A master-slave game scheduling method considering the interaction between the integrated charging, discharging and storage station and electric vehicles

Abstract

The application provides a master-slave game scheduling method considering interaction of charging-discharging-storage integrated station and electric vehicles, and steps are as follows: firstly, a charging-discharging-storage integrated station model is established, and multi-scenarios of the charging-discharging-storage integrated station are segmented; secondly, a dynamic road network model is established, and EV travel characteristics are combined to predict EV charging load space-time distribution under the constraint of urban area road network; then, according to the prediction result, an EV and charging-discharging-storage integrated station multi-objective master-slave game optimization scheduling model is established to coordinate multi-objective of EV users and charging-discharging-storage integrated station benefits; finally, KKT condition and dual theory are adopted to transform the EV and integrated station multi-objective master-slave game optimization scheduling model into a mixed integer linear programming model, so that the optimal solution is obtained. The application takes the charging-discharging-storage integrated station as the main body, guides both sides by using the electricity price, so that the optimal strategy under each scenario state is obtained, and the total system operation cost is effectively reduced.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of charging scheduling of electric vehicles, and particularly relates to a master-slave game scheduling method considering interaction between charging-discharging-storage integrated stations and electric vehicles. BACKGROUND

[0002] The power of EV single body is low, different types of EVs will also cause different problems, and the travel behavior of EV users is obviously different, which makes it difficult to determine the EV load. Therefore, using effective scheduling strategy to reasonably guide the charging behavior of EVs is beneficial to increase the overall economic and environmental benefits of microgrids.

[0003] Due to the uncertainty of EV charging load, many scholars have conducted a large number of researches on EV charging guidance strategy. Literature [Zhan Hua, Jiang Changxu, Su Qinglie. Electric vehicle charging guidance method based on hierarchical reinforcement learning [J]. Electric Power Automation Equipment, 2022, 42(10): 264-272.] proposes an EV charging guidance method based on hierarchical enhanced deep network reinforcement learning, which can make the optimal EV charging destination and travel path under random conditions; Literature [Shao Yinchi, Mu Yunfei, Lin Jiaying, et al. Electric vehicle fast charging guidance strategy under the multi-element demand of "vehicle-station-network" [J]. Power System Automation, 2019, 43(18): 60-66+101.] proposes an electric vehicle fast charging guidance strategy for the multi-element demand of electric vehicles (vehicles), fast charging stations (stations), and distribution networks (networks), which not only saves user charging costs but also improves charging station operation efficiency; Literature [Zhang Cong, Peng Ke, Xiao Chuanliang, et al. Electric vehicle charging guidance strategy based on "vehicle-road-network" coordination [J]. Electric Power Automation Equipment, 2022, 42(10): 125-133.] proposes a real-time user charging guidance strategy considering the running state of the road network, the running state of the charging station, and the running state of the distribution network, which reasonably relieves traffic pressure and ensures that the distribution network voltage is within the normal operating range; Literature [Zhou Chenrui, Sheng Guangzong, Li Sheng. Multi-objective optimization scheduling of microgrid considering electric vehicle access [J]. Proceedings of the Chinese Electrical Engineering Society, 2023, 18(01): 211-218.] considers the strong randomness and flexibility of electric vehicle usage based on the traditional microgrid model, and proposes a reasonable and orderly charging and discharging scheduling strategy for electric vehicles under different electricity price mechanisms, which significantly reduces user costs and microgrid operation costs; Literature [Ge Xiaolin, Cao Shipeng, Fu Yang, et al. Time-space dual-scale electric vehicle optimization scheduling based on regional decoupling [J]. Proceedings of the Chinese Electrical Engineering Society, 2022, 1-13.] considers the electrical characteristics and travel characteristics of EVs in different regions, and proposes a time-space dual-scale electric vehicle optimization scheduling method based on regional decoupling, which adopts different scheduling models according to different regional characteristics, and is more conducive to the implementation of actual scheduling strategies. The above literatures point out the influence of EV travel characteristics and charging behavior on EV load, and propose effective scheduling strategies considering the uncertainty of EV load. If the charging and discharging of EVs with energy storage devices are considered based on EV charging guidance, on the one hand, it can effectively alleviate the impact of EV charging load uncertainty on the grid side, and on the other hand, it can also improve the economic benefits of charging stations and EV users.

[0004] From the relevant literature research, compared with the traditional charging station, the integrated station can not only control the flow of electric energy through the dispatching center to realize the peak load shifting of the power grid, but also can centrally compensate the harmonic in the station to realize the optimization of power quality. On the other hand, when the power grid appears emergency, the EV charging and discharging and storage integrated station can operate in island mode to reduce economic loss. The literature [Chu Haoxiang, Jie Da. Charging and discharging control strategy of electric vehicle charging and discharging and storage integrated station considering power grid operation state [J]. Power automation equipment, 2018, 38(04): 96-101.] divides the battery group in the battery swap station and the tiered station into several parts, and proposes a new charging and discharging control strategy in combination with the real-time load level of the power grid, which can provide value-added services for the power grid under the premise of ensuring that the storage equipment in the station is in a full power state; The literature [Yuan Hongtao, Wei Gang, Zhang He, et al. Robust optimization scheduling of active distribution network considering charging and discharging and storage integrated station [J]. Proceedings of the Chinese Academy of Engineering, 2020, 40(08):2453-2468.] proposes a two-stage robust optimization scheduling model considering the charging and discharging and storage integrated station and the active distribution network, which takes the integrated station as a new type of controllable energy and reasonably participates in the optimization scheduling strategy, effectively reducing the total operation cost of the system. The literature [Yuan Hongtao, Wei Gang, Zhang He, et al. Optimal operation of distribution network with charging and discharging and storage integrated station based on model predictive control [J]. Power system automation, 2020, 44(05): 187-197.] proposes an optimization scheduling model considering the charging and discharging and storage integrated station and the active distribution network to reduce the risk of the distribution network after the large-scale access of EV charging load. The addition of the integrated station not only meets the demand of daily optimization scheduling, but also reduces the operation and maintenance cost of the active distribution network. SUMMARY

[0005] In view of the above background art, the present application proposes a master-slave game scheduling method considering the interaction between the charging and discharging and storage integrated station and the electric vehicle. The method takes the charging and discharging and storage integrated station as the main body, and guides both parties by using the electricity price to obtain the optimal strategy under various scene states. The integrated station is a new type of controllable energy, which reasonably participates in the optimization scheduling strategy, and effectively reduces the total system operation cost.

[0006] The technical scheme of the present application is as follows:

[0007] A master-slave game scheduling method considering the interaction between the charging and discharging and storage integrated station and the electric vehicle, the steps are as follows:

[0008] Step 1: Establish a charging and discharging and storage integrated station model, and segmentally set multiple scenes for the charging and discharging and storage integrated station;

[0009] Step two: Establish a dynamic road network model and combine EV travel characteristics to predict the spatio-temporal distribution of EV charging load under the constraints of urban road networks;

[0010] Step three: Based on the prediction results, establish a multi-objective master-slave game optimization scheduling model for EVs and integrated charging, discharging and storage stations to coordinate the multi-objective benefits of EV users and integrated charging, discharging and storage stations;

[0011] Step four: Convert the multi-objective master-slave game optimization scheduling model of EVs and integrated stations into a mixed integer linear programming model using KKT conditions and duality theory to obtain the optimal solution.

[0012] Preferably, the multi-scenario of the integrated charging, discharging and storage station includes three behaviors, namely, the storage behavior of purchasing electricity in the day-ahead market, the discharging behavior of selling electricity to the grid side, and the charging behavior of EV charging in the integrated station;

[0013] The multi-scenario setting of the integrated charging, discharging and storage station is divided into two stages:

[0014] 1) When the distribution network system is normally operated or operated at low load, the integrated station prioritizes the charging behavior; when the EV charging demand is met, the storage behavior is considered, at which time the integrated station behaves as a load characteristic;

[0015] 2) When the distribution network system reaches the peak load operation state, the integrated station prioritizes the discharging behavior; the integrated station sells electricity to the grid side, at which time the integrated station behaves as a distributed power source characteristic.

[0016] Preferably, the method for establishing the integrated charging, discharging and storage station model is:

[0017] The charge-discharge adjustment rate of the integrated station is:

[0018] (1)

[0019] When =1, it represents the charge-discharge status of the EV charging system of the integrated charging, discharging and storage station, and when , it represents the charge-discharge status of the energy storage system of the integrated charging, discharging and storage station; is the rated charging power of the charging system and the energy storage system; is the actual discharging power of the charging system and the energy storage system; when , it represents that the integrated charging, discharging and storage station is in the charging state, when it represents that the integrated charging, discharging and storage station is in the discharging state;

[0020] The energy storage system supports the charging system through a Boost circuit, and its operating power is , The relationship between the remaining power of the charging system and the remaining power of the energy storage system satisfies:​

[0021] (2)

[0022] In the formula, is the remaining power of the charging system; is the remaining power of the energy storage system; is the charging power of the nth EV at time t; is the charging system power at time t; is the energy storage system power at time t;

[0023] Due to the limitations of the energy storage device and the converter of the EV charging and energy storage integrated station, the energy storage system and the charging system are limited by boundary conditions, and the power constraints are as follows:

[0024] (3)

[0025] In the formula, is the charging and discharging power of the energy storage system of the charging and energy storage integrated station; is the minimum charging and discharging power of the energy storage system of the charging and energy storage integrated station, is the maximum charging and discharging power of the energy storage system of the charging and energy storage integrated station.

[0026] Preferably, the dynamic road network model comprises a dynamic traffic road network model, a city road resistance model, a power distribution network model and an EV charging price responsiveness model.

[0027] The dynamic traffic road network model is represented as:

[0028] (4)

[0029] In the formula, G is a traffic road network set; V is a set of all nodes in the traffic road network; E is a set of all road segments in the traffic road network; H is a set of divided time, i.e. dividing the whole day into T time periods; W is a set of road segment weights, representing the travel cost of vehicles passing through the road segment, which can be quantified by time consumption, passing speed and cost; the connection relationship between nodes in the traffic road network is described by matrix D; the element of matrix D is expressed as:

[0030] (5)

[0031] In the formula, is the connecting road segment between the ith node and the jth node; is the weight of the road segment at time t;

[0032] The city road resistance model is represented as:

[0033] (7)

[0034] wherein: is the length of road ij; is the waiting time for a red light at intersection i; , denotes the EV speed at time t; is the zero flow speed of road ij; is the capacity of road ij; is the road segment traffic volume of road ij at time t; is the road traffic level; is the adaptive coefficient for different road levels;

[0035] The distribution network model is represented as:

[0036] (8)

[0037] wherein: is the nth distribution network node; is the source node position; is the source node capacity position;

[0038] the total sum of the total EV charging power accessed by the nth distribution network node at time t is:

[0039] (9)

[0040] wherein: is the charging power of the nth EV at time t; is the total number of EVs charging at time t;

[0041] The EV charging price responsiveness model is represented as:

[0042] (10)

[0043] wherein: is the EV demand responsiveness at time t; is the difference between the price at time k and the price at time t at time t; is the starting threshold, is the saturation threshold; is the maximum EV demand responsiveness.

[0044] Preferably, the method for predicting the spatiotemporal distribution of EV charging load under the constraints of an urban area road network is:

[0045] obtaining the unit mileage according to the type of each EV obtaining the initial power Cap0 according to the EV battery capacity and the initial SOC, wherein the initial SOC is the battery state when the EV is fully charged;

[0046] The starting node and initial travel time t of each EV are allocated by Monte Carlo sampling s , combined with the initial travel time t s , the destination node is randomly sampled according to the corresponding OD probability matrix;

[0047] The travel trajectory of each EV is described by repeatedly calling the OD probability matrix of EVs in each period;

[0048] When the remaining power of the EV is greater than the charging threshold, the trip is completed according to the specified route, otherwise, the EV is charged quickly nearby to determine the space-time information of the fast charging load; repeat the above steps to traverse all EVs and calculate the charging load of each node of the distribution network.

[0049] Preferably, the unit mileage The remaining power Cap t of the EV at time t is calculated:

[0050] (12)

[0051] In the formula: is the remaining power of the EV at time t-1; is the energy consumption coefficient, which represents the power loss caused by starting and braking during driving; is the driving distance of the vehicle from time t-1 to t.

[0052] Preferably, the OD probability matrix is expressed as:

[0053] (13)

[0054] In the formula: represents the number of EVs starting from i and ending at j in the period from T to T+1; represents the probability of the EV staying in place in the period from T to T+1; represents the sum of the number of EVs from node i to any node in the period from T to T+1.

[0055] Preferably, the multi-objective master-slave game optimization scheduling model of the EV and the charging and discharging integrated station includes an integrated station objective function and EV integrated station constraint conditions, an EV objective function and EV constraint conditions;

[0056] The integrated station objective function expression is:

[0057] (14)

[0058] In the formula: N is the number of EVs charging at the integrated station in a day; is the time spent by the vehicle due to road impedance; the electricity purchase price of the integrated station to the grid at time period t, the electricity sale price of the integrated station to the grid at time period t; the electricity purchase amount of the integrated station from the real-time market at time period t, the electricity sale amount of the integrated station from the real-time market at time period t; the day-ahead contract electricity price at time period t; the day-ahead contract electricity amount at time period t; the charging electricity price of the nth EV at time period t;

[0059] The EV integrated station constraint conditions include:

[0060] the charging electricity price constraint:

[0061] (15)

[0062] In the formula: the highest electricity price at time period t, the lowest electricity price at time period t; the daily average electricity price;

[0063] the integrated station day-ahead electricity purchase amount constraint, the real-time electricity purchase and sale amount constraint:

[0064] (16)

[0065] In the formula: is a Boolean variable, indicating the electricity transaction state at time period t; M is a positive number; is the energy storage discharge amount of the integrated station at time period t;

[0066] the energy balance constraint:

[0067] (17)

[0068] In the formula: is the energy storage charging amount of the integrated station at time period t;

[0069] the integrated station energy storage device charging and discharging constraint:

[0070] (18)

[0071] In the formula: is a Boolean variable, indicating the state of the integrated station energy storage facility at time period t; is the maximum charging power of the integrated station energy storage device, is the maximum discharging power of the integrated station energy storage device;

[0072] the integrated station energy storage amount constraint:

[0073] (19)

[0074] wherein: is the energy storage capacity of the energy storage facility of the integrated station at time t; is the charging efficiency of the energy storage device of the integrated station, is the discharging efficiency of the energy storage device of the integrated station; is the maximum capacity of the energy storage device of the integrated station; is the initial energy of the energy storage device of the integrated station;

[0075] The EV objective function is expressed as:

[0076] (20)

[0077] The EV constraints include:

[0078] EV battery state of charge constraint:

[0079] (21)

[0080] wherein: is the user-desired battery state of charge; is the battery capacity of the nth EV; is the initial energy of the nth EV; represents the EV charging efficiency; T a is the EV charging duration;

[0081] Charging power constraint:

[0082] (22)

[0083] wherein: is the maximum charging power of the nth vehicle.

[0084] Preferably, the method of converting the EV and integrated station multi-objective master-slave game optimization scheduling model into a mixed integer linear programming model using KKT conditions and duality theory is:

[0085] Equivalent nonlinear programming conversion of master-slave game model:

[0086] For both parties of the game, the price is determined when the EV makes a decision, replace the linear programming equations (20)-(22) with the corresponding KKT conditions, and eliminate the optimization problem; let the dual variables be 、 、 、 The KKT conditions corresponding to the linear programming equations (20)-(22) are:

[0087] (23)

[0088] (24)

[0089] (25)

[0090] (26)

[0091] (27)

[0092] Constraints (25) and (26) are complementary slackness conditions, denotes that at most one of the scalars x and y can be strictly greater than 0; Equations (23)-(27) transform the lower-level optimization into constraints;

[0093] Linearization of the complementary slackness conditions:

[0094] Introduce Boolean variables and Transform constraints (25) and (26) into linear inequalities:

[0095] (28)

[0096] (29)

[0097] (30)

[0098] (31)

[0099] Linearization of the integrated station objective function:

[0100] The source of nonlinearity in the objective function is the product of the electricity price and the charging power ; According to the duality theorem of linear programming, transform linear programming equations (20)-(22) into:

[0101] (32)

[0102] Under the premise of satisfying the KKT conditions, the objective function equation (14) is equivalent to:

[0103] (33)

[0104] Equivalent mixed integer linear programming model of the master-slave game model:

[0105] The integrated station electricity price pricing game can be equivalent to the following mixed integer linear programming:

[0106] (34)

[0107] s. t. formula (15)-(29), formula (23)-(24), formula (27)-(31)

[0108] The optimal solution of the mixed integer linear programming formula (34) The Stackelberg equilibrium of the game formula (14)-(27), (20)-(22) is formed.

[0109] Preferably, in combination with the EV charging spatio-temporal distribution prediction model and the multi-objective master-slave game optimization scheduling model of the electric vehicle and the integrated station, an EV charging and discharging optimization scheduling model is proposed, and the specific scheduling steps are as follows:

[0110] According to the prediction result in step two, the spatio-temporal distribution of charging demand of each node is obtained, and the optimal price is obtained through master-slave game;

[0111] According to the load condition of each node, the charging price is formulated on the basis of the optimal price;

[0112] According to the optimal price, the optimal charging plan is formulated.

[0113] Compared with the prior art, the present application has the following beneficial effects:

[0114] 1) A multi-objective master-slave game optimization scheduling model of EV and charging and storage integrated station is established for the common interests of EV users and charging stations. The charging and storage integrated station is taken as the master, and the maximization of the interests of both parties is taken as the target, and the interaction between the EV and the charging and storage integrated station is considered. A master-slave game optimization scheduling strategy considering the interaction between the charging and storage integrated station and the EV is proposed. The response of the EV charging strategy to the price is comprehensively considered, so that the scheduling strategy is more close to reality.

[0115] 2) The strategy effectively reduces the cost of EV users by segmenting and optimizing the charging and storage integrated station in multiple scenarios. The introduction of the charging and storage integrated station greatly increases the income of the charging station, realizing the win-win of the charging and storage integrated station and the EV users. Compared with the traditional charging station, the charging and storage integrated station is a new type of controllable energy, which not only can reasonably participate in the optimization scheduling strategy, but also can effectively reduce the total operation cost of the system. By setting reasonable planning of the capacity of the energy storage equipment of the charging and storage integrated station and the lower limit of the lowest price, the income of the charging and storage integrated station can be increased, the total operation cost of the system can be reduced, and the construction cost of the charging and storage integrated station can be reduced. The charging and storage integrated station can also sell electricity to the grid side, effectively increasing the income of the charging and storage integrated station, and also playing a role in peak shaving and valley filling for the grid side. BRIEF DESCRIPTION OF DRAWINGS

[0116] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the accompanying drawings needed to be used in the embodiments or prior art description will be briefly introduced as follows. Obviously, the accompanying drawings in the following description are only some embodiments of the present application, and for those skilled in the art, other drawings can be obtained based on these drawings without creative labor.

[0117] Figure 1 A traffic network topology diagram of the present application.

[0118] Figure 2 A flow chart of EV charging load space-time distribution prediction of the present application.

[0119] Figure 3 A flow chart of EV charging and discharging optimization scheduling of the present application.

[0120] Figure 4 A road network diagram used in an example of the present application.

[0121] Figure 5 A space-time distribution diagram of charging load of each node measured by the method of the present application.

[0122] Figure 6 A comparison result of integrated station profit under different pricing lower limits by the method of the present application.

[0123] Figure 7 A comparison result of EV user charging cost under different pricing lower limits by the method of the present application.

[0124] Figure 8 A comparison result of charging station profit under different Smax by the method of the present application. DETAILED DESCRIPTION

[0125] The technical solutions in the embodiments of the present application will be described clearly and completely in combination with the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only some embodiments of the present application, but not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0126] The embodiment of the application provides a master-slave game scheduling method considering interaction of a charging-discharging-storing integrated station and an electric vehicle, first, a charging-discharging-storing integrated station model is established, and multi-scene setting is performed on the charging-discharging-storing integrated station; second, a dynamic road network model is established, and EV travel characteristics are combined to predict EV charging load space-time distribution under city area road network constraints; then, a multi-objective master-slave game optimization scheduling model of EVs and charging-discharging-storing integrated stations is established according to the prediction result, and multi-objective coordination is performed on EV user and charging-discharging-storing integrated station benefits; finally, the KKT condition and the dual theory are used to convert the multi-objective master-slave game optimization scheduling model of EVs and integrated stations into a mixed integer linear programming model, so that the optimal solution is obtained. The master-slave game model and the scheduling strategy can maximize the benefits of EV users and charging-discharging-storing integrated stations.

[0127] Multi-scene setting is a solution method for converting uncertain factors in a random process into deterministic factors. According to the characteristics of the charging-discharging-storing integrated station, the charging-discharging-storing integrated station is divided into three behaviors, namely, the power storage behavior of purchasing power in the day-ahead market, the power selling behavior to the grid side, and the charging behavior of EV charging in the integrated station. According to the above three behavior characteristics, an integrated station multi-scene model is established in combination with the operation state of the distribution network system, as shown in Table 1.

[0128] Table 1 Multi-scene description of the system

[0129]

[0130] The multi-scene setting of the integrated station is divided into two stages, and each stage is set separately.

[0131] 1) When the distribution network system is normally operated or low-load operated, the integrated station gives priority to the charging behavior; when the EV charging demand is met, the power storage behavior is considered, and at this time the integrated station shows the load characteristics.

[0132] 2) When the distribution network system reaches the high-load operation state, the integrated station gives priority to the discharging behavior; the integrated station sells power to the grid side, and at this time the integrated station plays the role of a distributed power source to achieve the effect of peak shaving. Compared with the first stage, at this time the integrated station shows the characteristics of a distributed power source.

[0133] In order to better realize the bidirectional interaction ability of the EV charging station and the electric energy and improve the EV grid scale, the EV charging-discharging-storing integrated station is established. The integrated station can not only provide charging services for EV users, but also can discharge to the grid side through the energy storage system. The charging-discharging adjustment rate of the integrated station is :

[0134] (1)

[0135] In the formula, when =1 represents the charging and discharging state of the EV charging and discharging integrated station charging system, and when =0 represents the charging and discharging state of the EV charging and discharging integrated station energy storage system. is the rated charging power of the charging system and the energy storage system; is the actual discharging power of the charging system and the energy storage system; when , the EV charging and discharging integrated station is in a charging state, , the EV charging and discharging integrated station is in a discharging state.

[0136] The energy storage system supports the charging system through a Boost circuit, and the operating power is , The relationship between the remaining power of the charging system and the remaining power of the energy storage system satisfies:

[0137] (2)

[0138] In the formula, is the remaining power of the charging system; is the remaining power of the energy storage system; is the charging power of the nth EV at t; is the charging system power at t; is the energy storage system power at t.

[0139] Due to the limitation of the energy storage device and the converter device of the EV charging and discharging integrated station, the energy storage system and the charging system are limited by boundary conditions, and the power constraints are as follows:

[0140] (3)

[0141] In the formula, is the charging and discharging power of the energy storage system of the charging and discharging integrated station; is the minimum charging and discharging power of the energy storage system of the charging and discharging integrated station, is the maximum charging and discharging power of the energy storage system of the charging and discharging integrated station.

[0142] The dynamic road network model includes a dynamic traffic road network model, a city road resistance model, a power distribution network model and an EV charging price responsiveness model.

[0143] The traffic network is modeled by using a graph theory analysis method. The traffic network topology structure diagram is as shown in Figure 1 .

[0144] In order to reflect the interactive characteristics of “vehicle-road-network”, the dynamic road network model is adopted to update the vehicle flow of each road section in a fixed period. The dynamic road network model is described as follows:

[0145] (4)

[0146] In the formula: G is a traffic road network set; V is a set of all nodes in the traffic road network; E is a set of all road sections in the traffic road network; H is a set of divided time, that is, the whole day is divided into T time periods; W is a set of road section weights, indicating the travel cost of a vehicle passing through the road section, which can be quantified by time consumption, passing speed and cost. The connection relationship between nodes in the traffic road network is described by matrix D; the element of matrix D is expressed as:

[0147] (5)

[0148] In the formula: is the connecting road section of the i-th and j-th nodes; is the weight of the road section of the t time period.

[0149] Due to the characteristics of multiple intersections and real-time changes of urban road network, the EV driving process will be affected not only by the road impedance, but also by the traffic signal, which will affect the driving time. In order to meet the charging demand of EVs on urban roads, a city road network impedance model is introduced. The driving time is modeled and analyzed as road impedance, and the speed-flow model is cited. The expression of EV driving speed

[0150] (6)

[0151] In the formula: is the zero flow speed of road ij; is the traffic capacity of road ij; is the road traffic flow of road ij at t time; is the road traffic level; is the adaptive coefficient at different road levels. The city road impedance model is expressed as:

[0152] (7)

[0153] In the formula: is the length of road ij; is the waiting time for red light at intersection i.

[0154] In the "vehicle-road-network" mode, the spatial coupling of the power distribution network and the road network needs to be realized, so a suitable power distribution network needs to be established to match the road network when the road network model is established. In order to obtain the influence of a large number of EV charging behaviors on the space-time distribution of road traffic flow and power grid load and the profit of users and charging stations, the power distribution network is used as the power supply side to trade electric energy with the charging station. The model expression of the n-th power distribution network node ​​​​

[0155] (8)

[0156] In the formula: The location of the source node; This indicates the location of the source node's capacity.

[0157] The total EV charging power connected to the nth distribution network node at time t is... for:

[0158] (9)

[0159] In the formula: Let be the charging power of the nth EV during time period t; The total number of EVs charged during time period t.

[0160] Under normal circumstances, EV users will choose the charging station with the shortest path, but this may lead to overload at certain times, which can seriously affect the stability of the power grid. Therefore, this invention introduces an EV charging electricity price responsiveness model to reasonably regulate EV charging behavior and balance the EV charging load distribution in different time periods.

[0161] EV users will only participate in the charging response process when the initial price threshold is met, and there is also a price saturation threshold. If the price difference exceeds the saturation threshold and continues to increase during the charging period, the number of EV users participating in the charging response will not increase. EV user demand responsiveness. Expressed as:

[0162] (10)

[0163] In the formula: EV demand responsiveness during time period t; The difference between the electricity price at time k and time t in time period t; As the activation threshold, This is the saturation threshold; This represents the maximum EV demand responsiveness.

[0164] The travel patterns of EV users determine their initial departure and return times. This invention assumes that the travel behavior of gasoline-powered vehicle users is similar to that of EV users. Therefore, it draws upon the probability distribution curves of EV initial departure and return times from household travel survey data to generate the EV initial departure time. The household travel survey data shows that different types of EV battery capacities... r It follows the gamma distribution of equation (11).

[0165] (11)

[0166] In the formula: Cap r For EV capacity.

[0167] Assume the initial state of charge (SOC) is the battery status when the EV is just charged. According to the EV battery capacity, the initial power Cap0 is obtained combined with the initial SOC. Since the EV power consumption increases linearly with the driving distance, the remaining power Cap t is:

[0168] (12)

[0169] In the formula: Cap0is the initial power of the EV; is the energy consumption coefficient, which represents the power loss caused by starting and braking during driving; is the driving distance of the vehicle from t-1 to t.

[0170] To analyze the space-time characteristics of EVs, the OD analysis method is introduced. By consulting historical data from the transportation department, the traffic volume of each type of EV on each road section in each period is obtained, and the OD matrix H in each period is calculated from the traffic volume according to the complex traffic network OD matrix calculation method, and the out-of-trip characteristics of different types of EVs are described.

[0171] A day is divided into 24 hours, so the OD matrix is divided into 24 parts, each part is a sub-matrix . m is the number of road nodes in the simulation area, , is the traffic volume of the vehicle's origin and destination from T to T+1 time period. Therefore, in the T to T+1 time period, the probability of EV starting from node i and ending at terminal node j is:

[0172] (13)

[0173] In the formula: represents the number of EVs starting from i and ending at j in the T to T+1 time period ; represents the probability of EV staying in place in the T to T+1 time period; represents the sum of the number of EVs from node i to any node in the T to T+1 time period.

[0174] EV charging demand prediction is a very important prerequisite for studying EV charging and discharging optimization control. In the ideal state, Monte Carlo sampling is used to allocate the starting node and initial departure time t s of each EV, combined with t sThe period corresponds to the OD probability matrix, and the destination node is generated by random sampling. The EV travel trajectory is described by repeatedly calling the OD probability matrix of each period EV. When the EV meets the charging demand, the EV user charging load can be predicted. In the prediction model, the influence of road impedance on EV is not considered, and the nearest charging station is selected for charging when the EV meets the charging demand. The specific process is shown in Figure 2 The unit mileage of each EV is obtained according to the type of each EV The initial power Cap0 is obtained according to the EV battery capacity and the initial SOC; wherein the initial SOC is the battery state when the EV is fully charged; secondly, the starting node and the initial travel time t s The initial travel time t s The destination node is generated by random sampling according to the corresponding OD probability matrix; then, the travel trajectory of each EV is described by repeatedly calling the OD probability matrix of each period EV; finally, when the residual power of the EV is greater than the charging threshold, the travel is completed according to the specified route, otherwise, the fast charging load space-time information is determined; the above steps are repeated to traverse all EVs, and the charging load of each node of the power distribution network is counted.

[0175] The application guides EV charging in the form of electricity price response, and EVs will choose the time period that is most beneficial to themselves according to different time electricity prices. The charging and discharging storage integrated station and the EV constitute a master-slave game, and the integrated station is the leader and the EV is the follower.

[0176] The maximum self-profit is the goal of the charging and discharging storage integrated station, and the integrated station profit includes four parts: first, the income of selling electricity to the real-time market; second, the cost of purchasing electricity in the day-ahead market; third, the cost of purchasing electricity in the real-time power grid; fourth, the charging income of EVs in the integrated station. Therefore, the expression of the integrated station target function is:

[0177] (14)

[0178] In the formula: N is the number of EVs charging in the integrated station in a day; is the time length spent by the vehicle due to road impedance; is the electricity purchasing price of the integrated station to the power grid in t period, is the electricity selling price of the integrated station to the power grid in t period; is the electricity purchasing amount of the integrated station from the real-time market in t period, is the electricity selling amount of the integrated station from the real-time market in t period; is the day-ahead contract electricity price in t period; is the day-ahead market contract electricity amount in t period; is the charging electricity price of the nth EV in t period.

[0179] EV integrated station constraints include:

[0180] 1) Charging price constraints:

[0181] (15)

[0182] wherein: is the highest electricity price at time t, is the lowest electricity price at time t; is the daily average electricity price.

[0183] 2) Integrated station day-ahead electricity purchase quantity constraints, real-time electricity purchase and sale quantity constraints:

[0184] (16)

[0185] wherein: is a Boolean variable, indicating the electricity transaction state at time t; M is a positive number; is the integrated station energy storage discharge quantity at time t.

[0186] 3) Energy balance constraints:

[0187] (17)

[0188] wherein: is the integrated station energy storage device charging quantity at time t.

[0189] 4) Integrated station energy storage device charging and discharging constraints:

[0190] (18)

[0191] wherein: is a Boolean variable, indicating the state of the integrated station energy storage facility at time t; is the maximum charging power of the integrated station energy storage device, is the maximum discharging power of the integrated station energy storage device.

[0192] 5) Integrated station energy storage quantity constraints:

[0193] (19)

[0194] wherein: is the energy storage quantity of the integrated station energy storage facility at time t; is the charging efficiency of the integrated station energy storage device, is the discharging efficiency of the integrated station energy storage device; is the maximum capacity of the integrated station energy storage device; is the initial energy quantity of the integrated station energy storage device.

[0195] The goal of EVs is to minimize charging costs, and its objective function is expressed as:

[0196] (20)

[0197] EV constraints include:

[0198] 1) EV battery charge level constraints:

[0199] The EV charge should bring the battery to the appropriate charge level, subject to the following constraints:

[0200] (twenty one)

[0201] In the formula: The battery charge level as desired by the user; Let n be the battery capacity of the nth EV. The initial battery level of the nth EV; Indicates EV charging efficiency; T a Charging time for EVs.

[0202] 2) Charging power constraints:

[0203] (twenty two)

[0204] In the formula: This represents the maximum charging power of the nth vehicle.

[0205] This invention uses KKT conditions and duality theory to transform the multi-objective master-slave game optimization scheduling model of EV and integrated station into a mixed integer linear programming model, thereby obtaining the optimal solution.

[0206] 1) Equivalent nonlinear programming transformation of the master-slave game model:

[0207] For both sides in the game, the price is fixed when making the EV decision. We can replace the linear programming equations (20)-(22) with the corresponding KKT conditions to eliminate the optimization problem; let the dual variable be... , , , The KKT conditions corresponding to the linear programming equations (20)-(22) are:

[0208] (twenty three)

[0209] (twenty four)

[0210] (25)

[0211] (26)

[0212] (27)

[0213] Constraints (25) and (26) are complementary slack conditions, which means that at most one of the scalar x and y can be strictly greater than 0; constraints (23)-(27) transform the lower-level optimization into constraints. Therefore, the objective functions (14) and (25), (26) are nonlinear, and the nonlinear problem is linearized in the following.

[0214] 2) Linearization of complementary slack conditions:

[0215] Introduce Boolean variables and Transform constraints (25) and (26) into linear inequalities:

[0216] (28)

[0217] (29)

[0218] (30)

[0219] (31)

[0220] 3) Linearization of the aggregator objective function:

[0221] The source of the nonlinearity of the objective function is the product of the electricity price and the charging power ; the duality theorem of linear programming states that the objective function values of the dual problem and the primal problem are the same at the optimal solution. Transform linear programming (20)-(22) into:

[0222] (32)

[0223] Under the premise of satisfying the KKT conditions, the objective function (14) is equivalent to:

[0224] (33)

[0225] Equation (33) is linear in terms of the decision variables.

[0226] 4) Equivalent mixed integer linear programming model of the master-slave game model:

[0227] The aggregator price setting game can be equivalent to the following mixed integer linear programming:

[0228] (34)

[0229] s. t. equations (15)-(29), equations (23)-(24), equations (27)-(31)

[0230] The optimal solution of the mixed integer linear programming equation (34) The Stackelberg equilibrium of the game equations (14)-(27), (20)-(22) is formed.

[0231] An EV charging and discharging optimization scheduling model is proposed by combining the EV charging and space-time distribution prediction model and the multi-objective master-slave game optimization scheduling model of electric vehicles and integrated stations. The specific scheduling process is as shown in Figure 3 The steps are as follows:

[0232] 1) According to the above prediction results, the space-time distribution of charging demand of each node is obtained, and the optimal price is obtained by master-slave game;

[0233] 2) According to the load condition of each node, the charging price is formulated on the basis of the optimal price;

[0234] 3) The optimal charging plan is formulated according to the optimal price.

[0235] Example analysis

[0236] In this example, a certain regional road network in a city is combined with an IEEE33 node power distribution network system to analyze the model of the application. The road network includes 31 nodes and 52 roads, and the average length of the roads is 1.44km. The specific road network diagram is as shown in Figure 4 .

[0237] Combined with the population and vehicle penetration of the region, 1000 EVs are introduced, including 250 home private cars, 450 taxis and 300 other public cars. The experimental software environment is MATLAB2021b, and the CPLEX solver is used for solution.

[0238] The charging demand of EV users needs to be judged according to the travel demand and the current SOC of the vehicle whether it needs to be charged, so the charging load of EV needs to be regulated. The basis of load regulation is EV charging load prediction, and the prediction model is used to predict EV load. The space-time distribution of charging load of each node is as shown in Figure 5 .

[0239] From Figure 5It can be seen that there are two peaks in a day, the first peak appears in the 08:00-09:00 period, the peak is about 1644KW; the second peak appears in the 18:00-19:00 period, the peak is about 4320KW. In addition, private cars have charging demand from 02:00 to 17:00, and reach the peak in the morning at ten o'clock; taxis and other public vehicles have charging demand throughout the day, and the charging peak appears in the afternoon at 7:00.

[0240] The day-ahead market electricity price is shown in Table 2. In general, the real-time market electricity purchase price will be higher than the day-ahead market electricity purchase price, so the upper limit of the retail electricity price is assumed to be 1.2 times the electricity price, the lower limit is 0.8 times the electricity price, and the average electricity price is 0.9 yuan / (kW·h). The experimental parameters are shown in Table 3.

[0241] Table 2 Day-ahead market electricity price

[0242]

[0243] Table 3 Experimental parameters

[0244]

[0245] Solving equation (32) can get the maximum benefit of the integrated station 4823 yuan, and the optimal strategy of the charging station is shown in Table 4. According to the comparison of the optimal price and the day-ahead price, the integrated station will set the upper and lower limits of the price in the EV day-ahead charging and non-charging period respectively to meet the average price. Because the real-time market electricity price is higher than the day-ahead market electricity price, it is not necessary to purchase electricity from the real-time market. This is to deal with some uncertain factors, such as EV temporarily leaving the charging station; on the other hand, the optimal charging time of EV obtained from the EV charging and discharging optimization scheduling model is in the 01:00-08:00 period, which is the lowest charging cost of EV, and the rational EV user has no tendency to deviate from this charging time. Therefore, under the optimal price, the charging behavior of EV is orderly behavior.

[0246] Table 4 Optimal strategy of integrated station

[0247]

[0248] From Table 4, it can be seen that when the distribution network system is normally running or running at low load, the distribution network system first meets the charging demand of EV in the integrated station, ensuring the normal driving of EV users. And after meeting the charging demand of EV in the integrated station, it purchases electricity in the period when the day-ahead market electricity price is low. It not only ensures the normal operation of the distribution network system, but also meets the charging demand of EV, reduces the electricity purchase cost, and obtains greater benefits, so as to achieve the optimal operation state of this scene.

[0249] To verify the advantages of the charging, discharging and storage integrated station compared with the traditional charging station, the Smax The optimal strategy of the traditional charging station is shown in Table 5.

[0250] Table 5 Optimal strategy of the traditional charging station

[0251]

[0252] From Table 5, it can be seen that the income of the traditional charging station is 3191 yuan combined with the day-ahead electricity price. From section 5.2.2, it can be seen that the income of the charging, discharging and storage integrated station is 4823 yuan. Therefore, the charging, discharging and storage integrated station can obtain greater income. Compared with the charging, discharging and storage integrated station, the traditional charging station does not have energy storage equipment, while the charging, discharging and storage integrated station can purchase electricity from the grid at a low electricity price and store it in the energy storage equipment, and discharge electricity to the grid at a high electricity price. Therefore, the charging, discharging and storage integrated station has more advantages than the traditional charging station.

[0253] Under the premise of keeping other parameters unchanged, the lower limit of the pricing is changed from 0.5 times the day-ahead market electricity price to 0.9 times the day-ahead market electricity price. The changes of the integrated station profit and the EV user charging cost are shown in Figure 6 、 7 .

[0254] From Figure 6 、 7 , it can be seen that when the lower limit of the charging electricity price is 0.5 times the day-ahead market electricity price, the maximum profit of the integrated station is 4830 yuan, and the EV user charging cost is 19172 yuan at this time, and the cost is the highest. This is because the lower limit of the charging electricity price is too low, and the average of the charging electricity price is unchanged, so that the charging electricity price in the EV user charging period is increased, thereby increasing the EV user charging cost. The integrated station can purchase electricity and store it at a low day-ahead market electricity price, and sell it at a high charging electricity price. Therefore, the profit of the integrated station is the highest at this time. When the lower limit of the charging electricity price is 0.5~0.8 times the day-ahead market electricity price, the profit of the integrated station and the EV user charging cost will slowly decrease with the increase of the lower limit of the pricing. When the lower limit of the charging electricity price is more than 0.8 times the day-ahead market electricity price, the profit of the integrated station and the EV user charging cost rapidly decrease. This is because the increase of the lower limit of the pricing will increase the pricing in the EV charging time period, but due to the limitation of the average of the pricing, the pricing in the multiple EV charging time period will decrease, thereby causing the profit of the charging station to decrease, and the EV user charging cost to decrease. Therefore, in order to realize the win-win of the charging station and the EV user, the lower limit of the pricing should be considered comprehensively.

[0255] Let S max vary from 4000 kW·h to 20000 kW·h, and the other parameters remain unchanged. The change of the charging station profit is shown in Figure 8 .

[0256] FromFigure 8 It can be seen that when S max From 4000 kW·h to 10000 kW·h, the profit of the integrated station increases rapidly from 4830 yuan to 7302 yuan, but when the value of S max exceeds 10000 kW·h, the profit of the integrated station increases slowly and no longer changes when the profit of the power station reaches 7375 yuan. This is because the larger the capacity of the energy storage device, the more electricity the integrated station can purchase and store at a lower price, saving the cost of purchasing electricity. In addition, as shown in Table 4, the integrated station sells 2500 kW·h of electricity to the real-time market in the time period 11:00-12:00 and 19:00, further increasing its own profit. However, the charging demand of EV users will not change much, and blindly increasing the capacity of the energy storage device of the integrated station will also increase the construction cost of the integrated station.

[0257] To alleviate the electricity demand during the peak period of the power grid, the EV charging price response model is combined to reasonably guide the EV to optimize the peak-shaving charging. In this process, the EV users will also save charging fees. As shown in Table 5, Figure 5 It can be seen that there are two peak periods in a day, the first peak period appears in the time period 08:00-09:00, and the second peak period appears in the time period 18:00-19:00. Therefore, these two time periods are taken as the EV charging regulation period. The EV charging situation is shown in Table 6.

[0258] Table 6 EV charging situation statistics

[0259]

[0260] The above only describes the preferred embodiments of the present application and is not used to limit the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principles of the present application shall be included in the protection scope of the present application.

Claims

1. A master-slave game scheduling method considering the interaction between integrated charging, discharging, and storage stations and electric vehicles, characterized in that, The steps are as follows: Step 1: Establish a charging, discharging and storage integrated station model, and set up segments for multiple scenarios of the charging, discharging and storage integrated station; The method for establishing the integrated charging and discharging storage station model is as follows: Charge and discharge regulation rate of integrated station for: (1) In the formula, when =1 indicates the charging and discharging status of the EV charging, discharging, and storage integrated station charging system. The time indicates the charging and discharging status of the energy storage system in the EV charging and discharging integrated station; The rated charging power for charging systems and energy storage systems; This refers to the actual discharge power of the charging system and the energy storage system; when This indicates that the EV charging, discharging, and storage integrated station is in charging mode. This indicates that the EV charging, discharging, and storage integrated station is in a discharging state; The energy storage system provides electrical support to the charging system through a boost converter circuit, and its operating power is... , The relationship between the remaining power of the charging system and the remaining power of the energy storage system satisfies: (2) In the formula, The remaining power of the charging system; The remaining power of the energy storage system; Let be the charging power of the nth EV during time period t; Let t be the power of the charging system. for Real-time energy storage system power; Due to limitations in the energy storage and power conversion devices of EV charging, discharging, and energy storage integrated stations, the energy storage system and charging system are subject to boundary conditions, with the following power constraints: (3) In the formula: The charging and discharging power of the integrated charging and discharging energy storage system; The minimum charging and discharging power for the integrated charging and discharging energy storage system. This refers to the maximum charging and discharging power of the integrated charging and discharging energy storage system. Step 2: Establish a dynamic road network model and combine it with EV travel characteristics to predict the spatiotemporal distribution of EV charging load under urban area road network constraints; Step 3: Based on the prediction results, establish a multi-objective master-slave game optimization scheduling model for EVs and integrated charging, discharging and storage stations, and coordinate the revenue of EV users and integrated charging, discharging and storage stations in multiple ways. Step 4: Using KKT conditions and duality theory, the multi-objective master-slave game optimization scheduling model of EV and integrated station is transformed into a mixed integer linear programming model, thereby obtaining the optimal solution.

2. The master-slave game scheduling method considering the interaction between the integrated charging, discharging, and storage station and electric vehicles as described in claim 1, characterized in that, The integrated charging, discharging, and energy storage station includes three types of behaviors across multiple scenarios: energy storage behavior involving purchasing electricity from the day-ahead market, discharging behavior involving selling electricity to the grid, and charging behavior involving EV charging within the integrated station. The multi-scenario setup of the integrated charging, discharging, and storage station will be divided into two phases: 1) When the distribution network system is operating normally or under low load, the integrated station prioritizes charging behavior; when the charging demand of EVs is met, its energy storage behavior is considered, and the integrated station exhibits load characteristics at this time. 2) When the distribution network system reaches the peak load operation state, the integrated station prioritizes discharge behavior; the integrated station sells electricity to the grid side, and at this time the integrated station exhibits the characteristics of distributed power source.

3. The master-slave game scheduling method considering the interaction between the integrated charging, discharging, and storage station and electric vehicles as described in claim 1, characterized in that... The dynamic road network model includes a dynamic traffic road network model, an urban road resistance model, a power distribution network model, and an EV charging electricity price responsiveness model. The dynamic traffic network model is represented as follows: (4) In the formula: G is the set of the transportation network; V is the set of all nodes in the transportation network; E is the set of all road segments in the transportation network; H is the set of time periods, that is, the whole day is divided into T time periods; W is the set of road segment weights, representing the travel cost of a vehicle passing through the road segment, which can be quantified by time, speed, and cost; the connection relationship between nodes in the transportation network is described by matrix D; the elements of matrix D... The expression is: (5) In the formula: This refers to the connection segment between the i-th and j-th nodes; For the road segment in time period t The weights; The urban road resistance model is represented as follows: (7) In the formula: Let be the length of road ij; The duration of waiting at a traffic light at an intersection; , This represents the speed of the EV at time t; Let be the zero velocity of road ij; The traffic capacity of road ij; Let be the traffic flow of road segment ij at time t; Road traffic clearance level; For different road grades, adaptive coefficients are used; The distribution network model is represented as follows: (8) In the formula: This refers to the nth distribution network node. The location of the source node; Location of the source node capacity; The total EV charging power connected to the nth distribution network node at time t is... for: (9) In the formula: Let be the charging power of the nth EV during time period t; The total number of EVs charged during period t; The EV charging price responsiveness model is expressed as follows: (10) In the formula: EV demand responsiveness during time period t; The difference between the electricity price at time k and time t in time period t; As the activation threshold, This is the saturation threshold; This represents the maximum EV demand responsiveness.

4. The master-slave game scheduling method considering the interaction between the integrated charging, discharging, and storage station and electric vehicles as described in claim 1, characterized in that, The method for predicting the spatiotemporal distribution of EV charging load under urban road network constraints is as follows: mileage is calculated based on the type of each EV. The initial charge Cap0 is obtained by combining the EV battery capacity with the initial SOC; where the initial SOC is the battery state when the EV is just fully charged. The Monte Carlo sampling method was used to assign the starting node and initial travel time t of each EV. s Combined with the initial travel time t s The destination node is generated by randomly sampling from the corresponding OD probability matrix; By repeatedly calling the OD probability matrix of EVs in different time periods, the travel trajectory of each EV can be depicted. When the remaining battery power of the EV is greater than the charging threshold, the trip is completed according to the prescribed route; otherwise, the EV is quickly charged at the nearest charging station to determine the spatiotemporal information of the fast charging load. The above steps are repeated to traverse all EVs and to count the charging load of each node in the power distribution network.

5. The master-slave game scheduling method considering the interaction between the integrated charging, discharging, and storage station and electric vehicles as described in claim 4, characterized in that, Based on unit mileage Calculate the remaining charge Cap of EV at time t. t : (12) In the formula: The remaining battery power of the EV at time t-1; The energy consumption coefficient represents the electrical energy loss caused by starting and braking of the vehicle during operation. Let t be the distance the vehicle travels from time t-1 to time t.

6. The master-slave game scheduling method considering the interaction between the integrated charging, discharging, and storage station and electric vehicles as described in claim 4, characterized in that, The OD probability matrix is ​​represented as follows: (13) In the formula: This represents the number of EVs starting at point i and ending at point j during the time interval from T to T+1. This represents the probability that EV remains in place during the time interval from T to T+1. This represents the sum of the number of EVs from node i to any node during the time interval from T to T+1, where m is the number of road nodes in the simulation area.

7. The master-slave game scheduling method considering the interaction between the integrated charging, discharging, and storage station and electric vehicles as described in claim 3, characterized in that, The multi-objective master-slave game optimization scheduling model for EV and integrated charging / discharging / storage station includes the integrated station objective function and EV integrated station constraints, and the EV objective function and EV constraints. The objective function expression for the integrated station is: (14) Where: N is the number of EVs charged at the integrated station in one day; This refers to the time it takes for a vehicle to travel due to road resistance. The electricity price purchased from the grid by the integrated substation during time period t. The electricity price sold from the integrated substation to the power grid during time period t; For the t-period integrated station, the amount of electricity purchased from the real-time market is... The electricity sold by the integrated station from the real-time market during time period t; The day-ahead contract electricity price for period t; This refers to the contracted electricity volume for the day-ahead market during time period t. Let be the charging electricity price for the nth EV during time period t; The constraints of the EV integrated station include: Charging electricity price constraints: (15) In the formula: The highest electricity price during period t. The lowest electricity price during period t; This refers to the average daily electricity price. Integrated station's daily purchase electricity limit and real-time purchase and sale electricity limit: (16) In the formula: This is a Boolean variable representing the status of the electrical transaction during time period t; M is a positive number. The energy storage and discharge capacity of the integrated station during time period t; Energy balance constraints: (17) In the formula: The charging amount of the integrated station energy storage equipment during time period t; Charge and discharge constraints of integrated station energy storage equipment: (18) In the formula: This is a Boolean variable representing the state of the integrated energy storage facility during time period t. This refers to the maximum charging power of the integrated station energy storage equipment. The maximum discharge power of the integrated station energy storage equipment; Integrated station energy storage power constraints: (19) In the formula: The energy storage capacity of the integrated station energy storage facility during time period t; To improve the charging efficiency of integrated station energy storage equipment. The discharge efficiency of the integrated station energy storage equipment; The maximum capacity of the integrated station energy storage equipment; This refers to the initial power of the integrated station energy storage equipment; The EV objective function is expressed as: (20) The EV constraints include: EV battery charge level constraints: (21) In the formula: The battery charge level as desired by the user; Let n be the battery capacity of the nth EV. The initial battery level of the nth EV; Indicates EV charging efficiency; T a EV charging time; Charging power constraints: (22) In the formula: This represents the maximum charging power of the nth vehicle.

8. The master-slave game scheduling method considering the interaction between the charging, discharging, and storage integrated station and electric vehicles as described in claim 7, characterized in that, The method for transforming the multi-objective master-slave game optimization scheduling model of EV and integrated station into a mixed integer linear programming model using KKT conditions and duality theory is as follows: Equivalent nonlinear programming transformation of the master-slave game model: For both sides of the game, the price is fixed when making the EV decision, and the linear programming equations (20)-(22) are replaced with the corresponding KKT conditions; Let the dual variable be , , , The KKT conditions corresponding to the linear programming equations (20)-(22) are: (23) (24) (25) (26) (27) Constraints (25) and (26) are complementary relaxation conditions. This indicates that at most one of the scalars x and y can be strictly greater than 0; Equations (23)-(27) transform the lower-level optimization into constraints; Linearization of complementary relaxation conditions: Introducing Boolean variables and Transform constraints (25) and (26) into linear inequalities: (28) (29) (30) (31) Linearization of the objective function of the integrated station: The source of the nonlinearity of the objective function is the product of electricity price and charging power. According to the duality theorem of linear programming, the linear programming equations (20)-(22) are transformed into: （32） Provided that the KKT conditions are satisfied, the objective function (14) is equivalent to: (33) Equivalent mixed-integer linear programming model for master-slave game theory: The pricing game of electricity at integrated power stations can be equivalent to the following mixed-integer linear programming problem: (34) st. (15)-(29), (23)-(24), (27)-(31) In the optimal solution of mixed integer linear programming equation (34) This forms the Stackelberg equilibrium of the game theory equations (14)-(27) and (20)-(22).

9. The master-slave game scheduling method considering the interaction between the charging, discharging, and storage integrated station and electric vehicles as described in claim 1, characterized in that, Combining the EV charging spatiotemporal distribution prediction model and the multi-objective master-slave game optimization scheduling model for electric vehicles and integrated charging stations, an EV charging and discharging optimization scheduling model is proposed. The specific scheduling steps are as follows: Based on the prediction results in step two, the spatiotemporal distribution of charging demand at each node is obtained, and the optimal electricity price is obtained through master-slave game theory. Based on the load conditions of each node, a charging electricity price will be formulated on the basis of optimized electricity prices. Develop the optimal charging plan based on the best electricity price.

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