Optimal configuration method of electric vehicle battery swap station based on non-cooperative game

Abstract

The embodiment of the invention provides a non-cooperative game-based optimal configuration method of an electric vehicle battery swap station, and belongs to the field of optimal configuration of the electric vehicle battery swap station. The method comprises the following steps: proposing an operation optimization strategy of the battery swap station according to the battery swap demand of a user in each time period and the peak clipping task of the battery swap station; formulating a non-cooperative game operation strategy in the process of participating in power grid peak clipping by the battery swap station; establishing an optimization target based on the user target satisfaction and the target daily comprehensive income of the battery swap station; and solving the non-cooperative game model by using an improved particle swarm algorithm to obtain an optimal strategy of each game participant, sharing the strategies by the two game parties, and determining a game equilibrium point for maximizing the benefits of the two game parties. Analysis is carried out from the unilateral angle of a battery swap station, and the service quality of a user side is emphasized; and moreover, peak clipping tasks and peak clipping capability margin constraints are considered, so that the condition of excessive or under-response of the battery swap station is avoided, multi-party gaming is performed, and multi-party benefits are maximized.

Description

Technical Field

[0001] The present invention relates to the technical field of optimized configuration of electric vehicle battery swap stations, and in particular to an optimized configuration method of electric vehicle battery swap stations based on non-cooperative game. Background Art

[0002] In the context of global advocacy of green living, the new energy vehicle market is expanding rapidly, especially the continuous increase in the number of electric vehicles (EVs). The traditional charging mode is difficult to meet the huge market demand, especially driven by the rapid growth of the number of electric vehicles and the national new energy policy, the demand for electric vehicle energy replenishment is increasing. As an innovative way of energy replenishment, battery swap stations have received widespread attention due to their advantages of on-demand power distribution, fast and convenient, and centralized management and maintenance of batteries. With the development of battery swap station technology, optimizing its operation mode has become the focus of research and enterprise attention.

[0003] Battery swap stations play an important role in the energy replenishment of electric vehicles. Their optimized operation can not only improve service efficiency, but also improve the reliability of battery management and energy utilization. Through centralized management and optimized configuration of batteries, battery swap stations can reduce operating costs and improve overall economic benefits while meeting peak battery demand. In addition, battery swap stations can also be combined with grid demand response to help the grid reduce peak loads and fill valleys, reduce grid burdens, and further promote the development of sustainable energy. Therefore, optimizing the capacity configuration and operation strategy of battery swap stations has important practical significance.

[0004] By reviewing the existing literature on the optimal configuration of electric vehicle battery swap stations, the following conclusions can be drawn: (1) The analysis is only conducted from the perspective of the battery swap station or the power grid, ignoring the service quality on the user side; (2) Although some studies have proposed peak load shifting strategies, they lack an effective reward and punishment mechanism to regulate the response behavior of the battery swap station, which may lead to over-response or under-response of the battery swap station; (3) Existing optimal configuration studies have failed to fully consider the dynamic changes in battery demand, resulting in the limited service capacity of the battery swap station when the battery supply is tight during peak hours. Therefore, it is necessary to further improve the optimization model of battery swap station capacity configuration and service capacity based on existing research. Summary of the invention

[0005] The purpose of the embodiments of the present invention is to provide an optimization configuration method for electric vehicle battery swap stations based on non-cooperative game, which is used to fully or at least partially solve the technical problems existing in the above-mentioned prior art.

[0006] In order to achieve the above-mentioned purpose, an embodiment of the present invention provides an optimization configuration method of electric vehicle battery swap stations based on non-cooperative game, comprising: According to the battery swapping needs of users in each period and the peak load shaving tasks of the battery swapping station, an operation optimization strategy of the battery swapping station is proposed, wherein the operation optimization strategy of the battery swapping station includes peak load shaving tasks, peak load reduction effect evaluation, tram travel distance model, battery swapping service requirements, service capacity margin constraints and peak load shaving task constraints of the battery swapping station; Formulate non-cooperative game operation strategies for battery swap stations in the process of participating in power grid peak shaving; Establish an optimization goal based on user target satisfaction and target daily comprehensive revenue of the battery swap station, where the daily comprehensive revenue of the battery swap station includes peak shaving subsidies, saved charging costs and investment costs; Considering the interests of all game participants, the optimization steps are completed. First, the operation optimization strategy of the battery swap station is input, and then the strategies of each game participant are initialized to generate the number of battery swap cars at each time period of each particle's battery swap station, and the infeasible solution is corrected to a feasible solution according to various constraints. The optimization is carried out with the goals of user satisfaction and maximizing the benefits of the battery swap station microgrid respectively. The non-cooperative game model is solved using the improved particle swarm algorithm to obtain the optimal strategy of each game participant. The two parties share the strategy and determine the game equilibrium point that maximizes the interests of both parties. Optionally, the peak shaving task is divided into two types, wherein the first is that the peak load balancing capability of the battery swap station is greater than the peak balancing requirement of the distribution network, and the peak balancing requirement is used as the peak shaving task of the battery swap station; the second is that the peak load balancing capability of the battery swap station is less than or equal to the peak balancing requirement of the distribution network.

[0007] Optionally, the peak load reduction effect evaluation expression is as follows: ; In the formula, The power saving coefficient is used to reflect the reduction degree of feeder load before and after peak shaving. , They are the maximum values ​​of the total load of the 10kV distribution feeder in a day before and after peak shaving, respectively.

[0008] Optionally, the tram travel distance model expression is as follows: ; In the formula, is the driving distance of the electric vehicle, is the total daily mileage of the electric vehicle, , The time when the electric vehicle starts running and the time when the battery needs to be replaced. It is the percentage of the electric vehicle’s mileage in each period to the total daily mileage.

[0009] Optionally, the battery swap service requirement expression is as follows: ; In the formula, for The number of EV batteries whose SOC values ​​meet the battery replacement conditions during the time period, is the battery shortage amount, The number of cars waiting in line due to insufficient number of replaceable batteries.

[0010] Optionally, the service capacity margin constraint expression is as follows: ; In the formula, The service capacity margin of the battery swap station in each period. for The number of EV batteries whose SOC values ​​meet the battery replacement conditions during the time period, is the number of EVs that need to be replaced during period t.

[0011] Optionally, the peak shaving task constraint expression of the battery swap station is as follows: ; In the formula, The peak shaving task of the battery swap station is is the actual peak shaving amount, The task completion degree of the battery swap station.

[0012] Optionally, the expression of user satisfaction is as follows: ; In the formula, To improve the user's satisfaction with battery replacement, is the number of EVs that are replaced in each period, is the total number of EVs that have undergone battery replacement.

[0013] Optionally, the daily comprehensive revenue expression of the battery swap station is as follows: ; In the formula, is the daily comprehensive income of the battery swap station, To provide subsidies for peak load reduction, The charging cost can be saved. Convert the daily equipment investment cost for the battery swap station.

[0014] Optionally, each game participant The optimal strategy expression of the round is as follows:

[0015] In the formula, For the The number of batteries waiting to be charged in each period, For the Total number of wheel chargers, For the The number of wheel battery chargers to be configured, For the Total number of wheel chargers, For the The number of wheel battery chargers to be configured.

[0016] Through the above technical scheme, it is possible to analyze from the unilateral perspective of the battery swap station or the power grid, and pay attention to the service quality on the user side; and consider the peak shaving task and the peak shaving capacity margin constraints to ensure that the battery swap station will not over-respond or under-respond; in addition, the existing optimization configuration research fails to fully consider the dynamic changes in battery demand, resulting in the service capacity of the battery swap station being limited when the battery supply is tight during peak hours. This application adds the dynamic changes in battery demand to further improve the optimization model of the capacity configuration and service capacity of the battery swap station; finally, a multi-party game is carried out to ensure high user satisfaction and maximize the interests of multiple parties.

[0017] Other features and advantages of the embodiments of the present invention will be described in detail in the subsequent detailed description. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The accompanying drawings are used to provide a further understanding of the embodiments of the present invention and constitute a part of the specification. Together with the following specific implementations, they are used to explain the embodiments of the present invention, but do not constitute a limitation on the embodiments of the present invention. In the accompanying drawings: Figure 1 It is an implementation flow chart of an optimization configuration method of an electric vehicle battery swap station based on a non-cooperative game provided in an embodiment of the present invention; Figure 2 This is a diagram of a microgrid structure including a battery swap station provided by an embodiment of the present invention; Figure 3 A probability density diagram of the start time of battery replacement for an electric vehicle provided by an embodiment of the present invention; Figure 4 A graph of the number of batteries replaced at different times of the day in an electric vehicle provided by an embodiment of the present invention; Figure 5 It is a non-cooperative game flow chart provided by an embodiment of the present invention; Figure 6 An embodiment of the present invention provides a graph of the number of iterations for solving Nash equilibrium. DETAILED DESCRIPTION

[0019] The specific implementation of the embodiment of the present invention is described in detail below in conjunction with the accompanying drawings. It should be understood that the specific implementation described here is only used to illustrate and explain the embodiment of the present invention, and is not used to limit the embodiment of the present invention.

[0020] See also Figure 1 As shown, it is an implementation flow chart of an optimization configuration method of an electric vehicle battery swap station based on a non-cooperative game provided by an embodiment of the present invention, which includes the following execution steps: Step 100: Propose an operation optimization strategy for the battery swap station based on the battery swap demand of users in each time period and the peak shaving task of the battery swap station.

[0021] Among them, the operation optimization strategy of the battery swap station includes peak shaving tasks, peak load reduction effect evaluation, tram travel distance model, battery swap service requirements, service capacity margin constraints and battery swap station peak shaving task constraints.

[0022] Specifically, the peak shaving tasks are divided into two types. The first type is that the peak load balancing capability of the battery swap station is greater than the peak balancing requirement of the distribution network, and the peak balancing requirement is used as the peak shaving task of the battery swap station; the second type is that the peak load balancing capability of the battery swap station is less than or equal to the peak balancing requirement of the distribution network.

[0023] More specifically, the first peak shaving task expression is as follows: ; In the formula, is the peak balancing requirement of the distribution network, It is the task of reducing the peak load of battery swap stations.

[0024] The second peak shaving task expression is as follows: ; In the formula, The peak load balancing capability of the battery swap station. It is the task of reducing the peak load of battery swap stations.

[0025] In some methods, the distribution network dispatching center (DC) needs to determine whether the battery swap station needs to participate in the peak shaving task based on the day-ahead load forecast. If necessary, it follows the following process: DC determines the peak shaving period through the high-load period of the feeder in a day, and sends the peak shaving notice to the battery swap station; the battery swap station predicts the day-ahead charging load based on the previous charging load information, and calculates its peak load balancing capacity, and feeds it back to the distribution network dispatching center; DC formulates a dispatching plan based on the information obtained, obtains the peak shaving task of the battery swap station, and then provides electricity price subsidies based on the actual task completion of the battery swap station, so that the daily comprehensive income of the battery swap station is maximized. As a type of intermediate service operator, the battery swap station needs to meet the service needs of the user side while ensuring the service quality on the microgrid side, that is, the two constraints of the battery swap station optimization operation strategy - peak shaving task constraints and service capacity constraints. Under the condition of ensuring service capacity and battery swap quality, the battery swap station responds to the time-of-use electricity price policy, reasonably adjusts the charging time of the EV battery and the capacity configuration of the battery swap station, effectively completes the peak shaving task, and reduces the cost of purchasing electricity. At the same time, it can obtain subsidies, which increases the profit margin of the entire microgrid battery swap station, such as Figure 2 shown.

[0026] Furthermore, the peak load reduction effect evaluation expression is as follows: ; In the formula, The power saving coefficient is used to reflect the reduction degree of feeder load before and after peak shaving. , They are the maximum values ​​of the total load of the 10kV distribution feeder in a day before and after peak shaving, respectively.

[0027] Furthermore, the model expression of the tram travel distance is as follows: ; In the formula, is the driving distance of the electric vehicle, is the total daily mileage of the electric vehicle, , The time when the electric vehicle starts running and the time when the battery needs to be replaced. It is the percentage of the electric vehicle's mileage in each period to the total daily mileage, and the expression is as follows:

[0028] In the formula, is the driving distance of the electric vehicle;

[0029] The battery replacement demand of electric vehicles can be determined by the battery replacement time of each electric vehicle. Assume that the electric vehicle is a private car, and assume that the charging and swapping stations in the integrated charging and swapping power station only provide battery replacement services, and the battery specifications are the same, such as Figure 2-3 shown.

[0030] Furthermore, the battery swap service requirements are expressed as follows: ; In the formula, for The number of EV batteries whose SOC values ​​meet the battery replacement conditions during the time period, is the battery shortage amount, The number of cars waiting in line due to insufficient number of replaceable batteries.

[0031] In some embodiments, when the state of charge (SOC) of the battery, that is, the ratio of the current battery power to the total battery capacity, is not less than 0.9, the battery replacement service can be supported.

[0032] Furthermore, the service capacity margin constraint expression is as follows: ; In the formula, The service capacity margin of the battery swap station in each period. for The number of EV batteries whose SOC values ​​meet the battery replacement conditions during the time period, is the number of EVs that need to be replaced during period t.

[0033] In order to evaluate users' battery swapping experience and satisfaction with battery swap station services, the changes in users' battery swapping needs are compared with the number of replaceable batteries in the corresponding period, and the service capacity margin of the battery swap station in each period is obtained, and its value range is set in (-1, 1).

[0034] In some embodiments, the urgency of battery supply at a battery swap station is reflected by the minimum service capacity margin of the battery swap station, which is expressed as follows:

[0035] The constraint expression of the service capacity margin of the battery swap station is as follows:

[0036] In the formula, In order to meet the normal battery swapping needs of the battery swap station, the service capacity margin limit is set to 0.05.

[0037] Furthermore, the constraint expression of the peak load shaving task of the battery swap station is as follows: ; In the formula, The peak shaving task of the battery swap station is is the actual peak shaving amount, The task completion degree of the battery swap station.

[0038] In some implementations, the reward and punishment system established according to the completion of the peak shaving task of the battery swap station is expressed as follows:

[0039] in: Subsidize electricity prices for peak shaving; The base peak shaving subsidy electricity price; is the reward and punishment coefficient, and its expression is as follows:

[0040] Where: is the reward coefficient, is the penalty coefficient, is the satisfaction threshold of task completion, is the unacceptable threshold for task completion. The value can be 1.2; The value is 0.9; The value is 0.8; The value is 0.5; It is 4 yuan / (kW·h).

[0041] Step 101: Formulate a non-cooperative game operation strategy for the battery swap station in the process of participating in power grid peak shaving.

[0042] Specifically, when executing step 101, the following steps may be specifically performed: S1010: Non-cooperative game between users and battery swap stations: The expression of the non-cooperative game between the user and the battery swap station is as follows:

[0043] in: For the game party; is the strategy set; is the payoff function, and the game player The expression is shown as follows:

[0044] Where: Replacement stations for game participants; For game participant users.

[0045] S1011: Policy Set: Policy Set The expression is shown as follows:

[0046] Where: is the number of batteries to be charged left by the battery swap station until the next moment, and the corresponding gaming party is the battery swap station; is the number of batteries configured; The number of battery chargers configured is Nc; where, , The corresponding game parties are users.

[0047] S1012: Profit function: Revenue Function The expression is shown as follows:

[0048] Where: Represents the revenue of the battery swap station, Represents the user's profit.

[0049] Step 102: Establish an optimization goal based on the user target satisfaction and the target daily comprehensive revenue of the battery swap station.

[0050] Among them, the daily comprehensive income of the battery swap station includes peak shaving subsidies, saved charging costs and investment costs.

[0051] Specifically, the expression of user satisfaction is as follows: ; In the formula, To improve the user's satisfaction with battery replacement, is the number of EVs that are replaced in each period, is the total number of EVs that have undergone battery replacement.

[0052] Furthermore, the daily comprehensive income takes into account the peak-shaving subsidies, the saved charging costs and the investment costs obtained by the battery swap station. The daily comprehensive income expression of the battery swap station is as follows: ; In the formula, is the daily comprehensive income of the battery swap station, To provide subsidies for peak load reduction, The charging cost can be saved. Convert the daily equipment investment cost for the battery swap station.

[0053] In some implementations, the peak shaving subsidy expression in the daily comprehensive income of the battery swap station is as follows:

[0054] Where: ; To optimize the distribution network The load that needs to be slashed during the peak period, After the distribution network The maximum load power, is the number of heavy load periods during which the distribution network needs to reduce peak load. It is the duration of a time period, and its value is 1h.

[0055] The expression of the charging cost that can be saved in the daily comprehensive income of the battery swap station is as follows:

[0056] in: The charging cost that can be saved for battery swap stations; To optimize the daily charging cost of the previous battery swap station; To optimize the charging cost of the battery swap station for one day, the expression is as follows:

[0057] Where: To optimize the load of the distribution network during the peak-cutting period; After the distribution network The maximum load power.

[0058] In some implementations, the investment cost expression in the daily comprehensive income of the battery swap station is as follows:

[0059] In the formula, Convert the daily equipment investment cost of the battery swap station to is the investment cost of the battery swap station, which represents the investment cost of the infrastructure of the battery swap station and the investment cost of the battery and charger. , Represents battery and charger respectively. is the installed capacity of the battery and charger, The useful life; is the depreciation rate, the battery life is 10, the charger life is 15, Take 7%.

[0060] In some implementations, the goal of optimizing the operation strategy of the battery swap station is to maximize the daily comprehensive benefit under the premise of meeting the service capacity margin requirements and peak shaving task requirements of the battery swap station, as shown in the following formula:

[0061] Step 103: Considering the interests of each game participant, the optimization step is completed. First, the operation optimization strategy of the battery swap station is input, and then the strategies of each game participant are initialized to generate the number of vehicles that start battery swapping at each time period of each particle's battery swap station, and the infeasible solution is corrected to a feasible solution according to the constraints.

[0062] Specifically, when executing step 103, the following steps may be specifically performed: the number of EV batteries that start charging at each time period of the battery swap station is used as a variable for optimization input, The total number of batteries to be charged in a time period is expressed as follows:

[0063] Where: for The total number of batteries to be charged during the time period, The number of batteries that may be left to be charged the day before; The number of batteries to be charged during this period. The 24 hours of the day are divided into 96 periods of 15 minutes each. for this period.

[0064] The number of batteries waiting to be charged in each period is determined by the number of batteries remaining in the previous period and the number of batteries newly added in this period, and the number of batteries charged in each period does not exceed the total number of batteries to be charged at this time. The number of batteries charged at the beginning of the period must satisfy the following formula:

[0065] Where: for The number of batteries to be charged at the start of the period.

[0066] At the same time, the number of batteries that can be charged at any one time is limited by the total number of chargers. time period, then The total number of batteries charged in a period is no more than the total number of chargers, as shown in the following formula:

[0067] Where: for The total number of batteries charged during a period; is the total number of chargers.

[0068] Among them, The expression for the number of batteries waiting to be charged in a period of time is as follows:

[0069] Input the number of battery swapping users in each time period, basic parameters of battery swapping stations, conventional load of distribution network, peak shaving tasks and other parameters; initialize the strategy of each game participant, randomly generate the number of EVs that start battery swapping at each time period of each particle's battery swapping station, and correct the infeasible solution to a feasible solution according to various condition constraints.

[0070] Step 104: Optimize the user satisfaction and the maximum benefit of the battery swap station microgrid respectively, use the improved particle swarm algorithm to solve the non-cooperative game model, obtain the optimal strategy of each game participant, the two parties share the strategy, and determine the game equilibrium point that maximizes the interests of both parties.

[0071] Specifically, the particle swarm algorithm is used to solve each profit function, and parameters such as population size and maximum number of iterations are input; the capacity of each game participant is optimized separately. According to the definition of Nash equilibrium, each game participant takes the maximization of its own interests as the optimization goal based on the strategies of other participants in the round, and obtains its own strategy plan for the round. The two parties share strategies, such as Figure 5 As shown in the figure, the number of battery swapping users in each period, the basic parameters of the battery swapping station, the conventional load of the distribution network, the peak shaving task and other parameters are input; the strategy of each game participant is initialized, and the number of EVs that start battery swapping at each battery swapping station of each particle is randomly generated. According to various conditional constraints, the infeasible solution is corrected to a feasible solution, and the optimization is performed with the maximum user satisfaction and the maximum additional profit of the battery swapping station microgrid as the goals. The particle swarm algorithm is used to solve each profit function, and parameters such as the population size and the maximum number of iterations are input to obtain the optimal strategy of each game participant in the jth round. The two parties in the game share strategies to determine whether the game equilibrium is reached. If so, the game ends, otherwise, the optimization and subsequent steps are continued with the maximum user satisfaction and the maximum profit of the battery swapping station microgrid as the goals.

[0072] Specifically, each game participant The optimal strategy expression of the round is as follows:

[0073] In the formula, For the The number of batteries waiting to be charged in each period, For the Total number of wheel chargers, For the The number of wheel battery chargers to be configured, is the total number of chargers in the j-1 round, is the number of battery chargers configured in the j-1th round.

[0074] In some implementations, the two parties in the game share strategies and determine the game equilibrium point that maximizes their interests, namely, the Nash equilibrium point, which is expressed as shown in the following formula:

[0075] In the formula, is the Nash equilibrium point, and the number of iterations is shown in the figure Figure 6 shown.

[0076] In some implementations, the interests of both the user and the battery swap station are comprehensively considered, a non-cooperative game model is used to balance the interests of both parties, and the scheduling results are analyzed.

[0077] The technical effects achieved by this application are as follows: an optimization model is constructed with users and microgrids containing battery swap stations as game participants, with the interests of each game participant being maximized, that is, the highest user satisfaction with battery swapping and the maximum daily comprehensive revenue of the microgrid battery swap station as the optimization goal. A battery swap method based on non-cooperative game is proposed, and an improved particle swarm algorithm is used to solve the model; it has the characteristics of high user satisfaction and maximization of the interests of multiple parties, and is feasible in actual engineering.

[0078] 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.

[0079] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes 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 device that provides the functions specified in a block or multiple blocks.

[0080] 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.

[0081] 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.

[0082] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0083] The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0084] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0085] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0086] The above are only embodiments of the present application and are not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included within the scope of the claims of the present application.

Claims

1. An optimization configuration method for electric vehicle battery swap stations based on non-cooperative game, characterized in that: include: According to the battery swapping needs of users in each period and the peak load shaving tasks of the battery swapping station, an operation optimization strategy of the battery swapping station is proposed, wherein the operation optimization strategy of the battery swapping station includes peak load shaving tasks, peak load reduction effect evaluation, tram travel distance model, battery swapping service requirements, service capacity margin constraints and peak load shaving task constraints of the battery swapping station; Formulate non-cooperative game operation strategies for battery swap stations in the process of participating in power grid peak shaving; Establish an optimization goal based on user target satisfaction and target daily comprehensive revenue of the battery swap station, where the daily comprehensive revenue of the battery swap station includes peak shaving subsidies, saved charging costs and investment costs; Considering the interests of all game participants, the optimization steps are completed. First, the operation optimization strategy of the battery swap station is input, and then the strategies of each game participant are initialized to generate the number of battery swap cars at each time period of each particle's battery swap station, and the infeasible solution is corrected to a feasible solution according to various constraints. The optimization is carried out with the goals of user satisfaction and maximizing the benefits of the battery swap station microgrid respectively. The non-cooperative game model is solved using the improved particle swarm algorithm to obtain the optimal strategy of each game participant. The two parties share the strategy and determine the game equilibrium point that maximizes the interests of both parties.

2. The method for optimizing the configuration of electric vehicle battery swap stations based on non-cooperative game according to claim 1, characterized in that: The peak shaving tasks are divided into two types. The first type is that the peak load balancing capability of the battery swap station is greater than the peak balancing requirement of the distribution network, and the peak balancing requirement is used as the peak shaving task of the battery swap station; the second type is that the peak load balancing capability of the battery swap station is less than or equal to the peak balancing requirement of the distribution network.

3. The method for optimizing the configuration of electric vehicle battery swap stations based on non-cooperative game according to claim 1, characterized in that: The peak load reduction effect evaluation expression is as follows: ； In the formula, The power saving coefficient is used to reflect the reduction degree of feeder load before and after peak shaving. , They are the maximum values ​​of the total load of the 10kV distribution feeder in a day before and after peak shaving, respectively.

4. The method for optimizing the configuration of electric vehicle battery swap stations based on non-cooperative game according to claim 1, characterized in that: The model expression of the tram travel distance is as follows: In the formula, is the driving distance of the electric vehicle, is the total daily mileage of the electric vehicle, , The time when the electric vehicle starts running and the time when the battery needs to be replaced. It is the percentage of the electric vehicle’s mileage in each period to the total daily mileage.

5. The method for optimizing the configuration of electric vehicle battery swap stations based on non-cooperative game according to claim 1, characterized in that: The battery replacement service requirements are as follows: ； In the formula, for The number of EV batteries whose SOC values ​​meet the battery replacement conditions during the time period, is the battery shortage amount, The number of cars waiting in line due to insufficient number of replaceable batteries.

6. The method for optimizing the configuration of electric vehicle battery swap stations based on non-cooperative game according to claim 1, characterized in that: The service capacity margin constraint expression is as follows: ； In the formula, The service capacity margin of the battery swap station in each period. for The number of EV batteries whose SOC values ​​meet the battery replacement conditions during the time period, is the number of EVs that need to be replaced during period t.

7. The method for optimizing the configuration of electric vehicle battery swap stations based on non-cooperative game according to claim 1, characterized in that: The constraint expression of the peak shaving task of the battery swap station is as follows: ； In the formula, The peak shaving task of the battery swap station is is the actual peak shaving amount, The task completion degree of the battery swap station.

8. The method for optimizing the configuration of electric vehicle battery swap stations based on non-cooperative game according to claim 1, characterized in that: The expression of user satisfaction is as follows: ； In the formula, To improve the user's satisfaction with battery replacement, is the number of EVs that are replaced in each period, is the total number of EVs that have undergone battery replacement.

9. The method for optimizing the configuration of electric vehicle battery swap stations based on non-cooperative game according to claim 1, characterized in that: The daily comprehensive income expression of the battery swap station is as follows: ； In the formula, is the daily comprehensive income of the battery swap station, To provide subsidies for peak load reduction, The charging cost can be saved. Convert the daily equipment investment cost for the battery swap station.

10. The method for optimizing the configuration of electric vehicle battery swap stations based on non-cooperative game according to claim 1, characterized in that: Each game participant The optimal strategy expression of the round is as follows: ； In the formula, For the The number of batteries waiting to be charged in each period, For the Total number of wheel chargers, For the The number of wheel battery chargers to be configured, For the Total number of wheel chargers, For the The number of wheel battery chargers to be configured.

Images