A method for joint configuration of multiple service area substations considering highway full-road charging demand

Abstract

The application discloses a method for joint configuration of multiple service area substations considering charging demand of expressways, relates to the technical field of traffic energy planning, and comprises the following steps: S1, establishing a traffic flow OD matrix based on the traffic flow distribution of electric vehicles at expressway entrances and exits; S2, establishing a travel behavior and charging demand model of electric vehicles based on the traffic flow OD matrix; S3, establishing a charging station queuing model and dynamically correcting the same; S4, constructing an electric vehicle charging decision probability model; S5, establishing a light-storage-charging multi-node coordination model based on the charging waiting cost of electric vehicle owners, system construction and operation and maintenance cost; and S6, optimizing the light-storage-charging multi-node coordination model to obtain an optimal solution of the joint configuration scheme of the light-storage-charging multiple service area substations considering the charging demand of expressways. The application fully considers the influence of queuing time, charging price and residual electric quantity on the charging behavior of electric vehicles, and also considers the influence of traffic flow on capacity configuration, so that efficient utilization of global resources is realized.

Description

Technical Field

[0001] This invention relates to the field of transportation energy planning technology, specifically to a method for the joint configuration of multiple service area substations that takes into account the charging needs of the entire highway. Background Technology

[0002] With the widespread adoption of mobile smart terminals (such as in-vehicle intelligent systems, mobile phones, and tablets) and the rapid development of wireless communication technology, more and more electric vehicle owners rely on real-time navigation technology to dynamically decide which charging station to choose. If the queue time at a charging station in a certain service area is long, vehicles may choose to drive to the next service area to charge, resulting in a significant spatiotemporal distribution of electric vehicle charging demand in the highway network. However, most existing technologies focus on optimizing the connection of photovoltaic, energy storage, and charging nodes in a single service area, with less consideration given to the spatiotemporal dynamics of charging demand within the highway network and its impact on charging station planning.

[0003] In real-world highway scenarios, the charging load within service areas exhibits a significant traffic flow shift effect, making independent optimization of a single service area insufficient to meet actual demand. Considering the randomness of drivers' charging station selection and the mutual influence of loads between regions, when a service area's charging stations approach saturation, some drivers may choose to charge at other service areas. The distribution of charging load is not only limited by the geographical location and infrastructure capacity of charging stations but also influenced by dynamic factors such as real-time electricity prices, charging demand, and queuing conditions. The spatial layout of charging stations is closely related to the spatiotemporal distribution of vehicles; coordinating these two aspects is a significant challenge in researching the optimal configuration of photovoltaic, energy storage, and charging integration into highway power supply and distribution systems. Summary of the Invention

[0004] The purpose of this invention is to overcome the shortcomings of the prior art and provide a method for the joint configuration of multiple service area substations that takes into account the charging needs of the entire highway.

[0005] The objective of this invention is achieved through the following technical solution: This invention discloses a method for jointly configuring substations in multiple service areas that considers the charging needs of the entire highway network, comprising the following steps: S1. Based on the traffic flow distribution of electric vehicles at highway entrances and exits, establish a vehicle flow OD matrix; S2. Based on the traffic flow OD matrix established in step S1, establish a model of electric vehicle travel behavior and charging demand. S3. Establish a queuing model for charging stations and dynamically correct it; S4. Construct a probabilistic model for electric vehicle charging decisions; S5. Based on the charging waiting costs for electric vehicle owners, system construction and operation and maintenance costs, establish a multi-node collaborative model for photovoltaic, energy storage and charging. S6. Optimize the multi-node collaborative model of photovoltaic, energy storage and charging established in step S5 to obtain the optimal solution of the joint configuration scheme of photovoltaic, energy storage and charging multi-service area substations that takes into account the charging needs of the entire highway.

[0006] Furthermore, step S1 specifically includes: the number of vehicles from entrance p to exit q in the constructed traffic flow OD matrix. Satisfying the flow balance constraint, i.e. Where m' represents the total number of highway entrances or exits. This indicates the traffic flow at entrance p. O represents the traffic flow at exit q, and O represents the total traffic flow within the highway network.

[0007] Preferably, in step S2, based on the constructed traffic OD matrix, the departure node i and destination node j of the electric vehicle are set, and the corresponding travel path is generated; then, the departure time of the electric vehicle is randomly set to follow a uniform probability distribution, and the initial state of charge of the electric vehicle is set to follow a normal distribution. By constructing an energy consumption model of the electric vehicle's operation, the remaining battery power of the electric vehicle when it arrives at the service area is calculated. The specific steps include: S21. Construct an energy consumption model for the operation of an electric vehicle, and calculate the electricity consumption of the electric vehicle as it travels from starting node i to destination node j. , ,in This represents the energy consumption per unit distance traveled. Indicates the air conditioner's power. This represents the travel distance from the starting node i to the destination node j. This represents the travel time from the starting node i to the destination node j; S22, through formula Calculate the remaining battery power of the electric vehicle when it reaches the service area. ,in Indicates battery capacity, This indicates the initial state of charge of the electric vehicle when it arrives at the highway.

[0008] Preferably, step S3 specifically includes the following steps: S31. Based on the M / M / S queuing model, calculate the initial average waiting time for electric vehicle users. , , where s represents the number of charging stations in a single service area. This represents the utilization rate of charging stations in the service area, and n represents the index variable for the summation operation. Indicates the arrival rate of electric vehicles; S32. Considering the impact of the electric vehicle queuing situation at time h-1 on time h, a correction variable is introduced, namely... The electric vehicle arrival rate at time h is corrected to obtain the corrected electric vehicle arrival rate at time h. , ,in This indicates the service efficiency of the charging station. This represents the arrival rate of electric vehicles at time h. This represents the number of vehicles that were not served at time h. This represents the number of vehicles that were not served at time h-1; S33. The corrected electric vehicle arrival rate at time h obtained in step S32. Substituting these values ​​into the initial average waiting time calculation formula described in step S31, the average waiting time of the electric vehicle at time h is obtained.

[0009] Preferably, in step S4, the charging decision interval is divided based on the remaining battery power of the electric vehicle when it arrives at the service area. Combining the utility function and the Logit model, the charging probability of the electric vehicle in each service area is calculated, thereby determining the charging load of the electric vehicle. Specifically, this includes: Through formula Calculate the minimum amount of electricity required for an electric vehicle to travel to the next service area or destination. ,in, This indicates the energy consumption of the electric vehicle as it travels to the next service area or destination; subsequently, it is based on the remaining battery power of the electric vehicle upon arrival at the service area. Calculate the charging probability of electric vehicles in service areas. ; If the remaining power Below minimum battery level ,Right now Then the charging probability ; If the remaining power Higher than battery capacity 60% of Then the charging probability ; If the remaining power Battery capacity 60% and minimum power Between, that is Then through the formula Calculate the charging probability ,in Indicates the remaining power of the electric vehicle A collection of accessible service areas Indicates electric vehicles ci The utility value of choosing to charge at service area q'. Let represent the utility value of user n' choosing to charge in service area q'; for any service area, user n''s utility function is: ,in, This represents the electricity price when the electric vehicle arrives at service area q'. This represents the average waiting time of electric vehicles at service area q'. This represents the cost per unit of time for workers. This indicates the cost of battery anxiety. The sensitivity coefficient representing electricity prices. The sensitivity coefficient representing the waiting time. Sensitivity coefficient indicating the degree of power shortage Indicates a pre-defined positive number; Based on the electric vehicle travel information and calculated charging probability generated in step S2 The number of electric vehicles charging in each service area during each time period is calculated to obtain the electric vehicle charging load of the service area.

[0010] Preferably, in step S5, with the goal of minimizing the annual comprehensive cost of the entire photovoltaic-storage-charging system, while taking into account the waiting costs of electric vehicles and the construction and operation costs of investors, the operational constraints of the power distribution system are introduced to construct a multi-node collaborative model for photovoltaic-storage-charging, specifically including: To minimize the annual comprehensive cost of the entire photovoltaic-storage-charging system, an objective function is constructed. , ,in, This represents the total comprehensive cost of the travel route. This represents the total investment and construction cost, and its calculation formula is: , This indicates the total number of service areas along the travel route. This represents the total cost of the z-th service area. This represents the total operating cost, and its calculation formula is: , This represents the investment and construction cost of the z-th service area. This represents the total user wait time cost, and its calculation formula is: , The operating cost of the z-th service area is represented by the following formula: , This represents the cost of waiting time per user. This represents the total number of time periods into which a running cycle is divided. cj Indicates a time period index; The operating constraints of the power distribution system are: ; in, This represents the active power at node m. This represents the voltage magnitude at node n. N Indicates the total number of nodes in the power distribution system. This represents the conductance between nodes m and n. This represents the susceptance between nodes m and n. This represents the reactive power at node m. This represents the voltage magnitude at node m. This represents the voltage phase angle between nodes m and n. This represents the power purchased at time t. This represents the photovoltaic output at time t. This represents the energy storage charging power at time t. This represents the energy storage discharge power at time t. This represents the charging power of the electric vehicle at time t. This represents the base load power at time t. This represents the network loss power at time t. Represents the voltage at node n The minimum value, Represents the voltage at node n The maximum value, This indicates the allowable installation capacity of distributed photovoltaic power. This indicates the maximum allowable installed capacity of distributed photovoltaic power. Indicates energy storage capacity. This represents the state of charge of the stored energy at time t+1. This represents the state of charge of the stored energy at time t. Indicates the energy storage charging status. Indicates energy storage charging efficiency. Indicates the energy storage discharge efficiency. Indicates the rated capacity of energy storage. Indicates the energy storage discharge state. Indicates the waiting time for electric vehicles. This indicates the maximum waiting time for an electric vehicle.

[0011] Preferably, in step S6, the optimal configuration scheme is obtained through a two-level iterative solution, using the configuration capacity of the photovoltaic and energy storage system and its access nodes as optimization variables. This specifically includes the following steps: S61. Initialize the particle swarm optimization and import typical meteorological year data and the electric vehicle charging load obtained in step S4 to construct an optimization model with the objective of minimizing the annual comprehensive cost. S62. By solving the optimization model, the optimization results are obtained; S63. The optimization result obtained in step S62 is used as a parameter to be input into the operation constraints of the power distribution system described in step S5 for verification; if all constraints are met, the equivalent cycle number of the energy storage system is calculated, and the equivalent life of the energy storage is determined; if the constraints are not met, an over-limit penalty term is introduced to correct the objective function. S64. The energy storage equivalent lifetime and annual operating cost obtained in step S63 are used as key parameters and fed back to the optimization model constructed in step S61 to calculate the annual investment cost of energy storage and the overall system cost. S64. Repeat steps S62-S63 until the upper limit of the number of iterations is reached or the change in the objective function value is less than the preset threshold, and generate the optimal solution of the joint configuration scheme of photovoltaic, energy storage and charging multi-service area substations.

[0012] The beneficial effects of this invention are: 1) This application improves the accuracy of electric vehicle charging load prediction by dynamically correcting the charging station queuing model; the established charging decision probability model fully considers the impact of queuing time, charging price and remaining power on electric vehicle charging behavior, which is more in line with the actual situation; at the same time, the optimization configuration method of this application considers the impact of traffic flow on capacity configuration, and realizes the efficient utilization of global resources. Attached Figure Description

[0013] Figure 1 This is a schematic diagram illustrating the steps of a multi-service area substation joint configuration method that considers the charging needs of the entire highway in an embodiment of the present invention. Detailed Implementation

[0014] The technical solution of the present invention will be clearly and completely described below with reference to the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0015] This invention discloses a method for the joint configuration of substations in multiple service areas, taking into account the charging needs of the entire highway network. This method can effectively coordinate the allocation of photovoltaic, energy storage, and charging resources in each service area, reduce user waiting times, and improve the overall system operating efficiency and service quality. A schematic diagram of the steps of the method is shown below. Figure 1 As shown, the specific steps include: S1. Based on the traffic flow distribution of electric vehicles at highway entrances and exits, establish a vehicle flow OD matrix; S2. Based on the traffic flow OD matrix established in step S1, establish a model of electric vehicle travel behavior and charging demand. S3. Establish a queuing model for charging stations and dynamically correct it; S4. Construct a probabilistic model for electric vehicle charging decisions; S5. Based on the charging waiting costs for electric vehicle owners, system construction and operation and maintenance costs, establish a multi-node collaborative model for photovoltaic, energy storage and charging. S6. Optimize the multi-node collaborative model of photovoltaic, energy storage and charging established in step S5 to obtain the optimal solution of the joint configuration scheme of photovoltaic, energy storage and charging multi-service area substations that takes into account the charging needs of the entire highway.

[0016] Specifically, step S1 includes: constructing the vehicle flow OD matrix as follows: The number of vehicles from entrance p to exit q Satisfying the flow balance constraint, i.e. Where m' represents the total number of highway entrances or exits. This indicates the traffic flow at entrance p. O represents the traffic flow at exit q, and O represents the total traffic flow within the highway network.

[0017] Specifically, in step S2, based on the constructed traffic OD matrix, the departure node i and destination node j of the electric vehicle are set, and the corresponding travel path is generated; then, the departure time of the electric vehicle is randomly set to follow a uniform probability distribution, and the initial state of charge of the electric vehicle is set to follow a normal distribution. By constructing an energy consumption model of the electric vehicle's operation, the remaining battery power of the electric vehicle when it arrives at the service area is calculated. The specific steps include: S21. Construct an energy consumption model for the operation of an electric vehicle, and calculate the electricity consumption of the electric vehicle as it travels from starting node i to destination node j. , ,in This represents the energy consumption per unit distance traveled. Indicates the air conditioner's power. This represents the travel distance (in km) from the starting node i to the destination node j. This represents the travel time from the starting node i to the destination node j (in hours). S22, through formula Calculate the remaining battery power of the electric vehicle when it reaches the service area. ,in Indicates battery capacity, This indicates the initial state of charge of the electric vehicle when it arrives at the highway.

[0018] Specifically, step S3 includes the following steps: S31. Based on the M / M / S queuing model, calculate the initial average waiting time for electric vehicle users. , , where s represents the number of charging stations in a single service area. This represents the utilization rate of charging stations in the service area, and n represents the index variable for the summation operation. Indicates the arrival rate of electric vehicles; S32. Considering the impact of the electric vehicle queuing situation at time h-1 on time h, a correction variable is introduced, namely... The electric vehicle arrival rate at time h is corrected to obtain the corrected electric vehicle arrival rate at time h. , ,in This indicates the service efficiency of the charging station. This represents the arrival rate of electric vehicles at time h. This represents the number of vehicles that were not served at time h. This represents the number of vehicles that were not served at time h-1; S33. The corrected electric vehicle arrival rate at time h obtained in step S32. Substituting these values ​​into the initial average waiting time calculation formula described in step S31, the average waiting time of the electric vehicle at time h is obtained.

[0019] Specifically, in step S4, the charging decision interval is divided based on the remaining battery power of the electric vehicle when it arrives at the service area. Combining the utility function and the Logit model, the charging probability of the electric vehicle in each service area is calculated, thereby determining the charging load of the electric vehicle. This includes: Through formula Calculate the minimum amount of electricity required for an electric vehicle to travel to the next service area or destination. ,in, This indicates the energy consumption of the electric vehicle as it travels to the next service area or destination; subsequently, it is based on the remaining battery power of the electric vehicle upon arrival at the service area. Calculate the charging probability of electric vehicles in service areas. ; If the remaining power Below minimum battery level ,Right now Then the charging probability ; If the remaining power Higher than battery capacity 60% of Then the charging probability ; If the remaining power Battery capacity 60% and minimum power Between, that is Then through the formula Calculate the charging probability ,in Indicates the remaining power of the electric vehicle A collection of accessible service areas Indicates electric vehicles ci The utility value of choosing to charge in service area q' (used in the Logit model to calculate the probability of an individual choosing a specific service area). Let represent the utility value of user n' choosing to charge in service area q'. The denominator is the sum of the utility values ​​over all available service areas, normalized to a probability. For any service area, user n''s utility function is: ,in, This represents the electricity price when the electric vehicle arrives at service area q'. This represents the average waiting time of electric vehicles at service area q'. This represents the cost per unit of time for workers. This indicates the cost of battery anxiety. The sensitivity coefficient representing electricity prices. The sensitivity coefficient representing the waiting time. Sensitivity coefficient indicating the degree of power shortage This indicates a preset positive number (used to avoid the denominator being zero and to ensure the stability of numerical calculations). Based on the electric vehicle travel information and calculated charging probability generated in step S2 The number of electric vehicles charging in each service area during each time period is calculated to obtain the electric vehicle charging load of the service area.

[0020] Specifically, in step S5, with the goal of minimizing the annual comprehensive cost of the entire photovoltaic-storage-charging system, while taking into account the waiting costs of electric vehicles and the construction and operation costs of investors, the operational constraints of the power distribution system are introduced to construct a multi-node collaborative model for photovoltaic-storage-charging, which includes: To minimize the annual comprehensive cost of the entire photovoltaic-storage-charging system, an objective function is constructed. , ,in, This represents the total comprehensive cost of the travel route. This represents the total investment and construction cost, and its calculation formula is: , This indicates the total number of service areas along the travel route. This represents the total cost of the z-th service area. This represents the total operating cost, and its calculation formula is: , This represents the investment and construction cost of the z-th service area. This represents the total user wait time cost, and its calculation formula is: , The operating cost of the z-th service area is represented by the following formula: , This represents the cost of waiting time per user. This represents the total number of time periods into which a running cycle is divided. cj Indicates a time period index; The operating constraints of the power distribution system are: ;in, This represents the active power at node m. This represents the voltage magnitude at node n. N Indicates the total number of nodes in the power distribution system. This represents the conductance between nodes m and n. This represents the susceptance between nodes m and n. This represents the reactive power at node m. This represents the voltage magnitude at node m. This represents the voltage phase angle between nodes m and n. This represents the power purchased at time t. This represents the photovoltaic output at time t. This represents the energy storage charging power at time t. This represents the energy storage discharge power at time t. This represents the charging power of the electric vehicle at time t. This represents the base load power at time t. This represents the network loss power at time t. Represents the voltage at node n The minimum value, Represents the voltage at node n The maximum value, This indicates the allowable installation capacity of distributed photovoltaic power. This indicates the maximum allowable installed capacity of distributed photovoltaic power. Indicates energy storage capacity. This represents the state of charge of the stored energy at time t+1. This represents the state of charge of the stored energy at time t. Indicates the energy storage charging status. Indicates energy storage charging efficiency. Indicates the energy storage discharge efficiency. Indicates the rated capacity of energy storage. Indicates the energy storage discharge state. Indicates the waiting time for electric vehicles. This indicates the maximum waiting time for an electric vehicle.

[0021] Specifically, in step S6, the optimal configuration scheme is obtained through a two-level iterative solution, using the configuration capacity of the photovoltaic and energy storage system and its access nodes as optimization variables. The entire optimization process continuously optimizes individual solutions and the global optimal solution through iterative updates of particle positions and velocities, and includes the following steps: S61. Initialize the particle swarm optimization and import typical meteorological year data and the electric vehicle charging load obtained in step S4 to construct an optimization model with the objective of minimizing the annual comprehensive cost. S62. By solving the optimization model, the optimization results are obtained; S63. The optimization result obtained in step S62 is used as a parameter to be input into the operation constraints of the power distribution system described in step S5 for verification; if all constraints are met, the equivalent cycle number of the energy storage system is calculated, and the equivalent life of the energy storage is determined; if the constraints are not met, an over-limit penalty term is introduced to correct the objective function. S64. The energy storage equivalent lifetime and annual operating cost obtained in step S63 are used as key parameters and fed back to the optimization model constructed in step S61 to calculate the annual investment cost of energy storage and the overall system cost. S64. Repeat steps S62-S63 until the upper limit of the number of iterations is reached or the change in the objective function value is less than a preset threshold (e.g., 10). -6 This generates the optimal solution for the joint configuration scheme of photovoltaic, energy storage, and charging substations in multiple service areas.

[0022] The above description is merely a preferred embodiment of the present invention. It should be understood that the present invention is not limited to the forms disclosed herein and should not be construed as excluding other embodiments. It can be used in various other combinations, modifications, and environments, and can be altered within the scope of the concept described herein through the above teachings or related technologies or knowledge. Modifications and variations made by those skilled in the art that do not depart from the spirit and scope of the present invention should be within the protection scope of the appended claims.

Claims

1. A method for jointly configuring substations in multiple service areas, considering the charging needs of the entire highway network, characterized in that: Includes the following steps: S1. Based on the traffic flow distribution of electric vehicles at highway entrances and exits, establish a vehicle flow OD matrix; S2. Based on the traffic flow OD matrix established in step S1, establish a model of electric vehicle travel behavior and charging demand. S3. Establish a queuing model for charging stations and dynamically correct it; S4. Construct a probabilistic model for electric vehicle charging decisions; S5. Based on the charging waiting costs for electric vehicle owners, system construction and operation and maintenance costs, establish a multi-node collaborative model for photovoltaic, energy storage and charging. S6. Optimize the multi-node collaborative model of photovoltaic, energy storage and charging established in step S5 to obtain the optimal solution of the joint configuration scheme of photovoltaic, energy storage and charging multi-service area substations that takes into account the charging needs of the entire highway.

2. The method for joint configuration of multi-service area substations considering the charging needs of the entire highway as described in claim 1, characterized in that, Step S1 specifically includes: the number of vehicles from entrance p to exit q in the constructed vehicle flow OD matrix. Satisfying the flow balance constraint, i.e. Where m' represents the total number of highway entrances or exits. This indicates the traffic flow at entrance p. O represents the traffic flow at exit q, and O represents the total traffic flow within the highway network.

3. The method for joint configuration of multi-service area substations considering the charging needs of the entire highway as described in claim 2, characterized in that, In step S2, based on the constructed traffic OD matrix, the departure node i and destination node j of the electric vehicle are set, and the corresponding travel path is generated; then, the departure time of the electric vehicle is randomly set to follow a uniform probability distribution, and the initial state of charge of the electric vehicle is set to follow a normal distribution. By constructing an energy consumption model of the electric vehicle's operation, the remaining battery power of the electric vehicle when it arrives at the service area is calculated. The specific steps include: S21. Construct an energy consumption model for the operation of an electric vehicle, and calculate the electricity consumption of the electric vehicle as it travels from starting node i to destination node j. , ,in This represents the energy consumption per unit distance traveled. Indicates the air conditioner's power. This represents the travel distance from the starting node i to the destination node j. This represents the travel time from the starting node i to the destination node j; S22, through formula Calculate the remaining battery power of the electric vehicle when it reaches the service area. ,in Indicates battery capacity, This indicates the initial state of charge of the electric vehicle when it arrives at the highway.

4. The method for jointly configuring multiple service area substations considering the charging needs of the entire highway as described in claim 3, characterized in that, Step S3 specifically includes the following steps: S31. Based on the M / M / S queuing model, calculate the initial average waiting time for electric vehicle users. , , where s represents the number of charging stations in a single service area. This represents the utilization rate of charging stations in the service area, and n represents the index variable for the summation operation. Indicates the arrival rate of electric vehicles; S32. Considering the impact of the electric vehicle queuing situation at time h-1 on time h, a correction variable is introduced, namely... The electric vehicle arrival rate at time h is corrected to obtain the corrected electric vehicle arrival rate at time h. , ,in This indicates the service efficiency of the charging station. This represents the arrival rate of electric vehicles at time h. This represents the number of vehicles that were not served at time h. This represents the number of vehicles that were not served at time h-1; S33. The corrected electric vehicle arrival rate at time h obtained in step S32. Substituting these values ​​into the initial average waiting time calculation formula described in step S31, the average waiting time of the electric vehicle at time h is obtained.

5. The method for jointly configuring multiple service area substations considering the charging needs of the entire highway as described in claim 4, characterized in that, In step S4, the charging decision interval is divided based on the remaining battery power of the electric vehicle when it arrives at the service area. Combining the utility function and the Logit model, the charging probability of the electric vehicle in each service area is calculated, thereby determining the charging load of the electric vehicle. Specifically, this includes: Through formula Calculate the minimum amount of electricity required for an electric vehicle to travel to the next service area or destination. ,in, This indicates the energy consumption of the electric vehicle as it travels to the next service area or destination; subsequently, it is based on the remaining battery power of the electric vehicle upon arrival at the service area. Calculate the charging probability of electric vehicles in service areas. ; If the remaining power Below minimum battery level ,Right now Then the charging probability ; If the remaining power Higher than battery capacity 60% of Then the charging probability ; If the remaining power In terms of battery capacity 60% and minimum power Between, that is Then through the formula Calculate the charging probability ,in Indicates the remaining power of the electric vehicle A collection of accessible service areas Indicates electric vehicles ci The utility value of choosing to charge at service area q'. Let represent the utility value of user n' choosing to charge in service area q'; for any service area, user n''s utility function is: ,in, This represents the electricity price when the electric vehicle arrives at service area q'. This represents the average waiting time of electric vehicles at service area q'. This represents the cost per unit of time for workers. This indicates the cost of battery anxiety. The sensitivity coefficient representing electricity prices. The sensitivity coefficient representing the waiting time. Sensitivity coefficient indicating the degree of power shortage Indicates a pre-defined positive number; Based on the electric vehicle travel information and calculated charging probability generated in step S2 The number of electric vehicles charging in each service area during each time period is calculated to obtain the electric vehicle charging load of the service area.

6. The method for jointly configuring multiple service area substations considering the charging needs of the entire highway as described in claim 5, characterized in that, In step S5, aiming to minimize the annual comprehensive cost of the entire photovoltaic-storage-charging system, while considering the waiting costs of electric vehicles and the construction and operation costs of investors, the operational constraints of the power distribution system are introduced to construct a multi-node collaborative model for photovoltaic-storage-charging, which specifically includes: To minimize the annual comprehensive cost of the entire photovoltaic-storage-charging system, an objective function is constructed. , ,in, This represents the total comprehensive cost of the travel route. This represents the total investment and construction cost, and its calculation formula is: , This indicates the total number of service areas along the travel route. This represents the total cost of the z-th service area. This represents the total operating cost, and its calculation formula is: , This represents the investment and construction cost of the z-th service area. This represents the total user wait time cost, and its calculation formula is: , The operating cost of the z-th service area is represented by the following formula: , This represents the cost of waiting time per user. This represents the total number of time periods into which a running cycle is divided. cj Indicates a time period index; The operating constraints of the power distribution system are: ;in, This represents the active power at node m. This represents the voltage magnitude at node n. N Indicates the total number of nodes in the power distribution system. This represents the conductance between nodes m and n. This represents the susceptance between nodes m and n. This represents the reactive power at node m. This represents the voltage magnitude at node m. This represents the voltage phase angle between nodes m and n. This represents the power purchased at time t. This represents the photovoltaic output at time t. This represents the energy storage charging power at time t. This represents the energy storage discharge power at time t. This represents the charging power of the electric vehicle at time t. This represents the base load power at time t. This represents the network loss power at time t. Represents the voltage at node n The minimum value, Represents the voltage at node n The maximum value, This indicates the allowable installation capacity of distributed photovoltaic power. This indicates the maximum allowable installed capacity of distributed photovoltaic power. Indicates energy storage capacity. This represents the state of charge of the stored energy at time t+1. This represents the state of charge of the stored energy at time t. Indicates the energy storage charging status. Indicates energy storage charging efficiency. Indicates the energy storage discharge efficiency. Indicates the rated capacity of energy storage. Indicates the energy storage discharge state. Indicates the waiting time for electric vehicles. This indicates the maximum waiting time for an electric vehicle.

7. The method for jointly configuring multiple service area substations considering the charging needs of the entire highway as described in claim 6, characterized in that, In step S6, the optimal configuration scheme is obtained through a two-level iterative solution, using the configuration capacity of the photovoltaic and energy storage system and its access nodes as optimization variables. This specifically includes the following steps: S61. Initialize the particle swarm optimization and import typical meteorological year data and the electric vehicle charging load obtained in step S4 to construct an optimization model with the objective of minimizing the annual comprehensive cost. S62. By solving the optimization model, the optimization results are obtained; S63. The optimization result obtained in step S62 is used as a parameter to be input into the operation constraints of the power distribution system described in step S5 for verification; if all constraints are met, the equivalent cycle number of the energy storage system is calculated, and the equivalent life of the energy storage is determined; if the constraints are not met, an over-limit penalty term is introduced to correct the objective function. S64. The energy storage equivalent lifetime and annual operating cost obtained in step S63 are used as key parameters and fed back to the optimization model constructed in step S61 to calculate the annual investment cost of energy storage and the overall system cost. S64. Repeat steps S62-S63 until the upper limit of the number of iterations is reached or the change in the objective function value is less than the preset threshold, and generate the optimal solution of the joint configuration scheme of photovoltaic, energy storage and charging multi-service area substations.

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