the specification and in claims of the application, means any combination of one or more of the associated listed items and all possible combinations, and includes these combinations.

[0067] The various structural diagrams according to the disclosed embodiments of the application are shown in the accompanying drawings. These diagrams are not drawn to scale, in which certain details are exaggerated for clarity and others omitted. The shapes and relative sizes of the various regions, layers, and the relative positions of these regions / layers shown in the drawings are merely examples, and in actuality, they can be different due to manufacturing tolerances or technical limitations, and a person skilled in the art can additionally design regions / layers with different shapes, sizes, and relative positions according to actual needs.

[0068] The embodiments of the application are described in detail below with reference to the accompanying drawings.

[0069] Embodiment 1

[0070] As shown in the accompanying drawings, the application provides a cell electric vehicle charging strategy calculation method based on Nash equilibrium, which comprises: Figure 1

[0071] Step one: simulate the data of electric vehicles arriving at the charging station to obtain electric vehicle power consumption data, including the number of electric vehicles, arrival and departure times, and the distribution of initial battery power and target charging power;

[0072] Step two: based on the electric vehicle power consumption data, describe the electric vehicle charging process in combination with the actual situation of the charging station, including the constraints of electric vehicle power demand and charging power upper and lower limits, to obtain the electric vehicle charging boundary conditions;

[0073] Step three: based on the dynamic electricity price and the aggregate game framework, the charging cost objective function of the electric vehicle user is calculated, and the charging cost objective function of the electric vehicle user and the electric vehicle charging boundary conditions are combined to obtain the electric vehicle charging optimization problem;

[0074] Step four: taking reducing the charging cost of the electric vehicle user as the target, the gradient projection parallel algorithm is used to solve the electric vehicle charging optimization problem to obtain the load curve under Nash equilibrium, and based on the load curve under Nash equilibrium, the current dynamic electricity price parameters and the electric vehicle load curve under the power consumption condition are obtained.

[0075] In this embodiment, step one: simulate the data of electric vehicles arriving at the charging station to obtain electric vehicle power consumption data, including:

[0076] First, the arrival and departure times and the required charging power of all electric vehicles are generated; in the formula , and ​EV n arrival and departure time and required charging power; with arrival time standard deviation , expected departure time standard deviation , expected , i.e. expected arrival time is 18:00 and departure time is 9:00; the battery power of the electric vehicle when it arrives at the community also approximately obeys normal distribution, and the power standard deviation , expected value , i.e. expected arrival power is 51.37%, and the variable probability density function formula is shown in (1)-(3);

[0077] (1)

[0078] (2)

[0079] (3).

[0080] In the embodiment, step two: based on the electric vehicle power consumption data, the electric vehicle charging process is described in combination with the actual situation of the charging station, including the electric vehicle power demand and the upper and lower limits of the charging power and other constraints, to obtain the electric vehicle charging boundary conditions, including:

[0081] The user arrival and departure time, the charging power upper and lower limits and the power consumption demand constraints are considered in the electric vehicle charging model, including:

[0082] (4)

[0083] wherein, describes EV n feasible charging set, electric vehicle set ; the n electric vehicle EV n charging time period set , wherein the electric vehicle charging power is 0 outside the arrival and departure time range; is the charging power of EV n in time period t , and and respectively represent the minimum and maximum charging power, is the electric vehicle charging efficiency, is the initial power of EV n when it arrives, is the upper limit of the battery power set to prevent overcharging of the electric vehicle.

[0084] In this embodiment, step three: The objective function for the charging cost of electric vehicle users is calculated based on the dynamic electricity price and aggregation game framework. The objective function for the charging cost of electric vehicle users is then combined with the boundary conditions for electric vehicle charging to obtain the electric vehicle charging optimization problem, including:

[0085] (5)

[0086] (6)

[0087] In the formula, For EV n The charging cost function, for t Total load of the community during the time period For all electric vehicle loads, For the fixed load of the community, To remove EV n Other electric vehicle charging power: ; for t Dynamic electricity pricing for different time periods and This refers to the dynamic electricity price coefficient.

[0088] Each electric vehicle adjusts its charging strategy to minimize the cost function. The optimization problem can be expressed as:

[0089] (7).

[0090] In this embodiment, step four: With the goal of reducing charging costs for electric vehicle users, a gradient projection parallel algorithm is used to solve the electric vehicle charging optimization problem, obtaining the load curve under Nash equilibrium, including:

[0091] (1) First, set the initial iteration strategy. To distribute the required charging amount evenly across all charging periods, a convergence criterion is defined based on the required convergence accuracy and the number of electric vehicles. Size;

[0092] (8)

[0093] In the formula For EV n The L2 norm of the difference between two adjacent iteration strategies. The value depends on the required convergence accuracy and the number of electric vehicles.

[0094] (2) Update the current strategy using the negative gradient of the cost function, so that the charging cost iterates in the decreasing direction. This is to prevent the new strategy from not satisfying the EV requirement. nthe charging constraints, find the vector closest to the new strategy in the feasible region as the strategy result of this iteration

[0095] (9)

[0096] (10)

[0097] where is the EV n the gradient of the cost function with respect to the power consumption of each time period, is the iteration step size, is the EV k of the n charging strategy ， is the operation of projecting the new strategy into the feasible region

[0098] (3) After each iteration, check if the convergence criterion (8) is met; if it is met, the algorithm ends, and the obtained load curve is the approximate solution of the Nash equilibrium; if it is not met, return to (2) to repeatedly update the charging strategy until the convergence criterion is met.

[0099] Example 2

[0100] First, generate the arrival and departure times and the required charging power of all electric vehicles. The arrival time and departure time are subject to normal distribution, with standard deviation , expected value , standard deviation , expected value , i.e. the expected arrival time is 18:00 and the departure time is 9:00. The battery power of the electric vehicle when it arrives at the community is also approximately subject to normal distribution, with standard deviation , expected value , i.e. the expected arrival power is 51.37%. The probability density function formulas of the above variables are shown in (7)-(9). The upper and lower limits of the charging power in the aforementioned formula (1) are set as , and , where is the maximum battery capacity of a single electric vehicle, and the charging efficiency is ; the dynamic price coefficient in formula (4) is . The algorithm is solved by calling Yalmip+Cplex solver in MATLAB R2020a environment.

[0101] (7)

[0102] (8)

[0103] ​(9)

[0104] performing a cell electric vehicle charging strategy calculation step based on Nash equilibrium: setting an initial iteration strategy , and determining a convergence criterion according to the number of electric vehicles and the required convergence precision , and then repeating the following steps until the convergence criterion is met: 1) calculating the negative gradient of the cost function according to the electric vehicle cost function, and updating the charging plan of each user according to the negative gradient. 2) To prevent the new strategy from being out of the feasible region, the new strategy needs to be projected into the feasible region. 3) According to the convergence criterion, it is judged whether the Nash equilibrium is reached, if so, the process is ended and the load curve is obtained, if not, the process returns to step 1).

[0105] The valley filling results are shown in Figure 2 . As can be seen from Figure 2 , with the increase of the number of electric vehicles participating in demand response, the valley filling effect is improved, and when the number of electric vehicles participating in demand response is small, the total load curve is arc-shaped, and when the number of electric vehicles reaches 3000 and 4000, the total load curve tends to be horizontal. It can be predicted that if the number of electric vehicles continues to increase until the electric vehicle charging power is much larger than the basic load, the total power curve will tend to be horizontal, which is consistent with the physical meaning when the Nash equilibrium is reached, proving the rationality of the algorithm results.

[0106] The calculation time and iteration number of the algorithm are described below. Considering the case of 100, 500, 1000, 1500, 2000, 3000, and 4000 electric vehicles participating, as shown in Figure 3 . When the number of electric vehicles is less than 2000, the iteration number and time increase with the increase of the number of electric vehicles, but when the number of electric vehicles reaches 4000, the algorithm calculation cost decreases obviously, which is due to the fact that the convergence condition in formula (2) becomes looser compared to the effect of the increase of the number of electric vehicles on the calculation cost. Under the same charging precision of a single electric vehicle, with the increase of the number of electric vehicles, the calculation time and iteration number required by the gradient projection parallel algorithm do not increase by orders of magnitude, which meets the calculation cost demand in the actual scene.

[0107] Embodiment 3

[0108] As shown in Figure 4 , the cell electric vehicle charging strategy calculation system based on Nash equilibrium provided by the application comprises:

[0109] The data acquisition module simulates data of the electric vehicle arriving at the charging station to obtain electric vehicle power consumption data, including the number of electric vehicles, arrival and departure times, and distribution of initial battery power and target charging power.

[0110] The boundary condition determination module describes the electric vehicle charging process based on the electric vehicle power consumption data and in combination with actual conditions of the charging station, including constraints such as electric vehicle power demand and upper and lower limits of charging power, to obtain electric vehicle charging boundary conditions.

[0111] The optimization problem determination module calculates a charging cost objective function of the electric vehicle user based on the dynamic electricity price and the aggregate game framework, and combines the charging cost objective function of the electric vehicle user and the electric vehicle charging boundary conditions to obtain an electric vehicle charging optimization problem.

[0112] The solving module solves the electric vehicle charging optimization problem by using a gradient projection parallel algorithm to reduce the charging cost of the electric vehicle user, obtains a load curve under Nash equilibrium, and obtains the current dynamic electricity price parameter and the electric vehicle load curve under the power consumption condition based on the load curve under Nash equilibrium.

[0113] Embodiment 4

[0114] The computer readable storage medium provided by the application stores a computer program, and the computer program realizes the steps of the cell electric vehicle charging strategy calculation method based on Nash equilibrium when executed by a processor.

[0115] Those skilled in the art should understand that the embodiments of the application can be provided as a method, a system, or a computer program product. Therefore, the application can be in the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the application can be in the form of a computer program product implemented on one or more computer usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer usable program code.

[0116] The application is described with reference to flowcharts and / or block diagrams according to the method, system and computer program product of the embodiments of the application. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, and the combination of the flows and / or blocks in the flowcharts and / or block diagrams can be implemented by computer program instructions. These computer program instructions can be provided to a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing device to produce a machine, so that the instructions executed by the computer or other programmable data processing device produce a device for implementing the functions described in the flowcharts and / or block diagrams. Figure 1 one flow or multiple flows and / or blocks Figure 1a system to perform the function specified in the individual block or blocks.

[0117] These computer program instructions can also be stored in a computer readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer readable memory produce an article of manufacture including instructions which implement the flow Figure 1 one or more flows and / or blocks Figure 1 the function specified in the individual block or blocks.

[0118] These computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the flow Figure 1 one or more flows and / or blocks Figure 1 the function specified in the individual block or blocks.

[0119] The above presents the basic principles and main features of the present application and the advantages of the present application. It is obvious for those skilled in the art that the present application is not limited to the details of the above exemplary embodiments, and the present application can be realized in other specific forms without departing from the spirit or essential characteristics of the present application. Therefore, the embodiments should be regarded as exemplary and non-limiting, and the scope of the present application is defined by the appended claims rather than the above description, and it is intended to encompass all variations falling within the meaning and scope of the equivalent elements of the claims. Any reference signs in the claims should not be regarded as limiting the claims to which they relate.

[0120] Furthermore, it should be understood that although the present specification is described in terms of embodiments, not every implementation embodies an independent technical solution, and the present specification is described in this way only for the sake of clarity, and those skilled in the art should consider the specification as a whole, and the technical solutions in each embodiment can also be appropriately combined to form other embodiments that those skilled in the art can understand. The above is only to illustrate the technical idea of the present application, and cannot limit the protection scope of the present application, and any modification made on the basis of the technical solutions falls within the protection scope of the claims of the present application.

Claims

1. A method for calculating a cell electric vehicle charging strategy based on Nash equilibrium, characterized in that, Comprise: Step one: simulate the data of electric vehicles arriving at charging stations to obtain electric vehicle power consumption data, including the number of electric vehicles, arrival and departure times, and the distribution of initial battery capacity and target charging capacity; Step two: based on the electric vehicle power consumption data, describe the electric vehicle charging process in combination with the actual situation of the charging station, including the constraints of electric vehicle power demand and charging power upper and lower limits, to obtain the electric vehicle charging boundary conditions; Step three: based on the dynamic electricity price and the aggregate game framework, the charging cost objective function of the electric vehicle user is calculated, and the charging cost objective function of the electric vehicle user and the electric vehicle charging boundary conditions are combined to obtain the electric vehicle charging optimization problem; Step four: to reduce the charging cost of electric vehicle users as the target, the gradient projection parallel algorithm is used to solve the electric vehicle charging optimization problem, and the load curve under Nash equilibrium is obtained, including: (1) First, set the initial iteration strategy To evenly distribute the required charging amount to all charging periods, define the algorithm convergence criterion according to the required convergence accuracy and the number of electric vehicles The size of the magnitude; (8) In the formula EVs n The two-norm of the difference between two adjacent iteration strategies, The value of is determined according to the required convergence accuracy and the number of electric vehicles, and N is the set of electric vehicles. (2) Update the current strategy using the negative gradient direction of the cost function, so that the charging cost is iterated in the descending direction to prevent the new strategy from not meeting the charging constraints of EVs n , and find the nearest vector to the new strategy in the feasible region as the strategy result of this iteration (9) (10) wherein, is the charging cost function for EV n , is the charging power for EV n , is the charging power for other electric vehicles except EV n , is the gradient of the charging cost function for EV n with respect to its individual period power consumption, is the iteration step size, is the charging power for EV n in period t , is the feasible region for EV n , is the set of charging periods for the n th electric vehicle EV n , is the EV n charging strategy for the k th iteration, ， is the operation of projecting the new strategy into the feasible region , denotes the arrival time of the n th electric vehicle EV n , denotes the departure time of the n th electric vehicle EV n , (3) After each iteration, check if the convergence criterion (8) is met; if met, the algorithm ends, and the obtained load curve is the approximate solution of Nash equilibrium; if not met, return to (2) to repeatedly update the charging strategy until the convergence criterion is met; Based on the load curve under Nash equilibrium, the current dynamic electricity price parameter and the electric vehicle load curve under the power consumption situation are obtained.

2. The method of claim 1, wherein, Step one: simulate the data of electric vehicles arriving at charging stations to obtain electric vehicle power consumption data, including: Firstly, the arrival-departure time and the required charging power of all electric vehicles are generated; the formula is The arrival-departure time of the i-th electric vehicle EVi is denoted as n n The required charging power of the i-th electric vehicle EVi is denoted as The arrival time of the i-th electric vehicle EVi is subject to normal distribution with the standard deviation , and the expected value The departure time of the i-th electric vehicle EVi is subject to normal distribution with the standard deviation , and the expected value , i.e. the expected arrival time is 18:00 and the expected departure time is 9:00; the battery power of the i-th electric vehicle EVi when arriving at the community is subject to normal distribution with the standard deviation , and the expected value , i.e. the expected arrival power is 51.37%, and the variable probability density function formula is shown as (1)-(3).​​ (1) (2) (3)。 3. The method of claim 2, wherein, Step two: based on the electric vehicle power consumption data, describe the electric vehicle charging process in combination with the actual situation of the charging station, including the constraints of electric vehicle power demand and charging power upper and lower limits, to obtain the electric vehicle charging boundary conditions, including: The electric vehicle charging model considers the constraints of user arrival and departure time, charging power upper and lower limit, and power demand, including: (4) wherein the set of electric vehicles ; the n set of electric vehicles EV n set of charging time periods wherein the electric vehicle charging power is 0 outside the arrival and departure time ranges; and denote the minimum and maximum charging power, respectively, is the electric vehicle charging efficiency, is the EV n initial state of charge at arrival, is the upper limit of the battery state of charge set to prevent overcharging of the electric vehicle.

4. The method of claim 1, wherein, Step three: based on the dynamic electricity price and the aggregate game framework, the charging cost objective function of the electric vehicle user is calculated, and the charging cost objective function of the electric vehicle user and the electric vehicle charging boundary conditions are combined to obtain the electric vehicle charging optimization problem, including: (5) (6) wherein is t periodic total load of the cell, is the total load of all electric vehicles, is the fixed load of the cell; ; is t periodic dynamic electricity price, and is the dynamic electricity price coefficient; Each electric vehicle adjusts its charging strategy to minimize the cost function, and the optimization problem is expressed as: (7)。 5. A system for computing a cell electric vehicle charging strategy based on Nash equilibrium, characterized in that, Including: The data acquisition module simulates the data of electric vehicles arriving at charging stations to obtain electric vehicle power consumption data, including the number of electric vehicles, arrival and departure times, and the distribution of initial battery capacity and target charging capacity; The boundary condition determination module, based on the electric vehicle power consumption data, describes the electric vehicle charging process in combination with the actual situation of the charging station, including the constraints of electric vehicle power demand and charging power upper and lower limits, to obtain the electric vehicle charging boundary conditions; The optimization problem determination module, based on the dynamic electricity price and the aggregate game framework, calculates the charging cost objective function of the electric vehicle user, and combines the charging cost objective function of the electric vehicle user and the electric vehicle charging boundary conditions to obtain the electric vehicle charging optimization problem; The solving module uses the gradient projection parallel algorithm to solve the electric vehicle charging optimization problem to reduce the charging cost of electric vehicle users as the target, and obtains the load curve under Nash equilibrium, including: (1) First, set the initial iteration strategy To evenly distribute the required charging amount to all charging periods, define the algorithm convergence criterion according to the required convergence accuracy and the number of electric vehicles The size of the magnitude; (8) wherein EVs n the two-norm of the difference between two adjacent iteration strategies, The value of is determined according to the required convergence accuracy and the number of electric vehicles, and N is the set of electric vehicles. (2) Update the current strategy using the negative gradient direction of the cost function, so that the charging cost is iterated in the descending direction to prevent the new strategy from not meeting the charging constraints of EVs n , and find the nearest vector to the new strategy in the feasible region as the strategy result of this iteration (9) (10) wherein, is the charging cost function for EV n , is the charging power for EV n , is the charging power for other electric vehicles except EV n , is the gradient of the charging cost function for EV n with respect to its individual period power consumption, is the iteration step size, is the charging power for EV n in period t , is the feasible region for EV n , is the set of charging periods for the n th electric vehicle EV n , is the EV n charging strategy for the k th iteration ， is the operation of projecting the new strategy into the feasible region , denotes the arrival time of the n th electric vehicle EV n , denotes the departure time of the n th electric vehicle EV n . (3) After each iteration, check if the convergence criterion (8) is met; if met, the algorithm ends, and the obtained load curve is the approximate solution of the Nash equilibrium; if not met, return to (2) to repeatedly update the charging strategy until the convergence criterion is met; Based on the load curve under the Nash equilibrium, the current dynamic electricity price parameter and the electric vehicle load curve under the electricity consumption are obtained.

6. The Nash Equilibrium based cell electric vehicle charging strategy computation system of claim 5, wherein, In the data acquisition module, the data of electric vehicles arriving at the charging station is simulated to obtain electric vehicle power consumption data, including: Firstly, the arrival-departure time and the required charging power of all electric vehicles are generated; the formula is The arrival-departure time of the i-th electric vehicle EVi is denoted as n n The required charging power of the i-th electric vehicle EVi is denoted as The arrival time of the i-th electric vehicle EVi is subject to normal distribution with standard deviation and expectation The departure time of the i-th electric vehicle EVi is subject to normal distribution with standard deviation and expectation , i.e. the expected arrival time is 18:00 and the expected departure time is 9:00; the battery power of the i-th electric vehicle EVi when it arrives at the community is also subject to normal distribution with standard deviation and expectation , i.e. the expected arrival power is 51.37%, and the variable probability density function formula is shown as (1)-(3).​​ (1) (2) (3)。 7. The Nash Equilibrium based cell electric vehicle charging strategy computation system of claim 6, wherein, In the boundary condition determination module, based on the electric vehicle power consumption data, the electric vehicle charging process is described in combination with the actual situation of the charging station, including the electric vehicle power demand and the upper and lower limits of the charging power constraints, to obtain the electric vehicle charging boundary conditions, including: In the electric vehicle charging model, the user arrival and departure time, the upper and lower limits of the charging power, and the power consumption demand constraints are considered, including: (4) wherein the set of electric vehicles ; the n set of electric vehicles EV n set of charging time periods wherein the electric vehicle charging power is 0 outside the arrival and departure time ranges; and denote the minimum and maximum charging power, respectively, is the electric vehicle charging efficiency, is the EV n initial state of charge at arrival, is the upper limit of the battery state of charge set to prevent overcharging of the electric vehicle.

8. The Nash Equilibrium based cell electric vehicle charging strategy computation system of claim 5, wherein, In the optimization problem determination module, the charging cost objective function of the electric vehicle user is calculated based on the dynamic electricity price and the aggregate game framework, and the charging cost objective function of the electric vehicle user and the electric vehicle charging boundary conditions are combined to obtain the electric vehicle charging optimization problem, including: (5) (6) wherein is t periodic cell total load, is all electric vehicle load, is cell fixed load, ; is t periodic dynamic electricity price, and is dynamic electricity price coefficient; Each electric vehicle adjusts its charging strategy to minimize the cost function, and the optimization problem is expressed as: (7)。 9. A computer-readable storage medium, characterized in that, The computer readable storage medium stores a computer program, and the computer program realizes the steps of the cell electric vehicle charging strategy calculation method based on the Nash equilibrium in any one of claims 1-4 when executed by the processor.

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