A method for site selection and capacity determination of electric vehicle charging stations

Abstract

The present invention proposes a method for site selection and capacity determination of electric vehicle charging stations, comprising: analyzing and processing the initial vehicle flow of each road node, using the flow correction coefficient to obtain the vehicle flow information of each traffic node to obtain the charging demand, and generating a city road CAD map according to the regional division of the planning area; calculating the distance from each node to the candidate charging station, and estimating the maximum number of stations and the minimum number of stations according to the maximum service capacity, minimum service capacity and charging demand of the charging station; randomly selecting charging stations according to the site of the candidate station according to the determined number range of charging stations, service range and charging station level; taking the sum of construction cost, operation and maintenance cost, network loss cost and distance loss cost as the total objective function, taking the charging demand of electric vehicles, the number and capacity of charging piles, and the power of the planning area as constraints, constructing a charging station site selection and capacity determination model; combining the vehicle flow and charging demand of each traffic node, and using the improved whale optimization algorithm to solve the charging station site selection and capacity determination model.

Description

Technical Field

[0001] The invention relates to a method for site selection and capacity determination of an electric vehicle charging station. Background Art

[0002] With the rapid development of urban economy and society, electric vehicle related technologies have made significant breakthroughs. The gradual popularization in China has also provided certain opportunities for the automotive industry and manufacturing industry. The production and promotion of electric vehicles and related charging facilities will also become the focus of future development of the industry. The technology of electric vehicles is relatively mature, but there is still no effective solution to the problem of battery capacity. Therefore, the site selection and capacity determination of charging stations are extremely important. The scientific and reasonable selection of charging station location construction is also of great significance to the rapid economic growth and the stable operation of the power grid. For example:

[0003] Yu et al. determined that the site selection of electric vehicle charging stations is a nonlinear combinatorial optimization problem when studying it, proposed an adaptive particle swarm optimization algorithm to solve it, and verified the feasibility and effectiveness of the algorithm and model (Yu Qing, Li Jinghua, Zhao Qianfu, Xing Chunyang. Optimal layout of urban electric vehicle charging stations based on chaotic quantum particle swarm algorithm with adaptive weight adjustment [J]. Electrical Measurement and Instrumentation, 2017, 54(13): 110-114+119);

[0004] Liu Zifa and others introduced constraints such as distribution transformer investment and traffic flow in the study of charging station location selection, and combined the probability expression and superposition state characteristics in quantum theory with particle swarms to plan charging stations, and the method was effective (Liu Zifa, Zhang Wei, Wang Zeli. Optimal layout of urban electric vehicle charging stations based on quantum particle swarm optimization algorithm [J]. Proceedings of the CSEE, 2012, 32(22): 39-45+20);

[0005] Zhang Yi et al. used Monte Carlo to derive the charging demand for electric vehicles, established a site selection model considering construction costs and waiting costs, and used a multi-population genetic algorithm to solve it (Zhang Yi, Zhu Pan. Research on site selection model for dedicated charging stations for electric taxis [J]. Journal of Geo-Information Science, 2021, 23(05): 802-811);

[0006] Yan Gangui takes minimizing the economic loss of users and the cost of charging stations as the goal, and takes Beijing's Haidian District as an example to propose an improved particle swarm algorithm solution model based on dynamic probability mutation of regional visit volume (Yan Gangui, Liu Huanan, Han Ninghui, Chen Songsong, Yu Dongmin. Optimization method for location and capacity of charging stations considering the spatiotemporal distribution of electric vehicles [J / OL]. Proceedings of the CSEE: 1-14 [2021-09-20]. http: / / kns.cnki.net / kcms / detail / 11.2107.TM.20210120.1747.010.html);

[0007] Bai et al. proposed a hybrid algorithm combining non-dominated sorting genetic algorithm with domain search, using a bi-objective mixed integer model based on the battery model to solve the location and capacity of electric vehicle charging stations (Xue Bai, Kwai-Sang Chin, Zhili Zhou. Abi-objective model for location planning of electric vehicle charging stations with GPS trajectory data [J]. Computers & Industrial Engineering, 2019, 128);

[0008] Jiao Dengjie et al. optimized the location and capacity of charging stations by using the catfish particle swarm algorithm, and selected the scheme based on the most cost-effective solution for the whole society (Jiao Dengjie, Su Xiaolin, Yan Xiaoxia, Wang Wenchao. A charging station location and capacity scheme based on Voronoi diagram and catfish particle swarm optimization algorithm [J]. Automation Technology and Applications, 2018, 37(03): 5-10);

[0009] Li Luyan improved the firefly algorithm by comparing light intensity, updating position, and moving step length. Taking the Tai'an fast charging station as an example, the model was solved to obtain its address and installation capacity (Li Luyan. Site selection and capacity determination of Tai'an electric vehicle fast charging station based on improved firefly algorithm [D]. Shandong University, 2020);

[0010] Li et al. took into account the investment of charging station operators and the travel costs of car owners, and used genetic algorithms to solve the problem using the taxi-hailing and charging data of Haikou and Shanghai as examples (Li Jiale, Liu Zhenbo, Wang Xuefei. Public charging station location determination for electric ride-hailing vehicles based on an improved genetic algorithm[J]. Sustainable Cities and Society, 2021, 74).

[0011] However, the construction of electric vehicle charging stations still has the following disadvantages:

[0012] The location planning of charging stations is not very reasonable, and the range overlap between stations is high, resulting in low charging efficiency of electric vehicles, a few charging stations are idle, and even cause traffic jams; most use traditional particle swarm to solve the problem of charging station site selection and sizing, while the particle swarm algorithm has the disadvantages of premature maturity and local convergence; the above problems will affect the rationality of the site selection and sizing of electric vehicle charging stations, and easily lead to excessively high construction costs. Summary of the invention

[0013] The purpose of the present invention is to provide a method for site selection and capacity determination of electric vehicle charging stations, which optimizes and solves the problem of site selection and capacity determination of charging stations by improving the traditional whale algorithm, while overcoming the problems of low charging efficiency of electric vehicles and idle charging stations.

[0014] The technical solution adopted by the present invention to solve the technical problem is:

[0015] A method for selecting a site and determining the capacity of an electric vehicle charging station comprises the following steps:

[0016] S1: Analyze and process the initial traffic flow of each road node, use the traffic correction coefficient to obtain the traffic flow information of each traffic node to obtain the charging demand, and generate the urban road CAD map according to the planning area division;

[0017] S2: The distance from each node to the candidate charging station is calculated, and the maximum and minimum number of charging stations are estimated based on the maximum service capacity, minimum service capacity and charging demand of the charging station; in the set planning area, i.e., residential area, commercial area, industrial area, the number range of charging stations, service range and charging station level of the charging station are determined, and the charging station is randomly selected according to the site of the candidate station, and its level is randomly selected;

[0018] S3: Taking the sum of construction cost, operation and maintenance cost, network loss cost and route loss cost as the total objective function, and taking electric vehicle charging demand, number and capacity of charging piles, and power in the planned area as constraints, a charging station site selection and capacity determination model is constructed;

[0019] S4: Combining the traffic volume and charging demand of each traffic node, the improved whale optimization algorithm is used to solve the charging station site selection and capacity determination model to obtain the optimal site selection and capacity determination plan.

[0020] In step S1, the charging demand of the traffic node is obtained by multiplying the traffic flow of the traffic node by the flow correction coefficient, and a city road CAD map is generated according to the regional division and the coordinates of the candidate station site.

[0021] In step S3, the objective function is specifically:

[0022] minF=F1+F2+F3+F4

[0023] Where: F represents the total cost of building and operating the charging station until the target year; F1 represents the fixed construction cost of the electric vehicle charging station in the target year converted to the planning period, including land cost, new charging pile cost, transformer cost and other construction costs; F2 represents the annual operation and maintenance cost of the electric vehicle charging station; F3 represents the annual network loss cost of the charging station; F4 represents the travel loss cost of the electric vehicle to the charging station;

[0024]

[0025] in:

[0026]

[0027] Where: S i represents the area of ​​the i-th charging station to be selected; f land,i represents the land unit price of the i-th candidate charging station area. In this paper, it is assumed that the planning area is divided into residential areas f land,1 , Commercial District land,2 and industrial areas land,3 ; N represents the number of charging piles in the charging station; f c Indicates the unit price of a single charging pile in the charging station; C i represents the infrastructure costs of charging stations and other costs; x i ={0,1}, that is, if a charging station is built at the candidate point I, the value is 1, and if a charging station is not built at the candidate point I, the value is the opposite; λ represents the annual operation and maintenance cost conversion factor, r0 is the depreciation rate; t k The planning period;

[0028]

[0029] Where: F2 represents the cost of daily maintenance, equipment loss, employee maintenance and wages of the charging station; the operation and maintenance costs are converted to the initial investment using the conversion coefficient; θ represents the above conversion coefficient;

[0030]

[0031] Where: L represents the unit electricity price; T d represents the average effective working time of the charging station per day; f L and f w They respectively represent the conversion coefficients of the charging pile line and its own losses converted to a single charging pile;

[0032]

[0033] Where: B represents the power consumption per kilometer of electric vehicle travel; d im represents the actual distance from the charging demand point m to the charging station of the electric vehicle; y imThe decision variable y represents whether the electric vehicle is charged. im ∈{0,1}, when y im When it is 1, it means go, and when it is 0, it means go; n m I represents the number of electric vehicles that have charging demand at charging demand point m on a typical day; i represents the set of candidate stations, I = {i|i = 1, 2, ..., m}; M represents the set of charging demand points {m|m = 1, 2, 3 ..., j};

[0034] The specific constraints are:

[0035] Single arrival constraint for electric vehicle demand points:

[0036]

[0037] This constraint indicates that user a can only be served by one charging station I at a time to meet its charging needs, where Z ia ={0,1} is a binary variable. If user a accepts charging service at the selected point I, it is 1;

[0038] Planning regional power constraints:

[0039]

[0040] This constraint indicates that the total power of the charging station should meet the charging demand within its service range. bc,i is the maximum power of the i-th charging station; P ps It is expressed as the total power of substations in the whole planning area;

[0041] The number of charging piles at a charging station is limited:

[0042]

[0043] This constraint condition indicates the limit on the number of charging piles in a charging station. The total power of charging station I must meet the charging demand within its service range. Each charging station has at least one charging pile, and the candidate points that are not selected as charging stations have no charging piles.

[0044] The number of charging piles at a charging station is limited:

[0045] n m Z ia P ia ≤T d N

[0046] Where P ia It represents the maximum probability of simultaneous charging of electric vehicles. The simultaneous charging rate in different planning areas is different. The simultaneous charging rate in residential areas is P 1a , the commercial area is P 2a , industrial area is P3a .

[0047] Step S4 specifically includes:

[0048] S41: Based on the random selection of charging stations and their levels, the charging stations that meet both conditions are screened out through the Voronoi diagram idea based on Floyd and the constraint conditions;

[0049] S42: Combining the objective function with the charging stations that meet the constraints and the corresponding charging station levels, the improved whale optimization algorithm is used to calculate the optimal value of the total social cost of the charging station;

[0050] S43: According to the optimal value of the total social cost of the charging station, the corresponding candidate site location of the charging station and its corresponding charging station grade are output, so as to obtain the most effective site selection and capacity determination plan.

[0051] Step S41 specifically includes:

[0052] The vehicles that need to be charged on the road are integrated into the road traffic nodes. The traffic nodes are regarded as charging demand points. The actual distance from the charging demand points within the service range of the charging station to the station should be less than or equal to the actual distance from the charging demand points to other charging stations. Then, according to the Floyd algorithm, the b×b order initial distance matrix C is generated according to the road node number b. (0) , the elements in the matrix represents the distance from road node l to road node j; if there is a direct road connecting nodes l to j, then is the actual road length between two nodes; if nodes l to j cannot be directly connected, then is infinite, as shown below:

[0053]

[0054] Where l,j=1,2,…,b; is the actual road length between road nodes l and j; for any two road nodes l and j, determine whether there is an intermediate node e such that the distance from node l to node j through node e is less than the existing distance. If such a node e exists, update the iterative distance matrix C (k) , as shown below:

[0055]

[0056] Follow the above steps to update the distance matrix C (k+1) , if C (k+1) ≠C (k) , then return to step 3; otherwise terminate and get the shortest path matrix, which is C (k) .

[0057] Step S42 specifically includes:

[0058] S421. Combined with the total objective function, the improved whale optimization algorithm is used to calculate the total social costs corresponding to multiple charging stations that meet the constraints.

[0059] S421. Select the minimum value from multiple total social costs as the optimal value.

[0060] The specific calculation process of the improved whale optimization algorithm is as follows:

[0061] T1: initialization parameters;

[0062] T2: The site selection and capacity determination coding of electric vehicle charging stations includes two parts, corresponding to the selection of the charging station address and the selection of the charging station scale level. The coding form adopts the combination of the charging station number and the charging station level. The specific method is as follows:

[0063] [x1 x2…x n m1 m2…m n ]n∈(n min ,n max ), where x1 x2…x n Number the charging stations, m1 m2…m n is the charging station level; x h is a non-repeating positive integer, indicating that the hth charging station is selected at the candidate location x h m h is a positive integer, indicating the scale level of the hth charging station;

[0064] T3: Calculate the charging station selection corresponding to each individual in the population and the total cost and F1, F2, F3, F4 in this case. The distance problems in the model are solved by the Floyd algorithm. The spiral bubble net predation strategy with improved convergence factor is used to update the population, including three search methods: encirclement predation, bubble predation, and random predation, to obtain a new population. Since the charging station number and charging station level are both integers, a rounding strategy needs to be used when updating the population.

[0065] T4: Use differential mutation and crossover of differential evolution to obtain the evolved population for the population obtained in T3. Similarly, a rounding strategy needs to be adopted when updating the population to ensure that the population code is always an integer.

[0066] T5: Calculate the antibody concentration of the population after evolution, calculate the affinity between antibodies, take the N individuals with the highest affinity as the new population, and record the global optimal fitness value.

[0067] The advantages of the present invention are:

[0068] 1. Compared with the prior art, the present invention proposes a charging station planning model with the best social cost for electric vehicle site selection and capacity determination, which balances the interests of charging station operators, electric vehicle users and power grid companies, and considers the impact of regional traffic flow on site selection, reflecting the rationality of site selection and capacity determination, which has certain practical guiding significance for future engineering construction;

[0069] 2. Compared with the prior art, the present invention proposes an improved whale optimization algorithm (IWOA), which introduces convergence factor, differential evolution, and artificial immune algorithm into the WOA algorithm, significantly improving the quality of the initial solution, maintaining the diversity of the population, and improving the algorithm's ability to jump out of the local optimal solution, thereby improving the overall optimization performance of the algorithm, and providing a new method for solving the problem of charging station site selection and capacity determination;

[0070] 3. The example of the present invention takes into account three types of land use, namely residential land, commercial land and industrial land, and adopts the Floyd algorithm to obtain the nearest real distance from the electric vehicle user to the charging station instead of the Euclidean distance in the traditional algorithm. The Voronoi diagram idea is combined to divide the service range of the charging station, which further improves the accuracy of the algorithm and the solution model in the example. BRIEF DESCRIPTION OF THE DRAWINGS

[0071] Figure 1 It is a structural schematic diagram of the method for selecting the site and determining the capacity of an electric vehicle charging station proposed by the present invention;

[0072] Figure 2 This is a schematic diagram of the solution process of the whale optimization algorithm;

[0073] Figure 3 is a schematic diagram of a 45-node transportation network area in an embodiment;

[0074] Figure 4 is a schematic diagram of a 45-node transportation network area divided by a Vorinoi graph in an embodiment;

[0075] Figure 5 Schematic diagram of solving the number of iterations in the embodiment. DETAILED DESCRIPTION

[0076] In order to make the purpose, technical scheme and advantages of the embodiments of the present invention clearer, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. The components of the embodiments of the present invention generally described and shown in the drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the drawings is not intended to limit the scope of the claimed invention, but merely represents selected embodiments of the present invention. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work belong to the scope of protection of the present invention.

[0077] like Figure 1 , Figure 2 As shown, the method for selecting the site and determining the capacity of an electric vehicle charging station proposed by the present invention comprises the following steps:

[0078] S1: Analyze and process the initial traffic flow of each road node, use the traffic correction coefficient to obtain the traffic flow information of each traffic node to obtain the charging demand, and generate the urban road CAD map according to the planning area division;

[0079] S2: The distance from each node to the candidate charging station is calculated, and the maximum and minimum number of charging stations are estimated based on the maximum service capacity, minimum service capacity and charging demand of the charging station. The number range, service range and level of charging stations are determined in the set planning area (residential area, commercial area, industrial area) according to the idea of ​​Voronoi diagram based on Floyd, and charging stations are randomly selected according to the site of the candidate station, and their levels are randomly selected;

[0080] S3: Taking the sum of construction cost, operation and maintenance cost, network loss cost and route loss cost as the total objective function, and taking electric vehicle charging demand, number and capacity of charging piles, and power in the planned area as constraints, a charging station site selection and capacity determination model is constructed;

[0081] S4: Combining the traffic volume and charging demand of each traffic node, the improved whale optimization algorithm is used to solve the charging station site selection and capacity determination model to obtain the optimal site selection and capacity determination plan.

[0082] In this embodiment, the site selection and capacity determination of electric vehicle charging stations are carried out by taking 45 nodes of the traffic network as an example. The traffic network of the area contains 45 main intersection nodes and 78 main roads. Taking into account the existence of 14 candidate sites and 3 substations, the total planned area of ​​the new area is 26.75km2, and the total power of the substation is 3MW. The length unit between nodes in the figure is km, the number of the intersection node is the number of the node, X is the horizontal coordinate of the intersection node, and Y is the vertical coordinate of the intersection node. It is assumed that the electric vehicle charging station will be put into use for 20 years, the unit power consumption of each electric vehicle is 0.15Kw·h / km, and the average battery capacity of the electric vehicle is 50Kw·h. This article makes a reasonable division assumption for each indicator of the four levels of electric vehicle charging stations. It is assumed that there are 14 candidate sites and the planning area is as follows: Figure 2 shown.

[0083] The IWOA algorithm parameters are set as follows: the whale population size is 100, the initial iteration number is 200, the spiral constant is 2, the scaling factor is set to 0.6, the crossover probability is 0.8, and the initial circle speed G is 1. The optimal construction cost, operation and maintenance cost, network loss cost, travel cost and the overall social cost corresponding to each n value can be obtained by calculation. The specific algorithm process is as follows: Figure 2 shown.

[0084] The method of the present invention is applied to this embodiment, and the specific process is as follows:

[0085] 1. Analyze the traffic flow of each node in the planning area, calculate the charging demand of each node according to the traffic flow coefficient, and estimate the maximum and minimum number of charging stations based on the maximum service capacity, minimum service capacity and charging demand of the charging station. ;

[0086] 2. Use the Floyd algorithm to determine the shortest distance from each node to each candidate charging station, and use the Vorinoi graph idea to divide the charging station service range;

[0087] 3. Determine the number and capacity of charging stations in the planned area and their corresponding levels, and determine the candidate sites for the stations to be built and their corresponding service scope and areas;

[0088] 4. Determine whether the charging stations at the candidate sites cover all nodes in the planned area and whether they meet various constraints;

[0089] 5. Calculate the total social cost of the charging station that meets the requirements of the site selection and capacity determination plan;

[0090] 6. Use the improved whale optimization algorithm to calculate the optimal value of the total social cost: calculate the total cost and F1, F2, F3, F4 (the distance problems in the model are all solved by the Floyd algorithm) of each individual in the population corresponding to the charging station selection, and the spiral bubble net predation strategy with improved convergence factor is used to update the population, including three search methods: encirclement predation, bubble predation, and random predation, to obtain a new population. Since the charging station number and charging station level are both integers, a rounding strategy is required when updating the population. After obtaining the new population, differential mutation and crossover of differential evolution are used to obtain the evolved population. Similarly, a rounding strategy is also required when updating the population. The antibody concentration of the evolved population is calculated, the affinity between antibodies is calculated, and the N individuals with the highest affinity are taken as the new population, and the global optimal fitness value is recorded.

[0091] 7. Determine whether the maximum number of iterations has been reached. If the iteration is not completed, continue with steps 3 to 6. If the iteration is completed, output the optimal value and the most effective site selection and capacity determination plan.

[0092] In step 2, the specific form is:

[0093] Draw a CAD map of urban roads according to the actual road conditions in the planning area, and number each road node. Generate a b×b order initial distance matrix C according to the road node number b (0) , the elements in the matrix represents the distance from road node l to road node j. If there is a direct road between nodes l and j, then is the actual road length between two nodes; if nodes l to j cannot be directly connected, then For infinity

[0094]

[0095] Where l,j=1,2,…,b; is the actual road length between road nodes l and j.

[0096] For any two road nodes l and j, determine whether there is an intermediate node e such that the distance from node l to node j through node e is less than the existing distance. If such a node e exists, update the iterative distance matrix C (k)

[0097]

[0098] The calculation formula for the optimal social cost of charging stations is:

[0099] minF=F1+F2+F3+F4

[0100] Where: F represents the total cost of building and operating the charging station until the target year; F1 represents the fixed construction cost of the electric vehicle charging station in the target year converted to the planning period, including land cost, new charging pile cost, transformer cost and other construction costs; F2 represents the annual operation and maintenance cost of the electric vehicle charging station; F3 represents the annual network loss cost of the charging station; F4 represents the travel loss cost of the electric vehicle to the charging station.

[0101]

[0102] in:

[0103]

[0104] Where: S i represents the area of ​​the i-th charging station to be selected; f land,i represents the land unit price of the i-th candidate charging station area. In this paper, it is assumed that the planning area is divided into residential areas f land,1 , Commercial District land,2 and industrial areas land,3 ; N represents the number of charging piles in the charging station; f c Indicates the unit price of a single charging pile in the charging station; C i represents the infrastructure costs of charging stations and other costs; x i ={0,1}, that is, if a charging station is built at the candidate point I, the value is 1, and if a charging station is not built at the candidate point I, the value is the opposite; λ represents the annual operation and maintenance cost conversion factor, r0 is the depreciation rate; t k For the planning period.

[0105]

[0106] Where: F2 represents the cost of daily maintenance, equipment loss, employee maintenance and wages of the charging station. Using the conversion factor, the operation and maintenance costs are converted to the initial investment consideration. θ represents the above conversion factor, which has the same meaning as above.

[0107]

[0108] Where: L represents the unit electricity price; T d represents the average effective working time of the charging station per day; f L and f w They respectively represent the conversion coefficients of the charging pile line and its own losses converted to a single charging pile.

[0109]

[0110] Where: B represents the power consumption per kilometer of electric vehicle travel; d im represents the actual distance from the charging demand point m to the charging station of the electric vehicle; yim The decision variable y represents whether the electric vehicle is charged. im ∈{0,1}, when y im When it is 1, it means go, and when it is 0, it means go; n m I represents the number of electric vehicles that have charging demand at charging demand point m on a typical day; i represents the set of candidate stations, I = {i|i = 1, 2, ..., m}; M represents the set of charging demand points {m|m = 1, 2, 3 ..., j}.

[0111] Single arrival constraint for electric vehicle demand points:

[0112]

[0113] This constraint indicates that user a can only be served by one charging station I at a time to meet its charging needs, where Z ia ={0,1} is a binary variable, and takes the value 1 if user a accepts charging service at the selected point I.

[0114] Planning regional power constraints:

[0115]

[0116] This constraint indicates that the total power of the charging station should meet the charging demand within its service range. bc,i is the maximum power of the i-th charging station; P ps It is expressed as the total power of substations in the entire planning area.

[0117] The number of charging piles at a charging station is limited:

[0118]

[0119] This constraint condition indicates the limit on the number of charging piles in a charging station (each charging station has at least one charging pile). There are no charging piles at the candidate points that are not selected as charging stations. The total power of charging station I must meet the charging demand within its service range.

[0120] The number of charging piles at a charging station is limited:

[0121] n m Z ia P ia ≤T d N

[0122] Where P ia It represents the maximum probability of simultaneous charging of electric vehicles. The simultaneous charging rate in different planning areas is different. The simultaneous charging rate in residential areas is P 1a , the commercial area is P 2a , industrial area is P 3a .

[0123] Finally, the optimal total social cost of this embodiment is 7.559 million yuan. Eight charging stations are built in the planned area, with levels of 1, 1, 3, 1, 4, 2, 4, and 2 respectively. The planned area and service scope are as follows: Figure 4 As shown, the algorithm’s iterative solution results are as follows Figure 5 shown.

[0124] The present invention aims to solve the mismatch between the existing utilization rate of electric vehicles and the construction of charging infrastructure. It comprehensively considers the construction costs of land, transformers, etc., the operation and maintenance costs such as power supply loss of charging stations, employee wages, and the travel of electric vehicle users, and considers the actual path of the road network structure and the land use nature of the region in the planning, so that the planning results are more scientific and reasonable.

Claims

1. A method for site selection and capacity determination of electric vehicle charging stations, characterized in that: The following steps are involved: S1: Analyze and process the initial traffic flow of each road node, use the traffic correction coefficient to obtain the traffic flow information of each traffic node to obtain the charging demand, and generate the urban road CAD map according to the planning area division; S2: The distance from each node to the candidate charging station is calculated, and the maximum and minimum number of charging stations are estimated based on the maximum service capacity, minimum service capacity and charging demand of the charging station; in the set planning area, i.e., residential area, commercial area, industrial area, the number range of charging stations, service range and charging station level of the charging station are determined, and the charging station is randomly selected according to the site of the candidate station, and its level is randomly selected; S3: Taking the sum of construction cost, operation and maintenance cost, network loss cost and route loss cost as the total objective function, and taking electric vehicle charging demand, number and capacity of charging piles, and power in the planned area as constraints, a charging station site selection and capacity determination model is constructed; S4: Combining the traffic volume and charging demand of each traffic node, the improved whale optimization algorithm is used to solve the charging station location and capacity determination model to obtain the optimal location and capacity determination plan; The specific calculation process of the improved whale optimization algorithm is: T1: initialization parameters; T2: The site selection and capacity determination coding of electric vehicle charging stations includes two parts, corresponding to the selection of the charging station address and the selection of the charging station scale level. The coding form adopts the combination of the charging station number and the charging station level. The specific method is as follows: [x1 x2…x n m1 m2…m n ]n∈(n min ,n max ), where x1 x2…x n Number the charging stations, m1 m2…m n is the charging station level; x h is a non-repeating positive integer, indicating that the hth charging station is selected at the candidate location x h m h is a positive integer, indicating the scale level of the hth charging station; T3: Calculate the charging station selection corresponding to each individual in the population and the total cost and F1, F2, F3, F4 in this case. The distance problems in the model are solved by the Floyd algorithm. The spiral bubble net predation strategy with improved convergence factor is used to update the population, including three search methods: encirclement predation, bubble predation, and random predation, to obtain a new population. Since the charging station number and charging station level are both integers, a rounding strategy needs to be used when updating the population. T4: Use differential mutation and crossover of differential evolution to obtain the evolved population for the population obtained in T3. Similarly, a rounding strategy needs to be adopted when updating the population to ensure that the population code is always an integer. T5: Calculate the antibody concentration of the population after evolution, calculate the affinity between antibodies, take the N individuals with the highest affinity as the new population, and record the global optimal fitness value.

2. The method for site selection and capacity determination of an electric vehicle charging station according to claim 1, characterized in that: In step S1, the charging demand of the traffic node is obtained by multiplying the traffic flow of the traffic node by the flow correction coefficient, and a city road CAD map is generated according to the regional division and the coordinates of the candidate station site.

3. The method for site selection and capacity determination of an electric vehicle charging station according to claim 1, characterized in that: In step S3, the objective function is specifically: minF=F1+F2+F3+F4 Where: F represents the total cost of building and operating the charging station until the target year; F1 represents the fixed construction cost of the electric vehicle charging station in the target year converted to the planning period, including land cost, new charging pile cost, transformer cost and other construction costs; F2 represents the annual operation and maintenance cost of the electric vehicle charging station; F3 represents the annual network loss cost of the charging station; F4 represents the travel loss cost of the electric vehicle to the charging station; in: Where: S i represents the area of ​​the i-th charging station to be selected; f land,i represents the land unit price of the i-th candidate charging station area. In this paper, it is assumed that the planning area is divided into residential areas f land,1 、Business District land,2 and industrial areas land,3 ; N represents the number of charging piles in the charging station; f c Indicates the unit price of a single charging pile in the charging station; C i represents the infrastructure costs of charging stations and other costs; x i ={0,1}, that is, if a charging station is built at the candidate point I, the value is 1, and if a charging station is not built at the candidate point I, the value is the opposite; λ represents the annual operation and maintenance cost conversion factor, r0 is the depreciation rate; t k The planning period; Where: F2 represents the daily maintenance, equipment loss, employee maintenance and salary costs of the charging station; the operation and maintenance costs are converted to the initial investment using the conversion coefficient; θ represents the above conversion coefficient; Where: L represents the unit electricity price; T d represents the average effective working time of the charging station per day; f L and f w They respectively represent the conversion coefficients of the charging pile line and its own losses converted to a single charging pile; Where: B represents the power consumption per unit kilometer of electric vehicle travel; d im represents the actual distance from the charging demand point m to the charging station of the electric vehicle; y im The decision variable y represents whether the electric vehicle is charged. im ∈{0,1}, when y im When it is 1, it means go, and when it is 0, it means go; n m I represents the number of electric vehicles that have charging demand at charging demand point m on a typical day; i represents the set of candidate stations, I = {i|i = 1, 2, ..., m}; M represents the set of charging demand points {m|m = 1, 2, 3 ..., j}; The specific constraints are: Single arrival constraint for electric vehicle demand points: This constraint indicates that user a can only be served by one charging station I at a time to meet its charging needs, where Z ia ={0,1} is a binary variable. If user a accepts charging service at the selected point I, it is 1; Planning regional power constraints: This constraint indicates that the total power of the charging station should meet the charging demand within its service range; where: P bc,i is the maximum power of the i-th charging station; P ps It is expressed as the total power of substations in the whole planning area; The number of charging piles at a charging station is limited: This constraint condition indicates the limit on the number of charging piles in a charging station. The total power of charging station I must meet the charging demand within its service range. Each charging station has at least one charging pile, and the candidate points that are not selected as charging stations have no charging piles. The number of charging piles at a charging station is limited: n m Z ia P ia ≤T d N Where P ia It represents the maximum probability of simultaneous charging of electric vehicles. The simultaneous charging rate in different planning areas is different. The simultaneous charging rate in residential areas is P 1a , the commercial area is P 2a , industrial area is P 3a .

4. The method for site selection and capacity determination of an electric vehicle charging station according to claim 1, characterized in that: Step S4 specifically includes: S41: Based on the random selection of charging stations and their levels, the charging stations that meet both conditions are screened out through the Voronoi diagram idea based on Floyd and the constraint conditions; S42: Combining the objective function with the charging stations that meet the constraints and the corresponding charging station levels, the improved whale optimization algorithm is used to calculate the optimal value of the total social cost of the charging station; S43: According to the optimal value of the total social cost of the charging station, the corresponding candidate site location of the charging station and its corresponding charging station grade are output, so as to obtain the most effective site selection and capacity determination plan.

5. The method for site selection and capacity determination of an electric vehicle charging station according to claim 4, characterized in that: Step S41 specifically includes: The vehicles that need to be charged on the road are integrated into the road traffic nodes. The traffic nodes are regarded as charging demand points. The actual distance from the charging demand points within the service range of the charging station to the station should be less than or equal to the actual distance from the charging demand points to other charging stations. Then, according to the Floyd algorithm, the b×b order initial distance matrix C is generated according to the road node number b. (0) , the elements in the matrix represents the distance from road node l to road node j; if there is a direct road connecting nodes l to j, then is the actual road length between two nodes; if nodes l to j cannot be directly connected, then is infinite, as shown below: Where l,j=1,2,…,b; is the actual road length between road nodes l and j; for any two road nodes l and j, determine whether there is an intermediate node e such that the distance from node l to node j through node e is less than the existing distance. If such a node e exists, update the iterative distance matrix C (k) , as shown below: Follow the above steps to update the distance matrix C (k+1) , if C (k+1) ≠C (k) , then return to step 3; otherwise terminate and get the shortest path matrix, which is C (k) .

6. A method for site selection and capacity determination of an electric vehicle charging station according to claim 4, characterized in that: Step S42 specifically includes: S421. Combined with the total objective function, the improved whale optimization algorithm is used to calculate the total social costs corresponding to multiple charging stations that meet the constraints. S421. Select the minimum value from multiple total social costs as the optimal value.

Images