Electric vehicle charging station locating and sizing method based on hybrid algorithm

Abstract

The invention belongs to the technical field of charging station planning, and particularly relates to an electric vehicle charging station locating and sizing method based on a hybrid algorithm, and the method comprises the steps: obtaining the charging demand of each node in a planning region according to historical traffic flow information and Monte Carlo simulation; taking the minimum annual total operating cost of the charging station and the minimum annual economic loss of the user as objective functions, and taking the total charging demand of the electric vehicle, the number of the charging stations, the service range and the upper limit of the total power of the planning area as constraint conditions to construct a charging station locating and sizing model; and solving the charging station locating and sizing model by combining an improved whale optimization algorithm and a doliolaria algorithm. Aiming at the problems that existing charging station planning is unreasonable and is difficult to adapt to large-scale electric vehicle grid connection in the future, the economic cost of a charging station operator and an electric vehicle user side is comprehensively considered, meanwhile, the requirement for safe operation of a power distribution network is considered, and a hybrid optimization algorithm is adopted for solving; and scientific guidance can be provided for future planning of the charging station.

Description

Technical Field

[0001] The present invention belongs to the technical field of charging station planning, and in particular relates to a method for site selection and capacity determination of an electric vehicle charging station based on a hybrid algorithm. Background Art

[0002] In recent years, electric vehicles, as an important alternative to traditional fossil fuel-powered vehicles, have attracted increasing attention from different stakeholders, including consumers, automakers, and government agencies, due to their low greenhouse gas emissions and high energy efficiency. However, the lack of charging infrastructure may directly hinder the promotion of electric vehicles, and the location and capacity of electric vehicle charging stations has become an important issue worthy of study. At present, there is a lack of mature theoretical guidance for the construction planning of electric vehicle charging stations. If charging station operators only carry out disorderly expansion, some charging piles will be overloaded and some charging piles will be idle, resulting in the problem of "cars without charging piles, and charging piles without cars". The former will affect the public acceptance of electric vehicles, and the latter will seriously waste public resources. Therefore, it is urgent to reasonably select the location and capacity of charging piles under the condition that the economic losses of all parties are minimized and the charging needs of users can be met, so as to alleviate the above problems. Summary of the invention

[0003] The purpose of the present invention is to provide a method for site selection and capacity determination of electric vehicle charging stations based on a hybrid algorithm to solve the technical problems of high economic losses in the traditional charging station planning process and inability to reasonably select sites according to user charging needs.

[0004] In order to solve the above technical problems, the specific technical solutions of the present invention are as follows:

[0005] In some embodiments of the present application, a method for selecting a site and determining the capacity of an electric vehicle charging station based on a hybrid algorithm is provided, comprising the following steps:

[0006] 1) Obtain the charging demand of each node in the planning area based on historical traffic flow information and Monte Carlo simulation;

[0007] 2) Taking the annualized total operating cost of charging stations and the lowest annual economic loss of users as the objective function, and the total charging demand of electric vehicles, the number of charging stations, the service scope, and the upper limit of the total power in the planned area as constraints, a charging station site selection and capacity determination model is constructed;

[0008] 3) The improved whale optimization algorithm is combined with the salp algorithm to solve the charging station site selection and sizing model.

[0009] In some embodiments of the present application, in step 1, the historical traffic flow information is to obtain the traffic O(origin)D(desti nation) probability matrix within a day through the historical traffic flow data, and a Monte Carlo simulation is performed in combination with the Markov chain and the electric vehicle charging behavior model to obtain the charging demand of each traffic node.

[0010] In some embodiments of the present application, the objective function in step 2 is:

[0011] C total =C1+C2

[0012] Where: C total is the annualized total economic cost; C1 is the annualized cost of the charging station; C2 is the annualized economic loss of the user.

[0013] In some embodiments of the present application, the annualized cost of the charging station in step 2 mainly includes the construction cost of the charging station, the purchase cost of the charging piles and the daily operation cost of the charging station, and is calculated as follows:

[0014]

[0015] Where: N is the number of charging stations; r0 is the average depreciation rate of charging stations; s l The maximum service life of the charging station; C c,i is the annual construction cost of the i-th charging station; C p,i C is the purchase cost of the charging pile of the i-th charging station; om,i is the annual operation and maintenance cost of the i-th charging station.

[0016] In some embodiments of the present application, C p,i The calculation method is:

[0017] C p,i =C ph ·n 1,i +C pl ·n 2,i

[0018] Where C ph is the unit price of high-power charging pile; n 1,i is the number of high-power charging piles in the i-th charging station; C pl is the unit price of low-power charging pile; n 2,i is the number of low-power charging piles in the i-th charging station.

[0019] In some embodiments of the present application, the annual operation and maintenance cost of the charging station in step 2 is calculated as follows:

[0020] C om,i =(α+β)P cap,i

[0021] P cap,i =365·T m ·(P ph ·n 1,i +P pl ·n 2,i )

[0022] P cap,i is the annual operating capacity of the i-th charging station; α is the labor cost ratio coefficient; β is the grid connection cost ratio coefficient; T m is the average operation time of the charging pile in a day; P ph is the power of the high-power charging pile; P pl is the power of the low-power charging station; 365 is the number of days in a year.

[0023] In some embodiments of the present application, the user's annual economic loss C2 in step 3 needs to comprehensively consider the user's travel time, queuing time, and the power consumption of the user on the way to the charging station. The calculation method is:

[0024]

[0025] Where: C w,i is the equivalent economic loss of the user's travel time when driving to the i-th charging station; C t,i is the equivalent economic loss of the user’s waiting time in the i-th charging station; C E,i The economic loss of electricity consumption caused by the user driving to the i-th charging station. w,i It is expressed as follows:

[0026]

[0027] Where: θ is the proportion of charged electric vehicles in the daily traffic flow; R s,i is the number of main traffic roads near the i-th charging station; F j is the number of electric vehicles on the jth road; v is the average speed of electric vehicles traveling from the jth road to the i-th charging station; D ij is the average distance from the jth road to the ith charging station; C utl It is the user's equivalent economic loss per unit time.

[0028] In some embodiments of the present application, the waiting time equivalent economic loss C of the user in the charging station is t,i It is related to the time when the electric vehicle arrives at the charging station, because the calculation formula is:

[0029]

[0030] Where: Q t,iis the number of EVs that arrive at the i-th charging station for charging at time t; T t,i is the average queuing time at the i-th charging station at time t. The economic loss of electricity consumption CCCi caused by the user driving to the i-th charging station is calculated as:

[0031]

[0032] Where: C upe is the unit price of electricity used by the user; η is the conversion coefficient between mileage and power consumption.

[0033] The constraints are:

[0034] The rated capacity of all charging stations shall not be less than the total demand of electric vehicle users in the target area:

[0035]

[0036] P d =θ·Q total ·C s (A s -A e )·(1+λ)

[0037] Where: Q total P is the total number of electric vehicles in the planning area; d is the total daily charging demand of the target area; C s is the average rated capacity of electric vehicles; A s Selecting the charging threshold for electric vehicles; A e is the average remaining power of the electric vehicle; λ is the average loss rate of the electric vehicle during the charging process.

[0038] The charging station coverage constraints are:

[0039] R s ≤w q,q+1 ≤2R s

[0040] Where: R s is the service range of the charging station; w q,q+1 is the actual distance between node q and node q+1.

[0041] The number of charging stations is constrained as follows:

[0042] N min ≤N≤N max

[0043] N max = ceil(P d / P min )

[0044] P min =T m ·P pl,i ·n 2,i

[0045] N min = ceil(P d / P max )

[0046] P max =T m ·P ph,i ·n 1,i

[0047] Where: ceil represents the upward rounding function; P max is the maximum capacity of a single charging station; P min is the minimum capacity of a single charging station; N min is the minimum number of charging stations; N max The maximum number of charging stations.

[0048] The total power constraint of the planning area is:

[0049]

[0050] Where: P r,i is the rated total charging power of charging station i; P B,max is the maximum power of the basic load excluding the electric vehicle charging load; P a,max The maximum power allowed to be connected to the grid.

[0051] In some embodiments of the present application, the improved whale optimization algorithm calculation method in step 3 is:

[0052] Improve the calculation of parameter A and random number l in the whale optimization algorithm. The calculation method of l is:

[0053] l=(a1-1)×a2+1

[0054] Where a2 is a random number between [0,1], a1 decreases exponentially from -1.26 to -1.73, and a1 is calculated as:

[0055]

[0056] After the calculation method is improved, the value range of l is -1.73 to 1.

[0057] The parameter A is calculated as:

[0058] A=2a·ra

[0059] Where r is an arbitrary vector in [0,1], and the value of a in the standard whale optimization algorithm decreases linearly from 2 to 0, calculated according to the following formula:

[0060]

[0061] Where t is the current iteration and MaxIter is the total number of iterations.

[0062] Improve the calculation method of A so that it changes exponentially from 2.7 to 1. The new a value in the calculation formula of A is calculated as follows:

[0063]

[0064] In some embodiments of the present application, in step 3, the improved whale optimization algorithm is combined with the salp algorithm to solve the charging station site selection and capacity determination model, and the specific steps are:

[0065] Initialize the parameters of the whale optimization algorithm and the salp algorithm;

[0066] There are N candidate charging station points. Whether to build a charging station at each candidate point is determined by the decision variable x i Indicates that x i =1 means to build a station at this candidate point, x i =0 means not to build a station at this candidate point and randomly generate M initial populations.

[0067] Calculate the fitness value of each individual, that is, the sum of the total economic costs under the charging station selection scheme corresponding to each individual in the population, and select the population with the lowest fitness as the best population.

[0068] Afterwards, the improved whale optimization algorithm or salp algorithm is used to update the position of the current population according to the parameter p, where the parameter p is a random number in the range [0,1].

[0069] When using parameter p, the probability of selecting the improved whale optimization algorithm or the salp algorithm when updating each optimal solution position is 50%. If the p value of the current population is less than or equal to 0.5, the improved whale optimization algorithm is used, and then according to The value of is used to update the population position using random search or prey surround search, otherwise the salp algorithm is used.

[0070] If the Salp Algorithm is used, the population is updated according to whether the current population is a leader or a follower. If the current population is a leader, that is, Then use the following formula to update the population position:

[0071]

[0072] In the formula, represents the updated population position, F j Represents the coordinate of the jth dimension of the food location in the K-dimensional search space, ub j and lb j is the boundary of the search space, c2 is an arbitrary number in the domain [0,1], and c1 is a random number used to stabilize the exploration and development phase.

[0073] Otherwise, if the current population is a follower, the prey encirclement strategy in the improved whale optimization algorithm is used to update the population.

[0074] Compared with the prior art, the beneficial effect of the present invention is that the present invention comprehensively considers the economic interests of the charging station operator and the user, and comprehensively considers the constraints such as the total charging demand of electric vehicles, the number of charging stations, the service scope, and the total power limit of the planning area. It can meet the daily charging needs while balancing the economic interests of all parties in society, and will not affect the operation of the distribution network. The improved whale optimization algorithm and the salp algorithm are combined to solve the problem. The exponential method is used to replace the linear method for the improvement of the parameters in the whale optimization algorithm, which improves the algorithm performance and the convergence speed. In the process, the two algorithms are selected and executed according to specific conditions, which can improve the ability of local search and global search and avoid falling into the local optimal situation. The method proposed by the present invention takes into account the user's satisfaction while avoiding the waste of public resources. The improved hybrid algorithm can combine the advantages of the two algorithms, improve the efficiency and robustness of the algorithm, and has more advantages in the search of the global optimal solution, which has guiding significance for the planning of practical problems. BRIEF DESCRIPTION OF THE DRAWINGS

[0075] Various other advantages and benefits will become apparent to those of ordinary skill in the art by reading the detailed description of the preferred embodiments below. The accompanying drawings are only for the purpose of illustrating the preferred embodiments and are not to be considered as limiting the present invention. Moreover, the same reference symbols are used throughout the accompanying drawings to represent the same components. In the accompanying drawings:

[0076] Figure 1 A schematic diagram of the application principle provided by an embodiment of the present invention;

[0077] Figure 2 A schematic diagram of the calculation principle of the charging station site selection and sizing model using the improved whale optimization algorithm combined with the salp algorithm provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0078] The specific implementation of the present invention is further described in detail below in conjunction with the accompanying drawings and examples. The following examples are used to illustrate the present invention, but are not intended to limit the scope of the present invention.

[0079] In order to better understand the purpose, structure and function of the present invention, the present invention is further described in detail below in conjunction with the accompanying drawings.

[0080] like Figure 1-2 As shown, the present invention discloses a method for selecting a site and determining the capacity of an electric vehicle charging station based on a hybrid algorithm, comprising the following steps:

[0081] 1: Obtain road traffic data through GPS tracking of the transportation department, obtain the OD probability matrix of traffic travel within a day based on the road traffic data through TransCAD software, obtain electric vehicle ownership data and road information data, and obtain the total demand for electric vehicle charging in the planning area and the charging demand for each traffic node in a day based on Monte Carlo simulation.

[0082] 2: Taking the annualized total operating cost of the charging station and the lowest annual economic loss of the user as the objective function, and the total charging demand of electric vehicles, the number of charging stations, the service scope, and the total power limit of the planned area as constraints, a charging station site selection and capacity determination model is constructed.

[0083] 3: The improved whale optimization algorithm is combined with the salp algorithm to solve the charging station site selection and capacity determination model.

[0084] In this embodiment, a planning area with a total area of ​​98.8 km2 and 234 main traffic sections is taken as an example.

[0085] The application process of the method of the present invention in the embodiment area is as follows Figure 1 As shown, the specific steps are as follows:

[0086] The traffic volume in the planning area is obtained according to the data of the transportation department. The O(origin)D(destination) probability matrix of traffic travel within a day is obtained through TransCAD software according to the road traffic data. Monte Carlo simulation is performed on electric vehicles in the planning area to obtain the total charging demand of electric vehicles during the charging peak period in the planning area and the charging demand of each traffic node.

[0087] The number of alternative charging stations is calculated based on the total charging demand in the planning area and the upper and lower limits of the planned capacity of the charging stations.

[0088] The objectives and constraints for planning the location and capacity of charging stations in this area are as follows:

[0089] The objective function is:

[0090] C total =C1+C2

[0091] Where: C total is the annualized total economic cost; C1 is the annualized cost of the charging station; C2 is the annualized economic loss of the user.

[0092] The annualized cost of a charging station mainly includes the construction cost of the charging station, the purchase cost of charging piles and the daily operation cost of the charging station. The calculation method is:

[0093]

[0094] Where: N is the number of charging stations; r0 is the average depreciation rate of charging stations; s l The maximum service life of the charging station; C c,i is the annual construction cost of the i-th charging station; C p,i C is the purchase cost of the charging pile of the i-th charging station; om,i is the annual operation and maintenance cost of the i-th charging station.

[0095] C p,i The calculation method is:

[0096] C p,i =C ph ·n 1,i +C pl ·n 2,i

[0097] Where C ph is the unit price of high-power charging pile; n 1,i is the number of high-power charging piles in the i-th charging station; C pl is the unit price of low-power charging pile; n 2,i is the number of low-power charging piles in the i-th charging station.

[0098] The calculation method of charging station operation and maintenance cost is:

[0099] C om,i =(α+β)P cap,i

[0100] P cap,i =365·T m ·(P ph ·n 1,i +P pl ·n 2,i )

[0101] P cap,i is the annual operating capacity of the i-th charging station; α is the labor cost ratio coefficient; β is the grid connection cost ratio coefficient; T m is the average operation time of the charging pile in a day; P ph is the power of the high-power charging pile; P pl is the power of the low-power charging station; 365 is the number of days in a year.

[0102] The user's economic loss cost C2 needs to comprehensively consider the user's travel time, queuing time, and the power consumption on the way to the charging station. The calculation method is:

[0103]

[0104] Where: C w,i is the equivalent economic loss of the user's travel time when driving to the i-th charging station; C t,i is the equivalent economic loss of the user’s waiting time in the i-th charging station; C E,i The economic loss of electricity consumption caused by the user driving to the i-th charging station. w,i It is expressed as follows:

[0105]

[0106] Where: θ is the proportion of charged electric vehicles in the daily traffic flow; R s,i is the number of main traffic roads near the i-th charging station; F j is the number of electric vehicles on the jth road; v is the average speed of electric vehicles traveling from the jth road to the i-th charging station; D ij is the average distance from the jth road to the ith charging station; C utl It is the user's equivalent economic loss per unit time.

[0107] The equivalent economic loss of the user's waiting time in the station C t,i It is related to the time when the electric vehicle arrives at the charging station. The calculation formula is:

[0108]

[0109] Where: Q t,i is the number of EVs that arrive at the i-th charging station for charging at time t; T t,i is the average queuing time at the i-th charging station at time t. The economic loss of electricity consumption generated by the user driving to the i-th charging station C E,i The calculation method is:

[0110]

[0111] Where: C upe is the unit price of electricity used by the user; η is the conversion coefficient between mileage and power consumption.

[0112] The constraints include:

[0113] (1) Constraints on total charging demand:

[0114]

[0115] P d=θ·Q total ·C s (A s -A e )·(1+λ)

[0116] Where: Q total P is the total number of electric vehicles in the planning area; d is the total daily charging demand of the target area; C s is the average rated capacity of electric vehicles; A s is the charge state threshold of the electric vehicle; A e is the average remaining power of the electric vehicle; λ is the average loss rate of the electric vehicle during the charging process.

[0117] (2) The coverage constraints of charging stations are:

[0118] R s ≤w q,q+1 ≤2R s

[0119] Where: R s is the service range of the charging station; w q,q+1 is the actual distance between node q and node q+1.

[0120] (3) The number of charging stations is constrained as follows:

[0121] N min ≤N≤N max

[0122] N max = ceil(P d / P min )

[0123] P min =T m ·P pl,i ·n 2,i

[0124] N min = ceil(P d / P max )

[0125] P max =T m ·P ph,i ·n 1,i

[0126] Where: ceil represents the upward rounding function; P max is the maximum capacity of a single charging station; P min is the minimum capacity of a single charging station; N min is the minimum number of charging stations; N maxThe maximum number of charging stations.

[0127] (4) The total power constraint of the planning area is:

[0128]

[0129] Where: P r,i is the rated total charging power of the i-th charging station; P B,max is the maximum power of the basic load excluding the electric vehicle charging load; P a,max The maximum power allowed to be connected to the grid.

[0130] In the above planning function, the objective function indicates that the annual operating cost of the charging station and the annual economic loss cost of the user are equally important, and the optimization goal is to minimize the sum of the two.

[0131] Constraint (1) indicates that it is necessary to ensure that the charging stations can meet the charging needs of electric vehicle users in the planning area during peak hours, and the rated capacity of all charging stations is not less than the total demand of electric vehicle users in the target area during peak hours.

[0132] Constraint (2) indicates that in order to ensure that the charging range of all charging stations can cover the entire planning area, the distance between every two adjacent charging stations is required to be no greater than twice the service radius of the charging station.

[0133] Constraint (3) indicates that the actual number of charging stations must be between the upper and lower limits of the number of charging stations.

[0134] Constraint (4) indicates that in order to ensure the normal operation of the distribution network, when there are multiple charging stations in the planning area, the sum of the rated total charging power of the charging station and the maximum power of other basic loads in the area should not exceed the maximum power allowed by the grid.

[0135] The parameter settings are as follows:

[0136] parameter value parameter value <![CDATA[r0]]> 3％ <![CDATA[C ph ]]> 5000 NTD <![CDATA[C c,i ]]> 1 million yuan <![CDATA[s l ]]> 10 years <![CDATA[C upe ]]> 1.8 yuan / (kW.h) <![CDATA[P ph ]]> 80kW θ 25.2％ <![CDATA[P pl ]]> 40kW α 0.01 v 40km / h A 0.3 <![CDATA[C utl ]]> 21.44 yuan / h A 0.9 β 0.0071 λ 10％ η 0.3 <![CDATA[C pl ]]> 20000 NTD R 1.5km

[0137] The solution process of the hybrid algorithm combining the whale optimization algorithm and the salp algorithm is as follows:

[0138] Set the initial parameters of the two algorithms;

[0139] The initial population size is 50, the maximum number of iterations is 500, and the fitness value of each population is calculated. The population with the lowest fitness, that is, the population with the smallest objective function, is selected as the optimal solution;

[0140] In each iteration, the values ​​of a and l are updated. The calculation method of a is:

[0141]

[0142] MaxIter is the total number of iterations.

[0143] Where r is a random number in [0,1], and the value of A changes exponentially from 2.7 to 1.

[0144] l is calculated as:

[0145] l=(a1-1)×a2+1

[0146] Where a2 is a random number between [0,1], a1 decreases exponentially from -1.26 to -1.73, and a1 is calculated as:

[0147]

[0148] The value range of l is -1.73 to 1.

[0149] Update parameters A, C and random number p in each iteration;

[0150] The parameter A is calculated as:

[0151] A=2a·ra

[0152] The parameter C is calculated as:

[0153] C=2·r

[0154] p is a random number in the range [0,1].

[0155] When using the parameter p, there is a 50% chance that the modified whale optimization algorithm or the salp algorithm will be selected when updating each optimal solution position. If the p value of the current population is less than or equal to 0.5, the modified whale optimization algorithm is used, and then the population position is updated by random search or prey encirclement search according to the value of |A|, otherwise the salp algorithm is used.

[0156] If the current p value is less than or equal to 0.5, the improved whale optimization algorithm is used for further judgment. When |A|≥1, a random search strategy is adopted to update the population. The calculation method is:

[0157] D=|C·X rand -X(t)|

[0158] X(t+1)=X rand -A.D

[0159] X rand represents the randomly selected reference population position, and D represents the population individual moving toward X rand The step vector is close to the current iteration, X(t) represents the population position, and X(t+1) represents the updated population position.

[0160] If |A| < 1, adopt the encirclement search strategy to update the population position, and the calculation method is:

[0161] X(t+1)=X * (t)-A·D

[0162] D=|C·X * (t)-X(t)|

[0163] X * (t) represents the current best candidate population position vector, and D represents the population individual to X * (t) the approaching step vector;

[0164] If the p value is greater than 0.5, the Salp Algorithm is used, in which the population is updated based on whether the current population is a leader or a follower. The current population is m. If Then the current population is the leader, and the population position is updated using the following formula:

[0165]

[0166] In the formula, represents the updated population position, F j Represents the coordinate of the jth dimension of the food location in the K-dimensional search space, ub j and lb j is the boundary of the search space, c2 is an arbitrary number in the [0,1] domain, and c1 is a random number used to stabilize the exploration and development phase. The calculation method is:

[0167]

[0168] Otherwise, the current population is a follower, and the population is updated by using the encircled prey search in the above improved whale optimization algorithm.

[0169] After reaching the maximum number of iterations, the optimal solution is output, which is the optimal location and capacity of the charging station in the planned area.

[0170] The whole process is like Figure 2 shown.

[0171] Through the above technical solution, the technical effects produced in the embodiments of the present application are as follows:

[0172] The present invention aims at the problem that the existing charging station planning is unreasonable and difficult to adapt to the future large-scale electric vehicle grid connection. It comprehensively considers the economic costs of the charging station operator and the electric vehicle user, and takes into account the requirements for the safe operation of the distribution network. It adopts a hybrid optimization algorithm for solution, which can provide scientific guidance for the planning of future charging stations. Compared with the prior art, the present invention comprehensively considers the economic interests of the charging station operator and the user, and comprehensively considers the constraints such as the total charging demand of electric vehicles, the number of charging stations, the service scope, and the total power limit of the planning area. While balancing the economic interests of all parties in society, it can meet the daily charging needs and will not affect the operation of the distribution network. Compared with the prior art, the method combining the improved whale optimization algorithm and the salp algorithm is used for solution. The improvement of the parameters in the whale optimization algorithm adopts an exponential method instead of a linear method, which improves the algorithm performance and the convergence speed. In the process, the execution is selected between the two algorithms according to specific conditions, which can improve the ability of local search and global search and avoid falling into the local optimum. The method proposed in the present invention takes into account user satisfaction while avoiding waste of public resources. The improved hybrid algorithm can combine the advantages of the two algorithms, improve the efficiency and robustness of the algorithm, and has more advantages in searching for the global optimal solution, which has guiding significance for the planning of practical problems.

[0173] In the description of the present application, it should be understood that the terms "center", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", etc., indicating orientations or positional relationships, are based on the orientations or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be understood as a limitation on the present application.

[0174] The terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features. In the description of this application, unless otherwise specified, "plurality" means two or more.

[0175] In the description of this application, it should be noted that, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in this application can be understood according to specific circumstances.

[0176] In this specification, each embodiment is described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the embodiments can be referred to each other. For the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method part.

[0177] The above description of the disclosed embodiments enables one skilled in the art to implement or use the present invention. Various modifications to these embodiments will be apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but rather to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for site selection and capacity determination of electric vehicle charging stations based on a hybrid algorithm, characterized in that: The following steps are involved: 1) Obtain the charging demand of each node in the planning area based on historical traffic flow information and Monte Carlo simulation; 2) Taking the annualized total operating cost of charging stations and the lowest annual economic loss of users as the objective function, and the total charging demand of electric vehicles, the number of charging stations, the service scope, and the upper limit of the total power in the planned area as constraints, a charging station site selection and capacity determination model is constructed; 3) The improved whale optimization algorithm is combined with the salp algorithm to solve the charging station site selection and sizing model.

2. The method for site selection and capacity determination of electric vehicle charging stations based on a hybrid algorithm according to claim 1, characterized in that: In step 1, the historical traffic flow information is to obtain the traffic O (origin) D (destination) probability matrix within a day through the historical traffic flow data, and the Monte Carlo simulation is performed in combination with the Markov chain and the electric vehicle charging behavior model to obtain the charging demand of each traffic node.

3. The method for site selection and capacity determination of electric vehicle charging stations based on a hybrid algorithm according to claim 1, characterized in that: The objective function in step 2 is: C total =C1+C2 Where: C total is the annualized total economic cost; C1 is the annualized cost of the charging station; C2 is the annualized economic loss of the user.

4. The method for site selection and capacity determination of electric vehicle charging stations based on a hybrid algorithm according to claim 1, characterized in that: The annualized cost of the charging station in step 2 mainly includes the construction cost of the charging station, the purchase cost of the charging piles and the daily operation cost of the charging station, and is calculated as follows: Where: N is the number of charging stations; r0 is the average depreciation rate of charging stations; s l The maximum service life of the charging station; C c,i is the annual construction cost of the i-th charging station; C p,i C is the purchase cost of the charging pile of the i-th charging station; om,i is the annual operation and maintenance cost of the i-th charging station.

5. The method for site selection and capacity determination of electric vehicle charging stations based on a hybrid algorithm according to claim 4, characterized in that: The C p,i The calculation method is: C p,i =C ph ·n 1,i +C pl ·n 2,i Where C ph is the unit price of high-power charging pile; n 1,i is the number of high-power charging piles in the i-th charging station; C pl is the unit price of low-power charging pile; n 2,i is the number of low-power charging piles in the i-th charging station.

6. The method for site selection and capacity determination of electric vehicle charging stations based on a hybrid algorithm according to claim 4, characterized in that: The calculation method of the annual operation and maintenance cost of the charging station in step 2 is: C om,i =(α+β)P cap,i P cap,i =365·T m ·(P ph ·n 1,i +P pl ·n 2,i ) P cap,i is the operating capacity of the i-th charging station in one year; α is the labor cost ratio coefficient; β is the grid connection cost ratio coefficient; T m is the average operation time of the charging pile in a day; P ph is the power of the high-power charging pile; P pl is the power of the low-power charging station; 365 is the number of days in a year.

7. The method for site selection and capacity determination of electric vehicle charging stations based on a hybrid algorithm according to claim 1, characterized in that: The user's annual economic loss C2 in step 3 needs to comprehensively consider the user's travel time, queuing time, and the power consumption of the user on the way to the charging station; the calculation method is: Where: C w,i is the equivalent economic loss of the user's travel time when driving to the i-th charging station; C t,i is the equivalent economic loss of the user’s waiting time in the i-th charging station; C E,i The economic loss of electricity consumption caused by the user driving to the i-th charging station; C w,i It is expressed as follows: Where: θ is the proportion of charged electric vehicles in daily traffic flow; R s,i is the number of main traffic roads near the i-th charging station; F j is the number of electric vehicles on the jth road; v is the average speed of electric vehicles traveling from the jth road to the i-th charging station; D ij is the average distance from the jth road to the ith charging station; C utl It is the user's equivalent economic loss per unit time.

8. The method for site selection and capacity determination of electric vehicle charging stations based on a hybrid algorithm according to claim 7, characterized in that: The equivalent economic loss C of the waiting time of the user in the charging station t,i It is related to the time when the electric vehicle arrives at the charging station, because the calculation formula is: Where: Q t,i is the number of EVs that arrive at the i-th charging station for charging at time t; T t,i is the average queuing time of the i-th charging station at time t; The calculation method of the economic loss of electricity consumption CCCi generated by the user driving to the i-th charging station is: Where: C upe is the unit price of electricity used by the user; η is the conversion coefficient between mileage and power consumption; The constraints are: The rated capacity of all charging stations shall not be less than the total demand of electric vehicle users in the target area: P d =θ·Q total ·C s (A s -A e )·(1+λ) Where: Q total P is the total number of electric vehicles in the planning area; d is the total daily charging demand of the target area; C s is the average rated capacity of electric vehicles; A s Selecting the charging threshold for electric vehicles; A e is the average remaining power of the electric vehicle; λ is the average loss rate of the electric vehicle during the charging process; The charging station coverage constraints are: R s ≤w q,q+1 ≤2R s Where: R s is the service range of the charging station; w q,q+1 is the actual distance between node q and node q+1; The number of charging stations is constrained as follows: N min ≤N≤N max N max =hide(P d / P min ) P min =T m ·P pl,i ·n 2,i N min =hide(P d / P max ) P max =T m ·P ph,i ·n 1,i Where: ceil represents the upward rounding function; P max is the maximum capacity of a single charging station; P min is the minimum capacity of a single charging station; N min is the minimum number of charging stations; N max is the maximum number of charging stations; The total power constraint of the planning area is: Where: P r,i is the rated total charging power of charging station i; P B,max is the maximum power of the basic load excluding the electric vehicle charging load; P a,max The maximum power allowed to be connected to the grid.

9. The method for site selection and capacity determination of electric vehicle charging stations based on a hybrid algorithm according to claim 1, characterized in that: The calculation method of the improved whale optimization algorithm in step 3 is: Improve the calculation of parameter A and random number l in the whale optimization algorithm. The calculation method of l is: l=(a1-1)×a2+1 Where a2 is a random number between [0,1], a1 decreases exponentially from -1.26 to -1.73, and a1 is calculated as: After the calculation method is improved, the value range of l is -1.73 to 1; The parameter A is calculated as: A=2a·ra Where r is an arbitrary vector in [0,1], and the value of a in the standard whale optimization algorithm decreases linearly from 2 to 0, calculated according to the following formula: Where t is the current iteration and MaxIter is the total number of iterations; Improve the calculation method of A so that it changes exponentially from 2.7 to 1. The new a value in the calculation formula of A is calculated as follows:

10. The method for site selection and capacity determination of electric vehicle charging stations based on a hybrid algorithm according to claim 1, characterized in that: In step 3, the improved whale optimization algorithm is combined with the salp algorithm to solve the charging station location and capacity determination model. The specific steps are: Initialize the parameters of the whale optimization algorithm and the salp algorithm; There are N candidate charging station points. Whether to build a charging station at each candidate point is determined by the decision variable x i Indicates that x i =1 means to build a station at this candidate point, x i =0 means not to build a station at this candidate point and randomly generate M initial populations; Calculate the fitness value of each individual, that is, the sum of the total economic costs of the charging station selection scheme corresponding to each individual in the population, and select the population with the lowest fitness as the best population; Afterwards, the improved whale optimization algorithm or salp algorithm is used to update the position of the current population according to the parameter p, where the parameter p is a random number in the range of [0,1]; When using parameter p, the probability of selecting the improved whale optimization algorithm or the salp algorithm when updating each optimal solution position is 50%; if the p value of the current population is less than or equal to 0.5, the improved whale optimization algorithm is used, and then according to The value of is selected to update the population position by random search or prey surround search, otherwise the salp algorithm is used; If the Salp Algorithm is used, the population is updated according to whether the current population is a leader or a follower. If the current population is a leader, that is, Then use the following formula to update the population position: In the formula, represents the updated population position, F j Represents the coordinate of the jth dimension of the food location in the K-dimensional search space, ub j and lb j is the boundary of the search space, c2 is an arbitrary number in the domain [0,1], and c1 is a random number used to stabilize the exploration and development phase; Otherwise, if the current population is a follower, the prey encirclement strategy in the improved whale optimization algorithm is used to update the population.

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