Vehicle-station-network three-party game electric vehicle orderly charging control method and system

Abstract

The application discloses a vehicle-station-network three-party game electric vehicle orderly charging control method and system, which comprises the following steps: predicting the space-time distribution characteristics of the electric vehicle charging load in the region according to the coupling relationship between the regional road network and the power grid and the user travel law; establishing a distribution system operator operation optimization model with the minimum network loss as the target under the premise of meeting the electric vehicle charging capacity; establishing an electric vehicle aggregator operation optimization model with the minimum charging cost as the target under the premise of meeting the electric vehicle charging capacity; establishing a distribution system operator and electric vehicle aggregator operation game model and solving the model to output the charging plan of the electric vehicle user. The application establishes a space-time characteristic model of the electric vehicle charging load distribution considering the coupling of the road network and the power grid, describes the game behavior of the EV user in the actual decision-making scene, avoids the idealization of the game conclusion, realizes the accurate prediction of the charging load of the charging station, and improves the accuracy of the orderly charging method.

Description

Technical Field

[0001] This invention relates to the field of electric vehicle operation control technology, specifically to an orderly charging control method and system for electric vehicles based on a three-way game between the vehicle, the station, and the network. Background Technology

[0002] In recent years, the electric vehicle (EV) industry has developed rapidly. However, due to the significant randomness and uncertainty of EV users' charging behavior, large-scale disorderly charging of EVs can lead to the superposition of charging load and base load, increasing the difficulty of grid control. Therefore, formulating effective orderly charging optimization strategies and rationally arranging EV charging is an effective way to reduce the aforementioned adverse effects and promote the long-term development of the EV industry. With the establishment of electricity market demand response encompassing demand-side adjustable resource capacity bidding and electricity energy bidding, electric vehicle aggregators (EVAs) that provide charging and discharging guidance services for EV users are gradually becoming an important part of the electricity market demand side. In addition, distribution system operators (DSOs) are responsible for the operation and management of distribution networks and provide value-added services to various users, making the management of operating rights more flexible.

[0003] Large-scale EVs with charging and discharging capabilities can participate in grid demand response by integrating their charging and discharging volumes through EVAs. Against the backdrop of power system reform on the retail side, the decision-making entities involved in EV charging and discharging scheduling scenarios are becoming more diversified, primarily considering DSOs, EVAs, and EV users. By integrating the vast and dispersed EV charging and discharging loads, EVAs possess capabilities that individual EVs lack, such as participating in electricity market bidding and providing ancillary services like peak shaving and valley filling to the grid. This not only lowers the barrier to entry for dispersed EVs but also enhances their ability to bid for low-cost electricity from the wholesale electricity market. EVAs provide cheaper electricity to EV users through bidding and profit from the price difference; simultaneously, this aggregated load and electricity market trading reduces the uncertainty brought about by large-scale EV charging, allowing DSOs to obtain some high-quality demand response resources and reducing grid peak-shaving pressure. This benefits all three parties: DSOs, EVAs, and EV users. In reality, DSOs, EVAs, and EV users, as different stakeholders, have different optimization objectives in their respective decision-making processes. On the one hand, each entity independently optimizes its own objectives; on the other hand, they are influenced by each other's actions, forming a complex game of interests among the stakeholders. Game theory, which can consider the interaction of decision-making entities with multiple interests or conflicts, and the coordination and balance of multiple objectives, is expected to become a powerful tool for studying the above problems. Summary of the Invention

[0004] This invention proposes an orderly charging control method for electric vehicles based on a three-way game between the vehicle, the station, and the network. The aim is to introduce the spatiotemporal distribution of electric vehicle charging load into a multi-party game competition model, thereby comprehensively considering the interests of all three parties and formulating a more scientific and reasonable charging method.

[0005] To achieve the above objectives, the present invention adopts the following technical solution: A method for orderly charging control of electric vehicles based on a three-way game between the vehicle, the station, and the network includes the following steps. Includes the following steps, S100: The power distribution network and charging stations within the region are managed by the power distribution system operator and the electric vehicle aggregator, respectively. Based on the coupling relationship between the regional road network and power grid and user travel patterns, the electric vehicle aggregator predicts the spatiotemporal distribution characteristics of the electric vehicle charging load in the region the following day, thereby obtaining the predicted values ​​of the electric vehicle charging load and charging amount at each node of the power distribution network, and reporting this information to the power distribution system operator; S200: With the goal of minimizing network losses, the power distribution system operator establishes an operation optimization model to determine the optimal electric vehicle charging load for each node of the power distribution network in the next 24 hours, while ensuring the charging capacity of electric vehicles. S300: Electric vehicle aggregators aim to minimize charging costs. Under the premise of meeting the charging volume of electric vehicles, they establish an operation optimization model for electric vehicle aggregators to determine the charging power that charging stations can provide for electric vehicles at different times. S400: Establish a game theory model between the power distribution system operator and the electric vehicle aggregator to obtain the optimal solution. Determine if the result is optimal. If so, end the solution process and announce to electric vehicle users the charging power available at each charging station at different times the following day; otherwise, return to S100 and readjust the charging plans of electric vehicle users.

[0006] Furthermore, step S100 includes the following specific steps: S101: Establish an attractiveness model for charging stations to electric vehicles. Assume the road network nodes where the charging stations and electric vehicles are located are respectively... and The model depends on the charging station at time t. Number of available charging stations Charging stations To road network nodes distance, This is the distance correction coefficient. The specific attraction model can be expressed as follows: (1) The charging station at time t can be calculated using the attraction model. For located The probability distribution of the attractiveness of electric vehicles at nodes The specific expression is as follows: (2) S102: Establish the probability distribution of non-charging destinations for electric vehicles. The spatiotemporal distribution of electric vehicle charging load is closely related to their travel paths. The probability distribution of non-charging destinations for EVs can be obtained through the origin-destination (OD) matrix, thus revealing the EV's location at each time point. Different OD matrices can generally be obtained through surveys and reverse engineering. Dividing the electric vehicle travel area into Ns regions based on the number of road network nodes, and dividing the day into 24 time periods, results in an Ns OD matrix. Ns A 24 matrix, where the OD matrix Ot within a certain time period t is an n-order square matrix of the following form: (3) In the formula, matrix elements This represents the number of vehicle travel records from area A to area B within time period t.

[0007] Based on matrix Ot, the probability distribution of the destination for non-charging trips can be obtained. During time period t, the probability that a vehicle departing from area A will travel to area B is... (4) element Constitute an Ns Ns The probability distribution matrix F of 24 represents the probability distribution of the EV's non-charging driving destination.

[0008] S103: Simulate the location of EVs at various times and their charging intentions at a certain time based on EV travel chains. Each travel chain can be decomposed into multiple "travel segments", and the start time ts of each travel segment follows a normal distribution as shown in equation (6). (5) In the formula: μ and σ are the mean and variance of the start time corresponding to different travel chains, respectively.

[0009] Let the maximum driving range of the EV be Lmax, and the driving length be... The initial state of charge of road AB is as follows: At time t1, the state of charge of the vehicle after leaving road AB is... The times t1 and t2 are respectively: (6) (7) in: Let t1 represent the congestion level of road segment AB. The design speed is for road sections AB.

[0010] The probability of EV charging intention at time t2 is as follows: (8) S104: Determine the spatiotemporal distribution of EV charging load in the network. Let the charging power of the EVs be... The state of charge is The charging efficiency is η, and the target state of charge is If B is the battery capacity, then the charging time is: (9) Monte Carlo simulations can be used to obtain the spatiotemporal distribution of all EV charging loads in the region, that is, to obtain the charging power of each charging station at various times during the next day. Assuming the charging stations... If the corresponding grid node is i, then the charging power of the corresponding grid node i at time t is obtained. and the station's charging volume the following day If no charging station is connected to node i of the power grid, then , .

[0011] Furthermore, in step S200, the power distribution system operator, with the goal of minimizing network losses and under the premise of meeting the charging needs of electric vehicles, determines the optimal electric vehicle charging load for each node of the power distribution network for the next 24 hours and assigns it to the electric vehicle aggregator; this includes the following specific steps: S201: Let sj be the name of a line in the distribution network, where s and j represent the start and end node numbers of the line, respectively. The objective function of the distribution system operator optimization model is as follows: (10) Where a and b are weighting coefficients; and Let S and S represent the current flowing through branch Sj and the resistance of the branch, respectively; E represents the set of all branches in the network; and N is the number of nodes in the distribution network. The charging power of electric vehicles located at node i is to be optimized by the distribution network operator. This refers to the electric vehicle charging load predicted in step S104. S202: The constraints of the power distribution system operator optimization model are as follows: (11) (12) (13) (14) (15) (16) Where pj,t and qj,t represent the active power and reactive power injected at node j, respectively; and These represent the active power and reactive power flowing out of node j, respectively. This indicates that node k is a downstream node of node j. and These represent the active power and reactive power passing through branch sj, respectively. B represents the set of all nodes in the branch network; S is the upstream node of node J. and These represent the upper and lower limits of the voltage, respectively. and These are the base active power and base reactive power of node j, respectively. For a time interval.

[0012] S203: Solve the above optimization problem to determine the optimal electric vehicle charging load for each node of the distribution network in the next 24 hours. .

[0013] Furthermore, the electric vehicle aggregator operation optimization model established in step S300 follows the following steps: S301: For electric vehicle aggregators, the goal is to minimize charging costs. Here, power deviation can be used to reflect costs, resulting in the following model: (17) Where C represents the weighting factor related to the power difference, and Pi,t is the optimized power variable of the electric vehicle aggregator. This refers to the electric vehicle charging load predicted in step S104.

[0014] S302: The constraints of the electric vehicle aggregator operation optimization model are: (18) (19) in, The maximum charging load that a charging station for node i can provide. For a time interval.

[0015] Furthermore, step S400 follows these steps: S401: Establish a master-slave game model Ω between power distribution system operators and electric vehicle aggregators, as follows: (20) In the formula: DSO and EVA represent the power distribution system operator and the electric vehicle aggregator, respectively, and S and E represent the strategy set and interest target set of each entity, respectively.

[0016] S402: Determine if the result is optimal or the maximum number of iterations has been reached. If so, end the solution process and announce the charging power available at each charging station at different times the next day to electric vehicle users; otherwise, adjust the probability of the electric vehicle user's destination or travel time, and return to S100 to re-predict the spatiotemporal distribution of charging power for electric vehicle users.

[0017] On the other hand, the present invention also discloses a computer system storing a computer program, which, when executed by a processor, causes the processor to perform the steps of the above-described method.

[0018] As can be seen from the above technical solution, the electric vehicle orderly charging control method based on the vehicle-station-network three-party game of the present invention includes: predicting the spatiotemporal distribution characteristics of electric vehicle charging load in the region on a daily basis according to the coupling relationship between the regional road network and the power grid and the user's travel patterns; establishing an operation optimization model for the power distribution system operator with the goal of minimizing network loss and on the premise of meeting the charging volume of electric vehicles; establishing an operation optimization model for the electric vehicle aggregator with the goal of minimizing charging cost and on the premise of meeting the charging volume of electric vehicles; establishing and solving the operation game model between the power distribution system operator and the electric vehicle aggregator, and outputting the charging plan for electric vehicle users.

[0019] Compared with existing technologies, the beneficial effects of this invention are reflected in: (1) A spatiotemporal characteristic model of electric vehicle charging load distribution considering road network-grid coupling was established to characterize the game behavior of EV users in actual decision-making scenarios, avoid idealization of game conclusions, realize accurate prediction of charging load of charging stations, and improve the accuracy of orderly charging method. (2) The model can capture the dynamic interaction characteristics of the three main entities: DSO, EVA, and EV users. It coordinates the economic interests among the three entities by achieving a balance between different levels of game. Attached Figure Description

[0020] Figure 1 This is a flowchart illustrating the present invention. Detailed Implementation

[0021] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are some embodiments of the present invention, but not all embodiments.

[0022] like Figure 1 As shown in this embodiment, the electric vehicle orderly charging control method based on a three-way game between the vehicle, station, and network includes the following steps: S100: The power distribution network and charging stations within the region are managed by the power distribution system operator and the electric vehicle aggregator, respectively. Based on the coupling relationship between the regional road network and power grid and user travel patterns, the electric vehicle aggregator predicts the spatiotemporal distribution characteristics of the electric vehicle charging load in the region the following day, thereby obtaining the predicted values ​​of the electric vehicle charging load and charging amount at each node of the power distribution network, and reporting this information to the power distribution system operator; S200: With the goal of minimizing network losses, the power distribution system operator establishes an operation optimization model to determine the optimal electric vehicle charging load for each node of the power distribution network in the next 24 hours, while ensuring the charging capacity of electric vehicles. S300: Electric vehicle aggregators aim to minimize charging costs. Under the premise of meeting the charging volume of electric vehicles, they establish an operation optimization model for electric vehicle aggregators to determine the charging power that charging stations can provide for electric vehicles at different times. S400: Establish a game theory model between the power distribution system operator and the electric vehicle aggregator to obtain the optimal solution. Determine if the result is optimal. If so, end the solution process and announce to electric vehicle users the charging power available at each charging station at different times the following day; otherwise, return to S100 and readjust the charging plans of electric vehicle users.

[0023] The following are detailed explanations: S100: The power distribution network and charging stations within the region are managed by the power distribution system operator and the electric vehicle aggregator, respectively. Based on the coupling relationship between the regional road network and power grid, and user travel patterns, the electric vehicle aggregator predicts the spatiotemporal distribution characteristics of the electric vehicle charging load in the region for the following day, thereby obtaining the predicted values ​​of the electric vehicle charging load and charging amount at each node of the power distribution network, and reporting this information to the power distribution system operator; step S100 includes the following specific steps: S101: Establish an attractiveness model for charging stations to electric vehicles. With the increasing popularity of EVs, users can share more resource information through real-time charging station operation information platforms and apps. Users will obtain richer charging station information and consider more factors when choosing a charging station. Since EVs' choice of charging stations is influenced by multiple factors and has a significant degree of subjectivity and randomness, an attractiveness model for charging stations to EVs is established to describe the appeal of charging stations to users. The model establishment approach is as follows: Assume that the road network nodes where the charging station and the electric vehicle are located are respectively and The model depends on t Moment Charging Station Number of available charging stations Charging stations To road network nodes distance, This is the distance correction coefficient. The specific attraction model can be expressed as follows: (1) From the attraction model, it can be calculated that t Moment Charging Station For located The probability distribution of the attractiveness of electric vehicles at nodes The specific expression is as follows: (2) S102: Establish the probability distribution of non-charging destinations for electric vehicles. Based on urban area information, the city is divided into residential areas, work areas, commercial areas and other locations. Using travel chains, the travel process of private cars can be simulated, and typical travel chains can be constructed with residential areas as the starting and ending points of the trip. Each travel chain can be decomposed into multiple "travel segments", and the start time ts of each travel segment follows the normal distribution shown in formula (5). Assume that taxis operate on a shift system and the vehicles are on the road 24 hours a day. Taxis provide travel services for all residents of the city, and their travel destinations are not as continuous as those of private cars, so OD analysis is used for research. The spatiotemporal distribution of electric vehicle charging load is closely related to its travel path. The probability distribution of non-charging destinations for EVs can be obtained through the origin-destination (OD) matrix, thereby obtaining the position of EVs at each time. Different OD matrices can generally be obtained through surveys and reverse inference. The electric vehicle travel area is divided into Ns areas according to the number of road network nodes, and the day is divided into 24 time periods, then the OD matrix is ​​a Ns Ns A 24 matrix, for a certain period of time t OD matrix within O t For a form of the following n Square matrix: (3) In the formula, matrix elements express t The number of vehicle travel records from area A to area B of the road network within a given time period.

[0024] Based on matrix O t This yields the probability distribution of the car's destination when it doesn't charge. (Time period) t Inside, from the area AThe probability that a departing vehicle will travel to area B is: (4) element constitute a Ns Ns 24 probability distribution matrix F , representing the probability distribution of the EV's non-charging driving destination.

[0025] S103: Simulates the location of EVs at various times and their charging intentions at a given moment based on EV mobility chains. Each mobility chain can be broken down into multiple "mobility segments," with each segment starting at a specific time. t s follows the normal distribution shown in equation (6). (5) In the formula: m and s These represent the mean and variance of the start times for different travel chains.

[0026] Assume the maximum driving range of the EV is Lmax The driving length is The initial state of charge of road AB is as follows: , at all times t 1. The state of charge of the vehicle after leaving road AB. and time t 2 are respectively: (6) (7) in: for t The level of congestion on road sections A and B at time 1. The design speed is for road sections AB.

[0027] get t The probability of EV charging intention at time 2 is as follows: (8) S104: Determine the spatiotemporal distribution of EV charging load in the network. Let the charging power of the EVs be... The state of charge is Charging efficiency is or The target state of charge is If B is the battery capacity, then the charging time is: (9) Monte Carlo simulations can be used to obtain the spatiotemporal distribution of all EV charging loads in the region, that is, to obtain the charging power of each charging station at various times during the next day. Assuming the charging stations... The corresponding power grid node is i Then the corresponding power grid is obtained. i Node at t Charging power at any time and the station's charging volume the following day If the power grid i If no charging station is connected to the node, then , .

[0028] S200: With the goal of minimizing network losses, distribution system operators establish an operational optimization model to determine the optimal electric vehicle charging load for each node in the distribution network over the next 24 hours, while ensuring sufficient charging capacity for electric vehicles. This is implemented according to the following specific steps: S201: The objective function for establishing the optimization model of the power distribution system operator.

[0029] Suppose that the name of a certain line in the distribution network is sj , s and j Let represent the first and last node numbers of the line, respectively. The objective function for establishing the power distribution system operator optimization model is as follows: (10) in, a and b These are the weighting coefficients; and Representing branch roads sj The current flowing through the branch and the resistance of the branch; E This represents the set of all branches in the network; N is the number of nodes in the distribution network. For distribution network operators, the nodes to be optimized i The charging power of electric vehicles, and This refers to the electric vehicle charging load predicted in step S104.

[0030] S202: The constraints of the power distribution system operator optimization model are given as follows: (11) (12) (13) (14) (15) (16) in: p j,t andq j,t Representing nodes respectively j Injected active and reactive power; and They are slave nodes j The outflow of active and reactive power; Represents a node k It is a node j Downstream nodes; and Branch roads sj The active and reactive power passing through; Represents a node s It is a node j The upstream node; B This represents the set of all nodes in a branch network; and These represent the upper and lower limits of the voltage, respectively. and They are nodes j The basic active power and basic reactive power; For a time interval.

[0031] S203: Solve the above optimization problem.

[0032] Determine the optimal electric vehicle charging load for each node of the distribution network in the following 24 hours. .

[0033] S300: Electric vehicle aggregators aim to minimize charging costs. While ensuring sufficient charging capacity for electric vehicles, they establish an operational optimization model to determine the charging power available at charging stations during different time periods. This is implemented through the following specific steps: S301 establishes the objective function for electric vehicle aggregators.

[0034] For electric vehicle aggregators, the goal is to minimize charging costs. Here, power deviation can be used to reflect costs, resulting in the following optimization model: (17) in, C This represents the weighting factor related to the power difference. P i,t For electric vehicle aggregators, the optimized power variable, and This refers to the electric vehicle charging load predicted in step S104.

[0035] S302: The constraints of the power distribution system operator optimization model are given as follows: (18) (19) in, The maximum charging load that a charging station for node i can provide. For a time interval.

[0036] S400: Establish a game theory model between the power distribution system operator and the electric vehicle aggregator to obtain the optimal solution. Determine if the result is optimal. If so, end the solution process and announce to electric vehicle users the charging power available at each charging station at different times the following day; otherwise, return to S100 and readjust the charging plans of electric vehicle users. This section follows the implementation scheme below: S401: Establish a master-slave game model Ω between power distribution system operators and electric vehicle aggregators, as follows: (20) In the formula: DSO and EVA represent the power distribution system operator and the electric vehicle aggregator, respectively, and S and E represent the strategy set and interest target set of each entity, respectively.

[0037] S402: Determine whether the result is optimal or the maximum number of iterations has been reached.

[0038] If so, the solution is terminated, and the charging power available at each charging station at different times the next day is announced to electric vehicle users; otherwise, the probability of the electric vehicle user's destination or travel time is corrected, and the process returns to S100 to re-predict the spatiotemporal distribution of the charging power for electric vehicle users.

[0039] In summary, this invention establishes a spatiotemporal characteristic model of electric vehicle charging load distribution considering road-grid coupling, characterizing the game behavior of EV users in actual decision-making scenarios, avoiding idealized game conclusions, achieving accurate prediction of charging load at charging stations, and improving the accuracy of orderly charging methods. This model can capture the dynamic interaction characteristics of the three main entities: DSO, EVA, and EV users. It coordinates the economic interests of the three entities by achieving a balance between game dynamics at different levels.

[0040] In another aspect, the present invention also discloses a computer-readable storage medium storing a computer program, which, when executed by a processor, causes the processor to perform the steps of any of the methods described above.

[0041] In another aspect, the present invention also discloses a computer device, including a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor performs the steps of any of the methods described above.

[0042] In another embodiment provided in this application, a computer program product containing instructions is also provided, which, when run on a computer, causes the computer to perform the steps of any of the methods described above.

[0043] It is understood that the system provided in the embodiments of the present invention corresponds to the method provided in the embodiments of the present invention, and the explanation, examples and beneficial effects of the relevant content can be referred to the corresponding parts of the above methods.

[0044] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments described above. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0045] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0046] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions 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. A method for orderly charging control of electric vehicles based on a three-way game between the vehicle, station, and network, characterized in that, Includes the following steps, S100: The power distribution network and charging stations within the region are managed by the power distribution system operator and the electric vehicle aggregator, respectively. Based on the coupling relationship between the regional road network and the power grid and the user's travel patterns, the electric vehicle aggregator predicts the spatiotemporal distribution characteristics of the electric vehicle charging load in the region the following day, thereby obtaining the predicted values ​​of the electric vehicle charging load and charging amount at each node of the power distribution network, and reporting this information to the power distribution system operator. S200: With the goal of minimizing network losses, the power distribution system operator establishes an operation optimization model to determine the optimal electric vehicle charging load for each node of the power distribution network in the next 24 hours, while ensuring the charging capacity of electric vehicles. S300: Electric vehicle aggregators aim to minimize charging costs. Under the premise of meeting the charging volume of electric vehicles, they establish an operation optimization model for electric vehicle aggregators to determine the charging power that charging stations can provide for electric vehicles at different times. S400: Establish a game theory model between the power distribution system operator and the electric vehicle aggregator to obtain the optimal solution; determine whether the result is optimal; if so, end the solution process and announce to electric vehicle users the charging power available at each charging station at different times the next day; otherwise, return to S100 and readjust the charging plans of electric vehicle users. Step S100 includes the following specific steps: S101: Establish an attractiveness model for charging stations to electric vehicles; assume that the road network nodes where the charging stations and electric vehicles are located are respectively... and The model depends on t Moment Charging Station Number of available charging stations Charging stations To road network nodes distance , This is the distance correction coefficient; the specific attraction model is described below: （1） From the attraction model, we can find t Moment Charging Station For located The probability distribution of the attractiveness of electric vehicles at nodes The specific expression is as follows: （2） S102: Establish the probability distribution of non-charging destinations for electric vehicles; the spatiotemporal distribution of electric vehicle charging load is closely related to its travel path. The probability distribution of non-charging destinations for EVs is obtained through the origin-destination matrix, thus revealing the EV's location at each time point. Different OD matrices are obtained through surveys and reverse engineering. The electric vehicle travel area is divided into Ns regions according to the number of road network nodes, and the day is divided into 24 time periods. The resulting OD matrix is... Ns Ns A 24 matrix, for a certain period of time t OD matrix within O t For a form of the following n Square matrix: （3） In the formula, matrix elements express t The number of vehicle travel records from road network area A to area B within a given time period; Based on matrix O t This yields the probability distribution of the destination for non-charging trips; time period t Inside, from the area A The probability that a departing vehicle will travel to area B is: （4） element constitute a Ns Ns 24 probability distribution matrix F , representing the probability distribution of non-charging driving destinations for EVs; S103: Based on the EV travel chain, simulate the location of EVs at various times and their charging intentions at a certain time; each travel chain is decomposed into multiple "travel segments", and the start time of each travel segment is... t s follows the normal distribution shown in equation (6). （5） In the formula: μ and σ These represent the mean and variance of the start times for different travel chains; Assume the maximum driving range of the EV is Lmax The driving length is The initial state of charge of road AB is as follows: , at all times t 1. The state of charge of the vehicle after leaving road AB. and time t 2 are respectively: （6） （7） in: for t The level of congestion on road sections A and B at time 1. The design speed for sections AB; get t The probability of EV charging intention at time 2 is as follows: （8） S104: Determine the spatiotemporal distribution of EV charging load in the network; assuming the charging power of the EV is... The state of charge is Charging efficiency is η The target state of charge is If B is the battery capacity, then the charging time is: （9） Monte Carlo simulation was used to calculate the spatiotemporal distribution of all EV charging loads in the region, that is, to obtain the charging power of each charging station at each time of the next day; assuming the charging station The corresponding power grid node is i Then the corresponding power grid is obtained. i Node at t Charging power at any time and the station's charging volume the following day If the power grid i If no charging station is connected to the node, then , .

2. The electric vehicle orderly charging control method based on a three-way game theory among vehicle, station, and network as described in claim 1, characterized in that: The specific steps in step S200 include: S201: Suppose the name of a certain line in the distribution network is... sj , s and j Let represent the first and last node numbers of the line, respectively. The objective function for establishing the power distribution system operator optimization model is as follows: （10） in, a and b These are the weighting coefficients; and Representing branch roads sj The current flowing through the branch and the resistance of the branch; E This represents the set of all branches in the network; N is the number of nodes in the distribution network. For distribution network operators, the nodes to be optimized i The charging power of electric vehicles, and This refers to the electric vehicle charging load predicted in step S104. S202: The constraints of the power distribution system operator optimization model are as follows: （11） （12） （13） （14） （15） （16） in: p j,t and q j,t Representing nodes respectively j Injected active and reactive power; and They are slave nodes j The outflow of active and reactive power; Represents a node k It is a node j downstream nodes, and Branch roads sj The active and reactive power passing through; Represents a node s It is a node j The upstream node; B This represents the set of all nodes in a branch network; and These represent the upper and lower limits of the voltage, respectively. and They are nodes j The basic active power and basic reactive power, For a time interval; S203: Solve the above optimization problem to determine the optimal electric vehicle charging load for each node of the distribution network in the next 24 hours. .

3. The electric vehicle orderly charging control method based on a three-way game theory among vehicle, station, and network as described in claim 2, characterized in that: The specific steps of step S300 are as follows: S301: For electric vehicle aggregators, the goal is to minimize charging costs; using power deviation to reflect costs, the following model is obtained: （17） in, C This represents the weighting factor related to the power difference. P i,t For electric vehicle aggregators, the optimized power variable, and This refers to the electric vehicle charging load predicted in step S104. S302: The constraints of the electric vehicle aggregator operation optimization model are: （18） （19） in, The maximum charging load that a charging station for node i can provide. For a time interval.

4. The electric vehicle orderly charging control method based on a three-way game theory among vehicle, station, and network as described in claim 3, characterized in that: The specific steps of step S400 are as follows: S401: Establish a master-slave game model Ω between power distribution system operators and electric vehicle aggregators, as follows: （20） In the formula: DSO and EVA represent the power distribution system operator and the electric vehicle aggregator, respectively; S and E represent the strategy set and interest target set of each entity, respectively. S402: Determine whether the result is optimal or the maximum number of iterations has been reached; If so, the solution is terminated, and the charging power available at each charging station at different times the next day is announced to electric vehicle users; otherwise, the probability of the electric vehicle user's destination or travel time is corrected, and the process returns to S100 to re-predict the spatiotemporal distribution of the charging power for electric vehicle users.

5. A computer system storing a computer program, which, when executed by a processor, causes the processor to perform the steps of the method as described in any one of claims 1 to 4.

Images