Electric vehicle charging station site selection planning method, device and terminal equipment

Abstract

The present invention provides a method, device and terminal equipment for site selection and planning of electric vehicle charging stations. The method comprises: for a target site selection area, taking the lowest operating cost of the charging station as the optimization target, taking the site selection coordinates of the charging station and the number of charging piles in the charging station with the site selection coordinates determined as optimization variables to construct an objective function; for the target site selection area, constructing constraints corresponding to the objective function based on the spatiotemporal distribution of user charging demand, investment cost, number of charging stations, total capacity of charging stations, number of charging piles configured at charging stations, charging node voltage, and charging line flow; optimizing and solving the objective function based on the constraints to obtain the site selection coordinates of the charging station in the target site selection area and the number of charging piles in the charging station with the site selection coordinates determined. The method, device and terminal equipment for site selection and planning of electric vehicle charging stations provided by the present invention can realize the reasonable site selection of electric vehicle charging stations, thereby reducing the waste of public resources.

Description

Technical Field

[0001] The present invention belongs to the technical field of charging station planning, and more specifically, relates to a method, device and terminal equipment for site selection planning of an electric vehicle charging station. Background Art

[0002] With the rapid and healthy development of the economy, the ownership rate of private cars continues to rise. At the same time, the environmental pollution and excessive energy consumption problems are becoming increasingly prominent. As a new type of energy vehicle, electric vehicles have quickly entered the public eye with their low emissions and low noise characteristics, and have attracted more and more attention. They have become an environmentally friendly choice to replace traditional fuel vehicles and reduce pollution. Therefore, electric vehicles have become a research hotspot. However, in order to vigorously promote and popularize electric vehicles, it is necessary to configure a complete and convenient basic charging facility network for electric vehicles. In order to promote the development of the electric vehicle market, meet the charging needs of electric vehicle users, improve the comprehensive social benefits, and solve the development problems such as backward charging infrastructure construction, imperfect charging station network, and unreasonable charging station site selection, it is particularly important to reasonably select the site for electric vehicle charging stations. Summary of the invention

[0003] The purpose of the present invention is to provide a method, device and terminal equipment for site selection planning of electric vehicle charging stations, so as to reasonably select the site of electric vehicle charging stations and avoid the waste of public resources.

[0004] A first aspect of an embodiment of the present invention provides a method for site selection and planning of an electric vehicle charging station, comprising:

[0005] For the target site selection area, the objective function is constructed with the lowest operating cost of the charging station as the optimization goal, the site selection coordinates of the charging station, and the number of charging piles in the charging station with the determined site selection coordinates as optimization variables;

[0006] For the target site selection area, the constraints corresponding to the objective function are constructed based on the spatiotemporal distribution of user charging demand, investment cost, number of charging stations, total capacity of charging stations, number of charging piles configured at charging stations, charging node voltage, and charging line flow;

[0007] The objective function is optimized and solved based on the constraint conditions to obtain the charging station site selection coordinates within the target site selection area and the number of charging piles in the charging station with the determined site selection coordinates.

[0008] In a possible implementation, the objective function is:

[0009]

[0010] Among them, F cost The operating cost of the charging station in the target site area, is the average annual cost of building a charging station numbered i, is the average annual cost of operating a charging station i, C 3 The time-consuming cost of users traveling to charging stations.

[0011] In one possible implementation, The calculation method is:

[0012]

[0013] in, is the total area occupied by the charging station numbered i. It represents the land price of the charging station with construction number i, and the construction scale S of the charging station with construction number i. i ∈{0,1,2,3,4}, each S i Corresponding to different preset construction areas, is the actual capacity of the charging station numbered i, C C is the actual construction cost per unit capacity, m is the operating time of the charging station with construction number i, in years, and r 0 For return on investment.

[0014] In one possible implementation, The calculation method is:

[0015]

[0016] Among them, V i th is the total power required by the vehicle that arrives at charging station i for charging within the tth hour of the day with date type h, is the price of electricity purchased from the grid by charging station i at hour t, is the electricity price sold by the grid to electric vehicle users at hour t, d h Indicates the number of days in the whole year whose date type is h. is the salary cost of the staff in charging station i, is the maintenance cost of charging station i;

[0017] in, W E The required power of the vehicle arriving at charging station i for charging, R E Y is the maximum mileage that the user's electric vehicle can continuously travel; ni is the shortest distance from the charging demand point numbered n to the charging station numbered i, Q n The remaining power of the user's electric vehicle corresponding to the charging demand point n;

[0018] in, is the set of charging demand points that can reach the charging station numbered i within the tth hour;

[0019] in, is the set of demand points that reach charging station i within the tth hour and demand points that reach charging station i' within the tth hour, where the actual distances of the demand points that reach charging station i and the actual distances of the demand points that reach charging station i' are both less than the preset threshold; G th It is the collection of all demand points in the target site selection area.

[0020] In one possible implementation, C 3 The calculation method is:

[0021]

[0022] Among them, c ji represents the unit time cost of the user going to charging station i from demand point j, q j is the charging demand quantity of demand point j; y i Refers to the actual number of charging piles of charging station i established at the candidate location, which is also the optimization variable; d ji Refers to the actual distance from demand point j to charging station i; Y ij ={0,1} is the actual decision variable. If demand point j goes to charging station i to charge, the value is 1, otherwise the value is 0. V refers to the actual speed of the user's electric vehicle.

[0023] In a possible implementation, the constraint conditions corresponding to the objective function include:

[0024] Constraints on the spatiotemporal distribution of user charging demand:

[0025]

[0026] Among them, X i (S i ) represents the number of charging piles in the station; γ is the preset unsatisfied rate of electric vehicle charging demand, where the unsatisfied rate of electric vehicle charging demand refers to the proportion of electric vehicles that have arrived at the charging station but have no charging piles for charging; η is the preset unreachable rate of charging demand, where the unreachable rate of charging demand refers to the proportion of electric vehicles that need to be charged but have insufficient remaining power to drive the electric vehicle to the nearest charging station; [·] represents rounding;

[0027] Investment cost constraints:

[0028]

[0029] in, is the total investment of charging station i, C maxFor maximum budget;

[0030] Constraints on the number of charging stations

[0031]

[0032] Where M is the number of charging stations that need to be established in the target site selection area; N sat is the number of addresses established for charging stations in the target site selection area; p is the index of the candidate location of the charging station (p = 1, 2, 3, ..., N sta ); x p It is a binary variable, 1 means that a charging station is built at the candidate location, and 0 means that a charging station is not built at the candidate location;

[0033] Total capacity constraints of charging stations:

[0034]

[0035] Among them, C total The actual total capacity of charging stations in the target site selection area; C pile The actual capacity of each charging station;

[0036] Constraints on the number of charging piles configured at a charging station:

[0037] 0≤y i ≤y max ,i=1,2,…,N sat

[0038] Among them, y max The ratio of charging piles in the preset charging station;

[0039] Charging node voltage constraints:

[0040]

[0041] Where k is the actual index of the charging node; Ω bus is the set of all charging nodes; U k is the charging voltage of the charging node; U N is the rated voltage of the entire charging system in the target site selection area; α% refers to the proportion of the allowed deviation of the charging voltage; β 1 P is the confidence level that the preset charging voltage exceeds the limit; r {} represents the probability of occurrence of {} event;

[0042] Charging line power flow constraints:

[0043] P r {I l >I l,max}≤β 2 ,l∈Ωbr

[0044] Among them, I l is the actual load current of the entire charging line in the target site selection area; Ω br is the set of all charging nodes; I l,max is the maximum current passing through the charging circuit l; β 2 Refers to the confidence level that the preset charging line flow exceeds the maximum value.

[0045] In a possible implementation, optimizing and solving the objective function based on the constraint condition to obtain the charging station site selection coordinates within the target site selection area and the number of charging piles in the charging station with the determined site selection coordinates includes:

[0046] The objective function is optimized and solved based on the constraint conditions and the genetic algorithm to obtain the charging station site selection coordinates within the target site selection area and the number of charging piles in the charging station with the determined site selection coordinates.

[0047] A second aspect of an embodiment of the present invention provides a device for site selection and planning of an electric vehicle charging station, comprising:

[0048] An objective function construction module is used to construct an objective function for a target site selection area, taking the lowest charging station operation cost as the optimization target and the charging station site selection coordinates as the optimization variables;

[0049] A constraint condition construction module is used to construct the constraint conditions corresponding to the objective function for the target site selection area based on the spatiotemporal distribution of user charging demand, investment cost, number of charging stations, total capacity of charging stations, number of charging piles configured at charging stations, charging node voltage, and charging line flow;

[0050] The site selection module is used to optimize and solve the objective function based on the constraint conditions to obtain the site selection coordinates of the charging station within the target site selection area and the number of charging piles in the charging station with the determined site selection coordinates.

[0051] According to a third aspect of an embodiment of the present invention, a terminal device is provided, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above-mentioned electric vehicle charging station site selection planning method when executing the computer program.

[0052] According to a fourth aspect of an embodiment of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the above-mentioned electric vehicle charging station site selection and planning method are implemented.

[0053] The beneficial effects of the electric vehicle charging station site selection planning method, device and terminal equipment provided by the embodiments of the present invention are:

[0054] The present invention constructs an objective function for the target site selection area, with the lowest operating cost of the charging station as the optimization target and the charging station site selection coordinates as the optimization variables. The constraint conditions corresponding to the objective function are constructed based on the spatiotemporal distribution of user charging demand, investment cost, number of charging stations, total capacity of charging stations, number of charging piles configured at charging stations, charging node voltage, and charging line flow. Finally, the objective function is optimized and solved based on the constraint conditions. That is, the present invention realizes the site selection optimization of the charging station by establishing the objective function, thereby reducing the operating cost of the charging station as much as possible and reducing the waste of public resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0056] Figure 1 A schematic diagram of a process flow of a method for site selection and planning of an electric vehicle charging station provided by an embodiment of the present invention;

[0057] Figure 2 A structural block diagram of a device for site selection and planning of an electric vehicle charging station provided by an embodiment of the present invention;

[0058] Figure 3 A schematic block diagram of a terminal device provided by an embodiment of the present invention;

[0059] Figure 4 A charging station site selection planning diagram of solution 1 provided in one embodiment of the present invention;

[0060] Figure 5 A charging station site selection planning diagram for solution 2 provided in one embodiment of the present invention;

[0061] Figure 6 A charging station site selection planning diagram of solution 3 provided in one embodiment of the present invention;

[0062] Figure 7 A schematic diagram of a road traveled by an electric vehicle provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0063] In the following description, specific details such as specific system structures, technologies, etc. are provided for the purpose of illustration rather than limitation, so as to provide a thorough understanding of the embodiments of the present invention. However, it should be clear to those skilled in the art that the present invention may be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to prevent unnecessary details from obstructing the description of the present invention.

[0064] In order to make the purpose, technical solutions and advantages of the present invention more clear, specific embodiments will be described below in conjunction with the accompanying drawings.

[0065] Please refer to Figure 1 , Figure 1 A schematic diagram of a process flow of a method for site selection and planning of an electric vehicle charging station provided by an embodiment of the present invention, the method comprising:

[0066] S101: For the target site selection area, an objective function is constructed with the lowest charging station operation cost as the optimization goal, the site selection coordinates of the charging station, and the number of charging piles in the charging station with the determined site selection coordinates as optimization variables.

[0067] S102: For the target site selection area, the constraints corresponding to the objective function are constructed based on the spatiotemporal distribution of user charging demand, investment cost, number of charging stations, total capacity of charging stations, number of charging piles configured at charging stations, charging node voltage, and charging line flow.

[0068] S103: Optimizing and solving the objective function based on the constraint conditions to obtain the location coordinates of the charging station within the target location area and the number of charging piles in the charging station with the determined location coordinates.

[0069] In this embodiment, the present invention constructs an objective function for the target site selection area, with the lowest charging station operating cost as the optimization goal and the charging station site selection coordinates as the optimization variables. The constraint conditions corresponding to the objective function are constructed based on the spatiotemporal distribution of user charging demand, investment cost, number of charging stations, total capacity of charging stations, number of charging piles configured at charging stations, charging node voltage, and charging line flow. Finally, the objective function is optimized and solved based on the constraint conditions. That is, the present invention realizes the site selection optimization of charging stations by establishing an objective function, thereby reducing the operating cost of charging stations as much as possible and reducing the waste of public resources.

[0070] In one possible implementation, the objective function is:

[0071]

[0072] Among them, F cost The operating cost of the charging station in the target site area, is the average annual cost of building a charging station numbered i, is the average annual cost of operating a charging station i, C 3 The time-consuming cost of users traveling to charging stations.

[0073] In one possible implementation, The calculation method is:

[0074]

[0075] in, is the total area occupied by the charging station numbered i. It represents the land price of the charging station with construction number i, and the construction scale S of the charging station with construction number i. i ∈{0,1,2,3,4}, each S i Corresponding to different preset construction areas, is the actual capacity of the charging station numbered i, C C is the actual construction cost per unit capacity, m is the operating time of the charging station with construction number i, in years, and r 0 For return on investment.

[0076] In this embodiment, m can be set to 20, r 0 The possible value is 0.12.

[0077] In this embodiment, the website construction scale correspondence table may be shown in Table 1 below.

[0078] Table 1 Site construction scale

[0079] <![CDATA[S i ]]> 0 1 2 3 4 Number of charging piles 0 8 15 30 45 Total capacity (kW) 0 50-150 150-250 200-500 500-750

[0080] In one possible implementation, The calculation method is:

[0081]

[0082] Among them, V i th is the total power required by the vehicle that arrives at charging station i for charging within the tth hour of the day with date type h, is the price of electricity purchased from the grid by charging station i at hour t, is the electricity price sold by the grid to electric vehicle users at hour t, d h Indicates the number of days in the whole year whose date type is h. is the salary cost of the staff in charging station i, is the maintenance cost of charging station i.

[0083] in, W EThe required power of the vehicle arriving at charging station i for charging, R E The maximum mileage that the user's electric vehicle can continuously travel. ni is the shortest distance from the charging demand point numbered n to the charging station numbered i, Q n is the remaining power of the user's electric vehicle corresponding to the charging demand point n.

[0084] in, is the set of charging demand points that can reach the charging station numbered i within the tth hour.

[0085] in, is the set of demand points that reach charging station i within the tth hour and demand points that reach charging station i' within the tth hour, where the actual distances of the demand points that reach charging station i and the actual distances of the demand points that reach charging station i' are both less than the preset threshold. th It is the collection of all demand points in the target site selection area.

[0086] In one possible implementation, C 3 The calculation method is:

[0087]

[0088] Among them, c ji represents the unit time cost of the user going to charging station i from demand point j, q j is the charging demand quantity of demand point j. i Refers to the actual number of charging piles of charging station i established at the candidate location, which is also the optimization variable. ji Refers to the actual distance from demand point j to charging station i. ij ={0,1} is the actual decision variable. If demand point j goes to charging station i to charge, the value is 1, otherwise the value is 0. V refers to the actual speed of the user's electric vehicle.

[0089] In a possible implementation, the constraints corresponding to the objective function include:

[0090] Constraints on the spatiotemporal distribution of user charging demand:

[0091]

[0092] Among them, X i (S i) represents the number of charging piles in the station. γ is the preset unsatisfied rate of electric vehicle charging demand, where the unsatisfied rate of electric vehicle charging demand refers to the proportion of electric vehicles that have arrived at the charging station but have no charging piles for charging. η is the preset unreachable rate of charging demand, where the unreachable rate of charging demand refers to the proportion of electric vehicles that need to be charged but have insufficient remaining power to drive the electric vehicle to the nearest charging station. [·] indicates rounding.

[0093] Investment cost constraints:

[0094]

[0095] in, is the total investment of charging station i, C max For maximum budget.

[0096] Constraints on the number of charging stations

[0097]

[0098] Where M is the number of charging stations that need to be established in the target site selection area. sat The number of addresses established for charging stations in the target site selection area. p is the index of the candidate location of the charging station (p = 1, 2, 3, ..., N sta ). p It is a binary variable. 1 indicates that a charging station is built at the candidate location, and 0 indicates that a charging station is not built at the candidate location.

[0099] Total capacity constraints of charging stations:

[0100]

[0101] Among them, C total The actual total capacity of charging stations in the target site selection area. pile The actual capacity of each charging pile.

[0102] Constraints on the number of charging piles configured at a charging station:

[0103] 0≤y i ≤y max ,i=1,2,…,N sat

[0104] Among them, y max It is the ratio of charging piles in the preset charging station.

[0105] Charging node voltage constraints:

[0106]

[0107] Where k is the actual index of the charging node. bus is the set of all charging nodes. k is the charging voltage of the charging node. N is the rated voltage of the entire charging system in the target site selection area. α% refers to the proportion of the allowed deviation of the charging voltage. β 1 The confidence level that the preset charging voltage exceeds the limit. r {} represents the probability of {} event occurring.

[0108] Charging line power flow constraints:

[0109] P r {I l >I l,max}≤β 2 ,l∈Ω br

[0110] Among them, I l is the actual load current of the entire charging line in the target site selection area. Ω br is the set of all charging nodes. l,max is the maximum current passing through the charging circuit l. 2 Refers to the confidence level that the preset charging line flow exceeds the maximum value.

[0111] In a possible implementation, the objective function is optimized and solved based on the constraint conditions to obtain the location coordinates of the charging station in the target location area and the number of charging piles in the charging station with the determined location coordinates, including:

[0112] Based on the constraints and genetic algorithm, the objective function is optimized and solved to obtain the location coordinates of the charging station in the target site selection area and the number of charging piles in the charging station with the determined location coordinates.

[0113] In this embodiment, a genetic algorithm model of mixed integer nonlinear programming can be used to optimize the algorithm. When the genetic algorithm is solved, the chromosome gene corresponds to the actual construction situation of a charging station, that is, S i ; Individuals correspond to candidate solutions for charging station construction, and f refers to n S i There is an orderly arrangement, that is, it represents the actual construction status of n different charging stations, and the population corresponds to a certain number of different individuals. The optimization process is to adopt a construction plan with a charging station operating cost less than a preset threshold, and then inherit excellent genes, and finally achieve the optimization solution of the objective function through many times of survival of the fittest.

[0114] Among them, the optimization solution based on genetic algorithm can include the following steps:

[0115] 1) For variable S iBinary coding is performed, and candidate locations are obtained according to η≤0.05, and then the scale S of charging piles that can be built is selected according to η≤0.02. i Perform calculations and preset 50 different "individuals" in this area to obtain the initial population.

[0116] 2) Calculate the fitness of all "individuals" based on the fitness function, that is:

[0117]

[0118] Among them, F f is the operating cost under different orders f, C max for The maximum value in .

[0119] 3) Set β = 1 × 10 4 By setting a larger penalty factor, individuals are killed (even if the individuals that cannot meet the actual constraint requirements have a particularly small fitness) to eliminate individuals that do not meet the requirements.

[0120] 4) When performing genetic operations, they compete with each other to obtain the best individuals, use a probability of 0.8 for crossover, and a probability of 0.1 for mutation, so that the size of the entire population is maintained. Then return to step 2) until the number of iterations is greater than the preset number of iterations.

[0121] In this embodiment, a specific simulation process is used to illustrate the effectiveness of the embodiment of the present invention.

[0122] First, in order to verify the effectiveness of the model and algorithm, this embodiment selects a target site selection area and designs a test instance with 30 demand points and 15 candidate sites in a plane area [0,50]×[0,50]. The locations of the candidate points and demand points in the area and the number of charging piles are shown in Table 2.

[0123] Table 2 Location coordinates and demand quantities of demand points and candidate points

[0124]

[0125]

[0126] On this basis, in order to obtain the electric vehicle charging station site selection plan with the lowest operating cost, the cost analysis of the three plans of setting up 5, 6 and 7 electric vehicle charging station site selection points is carried out respectively. The results are as follows: The results of plan 1 setting up 5 electric vehicle charging station site selection points are shown in Tables 3 and Figure 4 shown.

[0127] Table 3 Site selection points and corresponding demand points for Scheme 1

[0128] Site selection Allocated demand points 1 2、17、18、19、29 2 7、13、15、16、22、26、27 3 1、5、8、11、12、24、28 7 10、25 12 3、4、6、9、14、20、21、23、30

[0129] The results of setting 6 electric vehicle charging station site selection points in Scheme 2 are shown in Tables 4 and Figure 5 shown.

[0130] Table 4 Site selection points and corresponding demand points for Scheme 2

[0131] Site selection Allocated demand points 4 1、5、8、11、12、13、24、28 7 10、25、29 10 3、4、6、9、14、20、23 11 7、15、16、17、19、22、26、27 12 21、30 15 2、18

[0132] The results of setting 7 electric vehicle charging station site selection points in Scheme 3 are shown in Tables 5 and Figure 6 shown.

[0133] Table 5 Site selection points and corresponding demand points for Scheme 3

[0134] Site selection Allocated demand points 1 2、17、18、19、29 2 16、26、27 4 1、11、12、28 6 7、8、13、15、21、22 8 5、9、20、24 9 10、25、30 14 3、4、6、14、23

[0135] After calculation, the costs of the three solutions are shown in Table 6 below.

[0136] Table 6 Cost comparison of three solutions

[0137]

[0138] From the calculation results in the above table, it can be seen that the operating cost of Plan 2 is arable land, so 6 electric vehicle charging stations should be set up in the area to meet the electric vehicle electricity demand of users in the area.

[0139] Based on the above, it can be seen that the embodiments of the present invention have the following advantages compared with the prior art:

[0140] 1) Charging load has random characteristics. The present invention models the charging network planning problem as an uncertainty optimization problem based on its random characteristics.

[0141] 2) The present invention takes into account the requirements and technical constraints of both the transportation system and the power distribution system, and provides a reasonable charging network planning scheme.

[0142] That is, the present invention quantitatively analyzes the charging network service capacity, taking into account the charging needs of vehicles at traffic nodes as well as during driving. In addition, the present invention summarizes the site selection constraints of electric vehicle charging stations to form a constrained charging station site selection problem. Finally, the present invention uses a genetic algorithm to solve the optimization model. Simulation and experimental results show that the solution provided by the present invention can be applied to electric vehicle charging station planning occasions and has high universality.

[0143] When implementing the solution of the present invention, it is necessary to consider the quantification of the charging service capacity of the charging network. For example, it may be necessary to determine the decision variable Y ij={0,1}, that is, to judge whether demand point j goes to charging station i for charging, the value is 1, otherwise the value is 0. It may be necessary to calculate the unsatisfied rate and unreachable rate of electric vehicle charging demand. The following methods can be used:

[0144] The embodiment of the present invention uses a traffic demand model to calculate the charging service of the actual network, specifically:

[0145]

[0146] Among them, F catch It represents the actual flow of the electric vehicle charging network in the traffic system, which can well reflect the actual charging capacity of the network. If electric vehicles always travel along the shortest route, then P represents the set of the shortest routes in the entire network, p represents the index of the road traveled, and y p It indicates whether the actual flow of electric vehicles on the road can be intercepted (that is, corresponding to Y ij ={0,1}), if y p =1, it means it can be intercepted. If y p =0, which means it cannot be intercepted, f p is the actual traffic volume on the road, f p It can be calculated by the following formula:

[0147]

[0148] Among them, K O represents the weight of the starting point O of the road being traveled, K Z is the weight of the end point of the road traveled, l p is the length of time on the road traveled.

[0149] Among them, whether the actual traffic flow of the traveled road can be intercepted is very important for calculating the service capacity of the entire charging network. To implement the above solution, the embodiment of the present invention sets the following provisions: for the traveled road, after arriving at the charging station to charge the electric vehicle, if it can travel from the starting point to the final destination, and then travel from the destination to the starting point, it means that the traffic flow of the traveled road can be intercepted, that is, y p =1, otherwise y p = 0. If there is a charging station at the starting point, the car can be fully charged before driving, and the electric vehicle range L is initialized at this time. range For L max (The maximum range of an electric vehicle after charging). When an electric vehicle starts to travel from a starting point, if there is no charging station at the starting point, the range of the electric vehicle is initialized to 0.5 times the maximum range, that is, L range =Lmax / 2.

[0150] From the above description, it can be seen that whether the actual traffic flow on the road can be intercepted is closely related to the actual range L of the car when it is fully charged. max , the actual length of each road section, and whether there is a charging station at each road node.

[0151] The following examples illustrate the above provisions. Please refer to Figure 7 , Figure 7 It represents a road in the traffic network. There are four different nodes, including the starting point O, points X and Y, and the end point Z. Point X and point Y divide the entire road into three different segments, namely road OX, XY and YZ. In this example, L OX , L XY With L YZ to indicate their distance.

[0152] pass Figure 7 The first step is to initialize the range L of the electric vehicle when it starts from the starting point based on whether there is a charging pile at the starting point. range ; Then if the electric car starts from the starting point, passes through X, Y, Z and then returns to the starting point. Each time it passes a different node, the actual cruising range L of the car can be described by the following formula range , that is:

[0153] L range =L range -L travle

[0154] Among them, L travle It represents the distance traveled by the electric vehicle from the previous node to the current node. For example, when the electric vehicle travels from node X to node Y, L travle =L XY . The actual cruising range of the electric vehicle can be continuously updated at this time. If the actual cruising range updated in real time is less than zero, it means that the electric vehicle cannot drive normally and there is no way to reach the current node (corresponding to the calculation of the electric vehicle inaccessibility rate). There is also no way to return to the starting point and the actual traffic flow on this road cannot be intercepted. If the actual cruising range updated in real time is greater than zero, it means that the electric vehicle can reach the current node and can drive directly to the next node. If there is a charging station, the electric vehicle will be fully charged. At this time, the actual cruising range L is updated according to the following formula range :

[0155] L range =L max

[0156] If the electric car can finally return to the starting point, it means that the actual traffic flow on the road it is traveling on can be intercepted, otherwise it cannot be intercepted.

[0157] In this embodiment, the following factors may also be considered when performing simulation calculations: charging power, charging start time, charging end time, charging time, charging initial charge, etc.

[0158] 1) Charging power The existing charging pile system can provide two charging modes, fast charging and slow charging, for selection, but the output power of the two charging types is different. It is set that users can choose fast charging or slow charging modules to achieve constant output power charging for electric vehicles. The charging output power of a single electric vehicle follows the Bernoulli distribution, which is specifically expressed as follows:

[0159]

[0160] Among them, P chq is the actual charging power in fast charging mode, P chs is the actual charging power in slow charging mode, and p is the probability of the user using fast charging. The probability p is related to the initial remaining power SOC and the specific time of starting charging. Starting charging later will result in a smaller initial remaining power SOC, and the user is more likely to use fast charging mode for charging.

[0161] 2) Initial charge SOC

[0162] Initial charge SOC (also known as E 0 ) is related to the mileage d of the electric vehicle and can be estimated using the following method:

[0163]

[0164] Among them, E 0 The actual probability distribution characteristics of the random variable d can be described in detail using the lognormal distribution, that is:

[0165]

[0166] Among them, μ d With σ d are the mean and standard deviation of the random variable d after only using the logarithm, and the two values ​​can be 3.02 and 0.14 respectively. Among them, f d (x) is the probability distribution function with respect to d.

[0167] 3) Charging start time

[0168] The electric vehicle is charged after the last trip, so the end time of the last trip is the time when charging starts. SC . T SC The actual probability distribution is fitted using the probability distribution characteristics given below:

[0169]

[0170] in, About T SC The probability distribution function, μ sc can be 17.6, σ sc It can be 3.4.

[0171] 4) Charging duration.

[0172] Charging duration T c The initial charge E 0 , electric vehicle battery capacity E 0 With charging power P ch Jointly decide that:

[0173]

[0174] Among them, η' is the charging efficiency of the electric vehicle.

[0175] Corresponding to the electric vehicle charging station site selection and planning method in the above embodiment, Figure 2 This is a structural block diagram of an electric vehicle charging station site selection and planning device provided by an embodiment of the present invention. For ease of description, only the parts related to the embodiment of the present invention are shown. Figure 2 The electric vehicle charging station site selection and planning device 20 includes: an objective function construction module 21, a constraint condition construction module 22, and a site selection solution module 23.

[0176] The objective function construction module 21 is used to construct an objective function for the target site selection area, taking the lowest charging station operation cost as the optimization goal and taking the charging station site selection coordinates as the optimization variables.

[0177] The constraint condition construction module 22 is used to construct the constraint conditions corresponding to the objective function for the target site selection area based on the spatiotemporal distribution of user charging demand, investment cost, number of charging stations, total capacity of charging stations, number of charging piles configured at charging stations, charging node voltage, and charging line flow.

[0178] The site selection module 23 is used to optimize and solve the objective function based on the constraint conditions to obtain the site selection coordinates of the charging station in the target site selection area and the number of charging piles in the charging station with the determined site selection coordinates.

[0179] In one possible implementation, the objective function is:

[0180]

[0181] Among them, F cost The operating cost of the charging station in the target site area, is the average annual cost of building a charging station numbered i, is the average annual cost of operating a charging station i, C 3 The time-consuming cost of users traveling to charging stations.

[0182] In one possible implementation, The calculation method is:

[0183]

[0184] in, is the total area occupied by the charging station numbered i. It represents the land price of the charging station with construction number i, and the construction scale S of the charging station with construction number i. i ∈{0,1,2,3,4}, each S i Corresponding to different preset construction areas, is the actual capacity of the charging station numbered i, C C is the actual construction cost per unit capacity, m is the operating time of the charging station with construction number i, in years, and r 0 For return on investment.

[0185] In one possible implementation, The calculation method is:

[0186]

[0187] Among them, V i th is the total power required by the vehicle that arrives at charging station i for charging within the tth hour of the day with date type h, is the price of electricity purchased from the grid by charging station i at hour t, is the electricity price sold by the grid to electric vehicle users at hour t, d h Indicates the number of days in the whole year whose date type is h. is the salary cost of the staff in charging station i, is the maintenance cost of charging station i.

[0188] in, W E The required power of the vehicle arriving at charging station i for charging, R E The maximum mileage that the user's electric vehicle can continuously travel. niis the shortest distance from the charging demand point numbered n to the charging station numbered i, Q n is the remaining power of the user's electric vehicle corresponding to the charging demand point n.

[0189] in, is the set of charging demand points that can reach the charging station numbered i within the tth hour.

[0190] in, is the set of demand points that reach charging station i within the tth hour and demand points that reach charging station i' within the tth hour, where the actual distances of the demand points that reach charging station i and the actual distances of the demand points that reach charging station i' are both less than the preset threshold. th It is the collection of all demand points in the target site selection area.

[0191] In one possible implementation, C 3 The calculation method is:

[0192]

[0193] Among them, c ji represents the unit time cost of the user going to charging station i from demand point j, q j is the charging demand quantity of demand point j. i Refers to the actual number of charging piles of charging station i established at the candidate location, which is also the optimization variable. ji Refers to the actual distance from demand point j to charging station i. ij ={0,1} is the actual decision variable. If demand point j goes to charging station i to charge, the value is 1, otherwise the value is 0. V refers to the actual speed of the user's electric vehicle.

[0194] In a possible implementation, the constraints corresponding to the objective function include:

[0195] Constraints on the spatiotemporal distribution of user charging demand:

[0196]

[0197] Among them, X i (S i ) represents the number of charging piles in the station. γ is the preset unsatisfied rate of electric vehicle charging demand, where the unsatisfied rate of electric vehicle charging demand refers to the proportion of electric vehicles that have arrived at the charging station but have no charging piles for charging. η is the preset unreachable rate of charging demand, where the unreachable rate of charging demand refers to the proportion of electric vehicles that need to be charged but have insufficient remaining power to drive the electric vehicle to the nearest charging station. [·] indicates rounding.

[0198] Investment cost constraints:

[0199]

[0200] in, is the total investment of charging station i, C max For maximum budget.

[0201] Constraints on the number of charging stations

[0202]

[0203] Where M is the number of charging stations that need to be established in the target site selection area. sat The number of addresses established for charging stations in the target site selection area. p is the index of the candidate location of the charging station (p = 1, 2, 3, ..., N sta ). p It is a binary variable. 1 indicates that a charging station is built at the candidate location, and 0 indicates that a charging station is not built at the candidate location.

[0204] Total capacity constraints of charging stations:

[0205]

[0206] Among them, C total The actual total capacity of charging stations in the target site selection area. pile The actual capacity of each charging pile.

[0207] Constraints on the number of charging piles configured at a charging station:

[0208] 0≤y i ≤y max ,i=1,2,…,N sat

[0209] Among them, y max It is the ratio of charging piles in the preset charging station.

[0210] Charging node voltage constraints:

[0211]

[0212] Where k is the actual index of the charging node. bus is the set of all charging nodes. k is the charging voltage of the charging node. N is the rated voltage of the entire charging system in the target site selection area. α% refers to the proportion of the allowed deviation of the charging voltage. β 1 The confidence level that the preset charging voltage exceeds the limit. r {} represents the probability of {} event occurring.

[0213] Charging line power flow constraints:

[0214] P r {I l >I l,max}≤β 2 ,l∈Ω br

[0215] Among them, I l is the actual load current of the entire charging line in the target site selection area. Ω br is the set of all charging nodes. l,max is the maximum current passing through the charging circuit l. 2 Refers to the confidence level that the preset charging line flow exceeds the maximum value.

[0216] In a possible implementation, the site selection module 23 is specifically used to:

[0217] Based on the constraints and genetic algorithm, the objective function is optimized and solved to obtain the location coordinates of the charging station in the target site selection area and the number of charging piles in the charging station with the determined location coordinates.

[0218] See also Figure 3 , Figure 3 A schematic block diagram of a terminal device provided by an embodiment of the present invention. Figure 3 The terminal 300 in the embodiment shown may include: one or more processors 301, one or more input devices 302, one or more output devices 303 and one or more memories 304. The processors 301, input devices 302, output devices 303 and memories 304 communicate with each other via a communication bus 305. The memory 304 is used to store computer programs, which include program instructions. The processor 301 is used to execute the program instructions stored in the memory 304. The processor 301 is configured to call the program instructions to perform the following operations to perform the functions of the modules / units in the above-mentioned device embodiments, such as Figure 2 The functions of modules 21 to 23 are shown.

[0219] It should be understood that in the embodiment of the present invention, the processor 301 may be a central processing unit (CPU), and the processor may also be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0220] The input device 302 may include a touch panel, a fingerprint sensor (for collecting the user's fingerprint information and fingerprint direction information), a microphone, etc., and the output device 303 may include a display (LCD, etc.), a speaker, etc.

[0221] The memory 304 may include a read-only memory and a random access memory, and provide instructions and data to the processor 301. A portion of the memory 304 may also include a non-volatile random access memory. For example, the memory 304 may also store information about the device type.

[0222] In a specific implementation, the processor 301, input device 302, and output device 303 described in the embodiment of the present invention can execute the implementation methods described in the first and second embodiments of the electric vehicle charging station site selection and planning method provided in the embodiment of the present invention, and can also execute the implementation methods of the terminal described in the embodiment of the present invention, which will not be repeated here.

[0223] In another embodiment of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores a computer program, wherein the computer program includes program instructions, wherein the program instructions are executed by a processor to implement all or part of the processes in the above-mentioned embodiment method, and may also be completed by instructing the relevant hardware through the computer program, wherein the computer program may be stored in a computer-readable storage medium, and wherein the computer program may be executed by the processor to implement the steps of the above-mentioned various method embodiments. Among them, the computer program includes computer program code, and the computer program code may be in source code form, object code form, executable file or some intermediate form, etc. The computer-readable medium may include: any entity or device capable of carrying computer program code, recording medium, U disk, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electric carrier signal, telecommunication signal and software distribution medium, etc. It should be noted that the contents contained in the computer-readable medium may be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, the computer-readable medium does not include electric carrier signal and telecommunication signal.

[0224] The computer-readable storage medium may be an internal storage unit of the terminal of any of the foregoing embodiments, such as a hard disk or memory of the terminal. The computer-readable storage medium may also be an external storage device of the terminal, such as a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), etc. equipped on the terminal. Further, the computer-readable storage medium may also include both an internal storage unit of the terminal and an external storage device. The computer-readable storage medium is used to store computer programs and other programs and data required by the terminal. The computer-readable storage medium may also be used to temporarily store data that has been output or is to be output.

[0225] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described in terms of function in the above description. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0226] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the terminals and units described above can refer to the corresponding processes in the aforementioned method embodiments, and will not be repeated here.

[0227] In the several embodiments provided in the present application, it should be understood that the disclosed terminals and methods can be implemented in other ways. For example, the device embodiments described above are only schematic, for example, the division of units is only a logical function division, and there may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces or units, or it can be an electrical, mechanical or other form of connection.

[0228] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the embodiments of the present invention.

[0229] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0230] The above are only specific embodiments of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can easily think of various equivalent modifications or replacements within the technical scope disclosed by the present invention, and these modifications or replacements should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention shall be based on the protection scope of the claims.

Claims

1. A method for site selection and planning of electric vehicle charging stations, characterized in that: include: For the target site selection area, the objective function is constructed with the lowest operating cost of the charging station as the optimization goal, the site selection coordinates of the charging station, and the number of charging piles in the charging station with the determined site selection coordinates as optimization variables; For the target site selection area, the constraints corresponding to the objective function are constructed based on the spatiotemporal distribution of user charging demand, investment cost, number of charging stations, total capacity of charging stations, number of charging piles configured at charging stations, charging node voltage, and charging line flow; Optimizing and solving the objective function based on the constraint conditions to obtain the charging station site selection coordinates within the target site selection area and the number of charging piles in the charging station with the determined site selection coordinates; The objective function is: Among them, F cost The operating cost of the charging station in the target site area, is the average annual cost of building a charging station numbered i, is the average annual cost of operating charging station i, C3 is the time cost of users going to the charging station; The calculation method is: Among them, V i th is the total power required by the vehicle that arrives at charging station i for charging within the tth hour of the day with date type h, is the price of electricity purchased from the grid by charging station i at hour t, is the electricity price sold by the grid to electric vehicle users at hour t, d h Indicates the number of days in the whole year whose date type is h. is the salary cost of the staff in charging station i, is the maintenance cost of charging station i; in, W E The required power of the vehicle arriving at charging station i for charging, R E Y is the maximum mileage that the user's electric vehicle can continuously travel; ni is the shortest distance from the charging demand point numbered n to the charging station numbered i, Q n The remaining power of the user's electric vehicle corresponding to the charging demand point n; in, is the set of charging demand points that can reach the charging station numbered i within the tth hour; in, is the set of demand points that reach charging station i within the tth hour and demand points that reach charging station i' within the tth hour, where the actual distances of the demand points that reach charging station i and the actual distances of the demand points that reach charging station i' are both less than the preset threshold; G th It is the collection of all demand points in the target site selection area.

2. The electric vehicle charging station site selection and planning method according to claim 1, characterized in that: The calculation method is: in, is the total area occupied by the charging station numbered i. It represents the land price of the charging station with construction number i, and the construction scale S of the charging station with construction number i. i ∈{0,1,2,3,4}, each S i Corresponding to different preset construction areas, is the actual capacity of the charging station numbered i, C C is the actual cost per unit capacity, m is the operating time of the charging station with construction number i, in years, and r0 is the rate of return on investment.

3. The electric vehicle charging station site selection and planning method according to claim 1, characterized in that: The calculation method of C3 is: Among them, c ji represents the unit time cost of the user going to charging station i from demand point j, q j is the charging demand quantity of demand point j; y i Refers to the actual number of charging piles of charging station i established at the candidate location, which is also the optimization variable; d ji It refers to the actual distance from demand point j to charging station i; Yij={0,1} is the actual decision variable. If demand point j goes to charging station i to charge, the value is 1, otherwise the value is 0. V refers to the actual speed of the user's electric vehicle.

4. The electric vehicle charging station site selection and planning method according to claim 1, characterized in that: The constraints corresponding to the objective function include: Constraints on the spatiotemporal distribution of user charging demand: Among them, X i (S i ) represents the number of charging piles in the station; γ is the preset unsatisfied rate of electric vehicle charging demand, where the unsatisfied rate of electric vehicle charging demand refers to the proportion of electric vehicles that have arrived at the charging station but have no charging piles for charging; η is the preset unreachable rate of charging demand, where the unreachable rate of charging demand refers to the proportion of electric vehicles that need to be charged but have insufficient remaining power to drive the electric vehicle to the nearest charging station; [·] represents rounding; Investment cost constraints: in, is the total investment of charging station i, C max For maximum budget; Constraints on the number of charging stations Where M is the number of charging stations that need to be established in the target site selection area; N sat is the number of addresses established for charging stations in the target site selection area; p is the index of the candidate location of the charging station (p = 1, 2, 3, ..., N sta ); xp is a binary variable, 1 indicates that a charging station is built at the candidate location, and 0 indicates that a charging station is not built at the candidate location; Total capacity constraints of charging stations: Among them, C total The actual total capacity of charging stations in the target site selection area; C pile The actual capacity of each charging station; Constraints on the number of charging piles configured at a charging station: 0≤y i ≤y max ,i=1,2,…,N sat Among them, y max The ratio of charging piles in the preset charging station; Charging node voltage constraints: Where k is the actual index of the charging node; Ω bus is the set of all charging nodes; U k is the charging voltage of the charging node; U N is the rated voltage of the entire charging system in the target site selection area; α% refers to the proportion of the allowed deviation of the charging voltage; β1 is the confidence level that the preset charging voltage exceeds the limit; P r {} represents the probability of occurrence of {} event; Charging line power flow constraints: P r {I l >I l,max }≤β2,l∈Ω br Among them, I l is the actual load current of the entire charging line in the target site selection area; Ω br is the set of all charging nodes; I l,max is the maximum current passing through the charging line l; β2 refers to the confidence level that the preset charging line power flow exceeds the maximum value.

5. The method for site selection and planning of an electric vehicle charging station according to any one of claims 1 to 4, characterized in that: The optimizing and solving the objective function based on the constraint condition to obtain the charging station site selection coordinates within the target site selection area and the number of charging piles in the charging station with the determined site selection coordinates includes: The objective function is optimized and solved based on the constraint conditions and the genetic algorithm to obtain the charging station site selection coordinates within the target site selection area and the number of charging piles in the charging station with the determined site selection coordinates.

6. A device for site selection and planning of electric vehicle charging stations, characterized in that: include: An objective function construction module is used to construct an objective function for a target site selection area, taking the lowest operating cost of a charging station as the optimization goal, taking the site selection coordinates of the charging station, and the number of charging piles in the charging station that determines the site selection coordinates as optimization variables; A constraint condition construction module is used to construct the constraint conditions corresponding to the objective function for the target site selection area based on the spatiotemporal distribution of user charging demand, investment cost, number of charging stations, total capacity of charging stations, number of charging piles configured at charging stations, charging node voltage, and charging line flow; A site selection solution module, used to optimize and solve the objective function based on the constraint conditions, obtain the site selection coordinates of the charging station within the target site selection area and the number of charging piles in the charging station with the determined site selection coordinates; The objective function is: Among them, F cost The operating cost of the charging station in the target site area, is the average annual cost of building a charging station numbered i, is the average annual cost of operating charging station i, C3 is the time cost of users going to the charging station; The calculation method is: Among them, V i th is the total power required by the vehicle that arrives at charging station i for charging within the tth hour of the day with date type h, is the price of electricity purchased from the grid by charging station i at hour t, is the electricity price sold by the grid to electric vehicle users at hour t, d h Indicates the number of days in the whole year whose date type is h. is the salary cost of the staff in charging station i, is the maintenance cost of charging station i; in, W E The required power of the vehicle arriving at charging station i for charging, R E Y is the maximum mileage that the user's electric vehicle can continuously travel; ni is the shortest distance from the charging demand point numbered n to the charging station numbered i, Q n The remaining power of the user's electric vehicle corresponding to the charging demand point n; in, is the set of charging demand points that can reach the charging station numbered i within the tth hour; in, is the set of demand points that reach charging station i within the tth hour and demand points that reach charging station i' within the tth hour, where the actual distances of the demand points that reach charging station i and the actual distances of the demand points that reach charging station i' are both less than the preset threshold; G th It is the collection of all demand points in the target site selection area.

7. A terminal device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 5 are implemented.

8. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 5 are implemented.

Images