Cell electric vehicle charging strategy calculation method and system based on Nash equilibrium

Abstract

The invention discloses a cell electric vehicle charging strategy calculation method and system based on Nash equilibrium, and the method comprises the steps: simulating and generating the data of an electric vehicle arriving at a charging station, and obtaining the power utilization data of the electric vehicle; 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, and electric vehicle charging boundary conditions are obtained; combining the charging cost objective function of the electric vehicle user with the electric vehicle charging boundary condition to obtain an electric vehicle charging optimization problem; in order to reduce the charging cost of an electric vehicle user, a gradient projection parallel algorithm is adopted to solve an electric vehicle charging optimization problem, a load curve under Nash equilibrium is obtained, and then a current dynamic electricity price parameter and an electric vehicle load curve under the electricity consumption condition are obtained. The system comprises a data acquisition module, a boundary condition determination module, an optimization problem determination module and a solving module. According to the method, the night load curve of the whole community is smoothed while each user pursues individual benefits.

Description

Technical Field

[0001] The present invention belongs to the field of power systems, and in particular relates to a method and system for calculating a charging strategy for electric vehicles in a residential area based on Nash equilibrium. Background Art

[0002] With the integration of renewable energy and the development of smart grid technology, demand response management has become the key to improving grid efficiency and reliability. In recent years, with the maturity of electric vehicle technology and the popularization of intelligent power consumption technology, the scale of electric vehicles has continued to increase. However, the large-scale disorderly charging of a large number of electric vehicles will not only increase the load fluctuations in residential areas and aggravate the peak-to-valley difference of the power grid, but also increase the charging cost and affect the user experience of electric vehicles. Therefore, it is necessary to design a reasonable charging strategy to achieve electric vehicle charging control within the community.

[0003] Centralized charging optimization methods have the following difficulties: Electric vehicles have small controllable loads, large numbers, and many types, which makes it difficult for aggregators to centrally obtain their power consumption and arrival and departure times; when aggregators collect data from all users, there is a risk of data leakage and user privacy cannot be protected; when calculating large-scale electric vehicles, the efficiency of centralized optimization is generally not high; centralized systems are difficult to adapt flexibly when facing an increase in the number of users or changes in the environment, and have poor scalability.

[0004] Different from the traditional centralized optimization, each electric vehicle in the distributed optimization algorithm is an independent individual, solving its own small-scale problems, and these small-scale problems are coordinated with each other, and finally reach the overall approximate optimality, which well solves the dilemma faced by the above centralized methods. In recent years, the research on charging electric vehicles using dynamic electricity price regulation has received widespread attention. This study assumes that aggregators have the power to set electricity prices in their own jurisdiction. Dynamic electricity price means that the electricity price is a function of the load. If the load in a certain period is high, the electricity price will also be high, thereby guiding the user's load to transfer from this period to other periods with lower electricity prices, so as to achieve the purpose of flattening the load curve. The competitive relationship of non-cooperative games between users is considered under the dynamic electricity price framework, and the Nash equilibrium well describes the final balance achieved by the competitive relationship of this non-cooperative game. It refers to a state in which no participant can obtain better benefits by unilaterally changing his strategy. Summary of the invention

[0005] The purpose of the present invention is to provide a method and system for calculating the charging strategy of electric vehicles in a residential area based on Nash equilibrium, so that each user can pursue individual interests while smoothing the nighttime load curve of the entire residential area and reducing the operating pressure of the power grid. Users do not need to disclose their charging habits or itinerary arrangements to the central agency, thereby protecting the privacy of users, and at the same time, the central agency does not need to undertake a large amount of data processing and optimization tasks, thereby reducing operating costs.

[0006] In order to achieve the above object, the present invention adopts the following technical solution: The calculation method of community electric vehicle charging strategy based on Nash equilibrium includes: Step 1: Simulate and generate data on 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 power and target charging power; Step 2: Based on the electric vehicle electricity consumption data and the actual situation of the charging station, the electric vehicle charging process is described, including constraints such as the electric vehicle power demand and the upper and lower limits of the charging power, and the electric vehicle charging boundary conditions are obtained; Step 3: Based on the dynamic electricity price and the aggregation 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 4: With the goal of reducing the charging cost of electric vehicle users, the gradient projection parallel algorithm is used to solve the electric vehicle charging optimization problem and obtain the load curve under Nash equilibrium. Based on the load curve under Nash equilibrium, the current dynamic electricity price parameters and the electric vehicle load curve under electricity consumption conditions are obtained.

[0007] A further improvement of the present invention is that step 1: simulating and generating data of electric vehicles arriving at charging stations to obtain electric vehicle electricity consumption data includes: First, generate the arrival and departure times and required charging power of all electric vehicles; in the formula , and Respectively represent the Nth electric vehicle EV n Arrival and departure times and the amount of charge required; and All follow a normal distribution, and the standard deviation of arrival time ,expect , standard deviation of departure time ,expect , that is, 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 also approximately follows the normal distribution, and the standard deviation of the power is taken , expected value , that is, the expected arrival power is 51.37%, and the variable probability density function formula is shown in (1)-(3); (1) (2) (3).

[0008] The present invention is further improved in that, step 2: 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 constraints such as the electric vehicle power demand and the upper and lower limits of the charging power, and the electric vehicle charging boundary conditions are obtained, including: The electric vehicle charging model considers the user's arrival and departure time, the upper and lower limits of charging power, and the power demand constraints, including: (4) in, Describes EV n Feasible charging set, electric car set ;No. n Electric Vehicles EV n Charging time collection , where the charging power of electric vehicles outside the arrival and departure time range is 0; For EV n In the period t The charging power, and Respectively represent the minimum and maximum charging power, Charging efficiency for electric vehicles, For EV n Initial charge upon arrival, It is a battery charge limit set to prevent electric vehicles from overcharging.

[0009] The present invention is further improved in that, step three: based on the dynamic electricity price and the aggregated 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) In the formula, For EV n The charging cost function is: for t Total load of the cell during the period, For all electric vehicle loads, is the fixed load of the community, To remove EV nCharging power of other electric vehicles: ; for t Dynamic electricity prices during time periods, and is the dynamic electricity price coefficient; Each electric vehicle adjusts its charging strategy to minimize the cost function. The optimization problem is expressed as: (7).

[0010] The present invention is further improved in that, step 4: using a gradient projection parallel algorithm to solve the electric vehicle charging optimization problem with the goal of reducing the charging cost of electric vehicle users, and obtaining a load curve under Nash equilibrium, including: (1) First, set the initial iteration strategy In order to evenly distribute the required charging amount to all charging periods, the algorithm convergence criterion is defined according to the required convergence accuracy and the number of electric vehicles. size; (8) In the formula For EV n The bi-norm of the difference between two adjacent iteration strategies, The value of depends on the required convergence accuracy and the number 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 downward direction to prevent the new strategy from not satisfying the EV n The charging constraints are used to find the vector closest to the new strategy in the feasible domain as the strategy result of this iteration. (9) (10) in For EV n The gradient of the cost function for the power consumption in each period is: is the iteration step length, For the k EV of the next iteration n Charging strategy ， To project the new strategy into the feasible domain Operations; (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 the Nash equilibrium; if it is not met, return to (2) and repeatedly update the charging strategy until the convergence criterion is met.

[0011] The community electric vehicle charging strategy calculation system based on Nash equilibrium includes: The data acquisition module simulates and generates data on electric vehicles arriving at charging stations, and obtains 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; The boundary condition determination module describes the charging process of electric vehicles based on the electric vehicle power consumption data and the actual situation of the charging station, including constraints such as the electric vehicle power demand and the upper and lower limits of the charging power, and obtains the charging boundary conditions of electric vehicles; The optimization problem determination module calculates the charging cost objective function of electric vehicle users based on the dynamic electricity price and the aggregation game framework, and combines the charging cost objective function of electric vehicle users and the electric vehicle charging boundary conditions to obtain the electric vehicle charging optimization problem; 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 of electric vehicle users, and obtains the load curve under Nash equilibrium. Based on the load curve under Nash equilibrium, the current dynamic electricity price parameters and the electric vehicle load curve under electricity consumption conditions are obtained.

[0012] A further improvement of the present invention is that, in the data acquisition module, the data of the electric vehicle arriving at the charging station is simulated and generated to obtain the electric vehicle power consumption data, including: First, generate the arrival and departure times and required charging power of all electric vehicles; in the formula , and Respectively represent the Nth electric vehicle EV n Arrival and departure times and the amount of charge required; and All follow a normal distribution, and the standard deviation of arrival time ,expect , standard deviation of departure time ,expect , that is, 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 also approximately follows the normal distribution, and the standard deviation of the power is taken , expected value , that is, the expected arrival power is 51.37%, and the variable probability density function formula is shown in (1)-(3); (1) (2) (3).

[0013] A further improvement of the present invention is that, in the boundary condition determination module, based on the electric vehicle power consumption data, combined with the actual situation of the charging station, the electric vehicle charging process is described, including constraints such as the electric vehicle power demand and the upper and lower limits of the charging power, to obtain the electric vehicle charging boundary conditions, including: The electric vehicle charging model considers the user's arrival and departure time, the upper and lower limits of charging power, and the power demand constraints, including: (4) in, Describes EV n Feasible charging set, electric car set ;No. n Electric Vehicles EV n Charging time collection , where the charging power of electric vehicles outside the arrival and departure time range is 0; For EV n In the period t The charging power, and Respectively represent the minimum and maximum charging power, Charging efficiency for electric vehicles, For EV n Initial charge upon arrival, It is a battery charge limit set to prevent electric vehicles from overcharging.

[0014] A further improvement of the present invention is that 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) In the formula, For EV n The charging cost function is: for t Total load of the cell during the period, For all electric vehicle loads, is the fixed load of the community, To remove EV n Charging power of other electric vehicles: ; for t Dynamic electricity prices during time periods, and is the dynamic electricity price coefficient; Each electric vehicle adjusts its charging strategy to minimize the cost function. The optimization problem is expressed as: (7).

[0015] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of a method for calculating a charging strategy for electric vehicles in a residential area based on Nash equilibrium.

[0016] Compared with the prior art, the present invention has at least the following beneficial technical effects: The method and system for calculating the charging strategy of electric vehicles in a community based on Nash equilibrium provided by the present invention have the following four significant advantages. First, the distributed method can protect the privacy of users. Users do not need to upload their travel data and other sensitive information to the central server, reducing the risk of data leakage. Second, the distributed method supports large-scale electric vehicle calculations. Since each user EV n The charging strategy calculations are performed independently without coupling with each other, so the parallel calculation of the algorithm can be supported. This method can handle the optimization problems of a large number of users, and the parallel algorithm can quickly calculate the equilibrium results. Furthermore, the equilibrium method reduces the computational burden of the central agency. In traditional centralized optimization, the central agency needs to perform complex calculations to determine the optimal strategy after collecting data from all users. This method places high demands on the computing power of the central agency. Finally, compared with the centralized unified optimization method, the distributed optimization method allows the algorithm to be expanded to more users and more complex power grid environments. Each user can adjust the optimization strategy according to his or her own needs and preferences without relying on the unified dispatch of the central agency. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 This is a flow chart of a method for calculating a charging strategy for electric vehicles in a residential area based on Nash equilibrium according to the present invention; Figure 2 It is a schematic diagram showing the change of valley filling effect with the number of electric vehicles participating in equilibrium; Figure 3 It is a schematic diagram showing the relationship between iteration time and number of times as the number of electric vehicles changes; Figure 4 It is a structural block diagram of the community electric vehicle charging strategy calculation system based on Nash equilibrium of the present invention. DETAILED DESCRIPTION

[0018] In the following, only some exemplary embodiments are briefly described. As those skilled in the art will appreciate, the described embodiments may be modified in various ways without departing from the spirit or scope of the present invention. Therefore, the drawings and descriptions are considered to be exemplary and non-restrictive in nature.

[0019] It should be understood that when used in this specification and the appended claims, the terms "include" and "comprises" indicate the presence of described features, integers, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or combinations thereof.

[0020] It should also be understood that the terms used in the present specification are only for the purpose of describing specific embodiments and are not intended to limit the present invention. As used in the present specification and the appended claims, unless the context clearly indicates otherwise, the singular forms "a", "an" and "the" are intended to include plural forms.

[0021] It should be further understood that the term "and / or" used in the present description and the appended claims refers to any and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0022] Various structural schematic diagrams of the embodiments disclosed in the present invention are shown in the accompanying drawings. These figures are not drawn to scale, and some details are magnified and some details may be omitted for the purpose of clear expression. The shapes of various regions and layers shown in the figures and the relative sizes and positional relationships therebetween are only exemplary, and may deviate in practice due to manufacturing tolerances or technical limitations, and those skilled in the art may additionally design regions / layers with different shapes, sizes, and relative positions according to actual needs.

[0023] The embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0024] Example 1 like Figure 1 As shown, the method for calculating the charging strategy of electric vehicles in a residential area based on Nash equilibrium provided by the present invention includes: Step 1: Simulate and generate data on 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 power and target charging power; Step 2: Based on the electric vehicle electricity consumption data and the actual situation of the charging station, the electric vehicle charging process is described, including constraints such as the electric vehicle power demand and the upper and lower limits of the charging power, and the electric vehicle charging boundary conditions are obtained; Step 3: Based on the dynamic electricity price and the aggregation 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 4: With the goal of reducing the charging cost of electric vehicle users, the gradient projection parallel algorithm is used to solve the electric vehicle charging optimization problem and obtain the load curve under Nash equilibrium. Based on the load curve under Nash equilibrium, the current dynamic electricity price parameters and the electric vehicle load curve under electricity consumption conditions are obtained.

[0025] In this embodiment, step 1: simulating and generating data of electric vehicles arriving at charging stations to obtain electric vehicle electricity consumption data includes: First, generate the arrival and departure times and required charging power of all electric vehicles; in the formula , and Respectively represent the Nth electric car EV n Arrival and departure times and the amount of charge required; and All obey the normal distribution, and the standard deviation of arrival time ,expect , standard deviation of departure time ,expect , that is, 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 also approximately follows the normal distribution, and the standard deviation of the power is taken , expected value , that is, the expected arrival power is 51.37%, and the variable probability density function formula is shown in (1)-(3); (1) (2) (3).

[0026] In this embodiment, step 2: 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 constraints such as the electric vehicle power demand and the upper and lower limits of the charging power, and the electric vehicle charging boundary conditions are obtained, including: The electric vehicle charging model considers the user's arrival and departure time, the upper and lower limits of charging power, and the power demand constraints, including: (4) in, Describes EV n Feasible charging set, electric car set ;No. n Electric Vehicles EV n Charging time collection , where the charging power of electric vehicles outside the arrival and departure time range is 0; For EV n In the period t The charging power, and Respectively represent the minimum and maximum charging power, Charging efficiency for electric vehicles, For EV n Initial charge upon arrival, It is a battery charge limit set to prevent electric vehicles from overcharging.

[0027] In this embodiment, step three: based on the dynamic electricity price and the aggregated 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) In the formula, For EV n The charging cost function is: for t Total load of the cell during the period, For all electric vehicle loads, is the fixed load of the community, To remove EV n Charging power of other electric vehicles: ; for t Dynamic electricity prices during time periods, and is the dynamic electricity price coefficient; Each electric vehicle adjusts its charging strategy to minimize the cost function. The optimization problem is expressed as: (7).

[0028] In this embodiment, step 4: using the gradient projection parallel algorithm to solve the electric vehicle charging optimization problem with the goal of reducing the charging cost of electric vehicle users, and obtaining the load curve under Nash equilibrium, including: (1) First, set the initial iteration strategy In order to evenly distribute the required charging amount to all charging periods, the algorithm convergence criterion is defined according to the required convergence accuracy and the number of electric vehicles. size; (8) In the formula For EV n The bi-norm of the difference between two adjacent iteration strategies, The value of depends on the required convergence accuracy and the number 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 downward direction to prevent the new strategy from not satisfying the EV n The charging constraints are used to find the vector closest to the new strategy in the feasible domain as the strategy result of this iteration. (9) (10) in For EV n The gradient of the cost function for the power consumption in each period is: is the iteration step length, For the k EV of the next iteration n Charging strategy ， To project the new strategy into the feasible domain Operations; (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 the Nash equilibrium; if it is not met, return to (2) and repeatedly update the charging strategy until the convergence criterion is met.

[0029] Example 2 First, generate the arrival and departure times and required charging power of all electric vehicles. The arrival and departure times are normally distributed, and the standard deviation of the arrival time is ,expect , standard deviation of departure time ,expect , that is, 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 also approximately follows the normal distribution, and the standard deviation of the power is taken as , expected value. ., that is, the expected arrival power is 51.37%. The probability density function formulas of the above variables are shown in (7)-(9). The parameter settings of the above formula (1) take the upper and lower limits of charging power ,Pick ,in is the maximum battery capacity of a single electric vehicle, and the charging efficiency is ; The dynamic electricity price coefficient in formula (4) is The algorithm is solved by calling the Yalmip+Cplex solver in the MATLAB R2020a environment.

[0030] (7) (8) (9) Execute the calculation steps of the community electric vehicle charging strategy based on Nash equilibrium: Set the initial iteration strategy , and determine the convergence criterion according to the number of electric vehicles and the required convergence accuracy The size of , then repeat the following steps until the convergence criterion is met: 1) According to the electric vehicle cost function, calculate the negative gradient of the cost function, and update each user's charging plan according to the negative gradient. 2) In order to prevent the new strategy from being out of the feasible domain, it is necessary to project the new strategy into the feasible domain. 3) According to the convergence criterion, determine whether the Nash equilibrium is reached. If it is reached, end and obtain the load curve. If not, return to step 1).

[0031] Considering 100, 500, 1000, 1500, 2000, 3000, and 4000 electric vehicles participating in demand response, the valley filling results are as follows: Figure 2 As shown. Figure 2 It can be seen that as the number of electric vehicles participating in demand response increases, the valley filling effect improves. When the number of electric vehicles participating in demand response is small, the total load curve is curved, and when the number of electric vehicles reaches 3,000 and 4,000, the total load curve tends to be horizontal. It can be predicted that if the number of electric vehicles continues to increase until the charging power of electric vehicles is much greater than the basic load, the total power curve will tend to be horizontal, which is consistent with the physical meaning of reaching Nash equilibrium, proving the rationality of the algorithm results.

[0032] The following is an explanation of the algorithm's calculation time and number of iterations. Consider the cases where 100, 500, 1000, 1500, 2000, 3000, and 4000 electric vehicles participate. Figure 3 As shown in the figure. When the number of electric vehicles is less than 2000, the number of iterations and time increase with the increase in the number of electric vehicles, but when the number of electric vehicles reaches 4000, the algorithm computational cost decreases significantly. This is because the convergence condition in formula (2) becomes looser at this time, which has a greater impact on the computational cost than the increase in the number of electric vehicles. Under the same charging accuracy of a single electric vehicle, as the number of electric vehicles increases, the computational time and number of iterations required by the gradient projection parallel algorithm do not increase by an order of magnitude, which meets the computational cost requirements in actual scenarios.

[0033] Example 3 like Figure 4 As shown, the community electric vehicle charging strategy calculation system based on Nash equilibrium provided by the present invention includes: The data acquisition module simulates and generates data on electric vehicles arriving at charging stations, and obtains 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; The boundary condition determination module describes the charging process of electric vehicles based on the electric vehicle power consumption data and the actual situation of the charging station, including constraints such as the electric vehicle power demand and the upper and lower limits of the charging power, and obtains the charging boundary conditions of electric vehicles; The optimization problem determination module calculates the charging cost objective function of electric vehicle users based on the dynamic electricity price and the aggregation game framework, and combines the charging cost objective function of electric vehicle users and the electric vehicle charging boundary conditions to obtain the electric vehicle charging optimization problem; 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 of electric vehicle users, and obtains the load curve under Nash equilibrium. Based on the load curve under Nash equilibrium, the current dynamic electricity price parameters and the electric vehicle load curve under electricity consumption conditions are obtained.

[0034] Example 4 The present invention provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the steps of the method for calculating a charging strategy for electric vehicles in a residential area based on Nash equilibrium are implemented.

[0035] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present application may adopt 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 codes.

[0036] The present application is described with reference to the flowcharts and / or block diagrams of the methods, systems and computer program products according to the embodiments of the present application. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of the processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A system that specifies the functions of a box or multiple boxes.

[0037] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0038] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0039] The above shows and describes the basic principles and main features of the present invention and the advantages of the present invention. It is obvious to those skilled in the art that the present invention is not limited to the details of the above exemplary embodiments, and the present invention can be implemented in other specific forms without departing from the spirit or basic features of the present invention. Therefore, no matter from which point of view, the embodiments should be regarded as exemplary and non-restrictive. The scope of the present invention is defined by the attached claims rather than the above description, and it is intended that all changes falling within the meaning and scope of the equivalent elements of the claims are included in the present invention. Any figure mark in the claims should not be regarded as limiting the claims involved.

[0040] In addition, it should be understood that although this specification is described in accordance with the implementation modes, not every implementation mode contains only one independent technical solution. This description of the specification is only for the sake of clarity. Those skilled in the art should regard the specification as a whole. The technical solutions in each embodiment can also be appropriately combined to form other implementation modes that can be understood by those skilled in the art. The above content is only to illustrate the technical idea of ​​the present invention, and cannot be used to limit the protection scope of the present invention. Any changes made on the basis of the technical solution according to the technical idea proposed by the present invention shall fall within the protection scope of the claims of the present invention.

Claims

1. A calculation method for charging strategy of electric vehicles in residential areas based on Nash equilibrium, characterized in that: include: Step 1: Simulate and generate data on 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 power and target charging power; Step 2: Based on the electric vehicle electricity consumption data and the actual situation of the charging station, the electric vehicle charging process is described, including constraints such as the electric vehicle power demand and the upper and lower limits of the charging power, and the electric vehicle charging boundary conditions are obtained; Step 3: Based on the dynamic electricity price and the aggregation 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 4: With the goal of reducing the charging cost of electric vehicle users, the gradient projection parallel algorithm is used to solve the electric vehicle charging optimization problem and obtain the load curve under Nash equilibrium. Based on the load curve under Nash equilibrium, the current dynamic electricity price parameters and the electric vehicle load curve under electricity consumption conditions are obtained.

2. The method for calculating the charging strategy of electric vehicles in a residential area based on Nash equilibrium according to claim 1 is characterized in that: Step 1: Simulate and generate data of electric vehicles arriving at charging stations to obtain electric vehicle electricity consumption data, including: First, generate the arrival and departure times and required charging power of all electric vehicles; in the formula , and Respectively represent the Nth electric vehicle EV n Arrival and departure times and the amount of charge required; and All follow a normal distribution, and the standard deviation of arrival time ,expect , standard deviation of departure time ,expect , that is, 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 also approximately follows the normal distribution, and the standard deviation of the power is taken , expected value , that is, the expected arrival power is 51.37%, and the variable probability density function formula is shown in (1)-(3); (1) (2) (3)。 3. The method for calculating the charging strategy of electric vehicles in a residential area based on Nash equilibrium according to claim 1 is characterized in that: Step 2: Based on the electric vehicle electricity consumption data and the actual situation of the charging station, the electric vehicle charging process is described, including constraints such as the electric vehicle power demand and the upper and lower limits of the charging power, and the electric vehicle charging boundary conditions are obtained, including: The electric vehicle charging model considers the user's arrival and departure time, the upper and lower limits of charging power, and the power demand constraints, including: (4) in, Describes EV n Feasible charging set, electric car set ;No. n Electric Vehicles EV n Charging time collection , where the charging power of electric vehicles outside the arrival and departure time range is 0; For EV n In the period t The charging power, and Respectively represent the minimum and maximum charging power, Charging efficiency for electric vehicles, For EV n Initial charge upon arrival, It is a battery charge limit set to prevent electric vehicles from overcharging.

4. The method for calculating the charging strategy of electric vehicles in a residential area based on Nash equilibrium according to claim 1 is characterized in that: Step 3: Based on the dynamic electricity 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 electric vehicle charging boundary conditions are combined to obtain the electric vehicle charging optimization problem, including: (5) (6) In the formula, For EV n The charging cost function is: for t Total load of the cell during the period, For all electric vehicle loads, is the fixed load of the community, To remove EV n Charging power of other electric vehicles: ; for t Dynamic electricity prices during time periods, and is the dynamic electricity price coefficient; Each electric vehicle adjusts its charging strategy to minimize the cost function. The optimization problem is expressed as: (7)。 5. The method for calculating the charging strategy of electric vehicles in a residential area based on Nash equilibrium according to claim 1 is characterized in that: Step 4: To reduce the charging cost of electric vehicle users, the gradient projection parallel algorithm is used to solve the electric vehicle charging optimization problem and obtain the load curve under Nash equilibrium, including: (1) First, set the initial iteration strategy In order to evenly distribute the required charging amount to all charging periods, the algorithm convergence criterion is defined according to the required convergence accuracy and the number of electric vehicles. size; (8) In the formula For EV n The bi-norm of the difference between two adjacent iteration strategies, The value of depends on the required convergence accuracy and the number 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 downward direction to prevent the new strategy from not satisfying the EV n The charging constraints are used to find the vector closest to the new strategy in the feasible domain as the strategy result of this iteration. (9) (10) in For EV n The gradient of the cost function for the power consumption in each period is: is the iteration step length, For the k EV of the next iteration n Charging strategy ， To project the new strategy into the feasible domain Operations; (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 the Nash equilibrium; if it is not met, return to (2) and repeatedly update the charging strategy until the convergence criterion is met.

6. A community electric vehicle charging strategy calculation system based on Nash equilibrium, characterized in that: include: The data acquisition module simulates and generates data on electric vehicles arriving at charging stations, and obtains 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; The boundary condition determination module describes the charging process of electric vehicles based on the electric vehicle power consumption data and the actual situation of the charging station, including constraints such as the electric vehicle power demand and the upper and lower limits of the charging power, and obtains the charging boundary conditions of electric vehicles; The optimization problem determination module calculates the charging cost objective function of electric vehicle users based on the dynamic electricity price and the aggregation game framework, and combines the charging cost objective function of electric vehicle users and the electric vehicle charging boundary conditions to obtain the electric vehicle charging optimization problem; 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 of electric vehicle users, and obtains the load curve under Nash equilibrium. Based on the load curve under Nash equilibrium, the current dynamic electricity price parameters and the electric vehicle load curve under electricity consumption conditions are obtained.

7. The community electric vehicle charging strategy calculation system based on Nash equilibrium according to claim 6 is characterized in that: In the data acquisition module, the data of electric vehicles arriving at the charging station is simulated and generated to obtain the electric vehicle power consumption data, including: First, generate the arrival and departure times and required charging power of all electric vehicles; in the formula , and Respectively represent the Nth electric vehicle EV n Arrival and departure times and the amount of charge required; and All follow a normal distribution, and the standard deviation of arrival time ,expect , standard deviation of departure time ,expect , that is, 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 also approximately follows the normal distribution, and the standard deviation of the power is taken , expected value , that is, the expected arrival power is 51.37%, and the variable probability density function formula is shown in (1)-(3); (1) (2) (3)。 8. The community electric vehicle charging strategy calculation system based on Nash equilibrium according to claim 6 is characterized in that: In the boundary condition determination module, based on the electric vehicle power consumption data and combined with the actual situation of the charging station, the electric vehicle charging process is described, including constraints such as the electric vehicle power demand and the upper and lower limits of the charging power, and the electric vehicle charging boundary conditions are obtained, including: The electric vehicle charging model considers the user's arrival and departure time, the upper and lower limits of charging power, and the power demand constraints, including: (4) in, Describes EV n Feasible charging set, electric car set ;No. n Electric Vehicles EV n Charging time collection , where the charging power of electric vehicles outside the arrival and departure time range is 0; For EV n In the period t The charging power, and Respectively represent the minimum and maximum charging power, Charging efficiency for electric vehicles, For EV n Initial charge upon arrival, It is a battery charge limit set to prevent electric vehicles from overcharging.

9. The community electric vehicle charging strategy calculation system based on Nash equilibrium according to claim 6 is characterized in that: In the optimization problem determination module, the charging cost objective function of electric vehicle users is calculated based on the dynamic electricity price and the aggregation game framework. The charging cost objective function of electric vehicle users and the electric vehicle charging boundary conditions are combined to obtain the electric vehicle charging optimization problem, including: (5) (6) In the formula, For EV n The charging cost function is: for t Total load of the cell during the period, For all electric vehicle loads, is the fixed load of the community, To remove EV n Charging power of other electric vehicles: ; for t Dynamic electricity prices during time periods, and is the dynamic electricity price coefficient; Each electric vehicle adjusts its charging strategy to minimize the cost function. The optimization problem is expressed as: (7)。 10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the method for calculating a charging strategy for electric vehicles in a residential area based on Nash equilibrium according to any one of claims 1 to 5 are implemented.

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