Electric vehicle charging and discharging master-slave game optimization method and device and storage medium

Abstract

The invention discloses an electric vehicle charging and discharging master-slave game optimization method and device and a storage medium, belongs to the technical field of electric vehicle charging and discharging scheduling, and solves the problems of how to improve the resource utilization rate of a charging station, reduce the travel cost of a user and improve the overall scheduling efficiency of the charging station. According to the invention, an improved A * algorithm is adopted, real-time traffic data and charging station queuing information are combined, and the path planning of the electric vehicle is dynamically adjusted; based on the established electric vehicle user response model, an improved two-dimensional cloud model is adopted to quantify participation willingness of users under different excitation levels and time margins; a master-slave game model is introduced to carry out collaborative optimization on a charging station operation target and an electric vehicle user scheduling target; according to the method, the resource utilization rate of the charging station can be remarkably improved, the user travel cost is reduced, the overall scheduling efficiency of the system is improved, and an innovative solution is provided for intelligentization and optimization of charging and discharging scheduling of the electric vehicle.

Description

Technical Field

[0001] The present invention belongs to the technical field of electric vehicle charging and discharging scheduling, and relates to an optimization method, device and storage medium for the master-slave game of electric vehicle charging and discharging based on the "vehicle-road-network" mode, which is applied to the scenario composed of electric vehicle charging stations, electric vehicle users and distribution networks. Background Art

[0002] With the rapid development of the electric vehicle market, the charging demand of electric vehicles has gradually become an important factor affecting urban traffic and power grid operation. However, the charging process of electric vehicles not only depends on the layout of charging facilities and the availability of charging piles, but is also affected by various factors such as traffic network conditions, charging time, and user behavior. Therefore, how to optimize the charging and discharging scheduling of electric vehicles in a complex traffic environment and improve the operation efficiency of charging stations has become an important research topic in the field of electric vehicles.

[0003] At present, some studies have explored the path planning and charging strategies of electric vehicles. Traditional path planning methods such as the Dijkstra algorithm and the Floyd algorithm mainly consider static traffic networks and fixed routes. However, in a dynamic traffic environment, considering the influence of road conditions, traffic congestion, charging station queues and other factors, these algorithms have great limitations. With the continuous change of traffic flow and charging demand, how to provide real-time and flexible optimal path planning and charging strategies for electric vehicle users has become a technical problem that needs to be solved urgently. On the other hand, the user behavior of electric vehicles also has strong personalized characteristics. The travel decisions of users are not only affected by objective factors such as the geographical location of charging stations and charging time, but also by psychological factors such as incentive measures and time margins. Therefore, how to accurately quantify the user's responsiveness and design a reasonable incentive mechanism to guide users to participate in charging scheduling has become the key to improving the effect of the electric vehicle scheduling system. In this context, the existing electric vehicle scheduling methods generally ignore the personalized differences in user responses and the dynamic changes of traffic networks. Most of the existing technologies focus on the optimization of a single goal, such as the energy balance of charging stations and the minimization of charging costs, and lack a comprehensive consideration of the complex interaction relationship between charging stations and electric vehicle users. Summary of the Invention

[0004] The technical solution of the present invention is used to solve the problems of how to improve the resource utilization rate of charging stations, reduce the travel costs of users, and improve the overall scheduling efficiency of charging stations.

[0005] The present invention solves the above technical problems through the following technical solutions:

[0006] The present invention also provides an optimization method for the master-slave game of electric vehicle charging and discharging, including:

[0007] S1 Construct a dynamic traffic road network model, and analyze the minimum driving time cost for electric vehicles to travel to the charging station based on the improved A* algorithm;

[0008] S2 Construct a response model of electric vehicle users to different incentive levels, consider the travel time rules of electric vehicle users, establish an uncertainty response model of charging time margin, and quantify the response willingness of electric vehicle users to participate in scheduling according to the two-dimensional cloud model improved by the entropy weight method;

[0009] S3 Combine the user response model, and propose an optimization scheduling strategy based on the leader-follower game. The charging station is the leader of the game, and optimize the objectives on the charging station side;

[0010] S4 Establish an optimization model on the electric vehicle side for the energy utilization and scheduling scheme of electric vehicle users;

[0011] S5 Introduce the Karush-Kuhn-Tucker conditions, combine the objective functions and constraint conditions of the charging station and the electric vehicle to construct a leader-follower game model, and use the CPLEX solver to solve the leader-follower game model to obtain the optimal charging and discharging scheduling strategy and the electric vehicle decision-making scheme.

[0012] Furthermore, the construction of the dynamic traffic road network model is as follows:

[0013]

[0014] Among them, G is the road network; O is the set of all intersections in the road network G; L is the set of road segments in the road network G; T is the set of serial numbers of divided time periods, W is the set of weights of the road segment set L; o i is the i-th intersection, u is the total number of intersections; l ij is the road segment connecting the i-th intersection o i and the j-th intersection o j ; t is a certain time period, h is the number of time periods, ω ij (t) is the weight of the road segment l ij in the t-th time period.

[0015] Furthermore, the method for analyzing the minimum driving time cost for electric vehicles to travel to the charging station based on the improved A* algorithm is as follows:

[0016] Based on the dynamic traffic road network model, use Equation (2) to calculate the time cost required for electric vehicle travel:

[0017]

[0018] Among them, T drive,n,d (t) is the driving time cost of the n-th electric vehicle from the t-th time period to the d-th charging station; L n,dis the set of road segments passed by the nth electric vehicle on its way to the dth charging station;

[0019] Based on the M / M / s queuing theory to describe the waiting time of the vehicle owner in the queue, an electric vehicle user queuing model is constructed, and the queuing time cost T wait,n,d (t) required for the nth electric vehicle to select the dth charging station in the tth period is obtained;

[0020] Use Equation (3) to calculate the total time cost T n,d (t) of the nth electric vehicle in the tth period:

[0021] T n,d (t) = T drive,n,d (t) + T wait,n,d (t) (3)

[0022] Use the improved A* algorithm to solve the path of the nth electric vehicle to the optimal charging station, and obtain the optimal driving path with the minimum weight value in the set of road segments passed to reach charging station d And obtain the corresponding time cost

[0023] Furthermore, the method for constructing the response degree model of electric vehicle users to different incentive levels is as follows:

[0024] Use Equation (4) to analyze the user response deviation of the user response degree under different incentive levels:

[0025]

[0026] Among them, Δσ(n) is the response degree deviation of the nth electric vehicle participating in the scheduling; x(n) is the incentive level of the nth electric vehicle participating in the scheduling; x max is the incentive level when all electric vehicle users fully participate in the scheduling, where k1 and k2 are the variation coefficients of the response degree deviation with the change of the incentive level, x IP is the incentive level when the variation trend of Δσ(n) changes from increasing to decreasing;

[0027] Use Equation (5) to analyze the response degree of users under different incentive levels:

[0028]

[0029] Among them, σ(n) is the response degree of the nth electric vehicle participating in the power grid scheduling; k r is the slope of the user response degree changing with the incentive level;

[0030] Calculate the user response rate under specific incentives using Equation (6):

[0031]

[0032] Among them, f(n) is the response rate of the nth electric vehicle participating in the grid dispatching when the incentive level is σ(n); σ up (n) and σ down (n) are respectively the maximum and minimum values of the response degree when the nth electric vehicle participates in the dispatching; σ T is the critical response degree for electric vehicle users to participate in the dispatching and not to participate in the dispatching.

[0033] Furthermore, the method for establishing an uncertainty response model considering the charging time margin by taking into account the travel time law of electric vehicle users is as follows:

[0034] According to the on-grid time and off-grid time of electric vehicle users, use Equation (7) to calculate the shortest charging time required for the electric vehicle:

[0035]

[0036] Among them, T min (n) is the shortest charging time required for the nth electric vehicle; SOC in (n) and SOC out (n) are respectively the charge levels of the nth electric vehicle when it is on-grid and off-grid; C B is the rated capacity of the electric vehicle; P cd (n) is the charging power of the nth electric vehicle;

[0037] Use Equation (8) to calculate the time margin of the electric vehicle:

[0038]

[0039] Among them, T set (n) is the total duration of the nth electric vehicle connected to the grid, T(n) is the time margin of the nth electric vehicle; use Equation (9) to calculate the time margin response rate of the electric vehicle user;

[0040]

[0041] Among them, K(n) is the time margin response rate of the nth electric vehicle; T E (n) is the expected charging time margin of the nth electric vehicle.

[0042] Furthermore, the method for quantifying the response willingness of electric vehicle users to participate in the dispatching by using the two-dimensional cloud model improved according to the entropy weight method is as follows:

[0043] Perform standardization processing on the data, and use Equation (I0) to calculate the entropy weight value of the information entropy;

[0044]

[0045] Among them, ω(j) is the acceptance weight obtained from the j-th cloud model; EN(j) is the entropy of the j-th cloud model;

[0046] Use Equation (11) to calculate the acceptance calculation expression for the n-th electric vehicle:

[0047] p(n) = ω1f(n) + ω2K(n) (11)

[0048] Among them, p(n) is the acceptance of the n-th electric vehicle participating in the scheduling; ω1 and ω2 are the weights of the incentive level response rate and the time margin response rate, respectively.

[0049] Furthermore, combining the user response model, an optimization scheduling strategy based on the master-slave game is proposed. The charging station is the leader of the game, and the method for optimizing the charging station side target is as follows:

[0050] Use Equation (12) to construct the charging station side objective function:

[0051]

[0052] Among them, T is the total number of time periods; N is the number of electric vehicles; and are the charging incentive and discharging incentive levels of the n-th electric vehicle at the charging station d at time t, respectively; and are the charging power and discharging power of the n-th electric vehicle at the charging station d at time t, respectively; ΔT fn,d (t) is the discharging duration of the n-th electric vehicle at the charging station d; and are the energy output conversion coefficient and energy input conversion coefficient of the charging station d at time t, respectively; and are the energy output and energy input of the charging station d at time t, respectively;

[0053] Use Equation (13) to construct the charging and discharging parameter constraint conditions:

[0054]

[0055] Among them, and are the minimum incentive level and maximum incentive level of the charging station d at time t, respectively;

[0056] Use Equation (14) to construct the charging station's day-ahead energy input and energy output constraints:

[0057]

[0058] Among them, E d(t) is the day-ahead energy input of charging station d at time t; z d (t) is a Boolean variable of charging station d at time t; M is a positive number approaching infinity; is the energy discharge of the energy storage of charging station d at time t;

[0059] Construct the energy balance constraint using Equation (15):

[0060]

[0061] where, is the energy charge of the energy storage of charging station d at time t;

[0062] Construct the energy charge and discharge constraint of the energy storage using Equation (16):

[0063]

[0064] where, u d (t) is a Boolean variable of charging station d at time t; and are the maximum charging power and maximum discharging power of charging station d respectively;

[0065] Construct the energy storage capacity constraint using Equation (17):

[0066]

[0067] where, S(t) is the energy storage capacity of charging station d at time t; η + and η - are the charging efficiency and discharging efficiency of the energy storage respectively; S max is the maximum energy storage capacity.

[0068] Furthermore, for the energy utilization and scheduling scheme of electric vehicle users, the method for establishing the optimization model on the electric vehicle side is as follows:

[0069] Establish the objective function for electric vehicle users to participate in scheduling using Equation (18):

[0070]

[0071] where, T c is the electric vehicle charging period; T dis is the electric vehicle discharging period; ε is the degradation loss of the electric vehicle battery; Δt n,f is the delay time caused by road resistance during the f-th charge and discharge trip of the n-th electric vehicle; f cd is the number of charge and discharge times of the electric vehicle; λ is the time cost coefficient of the electric vehicle user;

[0072] Construct the electric vehicle state of charge constraint using Equation (19):

[0073]

[0074] Among them, ξ is the charge level expected by electric vehicle users; is the battery capacity of the nth electric vehicle; is the initial charge of the nth electric car; E c Power consumption for electric vehicles;

[0075] Use formula (20) to construct the electric vehicle charging power constraint:

[0076]

[0077] in, is the maximum charging power of the nth electric vehicle at charging station d;

[0078] Formula (21) is used to construct the electric vehicle discharge time constraint:

[0079]

[0080] Among them, E min The minimum charge value of an electric vehicle.

[0081] The present invention also provides a device including a memory and a processor, wherein the memory is used to store a program that supports the processor to execute the above-mentioned electric vehicle charging and discharging master-slave game optimization method, and the processor is configured to execute the program stored in the memory.

[0082] The present invention also provides a storage medium on which a computer program is stored. When the computer program is run by a processor, the steps of the above-mentioned electric vehicle charging and discharging master-slave game optimization method are executed.

[0083] The beneficial effects of the present invention are as follows:

[0084] The present invention adopts an improved A* algorithm, combined with real-time traffic data and charging station queue information, to dynamically adjust the path planning of electric vehicles. Compared with the traditional static path planning method, it significantly improves the driving efficiency of electric vehicles in complex traffic environments and the adaptability of charging decisions; based on psychological principles, an electric vehicle user response model is established, and an improved two-dimensional cloud model is used to quantify the user's willingness to participate under different incentive levels and time margins. It can effectively take into account individual differences of users and improve the user's enthusiasm for participating in scheduling and the response accuracy compared with the existing unified scheduling strategy; by introducing a master-slave game model, the charging station operation objectives and the electric vehicle user scheduling objectives are collaboratively optimized, which optimizes the resource allocation of the charging station and the charging scheduling of the electric vehicle, achieving a win-win situation between the charging station and the user, and further improving the overall efficiency and scheduling performance of the system compared with traditional methods. BRIEF DESCRIPTION OF THE DRAWINGS

[0085] Figure 1 This is a flow chart of the master-slave game optimization method for charging and discharging electric vehicles based on the "vehicle-road-network" model of the present invention;

[0086] Figure 2 This is a comparison result diagram of the loads of each charging station before and after optimization of the master-slave game optimization method for electric vehicle charging and discharging based on the "vehicle-road-network" mode of the present invention. DETAILED DESCRIPTION

[0087] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0088] The technical solution of the present invention is further described below with reference to the accompanying drawings and specific embodiments:

[0089] Example 1

[0090] like Figure 1 As shown, the master-slave game optimization method for charging and discharging an electric vehicle based on the "vehicle-road-network" mode according to the first embodiment of the present invention includes the following steps:

[0091] Step 1: Build a dynamic traffic network model and analyze the minimum travel time cost of electric vehicles to charging stations based on the improved A* algorithm.

[0092] Step 1.1: In the "vehicle-road-network" interaction model, the road network generally adopts a static model, and the traffic flow is non-time-varying. In order to reflect the dynamic characteristics of the road network, a dynamic traffic network model is constructed using formula (1);

[0093]

[0094] In formula (1), G is the road network; O is the set of all intersections in the road network G; L is the set of road sections in the road network G; T is the set of time period numbers, and W is the weight set of the road section set L; i is the i-th intersection, u is the total number of intersections; l ij To connect the i-th intersection o i and the jth intersection o j road section; t is a certain time period, h is the number of time periods, ω ij (t) is the road section l in the t-th period ij The weight of

[0095] Step 1.2: Calculate the time cost required for the electric vehicle by analyzing the travel time of the electric vehicle and the queue situation at the charging station.

[0096] Step 1.2.1: Based on the dynamic traffic road network model constructed in Step 1.1, use Equation (2) to calculate the time cost required for the electric vehicle to travel:

[0097]

[0098] In Equation (2): T drive,n,d (t) is the driving time cost of the nth electric vehicle from the tth time period to the dth charging station; L n,d is the set of road sections passed by the nth electric vehicle to the dth charging station;

[0099] Step 1.2.2: Based on the M / M / s queuing theory to describe the waiting time of the vehicle owner in line, construct an electric vehicle user queuing model, and obtain the queuing time cost T wait,n,d (t) required for the nth electric vehicle to select the dth charging station in the tth time period;

[0100] Step 1.2.3: According to the results obtained in Step 1.2.1 and 1.2.2, use Equation (3) to calculate the total time cost T n,d (t) of the nth electric vehicle in the tth time period:

[0101] T n,d (t) = T drive,n,d (t) + T wait,n,d (t) (3)

[0102] Step 1.2.4: Use the improved A* algorithm to solve the path of the nth electric vehicle to the optimal charging station, and obtain the optimal driving path with the minimum weight value in the set of road sections passed to the charging station d and obtain the corresponding time cost

[0103] Step Two: Quantify the response willingness of electric vehicle users to participate in scheduling under different charge-discharge incentives and time margins based on the principle of user psychology and the improved two-dimensional cloud model.

[0104] Step 2.1: Construct a response degree model of electric vehicle users to different incentive levels;

[0105] Step 2.1.1: Considering that different electric vehicle users have different response degrees to the incentive level, use Equation (4) to analyze the user response deviation of the user response degree under different incentive levels:

[0106]

[0107] In formula (4): Δσ(n) is the response degree deviation of the nth electric vehicle participating in the dispatching; x(n) is the incentive level of the nth electric vehicle participating in the dispatching; x max is the incentive level when all electric vehicle users fully participate in the dispatching, where k1 and k2 are the variation coefficients of the response degree deviation with respect to the incentive level, and x IP is the incentive level when the variation trend of Δσ(n) changes from increasing to decreasing.

[0108] Step 2.1.2: Use a linear model to model the user response situation, and analyze the response degree of users at different incentive levels using formula (5):

[0109]

[0110] In formula (5): σ(n) is the response degree of the nth electric vehicle participating in the grid dispatching; k r is the slope of the user response degree with respect to the incentive level.

[0111] Step 2.1.3: Based on the results obtained from the above steps, calculate the user response rate under a specific incentive using formula (6):

[0112]

[0113] In formula (6): f(n) is the response rate of the nth electric vehicle participating in the grid dispatching when the incentive level is σ(n); σ up (n) and σ down (n) are the maximum and minimum values of the response degree of the nth electric vehicle participating in the dispatching respectively; σ T is the critical response degree for electric vehicle users to participate in the dispatching and not participate in the dispatching.

[0114] Step 2.2: Consider the travel time law of electric vehicle users, and establish an uncertainty response model for the charging time margin;

[0115] Step 2.2.1: According to the grid connection time and off-grid time of electric vehicle users, calculate the shortest charging time required for the electric vehicle using formula (7):

[0116]

[0117] In formula (7): T min (n) is the shortest charging time required for the nth electric vehicle; SOC in (n) and SOC out (n) are the charge levels of the nth electric vehicle at the time of grid connection and off-grid respectively; C B is the rated capacity of the electric vehicle; P cd (n) is the charging power of the nth electric vehicle.

[0118] Step 2.2.2: Calculate the time margin of the electric vehicle using Equation (8):

[0119]

[0120] In Equation (8): T set (n) is the total grid connection duration of the nth electric vehicle, and T(n) is the time margin of the nth electric vehicle.

[0121] Step 2.2.3: Calculate the time margin response rate of the electric vehicle user using Equation (9);

[0122]

[0123] In Equation (9): K(n) is the time margin response rate of the nth electric vehicle; T E (n) is the expected charging time margin of the nth electric vehicle.

[0124] Step 2.3: Quantify the response willingness of electric vehicle users to participate in scheduling according to the two-dimensional cloud model improved by the entropy weight method;

[0125] Step 2.3.1: Standardize the data and calculate the entropy weight value of the information entropy using Equation (10);

[0126]

[0127] In Equation (10): ω(j) is the acceptance weight obtained from the jth cloud model; EN(j) is the entropy of the jth cloud model.

[0128] Step 2.3.2: Weight the two one-dimensional cloud models using the entropy weight value to obtain a two-dimensional cloud model determined by the incentive level and the margin coefficient, and calculate the acceptance calculation expression of the nth electric vehicle using Equation (11):

[0129] p(n) = ω1f(n) + ω2K(n) (11)

[0130] In Equation (11): p(n) is the acceptance of the nth electric vehicle to participate in scheduling; ω1 and ω2 are the weight values of the incentive level response rate and the time margin response rate respectively.

[0131] Step Three: Combine the user response model to propose an optimal scheduling strategy based on the master-slave game. The charging station is the leader of the game, and the charging station-side objective is optimized.

[0132] Step 3.1: The goal of the charging station is to improve its energy balance and system benefits, including two aspects: one is to optimize the charging and discharging process between the charging station and electric vehicles, and the other is to balance the energy flow between the charging station and the power grid. Use Equation (12) to construct the objective function on the charging station side.

[0133]

[0134] In Equation (12): T is the total number of time periods; N is the number of electric vehicles; and are the charging incentive and discharging incentive levels of the nth electric vehicle at the charging station d at time t, respectively; and are the charging power and discharging power of the nth electric vehicle at the charging station d at time t, respectively; ΔT fn,d (t) is the discharging duration of the nth electric vehicle at the charging station d; and are the energy output conversion coefficient and energy input conversion coefficient of the charging station d at time t, respectively; and are the energy output and energy input of the charging station d at time t, respectively.

[0135] Step 3.2: To optimize the resource utilization efficiency of the charging station, use Equation (13) to construct the charging and discharging parameter constraints:

[0136]

[0137] In Equation (13): and are the minimum incentive level and maximum incentive level of the charging station d at time t, respectively.

[0138] Step 3.3: Use Equation (14) to construct the charging station's day-ahead energy input and energy output constraints:

[0139]

[0140] In Equation (14): E d (t) is the day-ahead energy input of the charging station d at time t; z d (t) is the Boolean variable of the charging station d at time t; M is a positive number approaching infinity; is the energy storage discharging amount of the charging station d at time t.

[0141] Step 3.4: Use Equation (15) to construct the energy balance constraint:

[0142]

[0143] In Equation (15): The energy storage charging amount of charging station d at time t.

[0144] Step 3.5: Construct the energy storage charge and discharge amount constraint using Equation (16):

[0145]

[0146] In Equation (16): u d (t) is the Boolean variable of charging station d at time t; and are the maximum charging power and maximum discharging power of charging station d respectively.

[0147] Step 3.6: Construct the energy storage power constraint using Equation (17):

[0148]

[0149] In Equation (17): S(t) is the energy storage power of charging station d at time t; η + and η - are the charging efficiency and discharging efficiency of the energy storage respectively; S max is the maximum capacity of the energy storage.

[0150] Step 4: Regarding electric vehicle users as the followers of the game model, an optimization model on the electric vehicle side is established for the energy utilization and scheduling scheme of electric vehicle users.

[0151] Step 4.1: The objective function of electric vehicles comprehensively considers four aspects: one is the energy interaction efficiency of charge and discharge, the second is the battery life and health status, the third is the improvement of efficiency during the power transmission process, and the fourth is the impact of driving time and scheduling process on user experience. The objective function for electric vehicle users to participate in scheduling is established using Equation (18).

[0152]

[0153] In Equation (18): T c is the charging period of the electric vehicle; T dis is the discharging period of the electric vehicle; ε is the battery degradation loss of the electric vehicle; Δt n,f is the delay duration caused by road resistance during the nth charge and discharge trip of the fth electric vehicle; f cd is the number of charge and discharge times of the electric vehicle; λ is the time cost coefficient of the electric vehicle user.

[0154] Step 4.2: The charging amount of the electric vehicle should make the battery reach the corresponding state of charge. The state of charge constraint of the electric vehicle battery is constructed using Equation (19):

[0155]

[0156] In Equation (19): ξ is the state of charge level expected by the electric vehicle user; is the battery capacity of the nth electric vehicle; is the initial state of charge of the nth electric vehicle; E c is the power consumption during the electric vehicle's travel.

[0157] Step 4.3: The charging power of the electric vehicle connected to the grid should not exceed the limit of its maximum charging power. Use Equation (20) to construct the electric vehicle charging power constraint:

[0158]

[0159] In Equation (20): is the maximum charging power of the nth electric vehicle at charging station d.

[0160] Step 4.4: Use Equation (21) to construct the electric vehicle discharge duration constraint:

[0161]

[0162] In Equation (21): E min is the minimum state of charge of the electric vehicle.

[0163] Step Five: Introduce the Karush-Kuhn-Tucker conditions, combine the objective functions and constraint conditions of the charging station and the electric vehicle, and construct a master-slave game model. In this model, the charging station, as the "leader", formulates strategies first, and the electric vehicle user, as the "follower", optimizes its own decisions on this basis. Integrate the above master-slave game model into a mixed-integer linear programming problem. Use the CPLEX solver to solve this model to obtain the optimal charging and discharging scheduling strategy and the electric vehicle decision-making plan.

[0164] Figure 2 shows the comparison of the loads of each charging station before and after optimization, where (a) is before optimization and (b) is after optimization. It can be seen from the figure that before optimization, the load distribution of each charging station is unbalanced, with some stations having a high load and possibly an overload risk, while some other stations have a low load, resulting in insufficient resource utilization. After optimization, the load distribution of each charging station tends to be balanced, the peak value of the high-load stations decreases, the utilization rate of the low-load stations increases, and the overall power distribution is more reasonable. It effectively improves the stability and efficiency of the distribution network and at the same time improves the user experience.

[0165] The present invention constructs a dynamic traffic road network model, combines the travel demand of electric vehicles and the queuing situation of charging stations, applies an improved A* algorithm for real-time path planning, and optimizes the driving and charging paths of electric vehicles. Based on the principles of user behavior psychology, an improved two-dimensional cloud model is used to establish a response model for electric vehicle users, quantifying the willingness of users to participate in scheduling under different incentive levels and time margins. Through the framework of the master-slave game theory, the charging station is the leader, optimizing its charging and discharging strategies, and the electric vehicle users are the followers, optimizing their charging and discharging decisions, realizing the multi-objective collaborative optimization between the charging station and the electric vehicles. The present invention can significantly improve the utilization rate of charging station resources, reduce the travel cost of users, and enhance the overall scheduling efficiency of the system, providing an innovative solution for the intelligentization and optimization of electric vehicle charging and discharging scheduling.

[0166] Embodiment 2

[0167] A device includes a memory and a processor. The memory is used to store a program that supports the processor to execute the master-slave game optimization method for electric vehicle charging and discharging based on the "vehicle-road-network" mode in Embodiment 1, and the processor is configured to execute the program stored in the memory.

[0168] Embodiment 3

[0169] A storage medium stores a computer program. When the computer program is run by a processor, it executes the steps of the master-slave game optimization method for electric vehicle charging and discharging based on the "vehicle-road-network" mode in Embodiment 1.

[0170] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit them. Although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements for some of the technical features. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. An optimization method for the master-slave game of electric vehicle charging and discharging, characterized in that Including: S1 Construct a dynamic traffic road network model, and analyze the minimum driving time cost of electric vehicles to charging stations based on the improved A* algorithm; S2 Construct a response model of electric vehicle users to different incentive levels, consider the travel time rules of electric vehicle users, establish an uncertainty response model of charging time margin, and quantify the response willingness of electric vehicle users to participate in scheduling according to the two-dimensional cloud model improved by the entropy weight method; S3 Combine the user response model, and propose an optimization scheduling strategy based on the master-slave game. The charging station is the leader of the game, and optimize the charging station side target; S4 Establish an optimization model on the electric vehicle side for the energy utilization and scheduling scheme of electric vehicle users; S5 Introduce the Karush-Kuhn-Tucker conditions, combine the objective functions and constraint conditions of the charging station and electric vehicles to construct a master-slave game model, and use the CPLEX solver to solve the master-slave game model to obtain the optimal charging and discharging scheduling strategy and the electric vehicle decision-making scheme.

2. The main - slave game optimization method for electric vehicle charging and discharging according to claim 1, characterized in that, The construction of the dynamic traffic road network model is as follows: Among them, G is the road network; O is the set of all intersections in the road network G; L is the set of road segments in the road network G; T is the set of serial numbers of divided time periods, W is the set of weights of the road segment set L; o i is the i-th intersection, u is the total number of intersections; l ij is the road segment connecting the i-th intersection o i and the j-th intersection o j ; t is a certain time period, h is the number of time periods, ω ij (t) is the weight of the road segment l ij at the t-th time period.

3. The main - slave game optimization method for electric vehicle charging and discharging according to claim 2, wherein, The method for analyzing the minimum driving time cost of electric vehicles to charging stations based on the improved A* algorithm is as follows: Based on the dynamic traffic road network model, use Equation (2) to calculate the time cost required for electric vehicle travel: Among them, T drive,n,d (t) is the driving time cost of the nth electric vehicle from the t-th time period to the d-th charging station; L n,d is the set of road segments that the nth electric vehicle passes through to reach the d-th charging station; Describe the waiting time of car owners in line based on the M / M / s queuing theory, construct a queuing model for electric vehicle users, and obtain the queuing time cost T wait,n,d (t) required for the nth electric vehicle to select the dth charging station in the tth period; Calculate the total time cost T of the nth electric vehicle in the t-th period using Equation (3) n,d (t): T n,d (t) = T drive,n,d (t) + T wait,n,d (t) (3) The improved A* algorithm is used to solve the path for the nth electric vehicle to the optimal charging station, and the optimal driving path with the minimum weight value in the set of road sections passed to reach the charging station d is obtained. And the corresponding time cost is obtained.

4. The optimization method for the master-slave game of electric vehicle charging and discharging according to claim 1, wherein The method for constructing the response model of electric vehicle users to different incentive levels is as follows: Use Equation (4) to analyze the user response deviation of user response degree under different incentive levels: Among them, Δσ(n) is the response degree deviation of the nth electric vehicle participating in the dispatching; x(n) is the incentive level of the nth electric vehicle participating in the dispatching; x max is the incentive level when all electric vehicle users fully participate in the dispatching, where k1 and k2 are the variation coefficients of the response degree deviation changing with the incentive level, x IP is the incentive level when the change trend of Δσ(n) is from increasing to decreasing; Use Equation (5) to analyze the response degree of users under different incentive levels: Among them, σ(n) is the response degree of the nth electric vehicle participating in power grid dispatching; k r is the slope of the user response degree varying with the incentive level; Calculate the user response rate under specific incentives using Equation (6): Among them, f(n) is the response rate of the nth electric vehicle participating in the power grid dispatching when the incentive level is σ(n); σ up (n) and σ down (n) are respectively the maximum and minimum values of the response degree when the nth electric vehicle participates in the dispatching; σ T is the critical response degree for electric vehicle users to participate in the dispatching and not to participate in the dispatching.

5. The master-slave game optimization method for electric vehicle charging and discharging according to claim 4, wherein, The method for considering the travel time rules of electric vehicle users and establishing an uncertainty response model of charging time margin is as follows: According to the on-grid time and off-grid time of electric vehicle users, use Equation (7) to calculate the shortest charging time required for electric vehicles: Among them, T min (n) is the shortest charging time required for the nth electric vehicle; SOC in (n) and SOC out (n) are the charge levels of the nth electric vehicle when it connects to and disconnects from the grid respectively; C B is the rated capacity of the electric vehicle; P cd (n) is the charging power of the nth electric vehicle; Use Equation (8) to calculate the time margin of electric vehicles: Among them, T set (n) is the total grid connection duration of the nth electric vehicle, and T(n) is the time margin of the nth electric vehicle; Use Equation (9) to calculate the time margin response rate of electric vehicle users; Among them, K(n) is the time margin response rate of the nth electric vehicle; T E (n) is the expected charging time margin of the nth electric vehicle.

6. The optimization method for the master-slave game of electric vehicle charging and discharging according to claim 5, characterized in that, The method for quantifying the response willingness of electric vehicle users to participate in scheduling according to the two-dimensional cloud model improved by the entropy weight method is as follows: Perform standardized processing on the data, and use Equation (10) to calculate the entropy weight value of information entropy; Among them, ω(j) is the acceptance weight obtained from the jth cloud model; EN(j) is the entropy of the jth cloud model; Use Equation (11) to calculate the acceptance calculation expression of the nth electric vehicle: p(n) = ω1f(n) + ω2K(n) (11) Among them, p(n) is the acceptance of the nth electric vehicle to participate in scheduling; ω1 and ω2 are the weight values of the incentive level response rate and the time margin response rate respectively.

7. The main - slave game optimization method for electric vehicle charging and discharging according to claim 1, characterized in that, The method for combining the user response model, proposing an optimization scheduling strategy based on the master-slave game, and optimizing the charging station side target with the charging station as the leader of the game is as follows: Use Equation (12) to construct the charging station side objective function: Where, T is the total number of time periods; N is the number of electric vehicles; and are the charging incentive and discharging incentive levels of the nth electric vehicle at charging station d at time t, respectively; and are the charging power and discharging power of the nth electric vehicle at charging station d at time t, respectively; ΔT fn,d (t) is the discharging duration of the nth electric vehicle at charging station d; and are the energy output conversion coefficient and energy input conversion coefficient of charging station d at time t, respectively; and are the energy output and energy input of charging station d at time t, respectively; Use Equation (13) to construct the charging and discharging parameter constraint conditions: Among them, and are the minimum incentive level and the maximum incentive level of charging station d at time t, respectively; Use Equation (14) to construct the charging station's daily energy input and energy output constraints: Among them, E d (t) is the day-ahead energy input of charging station d at time t; z d (t) is the Boolean variable of charging station d at time t; M is a positive number approaching infinity; is the energy discharged from the energy storage of charging station d at time t; Use Equation (15) to construct the energy balance constraint: Among them, is the energy storage charging amount of charging station d at time t; Use Equation (16) to construct the energy storage charging and discharging amount constraint: Among them, u d (t) is a Boolean variable of charging station d at time t; and are the maximum charging power and the maximum discharging power of charging station d, respectively; Construct the energy storage power constraint by using Equation (17): Among them, S(t) is the energy storage power of charging station d at time t; η + and η - are the charging efficiency and discharging efficiency of the energy storage respectively; S max is the maximum capacity of the energy storage.

8. The method for optimizing the master-slave game of electric vehicle charging and discharging according to claim 1, wherein For the energy utilization and scheduling scheme of electric vehicle users, the method for establishing the optimization model on the electric vehicle side is as follows: Establish the objective function for electric vehicle users to participate in scheduling by using Equation (18): Among them, T c is the charging period of the electric vehicle; T dis is the discharging period of the electric vehicle; ε is the degradation loss of the electric vehicle battery; Δt n,f is the delay duration caused by road resistance during the nth charging / discharging trip of the fth electric vehicle; f cd is the number of charging / discharging times of the electric vehicle; λ is the time cost coefficient of the electric vehicle user; Construct the state of charge constraint of the electric vehicle battery by using Equation (19): where ξ is the desired state of charge for the electric vehicle user; is the battery capacity of the nth electric vehicle; is the initial charge of the nth electric vehicle; E c is the power consumption of the electric vehicle during driving; Construct the charging power constraint of the electric vehicle by using Equation (20): Among them, is the maximum charging power of the nth electric vehicle at charging station d; Construct the discharging duration constraint of the electric vehicle by using Equation (21): Among them, E min is the minimum value of the state of charge of the electric vehicle.

9. A device, comprising a memory and a processor, characterized in that, The memory is used to store the program for supporting the processor to execute the electric vehicle charging and discharging master-slave game optimization method described in any one of Claims 1 to 8, and the processor is configured to execute the program stored in the memory.

10. A storage medium has a computer program stored thereon, characterized in that, When the computer program is run by the processor, it executes the steps of the electric vehicle charging and discharging master-slave game optimization method described in any one of Claims 1 to 8.

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