Method, device, equipment, storage medium and program product for determining access position of charging station

Abstract

The application relates to a charging station access position determination method and device, equipment, a storage medium and a program product. The method comprises the following steps: constructing a power distribution network carbon emission flow model, the power distribution network carbon emission flow model is used for transmitting the carbon emission intensity of a power supply side along a power distribution network power flow path, and the carbon emission intensity corresponding to different charging station access positions in a region to be planned is determined; according to traffic demand information and vehicle driving power consumption information in the region to be planned, dynamic charging demand distribution information of the region to be planned is determined; under the condition of vehicle energy supplement constraint, the dynamic charging demand distribution information of the region to be planned is used to determine a candidate site set of the charging station in the region to be planned; a double-layer collaborative planning model of the charging station access position is constructed; linearization processing and iterative solving are performed on the double-layer collaborative planning model, and finally, the planning position data of the charging station access position is obtained. The method can improve the planning accuracy of the charging station access position.

Description

Technical Field

[0001] This application relates to the field of power electronics technology, and in particular to a method, apparatus, equipment, storage medium, and program product for determining the access location of a charging station. Background Technology

[0002] With the continued growth of electric vehicle scale, charging stations, as an important infrastructure for transportation electrification, are not only affected by the convenience of charging for users and the level of service guarantee, but also directly related to the safety of power distribution network operation, system investment costs and overall carbon emission levels.

[0003] In related technologies, separating charging station planning, power grid expansion, and carbon emission analysis makes it difficult to simultaneously consider the requirements of traffic demand response, grid connection capacity, and differences in nodal carbon intensity. This leads to difficulties in coordinating low-carbon, economical, and service-oriented planning results, and the phenomenon of directly selecting charging station sites without considering the dynamics of the transportation network, which can easily result in unbalanced site layout and insufficient service capacity. Therefore, the accuracy of the charging station access locations determined by the current method is not high. Summary of the Invention

[0004] Therefore, it is necessary to provide a method, apparatus, equipment, storage medium, and program product for determining the location of charging stations that can improve the planning accuracy of charging station access locations, in order to address the above-mentioned technical problems.

[0005] Firstly, this application provides a method for determining the access location of a charging station, including:

[0006] A carbon emission flow model for a power distribution network is constructed. This model is used to transmit the carbon emission intensity on the power supply side along the power flow path of the power distribution network and to determine the carbon emission intensity corresponding to the access location of different charging stations in the area to be planned.

[0007] Based on the traffic demand information and vehicle power consumption information in the area to be planned, determine the dynamic charging demand distribution information of the area to be planned;

[0008] Under the constraint of vehicle refueling, the candidate site set of charging stations in the area to be planned is determined based on the dynamic charging demand distribution information of the area to be planned.

[0009] A two-layer collaborative planning model for the charging station access locations is constructed, comprising an upper-layer sub-model and a lower-layer sub-model. The upper-layer sub-model optimizes the construction data of the power distribution network and the site selection and capacity determination data of the new energy generator units with the goal of minimizing the first cost data, and obtains the carbon emission intensity corresponding to different charging station access locations based on the power distribution network carbon emission flow model. The lower-layer sub-model performs site selection and capacity determination planning for the charging station access locations based on the carbon emission intensity corresponding to different charging station access locations and the candidate site set.

[0010] The two-layer collaborative planning model is linearized and iteratively solved to obtain the final planned location data of the charging station access location.

[0011] In one embodiment, determining the dynamic charging demand distribution information of the area to be planned based on traffic demand information and vehicle power consumption information in the area to be planned includes:

[0012] Based on the traffic demand information, the road segment impedance function is improved through user equilibrium theory, and the dynamic flow distribution of each path in the traffic network in the area to be planned is solved using the improved road segment impedance function.

[0013] Based on the dynamic traffic distribution of each path and the vehicle driving power consumption information, the energy replenishment demand information of each path in the area to be planned is determined;

[0014] The charging demand information of each path in the area to be planned is mapped to the initial preset station in the area to be planned to obtain the dynamic charging demand distribution information of the area to be planned.

[0015] The vehicle power consumption information includes at least one of the following: vehicle driving range information, power consumption per unit mileage information, and minimum safe state of charge requirement data at the origin and destination points.

[0016] In one embodiment, determining the candidate site set for charging stations in the area to be planned, based on the dynamic charging demand distribution information of the area to be planned under vehicle charging replenishment constraints, includes:

[0017] Under the vehicle charging constraint, the initial preset stations in the area to be planned are filtered according to the dynamic charging demand distribution information to obtain a set of candidate charging stations in the area to be planned.

[0018] The vehicle charging constraints include at least one of the following: maximum drivable distance constraint, minimum safe state of charge constraint at origin and destination, charging service capacity and site access capacity constraint.

[0019] In one embodiment, the upper-level sub-model is specifically used to optimize the construction data of the distribution network and the site selection and capacity determination data of the new energy generator sets with the goal of minimizing the first cost data, so as to obtain the generator set output data, the new energy power station site selection and capacity determination data, and the power grid flow operation data; the generator set output data, the new energy power station site selection and capacity determination data, and the power grid flow operation data are input into the distribution network carbon emission flow model to obtain the carbon emission intensity corresponding to the different charging station access locations;

[0020] The first cost data includes line expansion costs, new energy power plant construction costs, and electricity purchase and maintenance costs.

[0021] In one embodiment, the lower-level sub-model is specifically used to determine the carbon emission cost based on the carbon emission intensity corresponding to the different charging station access locations; with the goal of minimizing the second cost data, it performs site selection and capacity planning for the charging station access locations based on the candidate site set, and obtains charging station load result data based on the planned location data corresponding to the site selection and capacity planning;

[0022] The second cost data includes the construction and operation costs of the charging station, the carbon emission costs, and the penalty costs for unmet charging demand. The charging station load result data is associated with the electricity purchase and maintenance costs.

[0023] In one embodiment, the linearization and iterative solution of the two-layer collaborative planning model to obtain the final planned location data of the charging station access location includes:

[0024] The nonlinear terms in the bi-level collaborative programming model are linearized to obtain the transformed bi-level collaborative programming model.

[0025] The transformed two-layer collaborative planning model is iterated in both upper and lower layers until the charging station load result data of two adjacent iterations meet the convergence difference condition, at which point the final planned location data is output.

[0026] Secondly, this application also provides a device for determining the access location of a charging station, comprising:

[0027] The first construction module is used to construct a carbon emission flow model of the power distribution network. The carbon emission flow model of the power distribution network is used to transmit the carbon emission intensity on the power supply side along the power flow path of the power distribution network and determine the carbon emission intensity corresponding to the access location of different charging stations in the area to be planned.

[0028] The charging demand determination module is used to determine the dynamic charging demand distribution information of the area to be planned based on the traffic demand information and vehicle driving power consumption information in the area to be planned.

[0029] The candidate site determination module is used to determine the set of candidate charging stations in the area to be planned based on the dynamic charging demand distribution information of the area to be planned, under the constraint of vehicle refueling;

[0030] The second construction module is used to construct a two-layer collaborative planning model for the charging station access location. The two-layer collaborative planning model includes an upper-layer sub-model and a lower-layer sub-model. The upper-layer sub-model is used to optimize the construction data of the power distribution network and the site selection and capacity determination data of the new energy generator units with the goal of minimizing the first cost data, and to obtain the carbon emission intensity corresponding to different charging station access locations based on the power distribution network carbon emission flow model. The lower-layer sub-model is used to perform site selection and capacity determination planning for the charging station access location based on the carbon emission intensity corresponding to different charging station access locations and the candidate site set.

[0031] The solution module is used to linearize and iteratively solve the two-layer collaborative planning model to obtain the final planned location data of the charging station access location.

[0032] In one embodiment, the charging demand determination module is specifically used to improve the road segment impedance function based on the traffic demand information using user equilibrium theory, and use the improved road segment impedance function to solve for the dynamic flow distribution of each path in the traffic network in the area to be planned; determine the energy replenishment demand information of each path in the area to be planned based on the dynamic flow distribution of each path and the vehicle driving power consumption information; and map the energy replenishment demand information of each path in the area to be planned to the initial preset stations in the area to be planned to obtain the dynamic charging demand distribution information of the area to be planned.

[0033] The vehicle power consumption information includes at least one of the following: vehicle driving range information, power consumption per unit mileage information, and minimum safe state of charge requirement data at the origin and destination points.

[0034] In one embodiment, the candidate site determination module is specifically used to filter the initial preset sites in the area to be planned based on the dynamic charging demand distribution information under the vehicle charging constraint conditions, so as to obtain a set of candidate sites for charging stations in the area to be planned.

[0035] The vehicle charging constraints include at least one of the following: maximum drivable distance constraint, minimum safe state of charge constraint at origin and destination, charging service capacity and site access capacity constraint.

[0036] In one embodiment, the upper-level sub-model is specifically used to optimize the construction data of the distribution network and the site selection and capacity determination data of the new energy generator sets with the goal of minimizing the first cost data, so as to obtain the generator set output data, the new energy power station site selection and capacity determination data, and the power grid flow operation data; the generator set output data, the new energy power station site selection and capacity determination data, and the power grid flow operation data are input into the distribution network carbon emission flow model to obtain the carbon emission intensity corresponding to the different charging station access locations;

[0037] The first cost data includes line expansion costs, new energy power plant construction costs, and electricity purchase and maintenance costs.

[0038] In one embodiment, the lower-level sub-model is specifically used to determine the carbon emission cost based on the carbon emission intensity corresponding to the different charging station access locations; with the goal of minimizing the second cost data, it performs site selection and capacity planning for the charging station access locations based on the candidate site set, and obtains charging station load result data based on the planned location data corresponding to the site selection and capacity planning;

[0039] The second cost data includes the construction and operation costs of the charging station, the carbon emission costs, and the penalty costs for unmet charging demand. The charging station load result data is associated with the electricity purchase and maintenance costs.

[0040] In one embodiment, the solution module is specifically used to linearize the nonlinear terms in the two-layer collaborative planning model to obtain the transformed two-layer collaborative planning model; and to perform upper and lower layer iterative cycles on the transformed two-layer collaborative planning model until the charging station load result data of two adjacent iterations meet the convergence difference condition, at which point the final planned location data is output.

[0041] Thirdly, this application also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the method for determining the charging station access location described in the first aspect.

[0042] Fourthly, this application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method for determining the charging station access location described in the first aspect.

[0043] Fifthly, this application also provides a computer program product, including a computer program that, when executed by a processor, implements the method for determining the charging station access location described in the first aspect.

[0044] The aforementioned methods, devices, equipment, storage media, and program products for determining the access locations of charging stations are used to construct a distribution network carbon emission flow model. This model is used to transmit the carbon emission intensity from the power supply side along the power flow path of the distribution network, determining the carbon emission intensity corresponding to different charging station access locations in the planned area. Based on traffic demand information and vehicle power consumption information in the planned area, the dynamic charging demand distribution information of the planned area is determined. Under vehicle charging constraints, based on the dynamic charging demand distribution information of the planned area, a set of candidate charging stations in the planned area is determined. A charging station access location model is constructed. A two-layer collaborative planning model is proposed, comprising an upper-layer sub-model and a lower-layer sub-model. The upper-layer sub-model optimizes the construction data of the distribution network and the site selection and capacity determination data of new energy generator units with the goal of minimizing the first cost data, and obtains the carbon emission intensity corresponding to different charging station access locations based on the distribution network carbon emission flow model. The lower-layer sub-model performs site selection and capacity determination planning for charging station access locations based on the carbon emission intensity corresponding to different charging station access locations and the candidate site set. The two-layer collaborative planning model is linearized and iteratively solved to obtain the final planned location data for charging station access locations. By using the distribution network carbon emission flow model to finely divide the carbon emission intensity corresponding to different charging station access locations, and establishing a linkage mechanism between traffic demand information, vehicle driving power consumption information, and vehicle energy replenishment constraints, the candidate site set of charging stations in the area to be planned is accurately determined. Then, by using the two-layer collaborative planning model for charging station access locations in combination with the distribution network carbon emission flow model and the candidate site set, charging stations are preferentially guided to connect to low-carbon power nodes, thereby performing site selection and capacity determination planning for charging station access locations, effectively improving the planning accuracy of charging station access locations. Attached Figure Description

[0045] To more clearly illustrate the technical solutions in the embodiments of this application or related technologies, the drawings used in the description of the embodiments of this application or related technologies will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0046] Figure 1 An application environment diagram illustrating a method for determining the access location of a charging station provided in an embodiment of this application;

[0047] Figure 2 A flowchart illustrating a method for determining the access location of a charging station, provided in an embodiment of this application;

[0048] Figure 3 A flowchart illustrating another method for determining the access location of a charging station provided in an embodiment of this application;

[0049] Figure 4 A structural block diagram of a device for determining the access location of a charging station provided in an embodiment of this application;

[0050] Figure 5 This is an internal structural diagram of a computer device provided in an embodiment of this application. Detailed Implementation

[0051] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0052] The relevant technologies will be explained below.

[0053] With the continued growth of the electric vehicle market, charging stations, as a crucial infrastructure for transportation electrification, are increasingly important. Their planning and layout not only affect user charging convenience and service levels but also directly impact the operational safety of the power distribution network, system investment costs, and overall carbon emissions. Furthermore, the large-scale integration of renewable energy sources such as wind power provides an effective way to reduce the carbon intensity of power supply to charging loads. However, the location and capacity configuration of new energy sources can alter the power supply structure of various nodes in the distribution network, leading to significant differences in carbon emission responsibilities for different nodes. Simultaneously, electric vehicle charging demand exhibits significant spatiotemporal coupling characteristics, influenced by factors such as traffic demand, path flow distribution, driving range constraints, and user route selection behavior. All of these factors increase the difficulty of planning charging station locations.

[0054] In related technologies, separating charging station planning, power grid expansion, and carbon emission analysis makes it difficult to simultaneously consider the requirements of traffic demand response, grid connection capacity, and differences in nodal carbon intensity. This leads to difficulties in coordinating low-carbon, economical, and service-oriented planning results, and the phenomenon of directly selecting charging station sites without considering the dynamics of the transportation network, which can easily result in unbalanced site layout and insufficient service capacity. Therefore, the accuracy of the charging station access locations determined by the current method is not high.

[0055] Furthermore, in the planning of designated charging station locations, the carbon emission accounting in related technologies is rather crude, relying solely on static calculations based on average emission factors. This makes it difficult to accurately quantify the actual responsibility of charging stations (as electricity consumption units) in grid carbon emissions at different locations. Simultaneously, demand forecasting is statically lagging, estimating charging demand based only on static regional loads or empirical traffic coefficients, thus detaching from actual traffic congestion and dynamic changes, leading to planning mismatches.

[0056] To address the aforementioned issues, this application provides a method, apparatus, device, storage medium, and program product for determining the access location of charging stations. By using a power distribution network carbon emission flow model, the carbon emission intensity corresponding to different charging station access locations is finely divided. Furthermore, a linkage mechanism is established between traffic demand information, vehicle power consumption information, and vehicle energy replenishment constraints to accurately determine the candidate site set for charging stations in the planned area. Then, by utilizing a two-layer collaborative planning model for charging station access locations combined with the power distribution network carbon emission flow model and the candidate site set, charging stations are preferentially guided to connect to low-carbon power nodes. This enables site selection and capacity planning for charging station access locations, effectively improving the accuracy of charging station access location planning.

[0057] The following describes the application scenarios of the method for determining the access location of a charging station provided in the embodiments of this application.

[0058] The method for determining the access location of a charging station provided in this application embodiment can be applied to, for example, Figure 1 In the application environment shown, terminal 102 communicates with server 104 via a network. A data storage system can store the data that server 104 needs to process. The data storage system can be integrated onto server 104, or it can be located in the cloud or on another network server.

[0059] First, server 104 constructs a distribution network carbon emission flow model. This model transmits the carbon emission intensity from the power supply side along the power flow path of the distribution network, determining the carbon emission intensity corresponding to different charging station access locations in the planned area. Second, server 104 determines the dynamic charging demand distribution information of the planned area based on traffic demand information and vehicle power consumption information. Under vehicle charging constraints, it determines the candidate site set for charging stations in the planned area based on this dynamic charging demand distribution information. Third, server 104 constructs a two-layer collaborative planning model for charging station access locations. This model includes an upper-layer sub-model and a lower-layer sub-model. The upper-layer sub-model optimizes the construction data of the distribution network and the site selection and capacity determination data of new energy generator units with the goal of minimizing the first cost data, and obtains the carbon emission intensity corresponding to different charging station access locations based on the distribution network carbon emission flow model. The lower-layer sub-model performs site selection and capacity determination planning for charging station access locations based on the carbon emission intensity corresponding to different charging station access locations and the candidate site set. Finally, server 104 performs linearization and iterative solution on the two-layer collaborative planning model to obtain the final planned location data of the charging station access location, and sends the final planned location data of the charging station access location to terminal 102.

[0060] The terminal 102 can be, but is not limited to, various personal computers, laptops, smartphones, tablets, IoT devices, and portable wearable devices. IoT devices can include smart speakers, smart TVs, smart air conditioners, smart in-vehicle systems, and projection devices. Portable wearable devices can include smartwatches, smart bracelets, and head-mounted displays. Head-mounted displays can be virtual reality (VR) devices, augmented reality (AR) devices, and smart glasses. The server 104 can be a standalone physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing cloud computing services.

[0061] In one exemplary embodiment, such as Figure 2 As shown, a method for determining the access location of a charging station is provided, which can be applied to... Figure 1 Taking the server in the example, the explanation includes the following steps S201 to S205. Wherein:

[0062] S201. Construct a carbon emission flow model for the power distribution network.

[0063] Among them, the distribution network carbon emission flow model is used to transmit the carbon emission intensity on the power supply side along the power flow path of the distribution network and determine the carbon emission intensity corresponding to the access location of different charging stations in the area to be planned.

[0064] For example, the carbon emission intensity on the power supply side can be the carbon emission intensity or carbon emission responsibility data generated by generator units such as wind farms and thermal power plants during power generation. The planned area can be an area where charging stations are planned to be built.

[0065] In some embodiments, the above-mentioned distribution network carbon emission flow model can be established based on the proportional sharing principle. For example, the distribution network carbon emission flow model can be based on the proportional sharing principle to transfer the carbon emission intensity on the power supply side to the load side along the power flow path of the distribution network. During the transfer process, the generator output data, the site selection and capacity data of new energy power plants, and the power flow operation data of the power grid can be combined to obtain the carbon emission intensity corresponding to the access location of different charging stations in the area to be planned, thereby quantifying the actual carbon emission responsibility corresponding to different charging stations.

[0066] Among them, the site selection and capacity determination data of new energy power stations may include wind power access decision data, and the power flow operation data may include the power flow operation results of the distribution network.

[0067] S202. Based on the traffic demand information and vehicle power consumption information in the area to be planned, determine the dynamic charging demand distribution information of the area to be planned.

[0068] In some embodiments, when the server needs to plan the location of charging stations, it can also determine the dynamic charging demand distribution information of the area to be planned based on the traffic demand information and vehicle power consumption information in the area to be planned.

[0069] In some embodiments, the server can first improve the road segment impedance function based on traffic demand information using user equilibrium theory, and then use the improved road segment impedance function to solve for the dynamic traffic distribution of each path in the traffic network of the area to be planned. Subsequently, the server can determine the charging demand information for each path in the area to be planned based on the dynamic traffic distribution of each path and vehicle power consumption information. Finally, the server can map the charging demand information of each path in the area to be planned to initial preset stations in the area to obtain the dynamic charging demand distribution information of the area to be planned.

[0070] The aforementioned traffic demand information can be traffic origin-destination (OD) demand.

[0071] For example, the server can improve the Bureau of Public Roads (BPR) function based on traffic OD demand and User Equilibrium (UE) theory, and solve the dynamic flow distribution of each path in the traffic network by taking into account traffic capacity and delay, thereby obtaining the dynamic flow distribution of each path in the traffic network of the area to be planned.

[0072] The vehicle's power consumption information includes at least one of the following: vehicle driving range information, power consumption per unit mileage information, and minimum safe state of charge requirements data at the origin and destination points.

[0073] For example, the server can calculate the charging demand information for each path in the area to be planned based on one or more of the following requirements: vehicle range information, power consumption per unit mileage information, and minimum safe state of charge requirements at the origin and destination points. The server can then map the charging demand information for each path in the area to be planned to the initial preset stations in the area to be planned, thereby obtaining the dynamic charging demand distribution information for the area to be planned. This dynamic charging demand distribution information is used to indicate the dynamic charging demand distribution for each scenario and time period corresponding to the area to be planned.

[0074] In other embodiments, the server may also employ Dynamic Traffic Assignment (DTA) or a multi-agent-based traffic flow simulation model to determine the dynamic flow distribution of each path in the traffic network of the area to be planned based on traffic demand information.

[0075] S203. Under the constraint of vehicle energy replenishment, determine the set of candidate charging stations in the area to be planned based on the dynamic charging demand distribution information of the area to be planned.

[0076] In this step, once the server determines the dynamic charging demand distribution information of the area to be planned, it can determine the candidate site set of charging stations in the area to be planned based on the dynamic charging demand distribution information of the area to be planned, under the constraint of vehicle charging.

[0077] In some embodiments, the server can, under vehicle charging constraints, filter the initial preset stations in the area to be planned based on dynamic charging demand distribution information to obtain a set of candidate charging stations in the area to be planned.

[0078] Among them, the vehicle charging constraints include at least one of the following: maximum driving distance constraint, minimum safe state of charge constraint at the origin and destination points, charging service capacity and site access capacity constraint.

[0079] For example, the server can use the dynamic charging demand distribution information of the area to be planned as a basis, and introduce one or more vehicle charging constraints such as maximum driving distance constraint, minimum safe state of charge constraint at the origin and destination, charging service capacity and site access capacity constraint, so as to screen the initial preset sites in the area to be planned and determine the candidate site set that meets the vehicle charging constraints and dynamic charging demand distribution information.

[0080] The initial preset stations in the area to be planned can be set in advance, for example, by dividing the path according to preset distance intervals, thereby obtaining the initial preset stations in the area to be planned.

[0081] S204. Construct a two-layer collaborative planning model for charging station access locations.

[0082] The two-layer collaborative planning model includes an upper-layer sub-model and a lower-layer sub-model. The upper-layer sub-model is used to optimize the construction data of the distribution network and the site selection and capacity determination data of the new energy generator units with the goal of minimizing the first cost data, and to obtain the carbon emission intensity corresponding to different charging station access locations based on the carbon emission flow model of the distribution network. The lower-layer sub-model is used to perform site selection and capacity determination planning for charging station access locations based on the carbon emission intensity corresponding to different charging station access locations and the candidate site set.

[0083] The following is a description of the upper-level sub-model components.

[0084] In some embodiments, the upper sub-model is specifically used to optimize the construction data of the distribution network and the site selection and capacity determination data of the new energy generator sets with the goal of minimizing the first cost data, so as to obtain the generator set output data, the new energy power station site selection and capacity determination data and the power grid flow operation data; the generator set output data, the new energy power station site selection and capacity determination data and the power grid flow operation data are input into the distribution network carbon emission flow model to obtain the carbon emission intensity corresponding to different charging station access locations.

[0085] The first cost data may include line expansion costs, new energy power plant construction costs, and electricity purchase and maintenance costs.

[0086] For example, in the upper-level sub-model, an objective function can be constructed with the minimization of first cost data, including line expansion costs, renewable energy power station construction costs, and electricity purchase and maintenance costs. This objective function can then be used to optimize the distribution network construction data and the location and capacity allocation data of renewable energy generating units. Based on the optimized distribution network construction data and renewable energy generating unit location and capacity allocation data, the upper-level sub-model can then determine the generator output data, renewable energy power station location and capacity allocation data, and power grid flow operation data corresponding to the distribution network. Finally, the upper-level sub-model can input the generator output data, renewable energy power station location and capacity allocation data, and power grid flow operation data into the distribution network carbon emission flow model to obtain the carbon emission intensity corresponding to different charging station access locations calculated by the distribution network carbon emission flow model, and then provide the carbon emission intensity corresponding to different charging station access locations to the lower-level sub-model.

[0087] The following is a description of the lower-level sub-model components.

[0088] In some embodiments, the lower-level sub-model is specifically used to determine the carbon emission cost based on the carbon emission intensity corresponding to different charging station access locations; with the goal of minimizing the second cost data, it performs site selection and capacity planning for charging station access locations based on the candidate site set, and obtains charging station load result data based on the planned location data corresponding to the site selection and capacity planning;

[0089] The second cost data includes the construction and operation costs of charging stations, carbon emission costs, and penalty costs for unmet charging demand. The charging station load data is linked to electricity purchase and maintenance costs.

[0090] For example, the lower-level sub-model can call the carbon emission intensity corresponding to different charging station access locations to evaluate carbon emission costs. Subsequently, with the goal of minimizing the construction and operation costs, carbon emission costs, and penalty costs for unmet charging demand in the lower-level sub-model, the lower-level sub-model performs site selection and capacity planning for the charging stations under the boundary conditions of the upper-level sub-model, obtains the charging station load result data, and updates the charging station load result data to the upper-level sub-model.

[0091] For example, in the construction of constraints in the lower-level sub-model, carbon emission constraints for charging stations can be specifically set. These constraints verify whether the actual carbon emissions of the charging station exceed the limit. The calculation logic involves multiplying the charging output power of the charging station by the carbon emission intensity corresponding to different charging station access locations to ensure that the operation of the selected site meets the system's low-carbon requirements.

[0092] For example, during the site selection and capacity planning process for charging stations, the lower-level sub-model can also guide the site selection and construction of charging stations based on the carbon emission intensity corresponding to different charging station access locations, so that charging stations are preferentially located in nodes with lower carbon intensity, thereby meeting the requirements of feasible and low-carbon collaborative planning.

[0093] S205. Linearize and iteratively solve the two-layer collaborative planning model to obtain the final planned location data of the charging station access location.

[0094] In this step, once the carbon emission intensity corresponding to the different charging station access locations on the server is determined, the two-layer collaborative planning model can be linearized and iteratively solved to obtain the final planned location data for the charging station access locations.

[0095] In some embodiments, the server can first linearize the nonlinear terms in the two-level collaborative programming model to obtain the transformed two-level collaborative programming model. Subsequently, the server can perform iterative iterations on the transformed two-level collaborative programming model until the charging station load results data of two adjacent iterations meet the convergence difference condition, at which point the final planned location data is output.

[0096] The aforementioned nonlinear terms may include voltage drop constraints, dynamic charging demand distribution, and operational constraints corresponding to location information involved in the site selection and capacity planning.

[0097] For example, the server can use methods such as the Big M method or Lagrange relaxation to linearize the nonlinear terms in the bilevel co-programming model, resulting in a transformed bilevel co-programming model. This transformed bilevel co-programming model can be a Mixed Integer Linear Programming (MILP) model, which can be solved efficiently iteratively using conventional solvers (such as CPLEX / Gurobi).

[0098] For example, during the linearization transformation of voltage drop constraints, in order to describe the state changes brought about by the new line, equivalent substitutions can be made by introducing binary variables and new continuous variables, thereby eliminating the original nonlinear terms.

[0099] For example, when traffic flow is excessive, the constraints may not be met due to the dynamic charging demand distribution. Therefore, for the linearization transformation of the dynamic charging demand distribution, the Lagrange relaxation method can be used. Based on strong duality theory, the duality gap can be eliminated, and finally it can be transformed into linearized traffic assignment data under user equilibrium.

[0100] For example, the linearization transformation of location data (including nonlinear operational constraints such as vehicle battery state logic) can be achieved using the Big M method, by introducing auxiliary variables, to transform the Mixed Integer Non-Linear Programming (MINLP) problem into a MILP problem.

[0101] In some embodiments, the server may use a solver to iteratively solve the linearized model, or it may use a metaheuristic algorithm (such as particle swarm optimization or genetic algorithm) to perform approximate optimization. This application does not limit this.

[0102] In this application, by constructing a carbon emission flow model for the power distribution network, the limitations of the traditional static average carbon emission factor can be overcome. It can combine the power flow direction and the location of new energy sources to finely classify the carbon emission intensity (i.e., carbon emission responsibility) of the electricity consumed by charging stations.

[0103] This application comprehensively coordinates the allocation of new energy sources, the expansion of the power distribution network, and the layout of charging stations. With a slight increase in costs, it prioritizes the connection of charging stations to low-carbon power nodes, significantly reducing the total carbon emissions of the system and substantially reducing "unmet charging demand" (such as peak-hour queuing gaps).

[0104] This application establishes a linkage mechanism between traffic flow allocation, energy replenishment calculation, and site allocation, which can more accurately reflect the impact of dynamic behaviors such as traffic congestion on the spatiotemporal distribution of charging load, and greatly improve the matching degree between charging station layout and actual travel demand.

[0105] The method for determining the access location of charging stations provided in this application includes: constructing a distribution network carbon emission flow model, which transmits the carbon emission intensity on the power supply side along the power flow path of the distribution network to determine the carbon emission intensity corresponding to different charging station access locations in the planned area; determining the dynamic charging demand distribution information of the planned area based on traffic demand information and vehicle power consumption information in the planned area; determining the candidate site set of charging stations in the planned area based on the dynamic charging demand distribution information under vehicle charging replenishment constraints; constructing a two-layer collaborative planning model for charging station access locations, which includes an upper-layer sub-model and a lower-layer sub-model; the upper-layer sub-model is used to optimize the construction data of the distribution network and the site selection and capacity determination data of the new energy generator sets with the goal of minimizing the first cost data, and obtains the carbon emission intensity corresponding to different charging station access locations based on the distribution network carbon emission flow model; the lower-layer sub-model is used to perform site selection and capacity determination planning for charging station access locations based on the carbon emission intensity corresponding to different charging station access locations and the candidate site set; and linearizing and iteratively solving the two-layer collaborative planning model to obtain the final planned location data of the charging station access locations. By using a distribution network carbon emission flow model to finely divide the carbon emission intensity corresponding to different charging station access locations, and establishing a linkage mechanism between traffic demand information, vehicle driving power consumption information, and vehicle energy replenishment constraints, the set of candidate charging station sites in the area to be planned can be accurately determined. Then, by using a two-layer collaborative planning model for charging station access locations combined with the distribution network carbon emission flow model and the set of candidate sites, charging stations are preferentially guided to connect to low-carbon power nodes, thereby conducting site selection and capacity planning for charging station access locations, which effectively improves the planning accuracy of charging station access locations.

[0106] In one exemplary embodiment, such as Figure 3 As shown, another method for determining the location of a charging station is provided, which can be applied to... Figure 1 Taking the server in the example, the explanation includes the following steps S301 to S308. Wherein:

[0107] S301. Construct a carbon emission flow model for the power distribution network. The carbon emission flow model is used to transmit the carbon emission intensity on the power supply side along the power flow path of the power distribution network and determine the carbon emission intensity corresponding to the access location of different charging stations in the area to be planned.

[0108] S302. Based on traffic demand information, improve the road segment impedance function through user equilibrium theory, and use the improved road segment impedance function to solve for the dynamic flow distribution of each path in the traffic network in the area to be planned.

[0109] S303. Based on the dynamic traffic distribution and vehicle power consumption information of each path, determine the energy replenishment demand information of each path in the area to be planned.

[0110] S304. Map the charging demand information of each path in the area to be planned to the initial preset station in the area to be planned to obtain the dynamic charging demand distribution information of the area to be planned.

[0111] The vehicle's power consumption information includes at least one of the following: vehicle driving range information, power consumption per unit mileage information, and minimum safe state of charge requirements data at the origin and destination points.

[0112] S305. Under the constraint of vehicle energy replenishment, the initial preset stations in the area to be planned are screened according to the dynamic charging demand distribution information to obtain a set of candidate charging stations in the area to be planned.

[0113] Among them, the vehicle charging constraints include at least one of the following: maximum driving distance constraint, minimum safe state of charge constraint at the origin and destination points, charging service capacity and site access capacity constraint.

[0114] S306. Construct a two-layer collaborative planning model for the access location of charging stations, wherein the two-layer collaborative planning model includes an upper-layer sub-model and a lower-layer sub-model.

[0115] In some embodiments, the upper sub-model is specifically used to optimize the construction data of the distribution network and the site selection and capacity determination data of the new energy generator sets with the goal of minimizing the first cost data, so as to obtain the generator set output data, the new energy power station site selection and capacity determination data and the power grid flow operation data; the generator set output data, the new energy power station site selection and capacity determination data and the power grid flow operation data are input into the distribution network carbon emission flow model to obtain the carbon emission intensity corresponding to different charging station access locations.

[0116] The first cost data includes the cost of line expansion, the cost of new energy power plant construction, and the cost of electricity purchase and maintenance.

[0117] In some embodiments, the lower-level sub-model is specifically used to determine the carbon emission cost based on the carbon emission intensity corresponding to different charging station access locations; with the goal of minimizing the second cost data, it performs site selection and capacity planning for charging station access locations based on the candidate site set, and obtains charging station load result data based on the planned location data corresponding to the site selection and capacity planning.

[0118] The second cost data includes the construction and operation costs of charging stations, carbon emission costs, and penalty costs for unmet charging demand. The charging station load data is linked to electricity purchase and maintenance costs.

[0119] S307. Linearize the nonlinear terms in the bi-level collaborative programming model to obtain the transformed bi-level collaborative programming model.

[0120] S308. Perform iterative cycles on the transformed two-layer collaborative planning model until the charging station load results data of two adjacent iterations meet the convergence difference condition, and then output the final planning location data.

[0121] The method for determining the access location of charging stations provided in this application includes: constructing a distribution network carbon emission flow model, which transmits the carbon emission intensity on the power supply side along the power flow path of the distribution network to determine the carbon emission intensity corresponding to different charging station access locations in the planned area; determining the dynamic charging demand distribution information of the planned area based on traffic demand information and vehicle power consumption information in the planned area; determining the candidate site set of charging stations in the planned area based on the dynamic charging demand distribution information under vehicle charging replenishment constraints; constructing a two-layer collaborative planning model for charging station access locations, which includes an upper-layer sub-model and a lower-layer sub-model; the upper-layer sub-model is used to optimize the construction data of the distribution network and the site selection and capacity determination data of the new energy generator sets with the goal of minimizing the first cost data, and obtains the carbon emission intensity corresponding to different charging station access locations based on the distribution network carbon emission flow model; the lower-layer sub-model is used to perform site selection and capacity determination planning for charging station access locations based on the carbon emission intensity corresponding to different charging station access locations and the candidate site set; and linearizing and iteratively solving the two-layer collaborative planning model to obtain the final planned location data of the charging station access locations. By using a distribution network carbon emission flow model to finely divide the carbon emission intensity corresponding to different charging station access locations, and establishing a linkage mechanism between traffic demand information, vehicle driving power consumption information, and vehicle energy replenishment constraints, the set of candidate charging station sites in the area to be planned can be accurately determined. Then, by using a two-layer collaborative planning model for charging station access locations combined with the distribution network carbon emission flow model and the set of candidate sites, charging stations are preferentially guided to connect to low-carbon power nodes, thereby conducting site selection and capacity planning for charging station access locations, which effectively improves the planning accuracy of charging station access locations.

[0122] It should be understood that although the steps in the flowcharts of the above embodiments are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the above embodiments may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0123] Based on the same inventive concept, this application also provides a charging station access location determination device for implementing the charging station access location determination method described above. The solution provided by this device is similar to the solution described in the above method; therefore, the specific limitations in one or more charging station access location determination device embodiments provided below can be found in the limitations of the charging station access location determination method described above, and will not be repeated here.

[0124] In one exemplary embodiment, such as Figure 4 As shown, a charging station access location determination device 400 is provided, including: a first construction module 401, a charging demand determination module 402, a candidate site determination module 403, a second construction module 404, and a solution module 405, wherein:

[0125] The first construction module 401 is used to construct a carbon emission flow model for the power distribution network. The carbon emission flow model is used to transmit the carbon emission intensity on the power supply side along the power flow path of the power distribution network and determine the carbon emission intensity corresponding to the access location of different charging stations in the area to be planned.

[0126] The charging demand determination module 402 is used to determine the dynamic charging demand distribution information of the area to be planned based on the traffic demand information and vehicle driving power consumption information in the area to be planned.

[0127] The candidate site determination module 403 is used to determine the set of candidate charging stations in the area to be planned based on the dynamic charging demand distribution information of the area to be planned, under the constraint of vehicle charging.

[0128] The second construction module 404 is used to construct a two-layer collaborative planning model for charging station access locations. The two-layer collaborative planning model includes an upper-layer sub-model and a lower-layer sub-model. The upper-layer sub-model is used to optimize the construction data of the distribution network and the site selection and capacity determination data of the new energy generator units with the goal of minimizing the first cost data, and to obtain the carbon emission intensity corresponding to different charging station access locations based on the carbon emission flow model of the distribution network. The lower-layer sub-model is used to perform site selection and capacity determination planning for charging station access locations based on the carbon emission intensity corresponding to different charging station access locations and the candidate site set.

[0129] The solver module 405 is used to linearize and iteratively solve the two-layer collaborative planning model to obtain the final planned location data of the charging station access location.

[0130] In one embodiment, the charging demand determination module 402 is specifically used to improve the road segment impedance function based on traffic demand information using user equilibrium theory, and use the improved road segment impedance function to solve for the dynamic flow distribution of each path in the traffic network in the area to be planned; determine the energy replenishment demand information of each path in the area to be planned based on the dynamic flow distribution of each path and vehicle driving power consumption information; and map the energy replenishment demand information of each path in the area to be planned to the initial preset station in the area to be planned to obtain the dynamic charging demand distribution information of the area to be planned.

[0131] The vehicle's power consumption information includes at least one of the following: vehicle driving range information, power consumption per unit mileage information, and minimum safe state of charge requirements data at the origin and destination points.

[0132] In one embodiment, the candidate site determination module 403 is specifically used to filter the initial preset sites in the area to be planned based on the dynamic charging demand distribution information under the vehicle charging constraint, so as to obtain a set of candidate sites for charging stations in the area to be planned.

[0133] Among them, the vehicle charging constraints include at least one of the following: maximum driving distance constraint, minimum safe state of charge constraint at the origin and destination points, charging service capacity and site access capacity constraint.

[0134] In one embodiment, the upper-level sub-model is specifically used to optimize the construction data of the distribution network and the site selection and capacity determination data of the new energy generator sets with the goal of minimizing the first cost data, so as to obtain the generator set output data, the new energy power station site selection and capacity determination data, and the power grid flow operation data; the generator set output data, the new energy power station site selection and capacity determination data, and the power grid flow operation data are input into the distribution network carbon emission flow model to obtain the carbon emission intensity corresponding to different charging station access locations.

[0135] The first cost data includes the cost of line expansion, the cost of new energy power plant construction, and the cost of electricity purchase and maintenance.

[0136] In one embodiment, the lower-level sub-model is specifically used to determine the carbon emission cost based on the carbon emission intensity corresponding to different charging station access locations; with the goal of minimizing the second cost data, it performs site selection and capacity planning for charging station access locations based on the candidate site set, and obtains charging station load result data based on the planned location data corresponding to the site selection and capacity planning.

[0137] The second cost data includes the construction and operation costs of charging stations, carbon emission costs, and penalty costs for unmet charging demand. The charging station load data is linked to electricity purchase and maintenance costs.

[0138] In one embodiment, the solver module 405 is specifically used to linearize the nonlinear terms in the two-layer collaborative planning model to obtain the transformed two-layer collaborative planning model; and to perform upper and lower layer iterative cycles on the transformed two-layer collaborative planning model until the charging station load result data of two adjacent iterations meet the convergence difference condition, and then output the final planning location data.

[0139] Each module in the aforementioned charging station access location determination device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the memory of a computer device as software, so that the processor can call and execute the operations corresponding to each module.

[0140] In one exemplary embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as follows: Figure 5 As shown, this computer device includes a processor, memory, input / output interfaces (I / O), and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is also connected to the system bus via the I / O interfaces. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and a database. The internal memory provides the environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The database stores data. The I / O interfaces are used for exchanging information between the processor and external devices. The communication interface is used for communicating with external terminals via a network connection. When executed by the processor, the computer program implements a method for determining the location of a charging station.

[0141] Those skilled in the art will understand that Figure 5 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0142] In one exemplary embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the above-described method for determining the location of a charging station access point.

[0143] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the method for determining the location of the charging station access described above.

[0144] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the method for determining the location of the charging station access described above.

[0145] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, artificial intelligence (AI) processors, etc., and are not limited to these.

[0146] 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 application.

[0147] The above embodiments are merely illustrative of several implementation methods of this application, and their descriptions are relatively specific and detailed. However, they should not be construed as limiting the scope of this application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A method for determining the access location of a charging station, characterized in that, The method includes: A carbon emission flow model for a power distribution network is constructed. This model is used to transmit the carbon emission intensity on the power supply side along the power flow path of the power distribution network and to determine the carbon emission intensity corresponding to the access location of different charging stations in the area to be planned. Based on the traffic demand information and vehicle power consumption information in the area to be planned, determine the dynamic charging demand distribution information of the area to be planned; Under the constraint of vehicle refueling, the candidate site set of charging stations in the area to be planned is determined based on the dynamic charging demand distribution information of the area to be planned. A two-layer collaborative planning model for the charging station access locations is constructed, comprising an upper-layer sub-model and a lower-layer sub-model. The upper-layer sub-model optimizes the construction data of the power distribution network and the site selection and capacity determination data of the new energy generator units with the goal of minimizing the first cost data, and obtains the carbon emission intensity corresponding to different charging station access locations based on the power distribution network carbon emission flow model. The lower-layer sub-model performs site selection and capacity determination planning for the charging station access locations based on the carbon emission intensity corresponding to different charging station access locations and the candidate site set. The two-layer collaborative planning model is linearized and iteratively solved to obtain the final planned location data of the charging station access location.

2. The method according to claim 1, characterized in that, The step of determining the dynamic charging demand distribution information of the area to be planned based on traffic demand information and vehicle power consumption information in the area to be planned includes: Based on the traffic demand information, the road segment impedance function is improved through user equilibrium theory, and the dynamic flow distribution of each path in the traffic network in the area to be planned is solved using the improved road segment impedance function. Based on the dynamic traffic distribution of each path and the vehicle driving power consumption information, the energy replenishment demand information of each path in the area to be planned is determined; The charging demand information of each path in the area to be planned is mapped to the initial preset station in the area to be planned to obtain the dynamic charging demand distribution information of the area to be planned. The vehicle power consumption information includes at least one of the following: vehicle driving range information, power consumption per unit mileage information, and minimum safe state of charge requirement data at the origin and destination points.

3. The method according to claim 1, characterized in that, Under vehicle refueling constraints, the process of determining a candidate set of charging stations in the area to be planned, based on the dynamic charging demand distribution information of the area to be planned, includes: Under the vehicle charging constraint, the initial preset stations in the area to be planned are filtered according to the dynamic charging demand distribution information to obtain a set of candidate charging stations in the area to be planned. The vehicle charging constraints include at least one of the following: maximum drivable distance constraint, minimum safe state of charge constraint at origin and destination, charging service capacity and site access capacity constraint.

4. The method according to claim 1, characterized in that, The upper-level sub-model is specifically used to optimize the construction data of the distribution network and the site selection and capacity determination data of the new energy generator units with the goal of minimizing the first cost data, so as to obtain the unit output data of the generator units, the site selection and capacity determination data of the new energy power stations, and the power flow operation data of the power grid; the unit output data of the generator units, the site selection and capacity determination data of the new energy power stations, and the power flow operation data of the power grid are input into the carbon emission flow model of the distribution network to obtain the carbon emission intensity corresponding to the access location of the charging station; The first cost data includes line expansion costs, new energy power plant construction costs, and electricity purchase and maintenance costs.

5. The method according to claim 4, characterized in that, The lower-level sub-model is specifically used to determine the carbon emission cost based on the carbon emission intensity corresponding to the different charging station access locations; with the goal of minimizing the second cost data, it performs site selection and capacity planning for the charging station access locations based on the candidate site set, and obtains the charging station load result data based on the planned location data corresponding to the site selection and capacity planning. The second cost data includes the construction and operation costs of the charging station, the carbon emission costs, and the penalty costs for unmet charging demand. The charging station load result data is associated with the electricity purchase and maintenance costs.

6. The method according to claim 1, characterized in that, The linearization and iterative solution of the two-layer collaborative planning model to obtain the final planned location data of the charging station access location includes: The nonlinear terms in the bi-level collaborative programming model are linearized to obtain the transformed bi-level collaborative programming model. The transformed two-layer collaborative planning model is iterated in both upper and lower layers until the charging station load result data of two adjacent iterations meet the convergence difference condition, at which point the final planned location data is output.

7. A device for determining the access location of a charging station, characterized in that, The device includes: The first construction module is used to construct a carbon emission flow model of the power distribution network. The carbon emission flow model of the power distribution network is used to transmit the carbon emission intensity on the power supply side along the power flow path of the power distribution network and determine the carbon emission intensity corresponding to the access location of different charging stations in the area to be planned. The charging demand determination module is used to determine the dynamic charging demand distribution information of the area to be planned based on the traffic demand information and vehicle driving power consumption information in the area to be planned. The candidate site determination module is used to determine the set of candidate charging stations in the area to be planned based on the dynamic charging demand distribution information of the area to be planned, under the constraint of vehicle refueling; The second construction module is used to construct a two-layer collaborative planning model for the charging station access location. The two-layer collaborative planning model includes an upper-layer sub-model and a lower-layer sub-model. The upper-layer sub-model is used to optimize the construction data of the power distribution network and the site selection and capacity determination data of the new energy generator units with the goal of minimizing the first cost data, and to obtain the carbon emission intensity corresponding to different charging station access locations based on the power distribution network carbon emission flow model. The lower-layer sub-model is used to perform site selection and capacity determination planning for the charging station access location based on the carbon emission intensity corresponding to different charging station access locations and the candidate site set. The solution module is used to linearize and iteratively solve the two-layer collaborative planning model to obtain the final planned location data of the charging station access location.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.

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