A method and device for selecting a site for an electric vehicle charging station

Abstract

The present invention discloses a method and device for site selection of electric vehicle charging stations, the method comprising: obtaining a certain number of candidate charging stations and multiple candidate station construction plans based on nodes in a coupling network formed by a distribution network and a transportation network; constructing a mathematical model of target cost based on carbon emissions; calculating the total comprehensive cost of each candidate station construction plan according to the mathematical model, and selecting the top N candidate plans with the lowest total comprehensive cost; considering multiple uncertain factors to construct multiple combination scenarios and optimize and update the candidate plans; for multiple groups of new top N candidate plans, using a minimum maximum regret cost evaluation method to evaluate the best planning result. The method of the present invention takes into account the impact of carbon emissions on site selection results, presents the impact of carbon emissions on the environment in a numerical manner, and the analysis of carbon emissions can be traced and quantified, so that the impact of site selection results on the environment is reduced.

Description

Technical Field

[0001] The present invention relates to the fields of carbon trading and power system planning, and in particular to a method and device for selecting a site for an electric vehicle charging station. Background Art

[0002] In recent years, the research and technology related to electric vehicles have developed rapidly, and more and more electric vehicles have been put into use, resulting in more electricity consumption. The generation of electricity relies on the conversion of other energy sources, and currently mainly fossil energy. These energy sources produce a large amount of greenhouse gases during consumption, leading to worsening environmental problems. Due to the increasingly serious problems of energy shortage and environmental pollution, it is very important to reasonably plan the location of charging stations. On the one hand, a reasonable location of charging stations can help improve the convenience of users' travel, and on the other hand, it can also minimize the waste of electric energy, thereby indirectly reducing greenhouse gas emissions and moving closer to the goal of carbon neutrality.

[0003] The coupling of the traffic system (TS) and the distribution system (DS) provides an effective solution for the site selection and planning of electric vehicle charging stations. Through this coupling network, the carbon emissions generated during electricity consumption can be calculated, and the user travel situation can be counted, so as to plan the best location for charging stations. However, with the popularization of electrification, energy consumption is getting faster and faster, and greenhouse gas emissions are becoming more and more serious. How to reasonably plan the location of charging stations that can not only improve the user's travel experience, but also reduce energy waste and greenhouse gas emissions under the circumstances of increasing energy shortages and environmental pollution has become a focus of attention in the field of carbon trading and power system planning.

[0004] The planning and site selection of electric vehicle charging stations is a decision-making and planning issue, which requires detailed consideration of multiple uncertain factors and analysis and processing. At present, the factors considered in the site selection and planning of charging stations mainly include charging waiting time, battery loss, network loss, etc., and these planning methods have not yet taken energy and environmental issues into consideration, and cannot meet the planning requirements of future energy shortages and environmental deterioration, and cannot solve the future resource shortage dilemma and environmental deterioration problems. Therefore, it is necessary to explore new planning methods to solve the current dilemma. Summary of the invention

[0005] In view of the above problems, the present invention provides a method and device for site selection of electric vehicle charging stations, specifically relates to a method for site selection of electric vehicle charging stations taking into account carbon emission flow and a new traffic flow distribution model, which can realize reasonable planning of charging station construction locations.

[0006] A first aspect of the present invention provides a method for selecting a site for an electric vehicle charging station, the method comprising the following steps:

[0007] A certain number of candidate charging stations are obtained based on the nodes in the coupled network formed by the distribution network and the transportation network, and the required number of charging stations are selected from the candidate charging stations to obtain multiple candidate charging stations.

[0008] A mathematical model for target cost is constructed based on carbon emissions. The model includes: calculating the carbon emission intensity and carbon emission cost of each candidate charging station node according to the PSP principle, calculating the cost of building each candidate charging station, calculating the cost of operating each candidate charging station, and calculating the transportation cost of electric vehicles;

[0009] Calculate the total comprehensive cost of each candidate site construction plan based on the mathematical model, and select the top N candidate plans with the lowest total comprehensive cost;

[0010] Considering various uncertain factors, multiple combination scenarios are constructed. For each combination scenario, based on the top N candidate solutions, new candidate charging stations are added according to the increasing proportion of electric vehicle popularization, forming multiple new top N candidate solutions.

[0011] For multiple groups of new top N candidate plans, the minimax regret cost evaluation method is used to evaluate the best planning results.

[0012] Furthermore, the calculation of the transportation cost of electric vehicles is based on a new traffic flow distribution model. The specific model is: for a certain starting point and end point, when the battery is fully charged, find the charging stations Po and Pd in ​​the candidate station construction schemes that can be reached by the starting point and the end point respectively, find all the charging stations between the charging stations Po and Pd that can complete the travel task, and find the shortest distance path among the charging stations that can complete the travel task.

[0013] Furthermore, the carbon emission intensity and carbon emission cost of each candidate charging station node are calculated according to the PSP principle, where the carbon emission intensity ξ i,h,t The expression is:

[0014]

[0015] in They represent the coal energy supply device, zero carbon emission device and power supply branch when injecting active power directly into the candidate charging station node i, represents the carbon emission intensity of coal-fired energy supply, represents the output power of the coal-fired unit at time t, pf l,t represents the power flow of power supply branch l at time t in the distribution network system, represents the carbon emission intensity of power supply branch l at time t, represents the output power of the zero-carbon emission device at time t;

[0016] Carbon emission cost carb The expression is:

[0017]

[0018] where c carb represents the carbon emission cost coefficient, ξ i,h,t represents the carbon emission intensity of node i at the hth charging station at time t, represents the charging power of the hth charging station at the ith node at time t, Δt represents the time interval, and d year Indicates the planning period.

[0019] Furthermore, the cost of building each candidate charging station is calculated. The specific expression is:

[0020]

[0021] Where Ω CF represents the candidate charging station set, η h represents the binary decision variable for the hth charging station, represents the capital cost of building a charging station, z h represents the capacity of the hth charging station, represents the cost associated with the site selection location, Represents fixed costs.

[0022] Furthermore, the cost of operating each candidate charging station is calculated, and the specific expression is:

[0023] f oper =f pl +f vd

[0024] f pl represents the network loss cost, f vd It represents the additional cost caused by voltage deviation. The specific expression is:

[0025]

[0026]

[0027] where c pl represents the network loss factor, d year Indicates the planning period, Ω LD Represents the branch line of the distribution network system, Ω T represents the time interval set, G ij represents the real part of the node admittance matrix, U i,t ,U j,t represents the voltage of busbars i and j at time t, c vd represents the voltage deviation cost coefficient, ΩND represents the set of nodes in the power distribution system, represents the rated voltage of the distribution network node, θ ij,t represents the phase angle deviation of branch ij at time t.

[0028] Furthermore, the transportation cost of electric vehicles is calculated, and the specific expression is:

[0029] f inc =f deto +f fail

[0030] where f deto represents the additional travel cost due to the detour, f fail It represents the loss cost caused by travel failure. The specific expression is:

[0031]

[0032]

[0033] where c deto represents the detour cost coefficient, d year Indicates the planning period, Ω T represents the time interval set, Ω OD represents the set of starting point and end point pairs in the path, f t rs represents the electric vehicle traffic flow at time t, Indicates whether the path rs can travel successfully. represents the actual travel distance, represents the initial path travel distance, c fail A coefficient representing the cost of trip failure.

[0034] Furthermore, for multiple groups of new top N candidate solutions, the optimal planning result is evaluated using the minimax regret cost evaluation method. The specific method includes the following steps:

[0035] Calculate the total comprehensive cost of each candidate solution in the top N new candidate solutions in each group of scenarios, and get the minimum total comprehensive cost of each group of scenarios as F min ;

[0036] Count the cost and F of each candidate solution in all scenarios in multiple groups of the top N candidate solutions min The maximum value of the difference between diff-max ;

[0037] Find F diff-max The candidate with the smallest value is the best planning result.

[0038] A second aspect of the present invention provides a device for selecting a site for an electric vehicle charging station, comprising:

[0039] A station construction candidate scheme acquisition module is used to acquire a certain number of candidate charging stations based on nodes in a coupled network formed by a distribution network and a transportation network, select the required number of stations from the candidate charging stations, and obtain multiple station construction candidate schemes;

[0040] The target cost modeling module is used to build a mathematical model of the target cost based on carbon emissions. The model includes: calculating the carbon emission intensity and carbon emission cost of each candidate charging station node according to the PSP principle, calculating the cost of building each candidate charging station, calculating the cost of operating each candidate charging station, and calculating the transportation cost of electric vehicles;

[0041] The first candidate screening module is used to calculate the total comprehensive cost of each candidate site construction plan according to the mathematical model and select the top N candidate plans with the lowest total comprehensive cost;

[0042] The candidate solution optimization module is used to construct multiple combination scenarios by considering multiple uncertain factors. For each combination scenario, based on the top N candidate solutions, new candidate charging stations are added according to the increasing proportion of electric vehicle popularization to form multiple new top N candidate solutions.

[0043] The second candidate screening module is used to evaluate the best planning results for multiple groups of new top N candidate solutions using the minimax regret cost evaluation method.

[0044] A third aspect of the present invention provides an electric vehicle charging station site selection device, comprising: a processor; and a memory, wherein the memory stores a computer executable program, and when the computer executable program is executed by the processor, the above-mentioned electric vehicle charging station site selection method is executed.

[0045] A fourth aspect of the present invention is a storage medium having a program stored thereon, wherein when the program is executed by a processor, the processor executes the above-mentioned electric vehicle charging station site selection method.

[0046] The beneficial effects of the present invention are as follows: the method of the present invention takes into account the impact of carbon emissions on the site selection results, presents the impact of carbon emissions on the environment in a numerical manner, and the analysis of carbon emissions is traceable and quantifiable, so that the impact of the site selection results on the environment can be reduced to a minimum. At the same time, in terms of transportation, a new traffic flow distribution model is constructed, which is more in line with the travel habits of users, so that the site selection results will better meet the actual travel needs and can provide the greatest convenience for electric car owners. In addition, a variety of uncertain factors in the process of urban development are comprehensively considered, and a variety of combined scenarios are constructed for quantitative analysis. This ensures the robustness of the method and makes the planning more universal and instructive. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 1 is a flow chart of a method for selecting a site for an electric vehicle charging station according to an embodiment of the present invention;

[0048] Figure 2 1 is a flow chart of a novel traffic flow distribution model method according to an embodiment of the present invention;

[0049] Figure 3 Schematic diagram of the structure of the electric vehicle charging station site selection device in an embodiment of the present invention;

[0050] Figure 4 It is the architecture of the computer device in the embodiment of the present invention. DETAILED DESCRIPTION

[0051] In order to further explain the technical solution of the present invention in detail, this embodiment is implemented on the premise of the technical solution of the present invention, and provides a detailed implementation method and specific steps.

[0052] The present invention provides a method and device for selecting a site for an electric vehicle charging station, and provides the following embodiments:

[0053] Embodiment 1 based on the present invention

[0054] This embodiment is used to illustrate the site selection method of electric vehicle charging stations. Figure 1 As shown, the method comprises the following steps:

[0055] S1. Obtain a certain number of candidate charging stations based on the nodes in the coupled network formed by the distribution network and the transportation network, select the required number of stations from the candidate charging stations, and obtain multiple candidate station construction solutions;

[0056] During the specific implementation process, a certain number of nodes are evenly selected as candidate charging stations in the coupling network formed by the distribution network and the transportation network, and the required number of stations are selected from the candidate charging stations to obtain multiple candidate solutions for station construction.

[0057] In a preferred embodiment, the specific steps of obtaining candidate charging stations and multiple candidate station construction solutions are:

[0058] S11. According to planning requirements, select a distribution network and a transportation network with appropriate number of nodes for matching, so that the nodes in the two networks overlap as much as possible to facilitate the travel of car owners;

[0059] S12. In order to enable the car owner to meet his travel needs, m charging stations are uniformly selected from the entire network as candidate station nodes. These candidate station nodes include fast-charging stations (FCS) and battery-swapping stations (BSS), and the number of charging stations is the same.

[0060] S13. After determining the specific number of sites n (n<=m) according to actual planning requirements such as the size of the city, the popularity of electric vehicles in the city, etc., n nodes are randomly selected from the m candidate site nodes to form a plurality of candidate site solutions.

[0061] In a specific example, 10 nodes are selected as candidate locations for site construction in the entire network. Then, according to the actual planned number of sites to be built, 5 sites are randomly selected from the 10 candidate nodes to form a site construction plan. According to this method, there will be multiple candidate plans.

[0062] S2. Construct a mathematical model of target cost based on carbon emissions. The model includes: calculating the carbon emission intensity and carbon emission cost of each candidate charging station node according to the PSP principle, calculating the cost of building each candidate charging station, calculating the cost of operating each candidate charging station, and calculating the transportation cost of electric vehicles;

[0063] In the specific implementation process, the size of carbon emissions is related to the active power flow consumed by each node in the distribution network. At the same time, due to the different sources of energy, the carbon emission intensity generated by each energy source is different. Therefore, according to the Proportional Sharing Principle (PSP), the carbon emission intensity of each node can be calculated, and then the carbon emissions at a certain node are equal to the product of the carbon emission intensity of the node and the active power consumed at the point, so that the size of carbon emissions and carbon emission costs can be finally calculated. The carbon emission intensity and carbon emission cost of each candidate charging station node are calculated according to the PSP principle, where the carbon emission intensity ξ i,h,t The expression is:

[0064]

[0065] in They represent the coal energy supply device, zero carbon emission device and power supply branch when injecting active power directly into the candidate charging station node i, represents the carbon emission intensity of coal-fired energy supply, represents the output power of the coal-fired unit at time t, pf l,t represents the power flow of power supply branch l at time t in the distribution network system, represents the carbon emission intensity of power supply branch l at time t, represents the output power of the zero-carbon emission device at time t;

[0066] Carbon emission cost carb The expression is:

[0067]

[0068] where c carb represents the carbon emission cost coefficient, ξ i,h,t represents the carbon emission intensity of node i at the hth charging station at time t, represents the charging power of the hth charging station at the ith node at time t, Δt represents the time interval, and d year Indicates the planning period.

[0069] Furthermore, the cost of building each candidate charging station is calculated. The specific expression is:

[0070]

[0071] Where Ω CF represents the candidate charging station set, η h represents the binary decision variable for the hth charging station, represents the capital cost of building a charging station, z h represents the capacity of the hth charging station, represents the cost associated with the site selection location, Represents fixed costs.

[0072] Furthermore, the cost of operating each candidate charging station is calculated, and the specific expression is:

[0073] f oper =f pl +f vd

[0074] f pl represents the network loss cost, f vd It represents the additional cost caused by voltage deviation. The specific expression is:

[0075]

[0076]

[0077] where c pl represents the network loss factor, d year Indicates the planning period, Ω LD Represents the branch line of the distribution network system, Ω T represents the time interval set, G ij represents the real part of the node admittance matrix, U i,t ,U j,t represents the voltage of busbars i and j at time t, c vd represents the voltage deviation cost coefficient, Ω ND represents the set of nodes in the power distribution system, represents the rated voltage of the distribution network node, θ ij,t represents the phase angle deviation of branch ij at time t.

[0078] Furthermore, the calculation of the transportation cost of electric vehicles is based on a new traffic flow distribution model. The specific model is: for a certain starting point and end point, when the battery is fully charged, find the charging stations Po and Pd in ​​the candidate station construction schemes that can be reached by the starting point and the end point respectively, find all the charging stations between the charging stations Po and Pd that can complete the travel task, and find the shortest distance path among the charging stations that can complete the travel task.

[0079] During the specific implementation process, for each travel task, in order to find a travel path that can complete the task and has the shortest travel time, the Dijkstra or Floyd algorithm is used to find the shortest distance path as the travel path, and it is determined whether the maximum mileage of the electric vehicle can complete this travel path under this path. If it can, the travel path is successfully found, indicating that the electric vehicle does not need to be charged. If the owner cannot complete the trip under the shortest path, it means that at least one charge is required to complete the travel task. Then the best travel path can be found through the following steps, such as Figure 2 As shown,

[0080] S21, counting the locations of all charging stations that can be reached from the current position when the battery is fully charged, recorded as Po. When executed for the first time, the current position is the starting point;

[0081] S22, calculating the locations of all charging stations that can reach the destination when fully charged, recorded as Pd;

[0082] S23. If Po and Pd have an intersection, execute S24; otherwise, go to S21, and use the charging station found after the last execution of S11 as the current position, and all the charging stations found after the second execution of S11 are recorded as Pi (i=1, 2, 3...).

[0083] S24, calculate all possible paths that pass through Po and Pd and Pi (if any);

[0084] S25. According to the 214 statistical results, find the shortest path, which is used as the improved travel path. The selection of this travel path can maximize the user's travel experience and meet travel needs; it can save the owner's travel time, improve the owner's travel experience and increase the travel success rate.

[0085] Specifically, the transportation cost of electric vehicles is calculated as follows:

[0086] f inc =f deto +f fail

[0087] where f deto represents the additional travel cost due to the detour, f fail It represents the loss cost caused by travel failure. The specific expression is:

[0088]

[0089]

[0090] where c deto represents the detour cost coefficient, d year Indicates the planning period, Ω T represents the time interval set, Ω OD represents the set of starting point and end point pairs in the path, f t rs represents the electric vehicle traffic flow at time t, Indicates whether the path rs can travel successfully. represents the actual travel distance, represents the initial path travel distance, c fail A coefficient representing the cost of trip failure.

[0091] S3. Calculate the total comprehensive cost of each candidate site construction plan according to the mathematical model, and select the top N candidate plans with the lowest total comprehensive cost;

[0092] The total comprehensive cost includes four parts: the construction cost of the charging station, the operating cost of the charging station, the carbon emission cost, and the transportation cost. The construction cost includes two parts, one is the cost of building a fast charging station, and the other is the cost of building a battery replacement charging station; the operating cost of the charging station refers to the electricity cost paid for operating the charging station, including electricity purchase, network loss, etc.; the carbon emission cost refers to the quantified cost of carbon emissions generated by the energy consumed to meet the travel needs of users; the transportation cost includes the loss cost caused by travel failure and the additional cost caused by detours to complete the trip.

[0093] The total comprehensive cost expression of the mathematical model for building target cost based on carbon emissions is:

[0094] F=f con +f oper +f carb +f inc

[0095] where f con represents the cost of building a charging station, f oper represents the operating cost of the charging station, f carb represents the carbon emission cost, f inc Represents the transportation cost. S1-S3 uses the carbon flow model to quantify the carbon emissions generated during energy consumption, so as to find the most robust site selection plan.

[0096] S4, considering multiple uncertain factors to construct multiple combination scenarios, for each combination scenario, based on the first N candidate solutions, add new candidate charging stations to form multiple new groups of first N candidate solutions, S4 improves the versatility and guidance of the method of the present invention, taking multiple uncertain factors into consideration;

[0097] During the specific implementation process, various uncertain factors are taken into consideration, including: changes in electric vehicle types, changes in land use nature, the proportion of new clean energy investment, traffic flow and the rate of change of distribution network load. Multiple combination scenarios are simulated and constructed, and then the planning results of each plan in each scenario are calculated based on the decision rules.

[0098] In the preferred implementation method, the various uncertain factors affecting the site selection of electric vehicles during urban development are analyzed, including:

[0099] (1) Changes in the nature of urban land use, such as residential areas evolving into commercial areas as urban planning develops; (2) Changes in the proportion of electric vehicle types. Assuming that there are the same number of fast-charging and battery-swappable vehicles, this proportion will fluctuate in the future as development progresses; (3) Changes in the proportion of traffic flow load, such as an increase in the number of vehicles in residential areas and an increase in traffic flow, such as a 10% increase in traffic flow; (4) Changes in distribution network load, such as a 10% increase in load; (5) Changes in energy use types. Currently, all energy sources are fossil energy with strong carbon emissions. The introduction of clean energy in the future will affect the carbon emission intensity of the network.

[0100] Considering the changes of each factor, multiple scenarios are combined. In the preferred implementation method, the traffic flow and distribution network load remain unchanged; the ratio of fast-charging cars and battery replacement cars changes from 1:1 to 1:4; the locations of residential areas and industrial areas change; residential areas introduce clean energy supply, and industrial areas maintain fossil energy supply. The above situation constitutes a possible scenario in the future. According to the above method, there will be multiple simulated change scenarios.

[0101] For each combination scenario, on the basis of the first N candidate solutions, new candidate charging stations are added to form multiple groups of new first N candidate solutions. The specific method includes: adding candidate charging stations by permutation and combination from the candidate charging stations obtained in S1 except for the candidate charging stations included in the first N candidate solutions. The number of newly added candidate charging stations is proportionally increased according to the increase in the popularity of electric vehicles. The number of stations to be built will form multiple combinations, which are recorded as M. Then, in each combination scenario, on the basis of the first N candidate solutions, each solution is combined with the newly added M combinations to obtain M candidate solutions. For these M candidate solutions, the total comprehensive cost of each station construction candidate solution is calculated according to the mathematical model, and the candidate solution with the lowest total comprehensive cost is selected. Thus, the optimal location of newly added candidate charging stations for these N solutions in different combination scenarios can be calculated, and finally multiple groups of new first N candidate solutions are formed.

[0102] S5. For multiple groups of new top N candidate plans, the minimax regret cost evaluation method is used to evaluate the best planning results.

[0103] The specific method includes the following steps:

[0104] Calculate the total comprehensive cost of each candidate solution in the top N new candidate solutions in each group of scenarios, and get the minimum total comprehensive cost of each group of scenarios as F min ;

[0105] Count the cost and F of each candidate solution in all scenarios in multiple groups of the top N candidate solutions min The maximum value of the difference between diff-max ;

[0106] Find F diff-max The candidate with the smallest value is the best planning result.

[0107] The use of the minimax regret cost evaluation method can find the best site selection plan under the most undesirable conditions, making the site selection results more robust and having the strongest ability to resist uncertainty risks.

[0108] Embodiment 2 based on the present invention

[0109] This embodiment refers to Figure 3 To describe the device corresponding to the method according to embodiment 1 of the present disclosure, a device for selecting a site for an electric vehicle charging station, the device 100 includes: a station construction candidate scheme acquisition module 101, which is used to obtain a certain number of candidate charging stations based on the nodes in the coupled network formed by the distribution network and the transportation network, select the required number of stations from the candidate charging stations, and obtain multiple station construction candidate schemes; a target cost modeling module 102, which is used to construct a mathematical model of the target cost based on carbon emissions, and the model includes: calculating the carbon emission intensity and carbon emission cost of each candidate charging station node according to the PSP principle, calculating the cost of building each candidate charging station, and calculating the cost of operating each candidate charging station. The cost of electric vehicles is calculated; the candidate solution first screening module 103 is used to calculate the total comprehensive cost of each candidate solution for station construction according to the mathematical model, and select the top N candidate solutions with the lowest total comprehensive cost; the candidate solution optimization module 104 is used to consider a variety of uncertain factors to construct a variety of combination scenarios, for each combination scenario, based on the top N candidate solutions, according to the increase in the proportion of electric vehicle popularization, add new candidate charging stations to form multiple groups of new top N candidate solutions; the candidate solution second screening module 105 is used to evaluate the best planning results for multiple groups of new top N candidate solutions using the minimum maximum regret cost evaluation method. In addition to the above five modules, the device 100 may also include other components, however, since these components are irrelevant to the content of the embodiments of the present disclosure, their illustrations and descriptions are omitted here.

[0110] The specific working process of the electric vehicle charging station site selection device 100 refers to the description of the above-mentioned electric vehicle charging station site selection method embodiment 1, and will not be repeated here.

[0111] Embodiment 3 based on the present invention

[0112] The device according to the embodiment of the present invention can also be used by Figure 4 The architecture of the computing device shown is implemented. Figure 3 The architecture of the computing device is shown. Figure 4As shown, computer system 201, system bus 203, one or more CPUs 204, input / output 202, memory 205, etc. Memory 205 can store various data or files used for computer processing and / or communication and program instructions including the method of embodiment 1 executed by the CPU. Figure 4 The architecture shown is only exemplary and can be adjusted according to actual needs when implementing different devices. Figure 4 One or more components in .

[0113] Embodiment 4 based on the present invention

[0114] The embodiment of the present invention may also be implemented as a computer-readable storage medium. The computer-readable storage medium according to embodiment 4 stores computer-readable instructions. When the computer-readable instructions are executed by a processor, the electric vehicle charging station site selection method according to embodiment 1 of the present invention described with reference to the above figures may be executed.

[0115] The embodiments of the present invention are directed to the above-mentioned electric vehicle charging station site selection method, device and storage medium, and the technical problem to be solved is: to provide an electric vehicle charging station site selection planning method based on a new traffic flow distribution model and a combination of carbon emissions, which can realize the reasonable planning of the charging station construction location. The method of the present invention takes into account the impact of carbon emissions on the site selection results, presents the impact of carbon emissions on the environment in a numerical manner, and the analysis of carbon emissions has the characteristics of traceability and quantification, so that the impact of the site selection results on the environment can be reduced to a minimum. At the same time, in terms of traffic travel, a new traffic flow distribution model is constructed, which is more in line with the travel habits of users, so that the site selection results will better meet the actual travel needs and can provide the greatest convenience for electric vehicle owners. In addition, a variety of uncertain factors in the process of urban development are comprehensively considered, and a variety of combined scenarios are constructed for quantitative analysis. Thereby ensuring the robustness of the method, making the planning more universal and instructive.

[0116] In this document, the terms "comprises," "comprising" or any other variations thereof are intended to cover non-exclusive inclusion, such that a step or method that includes a series of elements includes not only those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such step or method.

[0117] The above contents are further detailed descriptions of the present invention in combination with specific preferred embodiments, and it cannot be determined that the specific implementation of the present invention is limited to these descriptions. For ordinary technicians in the technical field to which the present invention belongs, several simple deductions or substitutions can be made without departing from the concept of the present invention, which should be regarded as falling within the protection scope of the present invention.

Claims

1. A method for selecting a site for an electric vehicle charging station, characterized in that: The method comprises the following steps: A certain number of candidate charging stations are obtained based on the nodes in the coupled network formed by the distribution network and the transportation network, and the required number of stations are selected from the candidate charging stations to obtain multiple candidate station construction schemes; A mathematical model for target cost is constructed based on carbon emissions. The model includes: calculating the carbon emission intensity and carbon emission cost of each candidate charging station node according to the PSP principle, calculating the cost of building each candidate charging station, calculating the cost of operating each candidate charging station, and calculating the transportation cost of electric vehicles; The calculation of the transportation cost of electric vehicles is based on a new traffic flow allocation model. Specifically, the model is as follows: when the vehicle is fully charged, the charging stations Po and Pd in ​​the candidate station construction schemes that can be reached by the starting point and the end point are found respectively, and all the charging stations between the charging stations Po and Pd that can complete the travel task are found, and the shortest distance path is found among the charging stations that can complete the travel task; Calculate the total comprehensive cost of each candidate site construction plan based on the mathematical model, and select the top N candidate plans with the lowest total comprehensive cost; Considering various uncertain factors, multiple combination scenarios are constructed. For each combination scenario, based on the top N candidate solutions, new candidate charging stations are added according to the increasing proportion of electric vehicle popularization, forming multiple new top N candidate solutions. For multiple groups of new top N candidate plans, the minimax regret cost evaluation method is used to evaluate the best planning results.

2. The method for selecting a site for an electric vehicle charging station according to claim 1, characterized in that: According to the PSP principle, the carbon emission intensity and carbon emission cost of each candidate charging station node are calculated, where the carbon emission intensity ξ i,h,t The expression is: in They represent the coal energy supply device, zero carbon emission device and power supply branch when injecting active power directly into the candidate charging station node i, represents the carbon emission intensity of coal-fired energy supply, represents the output power of the coal-fired unit at time t, pf l,t represents the power flow of power supply branch l at time t in the distribution network system, represents the carbon emission intensity of power supply branch l at time t, represents the output power of the zero-carbon emission device at time t; Carbon emission cost carb The expression is: Where Ω CF represents the candidate charging station set, Ω T represents the time interval set, c carb represents the carbon emission cost coefficient, ξ i,h,t represents the carbon emission intensity of node i at the hth charging station at time t, represents the charging power of the hth charging station at the ith node at time t, Δt represents the time interval, and d year Indicates the planning period.

3. The method for selecting a site for an electric vehicle charging station according to claim 1, characterized in that: Calculate the cost of building each candidate charging station. The specific expression is: Where Ω CF represents the candidate charging station set, η h represents the binary decision variable for the hth charging station, represents the capital cost of building a charging station, z h represents the capacity of the hth charging station, represents the cost associated with the site selection location, Represents fixed costs.

4. The method for selecting a site for an electric vehicle charging station according to claim 1, characterized in that: Calculate the cost of operating each candidate charging station. The specific expression is: f oper =f pl +f vd f pl represents the network loss cost, f vd It represents the additional cost caused by voltage deviation. The specific expression is: where c pl represents the network loss factor, d year Indicates the planning period, Ω LD Represents the branch line of the distribution network system, Ω T represents the time interval set, G ij represents the real part of the node admittance matrix, U i,t ,U j,t represents the voltage of busbars i and j at time t, c vd represents the voltage deviation cost coefficient, Ω ND represents the set of nodes in the power distribution system, represents the rated voltage of the distribution network node, θ ij,t represents the phase angle deviation of branch ij at time t.

5. The method for selecting a site for an electric vehicle charging station according to claim 1, characterized in that: Calculate the transportation cost of electric vehicles. The specific expression is: f inc =f deto +f fail where f deto represents the additional travel cost due to the detour, f fail It represents the loss cost caused by travel failure. The specific expression is: where c deto represents the detour cost coefficient, d year Indicates the planning period, Ω T represents the time interval set, Ω OD represents the set of starting point and end point pairs in the path, f t rs represents the electric vehicle traffic flow at time t, Indicates whether the path rs can travel successfully. represents the actual travel distance, represents the initial path travel distance, c fail A coefficient representing the cost of trip failure.

6. The method for selecting a site for an electric vehicle charging station according to claim 1, characterized in that: For multiple groups of new top N candidate solutions, the minimax regret cost evaluation method is used to evaluate the best planning result. The specific method includes the following steps: Calculate the total comprehensive cost of each candidate solution in the top N new candidate solutions in each group of scenarios, and get the minimum total comprehensive cost of each group of scenarios as F min ; Count the cost and F of each candidate solution in all scenarios in multiple groups of the top N candidate solutions min The maximum value of the difference between diff-max ; Find F diff-max The candidate with the smallest value is the best planning result.

7. A site selection device for an electric vehicle charging station, characterized in that: include: A station construction candidate scheme acquisition module is used to acquire a certain number of candidate charging stations based on nodes in a coupled network formed by a distribution network and a transportation network, select the required number of stations from the candidate charging stations, and obtain multiple station construction candidate schemes; The target cost modeling module is used to build a mathematical model of the target cost based on carbon emissions. The model includes: calculating the carbon emission intensity and carbon emission cost of each candidate charging station node according to the PSP principle, calculating the cost of building each candidate charging station, calculating the cost of operating each candidate charging station, and calculating the transportation cost of electric vehicles; the transportation cost calculation of electric vehicles is based on a new traffic flow allocation model. The model is specifically as follows: in the case of full power, find the charging stations Po and Pd in ​​the candidate station construction scheme that can be reached by the starting point and the end point respectively, find all the charging stations between charging stations Po and Pd that can complete the travel task, and find the shortest distance path among the charging stations that can complete the travel task; The first candidate screening module is used to calculate the total comprehensive cost of each candidate site construction plan according to the mathematical model and select the top N candidate plans with the lowest total comprehensive cost; The candidate solution optimization module is used to construct multiple combination scenarios by considering multiple uncertain factors. For each combination scenario, based on the top N candidate solutions, new candidate charging stations are added according to the increasing proportion of electric vehicle popularization to form multiple new top N candidate solutions. The second candidate screening module is used to evaluate the best planning results for multiple groups of new top N candidate solutions using the minimax regret cost evaluation method.

8. A site selection device for an electric vehicle charging station, characterized in that: include: processor; and a memory, wherein a computer executable program is stored in the memory, and when the computer executable program is executed by the processor, the electric vehicle charging station site selection method according to any one of claims 1 to 6 is executed.

9. A storage medium having a program stored thereon, characterized in that: When the program is executed by a processor, the electric vehicle charging station site selection method according to any one of claims 1 to 6 is implemented.

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