Multi-target charging station locating and sizing method, device, equipment and medium

Abstract

The invention relates to the technical field of electric vehicle charging station locating and sizing, in particular to a multi-target charging station locating and sizing method, device and equipment and a medium, and the method comprises the steps: determining initial configuration based on an M / M / c queuing theory model; modeling the fault rate of the charging pile in the charging station to form a multi-target charging station locating and sizing optimization model; the service range of the charging station is determined based on the Voronoi graph theory, a final locating and sizing scheme of the charging station is determined, and a capacity model of the charging station is established; performing iterative solution on the addressing and sizing model through a simulated annealing intelligent algorithm to obtain a multi-target optimal charging station addressing and sizing scheme; the method considers the influence of the fault rate of the charging pile on the revenue of the charging station, considers the optimization of the time and economical efficiency of selecting the charging station by the electric vehicle user based on the queuing theory, comprehensively considers the cost of the electric vehicle user and the revenue of the charging station, and obtains a multi-target optimal locating and sizing scheme for the electric vehicle charging station.

Description

Technical Field

[0001] The present invention relates to the technical field of site selection and capacity determination for electric vehicle charging stations, and in particular to a method, device, equipment and medium for site selection and capacity determination for multi-target charging stations. Background Art

[0002] With the rapid growth of electric vehicle ownership, the site selection and capacity configuration of charging stations have become key issues in the construction of electric vehicle infrastructure. In order to meet the charging needs of electric vehicle users in different regions and optimize the layout and capacity configuration of charging stations, existing technologies usually consider factors such as queuing theory and demand forecasting, and use different methods to determine the optimal location of charging stations and the required number of charging piles.

[0003] In the existing technology, the site selection and capacity determination model based on queuing theory is mainly used to model the probability of electric vehicle users arriving at charging stations and the charging demand. Through this method, the average waiting time and charging time of vehicles at charging stations can be calculated, and the configuration of charging equipment can be optimized in combination with the operating cost of the charging station and the charging convenience of users. However, with the rapid growth of the number of electric vehicles, the existing technology still has some shortcomings in the process of site selection and capacity determination:

[0004] In practical applications, the failure rate of charging pile equipment will significantly affect the service efficiency and user experience of the charging station, and thus affect the revenue of the charging station. However, in the existing technology, the modeling of the failure rate of charging piles and its cost evaluation are often ignored, resulting in a mismatch between the configuration of the charging equipment and the actual usage, affecting the long-term operational efficiency of the charging station.

[0005] The information disclosed in this background technology section is only intended to deepen the understanding of the overall background technology of the present invention, and should not be regarded as acknowledging or suggesting in any form that the information constitutes the prior art already known to those skilled in the art. Summary of the invention

[0006] The present invention provides a method, device, equipment and medium for site selection and capacity determination of a multi-target charging station, thereby effectively solving the problems in the background technology.

[0007] In order to achieve the above object, the technical solution adopted by the present invention is: a multi-objective charging station site selection and capacity determination method, comprising the following steps:

[0008] S10: Based on the M / M / c queuing theory model and the probability distribution of electric vehicle users arriving at and using charging stations, the average queue length, average waiting time and stay time of electric vehicles at charging stations are calculated to determine the preliminary configuration of charging equipment in the charging stations;

[0009] S20: Based on the preliminary configuration of the charging equipment in the charging station, the failure rate of the charging piles in the charging station is modeled, and based on the average maintenance time, the guarantee delay time and the component repair rate, a failure cost model considering the failure rate of the charging pile equipment is constructed, and combined with the satisfaction of electric vehicle users and the construction and operation costs of the charging station, a multi-objective charging station location and capacity optimization model is formed;

[0010] S30: Determine the service range of each charging station based on the Voronoi graph theory, determine the initial coordinates of the charging station by planning the charging needs of electric vehicle users in the area, adjust the location of the charging station by combining the multi-objective charging station site selection and capacity optimization model and the Voronoi graph theory, and determine the final charging station site selection and capacity solution. By dividing the service range of each charging station, the total demand for charging capacity within the range can be known, the demand for charging piles installed at the charging station is determined, and the capacity model of each charging station is established;

[0011] S40: Based on the capacity model of each charging station, combined with the satisfaction of the electric vehicle users and the construction and operation costs of the charging station, a comprehensive evaluation index is constructed, and the site selection and capacity determination model is iteratively solved through a simulated annealing intelligent algorithm to obtain a multi-objective optimal charging station site selection and capacity determination plan.

[0012] Further, in step S10, based on the M / M / c queuing theory model, for the probability distribution of a large number of electric vehicle users arriving at the charging station and using the charging station, the average queue length, average waiting time and stay time of electric vehicles at the charging station are calculated, and the preliminary configuration of the charging equipment in the charging station is determined; including:

[0013] S11: Based on the M / M / c queuing theory model, the Poisson distribution characteristics of electric vehicle users arriving at charging stations and the negative exponential distribution law of charging time are described, and the system state transfer difference equation is established to form a statistical model of the queuing system of the charging station;

[0014] S12: Based on the statistical model, the probability of charging demand in the charging station is counted to obtain the probability of no vehicle demanding charging in the charging station and the probability model of n electric vehicles needing charging;

[0015] S13: Utilizing the statistical model and the probability model, the average queue length, average waiting time and average stay time of the charging station queue system are calculated, and a preliminary configuration optimization objective function model of the charging equipment is constructed with minimizing the average waiting time as the optimization goal.

[0016] Further, in step S13, the model for calculating the average queue length, average waiting time and average stay time of the charging station queue system includes:

[0017]

[0018] Where, L q represents the average queue length, W q represents the average waiting time, W s represents the average stay time, λ represents the Poisson parameter, k represents the cumulative function subscript, μ represents the number of services provided by each charging pile per hour, and N ch represents the number of charging piles in the charging station, P0 represents the probability that there is no need to charge in the charging station, ρ = λ / μN ch Indicates the charging pile service intensity, and its value is less than 1.

[0019] Furthermore, in step S13, with minimizing the average waiting time as the optimization goal, a preliminary configuration optimization objective function model of the charging equipment is constructed, including:

[0020] f1=C0N ch I0+C w W q ;

[0021] W q ≤W max ;

[0022] N ch,min ≤N ch ≤N ch,max ;

[0023] In the formula, C0 represents the idle cost of charging facilities, C w represents the charging service loss cost, N ch Indicates the number of charging piles in the charging station, N ch,min and N ch,max Respectively represent the minimum number of charging piles and the maximum number of charging piles, W q represents the average waiting time, W max represents the maximum waiting time of the vehicle, I0 represents the equipment idle probability of the charging station queuing system, and I0 is equal to

[0024]

[0025] Furthermore, in step S20, based on the preliminary configuration of the charging equipment in the charging station, the failure rate of the charging piles in the charging station is modeled, and based on the mean maintenance time, the guarantee delay time and the component repair rate, a failure cost model considering the failure rate of the charging pile equipment is constructed, and combined with the satisfaction of electric vehicle users and the construction and operation costs of the charging station, a multi-objective charging station site selection and capacity optimization model is formed; including:

[0026] S21: Modeling component failure rate and component repair rate based on Weibull distribution, and deriving the average guarantee delay time of the component from the component failure rate and the component repair rate to obtain the average guarantee delay time;

[0027] S22: According to the component failure rate, model the component failure rate based on binomial distribution, calculate the failure probability of the charging pile component, and derive the failure cost model of the charging station;

[0028] S23: Considering the user's travel distance, charging time and charging cost, a user satisfaction index is constructed;

[0029] S24: Based on the failure cost model of the charging station and the user satisfaction index, the construction and operation and maintenance costs of the charging station are integrated to construct the total construction cost of the charging station. The total construction cost of the charging station is the objective function of the multi-objective charging station site selection and sizing optimization model.

[0030] Furthermore, in step S22, according to the component failure rate, the component failure rate is modeled based on binomial distribution, the failure probability of the charging pile component is calculated, and the failure cost of the charging station is derived. The model includes:

[0031]

[0032] f2=(1-P l,ch +P f,ch ) re,ev C b,ev t mldt ;

[0033] Where f2 represents the failure cost of the charging station, P f,ch represents the failure rate of charging pile components, P l,ch represents the failure probability of the faulty component of the charging pile, l represents the number of charging piles in operation, P re,ev represents the expected output of the charging pile, C b,ev represents the unit charging electricity price, λ A represents the reliability constant, ω(t) represents the component failure rate, γ represents the Weibull shape parameter, and θ represents the characteristic life.

[0034] Furthermore, in step S23, the user's travel distance, charging time and charging cost are comprehensively considered to construct a user satisfaction index, and the model includes:

[0035]

[0036] In the formula, f3 represents the user satisfaction index, d ij represents the distance between electric vehicle i and target charging station j, t ijrepresents the time it takes for electric vehicle i to reach the target charging station j, C ij Indicates the total charging cost. represents the total charging price at charging station j, represents the basic parking fee of charging station j, t c,i represents the charging time of electric vehicle i, C dis represents the benefit of electric vehicle i receiving discharge, P ev Represents the average charging power, P dis,i represents the average discharge power, t w represents the waiting time of electric vehicles at the charging station, t w,i represents the queue time of electric vehicle i, t dis,i Indicates the discharge time of electric vehicle i.

[0037] Further, in step S30, the service range of each charging station is determined based on the Voronoi graph theory, the initial coordinates of the charging station are determined by planning the charging needs of electric vehicle users in the area, the location of the charging station is adjusted by combining the multi-objective charging station site selection and capacity optimization model and the Voronoi graph theory, and the final charging station site selection and capacity solution is determined. Through the divided service ranges of each charging station, the total demand for charging capacity within the range can be known, the demand for charging piles installed at the charging station is determined, and the capacity model of each charging station is established; including:

[0038] S31: Based on the historical data of electric vehicle traffic, a probability distribution model of traffic flow is established, and the Monte Carlo method is used to simulate the historical data to calculate the average traffic flow index; and the number, longitude and latitude coordinates and daily mileage of each electric vehicle are generated to provide basic data for the site selection of charging stations;

[0039] S32: Based on the basic data, the location of the charging station is selected using Voronoi graph theory, and the charging station set is used as the growth point set H = {H1, H2, H3, ..., H L},3≤L≤∞, then the distance from the demand point in the charging station service area to the charging station is less than or equal to the distance from the electric vehicle user to other charging stations, then the constraint condition of the multi-objective charging station location and capacity optimization model must satisfy Vor(H b )={u∈Vor(H b )d(u,H b )≤d(u,H l )};

[0040] In the formula, H L represents the different points on the plane, L represents the number of points Vor(H b ) is H b V polygon with vertices, d(u,Hl ) represents the point u and H l The distance between.

[0041] Furthermore, in step S40, the site selection and capacity determination model is iteratively solved by a simulated annealing intelligent algorithm to obtain a multi-objective optimal charging station site selection and capacity determination solution, including:

[0042] S41: The CRITIC method in the objective weighting method is used to assign weights to each objective function. The objective function can be dimensionless for negative indicators and positive indicators respectively. And calculate the final weight w j ;

[0043] In the formula, x′ ij represents the dimensionless value of a certain objective function, x ij represents the value of the function before it is dimensionless, max(x j ) and min(x j ) represent the maximum and minimum values ​​of the function before non-dimensionalization;

[0044] S42: Based on the final weight w j , solving it through simulated annealing intelligent algorithm, initializing it through the data of electric vehicle users and the initial selected location of the charging station generated by the Voronoi diagram theory, and weighting the objective function value through the CRITIC method, solving the objective function value under the initial conditions, and generating a domain solution;

[0045] S43: By comparing the difference between the domain solution objective function and the initial objective function, an iterative solution is performed with the help of the temperature factor update mechanism of the simulated annealing algorithm, and the quality of the solution is gradually adjusted and optimized until the convergence condition is met, thereby obtaining the optimal method for the location and capacity of the final charging station.

[0046] The present invention also includes a multi-objective charging station location and capacity determination device based on failure rate influence and queuing theory, using the above method, including:

[0047] A queue model calculation module is used to calculate the average queue length, average waiting time and stay time of electric vehicles at the charging station based on the M / M / c queue theory model and the probability distribution of electric vehicle users arriving at the charging station and using the charging station, and determine the preliminary configuration of charging equipment in the charging station;

[0048] A failure rate modeling and cost calculation module is used to model the failure rate of charging piles in the charging station based on the preliminary configuration of the charging equipment in the charging station, and to build a failure cost model that takes into account the failure rate of charging pile equipment based on the average maintenance time, the guarantee delay time and the component repair rate, and to form a multi-objective charging station location and capacity optimization model in combination with the satisfaction of electric vehicle users and the construction and operation costs of the charging station;

[0049] A service range determination module is used to determine the service range of each charging station based on the Voronoi diagram theory, determine the initial coordinates of the charging station by planning the charging needs of electric vehicle users in the area, adjust the location of the charging station by combining the multi-objective charging station site selection and capacity optimization model with the Voronoi diagram theory, and determine the final charging station site selection and capacity solution;

[0050] The capacity model generation module is used to determine the total demand for charging capacity within the service range of each charging station, determine the demand for charging piles installed at the charging station, and establish the capacity model of each charging station;

[0051] The multi-objective optimization solution module is used to construct a comprehensive evaluation index based on the capacity model of each charging station, combined with the satisfaction of the electric vehicle users and the construction and operation costs of the charging station, and iteratively solve the site selection and capacity determination model through a simulated annealing intelligent algorithm to obtain a multi-objective optimal charging station site selection and capacity determination plan.

[0052] The present invention also includes a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the method described above is implemented.

[0053] The present invention also includes a storage medium on which a computer program is stored. When the computer program is executed by a processor, the method described above is implemented.

[0054] The beneficial effects of the present invention are:

[0055] The impact of charging pile failure rate on charging station revenue is taken into account, and the time and economic optimization of electric vehicle users' selection of charging stations are considered based on queuing theory. At the same time, the service scope of the charging station is generated through Voronoi diagram theory, and the optimal site selection and capacity determination plan is determined by comparing the convenience of selecting charging stations at different locations and capacities for electric vehicle users and the revenue cost of charging stations. Finally, the above model is iteratively solved through simulated annealing intelligent algorithm, and the multi-objective optimal electric vehicle charging station site selection and capacity determination plan is obtained by comprehensively considering the electric vehicle user cost and charging station revenue, achieving the global optimal solution for site selection and capacity configuration. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, 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 recorded in the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0057] Figure 1 A flowchart of the method for site selection and capacity determination of multi-objective charging stations is provided;

[0058] Figure 2 A framework diagram for the multi-objective charging station site selection and capacity determination method;

[0059] Figure 3 Generate a schematic diagram of charging station location for Voronoi diagram theory;

[0060] Figure 4 The flowchart of the site selection and capacity determination solution of the simulated annealing algorithm based on CRITIC evaluation;

[0061] Figure 5 A schematic diagram of the structure of a device for selecting a location and determining the capacity of a multi-target charging station;

[0062] Figure 6 A schematic diagram of the structure of a computer device. DETAILED DESCRIPTION

[0063] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments.

[0064] like Figures 1 to 4 A multi-objective charging station site selection and capacity determination method is shown, comprising the following steps:

[0065] S10: Based on the M / M / c queuing theory model, for the probability distribution of a large number of electric vehicle users arriving at and using the charging station, the average queue length, average waiting time and stay time of electric vehicles at the charging station are calculated, and the preliminary configuration of charging equipment in the charging station is determined; this step provides a basic probability model to calculate the queuing situation of electric vehicles;

[0066] S20: Based on the preliminary configuration of the charging equipment in the charging station, the failure rate of the charging piles in the charging station is modeled, and based on the average maintenance time, the guarantee delay time and the component repair rate, a failure cost model considering the failure rate of the charging pile equipment is constructed, and combined with the satisfaction of electric vehicle users and the construction and operation costs of the charging station, a multi-objective charging station location and capacity optimization model is formed; this step is based on the preliminary configuration, and further considers equipment failures and their impacts to form a more detailed optimization model;

[0067] S30: Determine the service scope of each charging station based on Voronoi diagram theory, determine the initial coordinates of the charging station by planning the charging needs of electric vehicle users in the area, adjust the location of the charging station by combining the multi-objective charging station site selection and capacity optimization model with Voronoi diagram theory, and determine the final charging station site selection and capacity solution. By dividing the service scope of each charging station, the total demand for charging capacity within the scope can be known, the demand for charging piles installed at the charging station is determined, and the capacity model of each charging station is established; this step uses spatial planning tools (Voronoi diagram) to optimize the service scope and capacity configuration in geographic space;

[0068] S40: Based on the capacity model of each charging station, combined with the satisfaction of electric vehicle users and the construction and operation costs of the charging station, a comprehensive evaluation index is constructed, and the site selection and capacity determination model is iteratively solved through the simulated annealing intelligent algorithm to obtain the multi-objective optimal charging station site selection and capacity determination plan; this step performs global optimization, balances user satisfaction and site costs, and obtains the optimal plan.

[0069] Under the background of rapid growth of electric vehicle ownership and increasing demand for charging station planning, the present invention takes into account the impact of charging pile failure rate on charging station revenue, and considers the time and economic optimization of electric vehicle users selecting charging stations based on queuing theory; at the same time, the service scope of the site selection charging station is generated through Voronoi graph theory, and the optimal site selection and capacity determination plan is determined by comparing the convenience of selecting charging stations of different locations and capacities for electric vehicle users and the revenue cost of the charging stations; finally, the above model is iteratively solved through a simulated annealing intelligent algorithm, and the multi-objective optimal site selection and capacity determination plan for electric vehicle charging stations is obtained by comprehensively considering the cost of electric vehicle users and the revenue of charging stations.

[0070] The present invention constructs a multi-objective planning model for the site selection and sizing of electric vehicle charging stations, which fully considers the satisfaction indicators of the charging waiting time and cost structure of electric vehicle users, as well as the total cost of planning, operation and maintenance within the service scope of each charging station, and comprehensively formulates a site selection and sizing planning scheme that takes into account the two multiple objectives.

[0071] This paper proposes a multi-objective location and capacity determination method for charging stations. For the location and capacity determination problem of charging stations, the benefits of both electric vehicle users and charging stations are comprehensively considered. For electric vehicle users, the time and cost spent on charging constitute the user satisfaction index. For charging stations, the additional cost caused by the failure rate of charging piles in the charging station and the location planning and maintenance cost of the charging station are considered. The multi-objective function is solved by the objective weighting method, and the optimal location and capacity determination plan for the charging station is obtained by the simulated annealing algorithm.

[0072] Compared with the existing technology, this technical solution constructs a failure cost model by modeling the failure rate of charging pile equipment and combining parameters such as average maintenance time and guaranteed delay time. This multi-objective optimization method considering charging pile failures effectively solves the problem that the existing technology does not fully consider the impact of equipment failures on charging station operations, improves the overall reliability and stability of the charging station, and reduces downtime and service interruptions caused by failures.

[0073] This solution calculates the average queue length, waiting time and stay time of charging stations through the M / M / c queuing theory model, optimizes the configuration of charging equipment, and reduces the waiting time of users. At the same time, combined with the user satisfaction model, it fully considers the economic issues of charging costs, service quality and user charging behavior, so that the site selection and capacity determination plan can maximize the user's charging satisfaction while improving the user experience.

[0074] This technical solution uses Voronoi graph theory to reasonably divide the service range of charging stations, and combines the charging needs of electric vehicle users to ensure that the location and capacity of each charging station are reasonably configured. This solves the problem of inaccurate planning of the service range of charging stations in the prior art, avoids waste of resources or excessive congestion of sites, and thus improves resource utilization and system service efficiency.

[0075] Through the multi-objective optimization model of failure rate modeling, user satisfaction and planning and maintenance costs of charging stations, this technical solution effectively balances user needs and site operating costs. Finally, global optimization is performed through simulated annealing intelligent algorithm to ensure that the system can reduce construction and maintenance costs and improve the operating efficiency of charging stations while meeting user needs.

[0076] This technical solution uses a simulated annealing intelligent algorithm to iteratively solve the entire site selection and capacity determination model, combining multiple objective factors such as user satisfaction, capacity demand within the service scope, and failure rate cost, and ultimately obtains the optimal site selection and capacity determination plan. This global optimization method effectively solves the problem of insufficient consideration of user needs and site operating costs in existing technologies, and achieves the global optimal solution for site selection and capacity configuration.

[0077] As a preferred embodiment of the above, in step S10, based on the M / M / c queuing theory model, for the probability distribution of a large number of electric vehicle users arriving at the charging station and using the charging station, the average queue length, average waiting time and stay time of the electric vehicles at the charging station are calculated, and the preliminary configuration of the charging equipment in the charging station is determined; including:

[0078] When an electric vehicle user has a charging demand, the probability of arriving at the charging station follows the Poisson distribution. At the same time, the user's charging time conforms to the law of negative exponential distribution. Based on these characteristics, the behavior of electric vehicle users during charging can be described as the M / M / c queuing model:

[0079] S11: Based on the M / M / c queuing theory model, describe the Poisson distribution characteristics of electric vehicle users arriving at the charging station and the negative exponential distribution law of the charging time, establish the system state transfer difference equation, and form a statistical model of the queuing system of the charging station; including:

[0080]

[0081] Where μ represents the number of services provided by each charging pile per hour, N ch Indicates the number of charging piles in the charging station, P n represents the probability that there are n vehicles that need to be charged in the charging station, P0 represents the probability that there is no vehicle that needs to be charged in the charging station, λ represents the Poisson parameter, P1, P n-1 , P n+1 They represent the probability that there are 1, n-1, and n+1 electric vehicles that need to be charged in the charging station; n represents the number of electric vehicles that need to be charged;

[0082] S12: Based on the statistical model, the probability of charging demand in the charging station is counted to obtain the probability of no vehicle demanding charging in the charging station and the probability model of n electric vehicles needing charging; including:

[0083]

[0084] Where ρ = λ / μN ch Indicates the service intensity of the charging pile, and its value is less than 1;

[0085] S13: Using statistical models and probability models, the average queue length, average waiting time and average stay time of the charging station queue system are calculated, and the preliminary configuration optimization objective function model of the charging equipment is constructed with minimizing the average waiting time as the optimization goal.

[0086] Through the combination of steps S11, S12 and S13, the average queue length, average waiting time and average stay time of the charging station can be accurately calculated. These performance indicators directly reflect the service efficiency and user experience of the charging station. Among them, the average queue length helps to predict the number of vehicles in the queue, thereby optimizing the configuration of charging piles to avoid excessive queues or excessive waste of resources; the average waiting time directly affects the user experience, and a shorter waiting time can improve user satisfaction; the average stay time includes charging time and queuing time, which comprehensively reflects the total time spent by users at the charging station.

[0087] In this embodiment, in step S13, the model for calculating the average queue length, average waiting time and average stay time of the charging station queue system includes:

[0088]

[0089] Where, L q represents the average queue length, W q represents the average waiting time, W s represents the average stay time, λ represents the Poisson parameter, k represents the cumulative function subscript, μ represents the number of services provided by each charging pile per hour, and N ch represents the number of charging piles in the charging station, P0 represents the probability that there is no need to charge in the charging station, ρ = λ / μN ch Indicates the charging pile service intensity, and its value is less than 1.

[0090] The above formulas show how to calculate the performance indicators of the charging station queuing system, including average queue length, average waiting time, and average stay time. These formulas can help optimize the equipment configuration of the charging station and ensure efficient operation of the charging station under vehicle load.

[0091] In step S13, the optimization objective is to minimize the average waiting time and construct a preliminary configuration optimization objective function model for the charging equipment, including:

[0092] f1=C0N ch I0+C w W q ;

[0093] W q ≤W max ;

[0094] N ch,min ≤N ch ≤N ch,max ;

[0095] In the formula, C0 represents the idle cost of charging facilities, C w represents the charging service loss cost, N ch Indicates the number of charging piles in the charging station, N ch,min and N ch,max Respectively represent the minimum number of charging piles and the maximum number of charging piles, W q represents the average waiting time, W max represents the maximum waiting time of the vehicle, I0 represents the equipment idle probability of the charging station queuing system, and I0 is equal to

[0096]

[0097] Through the above model formula, the configuration of charging equipment can be optimized to achieve the goals of minimizing the average waiting time and maximizing the system operation efficiency.

[0098] As a preferred embodiment of the above, in step S20, based on the preliminary configuration of the charging equipment in the charging station, the failure rate of the charging piles in the charging station is modeled, and based on the average maintenance time, the guarantee delay time and the component repair rate, a failure cost model considering the failure rate of the charging pile equipment is constructed, and combined with the satisfaction of electric vehicle users and the construction and operation costs of the charging station, a multi-objective charging station site selection and capacity optimization model is formed; including:

[0099] S21: Model the component failure rate and component repair rate based on Weibull distribution, deduce the average component guarantee delay time from the component failure rate and component repair rate, and obtain the average guarantee delay time;

[0100] In the power system, the component failure rate uses the Weibull distribution To describe, the probability density function is The component repair rate is The average component guarantee delay time can be derived from the component failure rate and component repair rate. The average guarantee delay time can be calculated by Give a description;

[0101] Where ω(t) represents the component failure rate, γ represents the Weibull shape parameter, θ represents the characteristic life, and α r , β r denote the shape and size parameters of the exponential, η r represents the Weibull scaling parameter, t represents the component working time, and Γ represents the gamma function;

[0102] S22: Based on the component failure rate, for the components of electric vehicle charging piles, the charging piles of the same model operate independently of each other. The component failure rate is modeled based on the binomial distribution. In addition to the successful repair of the faulty components, the charging pile components also have a probability of failure. The failure probability of the charging pile components is calculated, and the failure cost model of the charging station is derived;

[0103] S23: Considering the user's travel distance, charging time and charging cost, a user satisfaction index is constructed;

[0104] S24: Based on the failure cost model of the charging station and the user satisfaction index, the construction and operation and maintenance costs of the charging station are integrated to construct the total construction cost of the charging station. The total construction cost of the charging station is the objective function of the multi-objective charging station location and capacity optimization model. Among them, the construction cost is The operation and maintenance cost is The two together constitute the construction cost of the charging station f4 = C b +C m .

[0105] Among them, K evc represents the investment equivalent coefficient, c evcrepresents the unit investment price, n i,ch represents the number of charging piles in the i-th charging station, ρ evc represents the cost of the charging pile, μ evc Indicates the maintenance cost transferred from charging, taking 5%ρ evc .

[0106] By combining the Weibull distribution and the binomial distribution to model the failure rate and failure probability of charging piles, the site selection and capacity configuration of charging stations are optimized based on the reliability of equipment. This method avoids the problem of excessive maintenance or waste of resources due to equipment failure, and achieves the optimal configuration of charging stations in terms of system reliability. The failure rate and failure probability are used to derive the failure cost of charging stations, so that the failure cost can be accurately estimated, thereby helping charging station operators to formulate reasonable operation and maintenance plans. By reasonably allocating operation and maintenance resources, the maintenance and repair costs in long-term operations are reduced. By building a user satisfaction model and comprehensively considering the user's travel distance, charging time and charging costs, this solution ensures the importance of user experience in the entire site selection and capacity planning process. Under the premise of ensuring user satisfaction, this solution achieves a balance between economic cost and user experience, avoiding the problem of affecting user charging experience due to excessive compression of construction and operation and maintenance costs.

[0107] In this embodiment, in step S22, according to the component failure rate, the component failure rate is modeled based on the binomial distribution, the failure probability of the charging pile component is calculated, and the failure cost of the charging station is derived. The model includes:

[0108]

[0109] f2=(1-P l,ch +P f,ch ) re,ev C b,ev t mldt ;

[0110] Where f2 represents the failure cost of the charging station, P f,ch represents the failure rate of charging pile components, P l,ch represents the failure probability of the faulty component of the charging pile, l represents the number of charging piles in operation, P re,ev represents the expected output of the charging pile, C b,ev represents the unit charging electricity price, λ A represents the reliability constant, ω(t) represents the component failure rate, γ represents the Weibull shape parameter, and θ represents the characteristic life.

[0111] Based on Weibull distribution and binomial distribution, the failure rate and failure probability of components are accurately calculated, and then the failure cost is derived, which helps operators to rationally plan maintenance and repair resources and reduce economic losses caused by unexpected downtime.

[0112] In step S23, the user's travel distance, charging time and charging cost are comprehensively considered to construct a user satisfaction index, and the model includes:

[0113]

[0114] In the formula, f3 represents the user satisfaction index, d ij represents the distance between electric vehicle i and target charging station j, t ij represents the time it takes for electric vehicle i to reach the target charging station j, C ij Indicates the total charging cost. represents the total charging price of charging station j (including basic charging unit price and basic service fee), represents the basic parking fee of charging station j, t c,i represents the charging time of electric vehicle i, C dis represents the benefit of electric vehicle i receiving discharge, for P ev (t), which is given by P c,i -P dis,i Composition, P ev Represents the average charging power, P dis,i represents the average discharge power, t w represents the waiting time of electric vehicles at the charging station, t w,i represents the queue time of electric vehicle i, t dis,i Indicates the discharge time of electric vehicle i.

[0115] The model comprehensively considers the user's distance, time and cost, and can comprehensively measure the user's experience at the charging station, providing a reliable basis for the optimization of site selection and capacity planning; through the reasonable calculation of charging costs, parking fees and charging time, the charging station can ensure that the user's costs are reasonable while providing services, avoiding high charges that lead to decreased user satisfaction; by modeling the user's waiting time and queuing time, the solution can optimize the user's waiting experience at the charging station, reduce queuing time, and improve system efficiency and user satisfaction.

[0116] As a preferred embodiment of the above, in step S30, the service range of each charging station is determined based on the Voronoi graph theory, the initial coordinates of the charging station are determined by planning the charging needs of electric vehicle users in the area, the location of the charging station is adjusted by combining the multi-objective charging station site selection and capacity optimization model with the Voronoi graph theory, and the final charging station site selection and capacity solution is determined. Through the divided service ranges of each charging station, the total demand for charging capacity within the range can be known, the demand for charging piles installed at the charging station is determined, and the capacity model of each charging station is established; including:

[0117] S31: Based on the historical data of electric vehicle traffic, a probability distribution model of traffic flow is established, and the Monte Carlo method is used to simulate the historical data to calculate the average traffic flow index; and the number, longitude and latitude coordinates and daily mileage of each electric vehicle are generated to provide basic data for the site selection of charging stations;

[0118] S32: Based on the basic data, the location of the charging station is selected using Voronoi graph theory, and the charging station set is used as the growth point set H = {H1, H2, H3, ..., H L},3≤L≤∞, then the distance from the demand point to the charging station in the charging station service area is less than or equal to the distance from the electric vehicle user to other charging stations, then the constraint condition of the multi-objective charging station location and capacity optimization model must satisfy Vor(H b )={u∈Vor(H b )d(u,H b )≤d(u,H l )}, that is, to meet the principle that electric vehicle users choose the nearest charging station;

[0119] In the formula, H L represents the different points on the plane, L represents the number of points Vor(H b ) is H b V polygon with vertices, d(u,H l ) represents the point u and H l The distance between.

[0120] By analyzing the historical data of electric vehicle traffic and using the Monte Carlo method for simulation, the solution can more accurately generate relevant data such as the number, longitude and latitude of each electric vehicle, and daily mileage. Combined with these basic data, the initial coordinates of the charging station can be reasonably determined to ensure that the site selection process is data-driven, which improves the accuracy and scientificity of the charging station site selection; the Voronoi graph theory is used to divide the charging station service area, and by selecting the charging station with the shortest service distance as the user's first choice, the principle of electric vehicle users choosing the nearest charging station is met. This method not only takes into account the optimal distribution in geographic space, but also reduces the driving distance of users between charging stations, improving the convenience and satisfaction of users charging.

[0121] In this embodiment, in step S40, the site selection and capacity determination model is iteratively solved by a simulated annealing intelligent algorithm to obtain a multi-objective optimal charging station site selection and capacity determination solution, including:

[0122] S41: The CRITIC (Criteria Importance Through Intercriteria Correlation) method in the objective weighting method is used to assign weights to each objective function. The objective function can be dimensionless for negative indicators and positive indicators respectively. Get the standard deviation σ of each indicator j The correlation coefficient R between each indicator ij Then, find the information amount C of each indicator i , and calculate the final weight w j ;Therefore, it can provide support for the optimal decision-making of charging station site selection and capacity determination;

[0123] In the formula, x′ ij represents the dimensionless value of a certain objective function, x ij represents the value of the function before it is dimensionless, max(x j ) and min(x j ) represent the maximum and minimum values ​​of the function before non-dimensionalization;

[0124] S42: Based on the final weight w j , the solution is solved by the simulated annealing intelligent algorithm, the initial selection position of the charging station generated by the electric vehicle user data and the Voronoi diagram theory is initialized, and the objective function value is weighted by the CRITIC method to solve the objective function value under the initial conditions and generate the domain solution; this step ensures that the algorithm starts to optimize from a reasonable initial position;

[0125] S43: By comparing the difference between the domain solution objective function and the initial objective function, the temperature factor update mechanism of the simulated annealing algorithm is used to iteratively solve the problem, and the quality of the solution is gradually adjusted and optimized until the convergence condition is met, and the optimal method for the location and capacity of the final charging station is obtained. This step determines whether to accept the domain solution based on the temperature factor, and updates the temperature factor until the result meets the stop condition, and the optimal method for the location and capacity of the final charging station is obtained, ensuring that the algorithm can escape the local optimum and finally find the global optimal solution.

[0126] By using the CRITIC method to non-dimensionalize positive and negative indicators, and combining the standard deviation and correlation coefficient to calculate the weight of each objective function, the objectivity and scientificity of weight distribution are ensured. This data-based objective weighting method avoids the influence of subjective factors, so that each objective function can fairly reflect its actual impact during the optimization process, and improves the rationality and effectiveness of the site selection and capacity determination plan.

[0127] The iterative solution using the simulated annealing intelligent algorithm can effectively avoid the optimization process from falling into the local optimal solution. The simulated annealing algorithm gradually updates the temperature factor and combines the objective function difference between the domain solution and the initial solution to gradually optimize the quality of the solution and finally converge to the global optimal solution. This ensures that the site selection and capacity configuration of the charging station can obtain the best solution under multi-objective optimization.

[0128] The present invention also includes a multi-objective charging station location and capacity determination device based on failure rate influence and queuing theory, using the above method, such as Figure 5 As shown, including:

[0129] A queue model calculation module is used to calculate the average queue length, average waiting time and stay time of electric vehicles at the charging station based on the M / M / c queue theory model and the probability distribution of electric vehicle users arriving at the charging station and using the charging station, and determine the preliminary configuration of charging equipment in the charging station;

[0130] The failure rate modeling and cost calculation module is used to model the failure rate of charging piles in the charging station based on the preliminary configuration of the charging equipment in the charging station, and to build a failure cost model that takes into account the failure rate of charging pile equipment based on the average maintenance time, guarantee delay time and component repair rate. In addition, it combines the satisfaction of electric vehicle users and the construction and operation costs of the charging station to form a multi-objective charging station location and capacity optimization model;

[0131] The service range determination module is used to determine the service range of each charging station based on the Voronoi diagram theory. By planning the charging needs of electric vehicle users in the area, the initial coordinates of the charging station are determined. The location of the charging station is adjusted by combining the multi-objective charging station site selection and capacity optimization model with the Voronoi diagram theory to determine the final charging station site selection and capacity solution.

[0132] The capacity model generation module is used to determine the total demand for charging capacity within the service range of each charging station, determine the demand for charging piles installed at the charging station, and establish the capacity model of each charging station;

[0133] The multi-objective optimization solution module is used to construct a comprehensive evaluation index based on the capacity model of each charging station, combined with the satisfaction of electric vehicle users and the construction and operation costs of the charging station, and iteratively solve the site selection and capacity determination model through the simulated annealing intelligent algorithm to obtain the multi-objective optimal charging station site selection and capacity determination plan.

[0134] See also Figure 6A computer device 400 provided in an embodiment of the present application includes: a processor 410 and a memory 420, wherein the memory 420 stores a computer program executable by the processor 410, and when the computer program is executed by the processor 410, the above method is executed.

[0135] The embodiment of the present application further provides a storage medium 430 on which a computer program is stored. When the computer program is run by the processor 410, the above method is executed.

[0136] Among them, the storage medium 430 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable red-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, disk or optical disk.

[0137] In the description of the present invention, 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. Therefore, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. "Multiple" means two or more, unless otherwise clearly and specifically defined.

[0138] In the present invention, unless otherwise clearly specified and limited, the terms "installed", "connected", "connected", "fixed" and the like 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 an indirect connection through an intermediate medium, it can be the internal connection of two elements or the interaction relationship between two elements. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0139] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, unless they are contradictory.

[0140] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, segment or portion of code that includes one or more executable instructions for implementing the steps of a specific logical function or process, and the scope of the preferred embodiments of the present invention includes alternative implementations in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in the reverse order depending on the functions involved, which should be understood by those skilled in the art to which the embodiments of the present invention belong.

[0141] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in combination with these instruction execution systems, devices or apparatuses. For the purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in combination with these instruction execution systems, devices or apparatuses. More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic devices), a portable computer disk box (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), a fiber optic device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium and then editing, interpreting or processing in other suitable ways if necessary, and then stored in a computer memory.

[0142] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, a plurality of steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0143] A person skilled in the art may understand that all or part of the steps in the method for implementing the above-mentioned embodiment may be completed by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, which, when executed, includes one or a combination of the steps of the method embodiment.

[0144] The storage medium mentioned above may be a read-only memory, a magnetic disk or an optical disk, etc. Although the embodiments of the present invention have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limiting the present invention. A person of ordinary skill in the art may change, modify, replace and modify the above embodiments within the scope of the present invention.

Claims

1. A multi-objective charging station site selection and capacity determination method, characterized in that: The steps include: S10: Based on the M / M / c queuing theory model and the probability distribution of electric vehicle users arriving at and using charging stations, the average queue length, average waiting time and stay time of electric vehicles at charging stations are calculated to determine the preliminary configuration of charging equipment in the charging stations; S20: Based on the preliminary configuration of the charging equipment in the charging station, the failure rate of the charging piles in the charging station is modeled, and based on the average maintenance time, the guarantee delay time and the component repair rate, a failure cost model considering the failure rate of the charging pile equipment is constructed, and combined with the satisfaction of electric vehicle users and the construction and operation costs of the charging station, a multi-objective charging station location and capacity optimization model is formed; S30: Determine the service range of each charging station based on the Voronoi graph theory, determine the initial coordinates of the charging station by planning the charging needs of electric vehicle users in the area, adjust the location of the charging station by combining the multi-objective charging station site selection and capacity optimization model and the Voronoi graph theory, and determine the final charging station site selection and capacity solution. By dividing the service range of each charging station, the total demand for charging capacity within the range can be known, the demand for charging piles installed at the charging station is determined, and the capacity model of each charging station is established; S40: Based on the capacity model of each charging station, combined with the satisfaction of the electric vehicle users and the construction and operation costs of the charging station, a comprehensive evaluation index is constructed, and the site selection and capacity determination model is iteratively solved through a simulated annealing intelligent algorithm to obtain a multi-objective optimal charging station site selection and capacity determination plan.

2. The multi-objective charging station site selection and capacity determination method according to claim 1 is characterized in that: In step S10, based on the M / M / c queuing theory model, for the probability distribution of a large number of electric vehicle users arriving at the charging station and using the charging station, the average queue length, average waiting time and stay time of electric vehicles at the charging station are calculated, and the preliminary configuration of the charging equipment in the charging station is determined; including: S11: Based on the M / M / c queuing theory model, the Poisson distribution characteristics of electric vehicle users arriving at charging stations and the negative exponential distribution law of charging time are described, and the system state transfer difference equation is established to form a statistical model of the queuing system of the charging station; S12: Based on the statistical model, the probability of charging demand in the charging station is counted to obtain the probability of no vehicle demanding charging in the charging station and the probability model of n electric vehicles needing charging; S13: Utilizing the statistical model and the probability model, the average queue length, average waiting time and average stay time of the charging station queue system are calculated, and a preliminary configuration optimization objective function model of the charging equipment is constructed with minimizing the average waiting time as the optimization goal.

3. The multi-objective charging station site selection and capacity determination method according to claim 2 is characterized in that: In step S13, the model for calculating the average queue length, average waiting time and average stay time of the charging station queue system includes: Where, L q represents the average queue length, W q represents the average waiting time, W s represents the average stay time, λ represents the Poisson parameter, k represents the cumulative function subscript, μ represents the number of services provided by each charging pile per hour, and N ch represents the number of charging piles in the charging station, P0 represents the probability that there is no need to charge in the charging station, ρ = λ / μN ch Indicates the charging pile service intensity, and its value is less than 1.

4. The multi-objective charging station site selection and capacity determination method according to claim 2, characterized in that: In step S13, taking minimizing the average waiting time as the optimization goal, a preliminary configuration optimization objective function model of the charging equipment is constructed, including: f1=C0N ch I0+C w W q ; IN q ≤In max ; N ch,min ≤N ch ≤N ch,max ; In the formula, C0 represents the idle cost of charging facilities, C w represents the charging service loss cost, N ch Indicates the number of charging piles in the charging station, N ch,min and N ch,max Respectively represent the minimum number of charging piles and the maximum number of charging piles, W q represents the average waiting time, W max represents the maximum waiting time of the vehicle, I0 represents the equipment idle probability of the charging station queuing system, and I0 is equal to 5. The multi-objective charging station site selection and capacity determination method according to claim 1, characterized in that: In step S20, based on the preliminary configuration of the charging equipment in the charging station, the failure rate of the charging piles in the charging station is modeled, and based on the average maintenance time, the guarantee delay time and the component repair rate, a failure cost model considering the failure rate of the charging pile equipment is constructed, and combined with the satisfaction of electric vehicle users and the construction and operation costs of the charging station, a multi-objective charging station site selection and capacity optimization model is formed; including: S21: Modeling component failure rate and component repair rate based on Weibull distribution, and deriving the average guarantee delay time of the component from the component failure rate and the component repair rate to obtain the average guarantee delay time; S22: According to the component failure rate, model the component failure rate based on binomial distribution, calculate the failure probability of the charging pile component, and derive the failure cost model of the charging station; S23: Considering the user's travel distance, charging time and charging cost, a user satisfaction index is constructed; S24: Based on the failure cost model of the charging station and the user satisfaction index, the construction and operation and maintenance costs of the charging station are integrated to construct the total construction cost of the charging station. The total construction cost of the charging station is the objective function of the multi-objective charging station site selection and sizing optimization model.

6. The multi-objective charging station site selection and capacity determination method according to claim 5 is characterized in that: In step S22, according to the component failure rate, the component failure rate is modeled based on binomial distribution, the failure probability of the charging pile component is calculated, and the failure cost of the charging station is derived. The model includes: f2=(1-P l,ch +P f,ch )P re,ev C b,ev t mldt ; Where f2 represents the failure cost of the charging station, P f,ch represents the failure rate of charging pile components, P l,ch represents the failure probability of the faulty component of the charging pile, l represents the number of charging piles in operation, P re,ev represents the expected output of the charging pile, C b,ev represents the unit charging electricity price, λ A represents the reliability constant, ω(t) represents the component failure rate, γ represents the Weibull shape parameter, and θ represents the characteristic life.

7. The multi-objective charging station site selection and capacity determination method according to claim 5, characterized in that: In step S23, the user's travel distance, charging time and charging cost are comprehensively considered to construct a user satisfaction index, and the model includes: In the formula, f3 represents the user satisfaction index, d ij represents the distance between electric vehicle i and target charging station j, t ij represents the time it takes for electric vehicle i to reach the target charging station j, C ij Indicates the total charging cost. represents the total charging price at charging station j, represents the basic parking fee of charging station j, t c,i represents the charging time of electric vehicle i, C dis represents the benefit of electric vehicle i receiving discharge, P ev Represents the average charging power, P dis,i represents the average discharge power, t w represents the waiting time of electric vehicles at the charging station, t w,i represents the queue time of electric vehicle i, t dis,i Indicates the discharge time of electric vehicle i.

8. The multi-objective charging station site selection and capacity determination method according to claim 1, characterized in that: In step S30, the service range of each charging station is determined based on the Voronoi graph theory, the initial coordinates of the charging station are determined by planning the charging needs of electric vehicle users in the area, the location of the charging station is adjusted by combining the multi-objective charging station site selection and capacity optimization model and the Voronoi graph theory, and the final charging station site selection and capacity solution is determined. Through the divided service ranges of each charging station, the total demand for charging capacity within the range can be known, the demand for charging piles installed at the charging station is determined, and the capacity model of each charging station is established; including: S31: Based on the historical data of electric vehicle traffic, a probability distribution model of traffic flow is established, and the Monte Carlo method is used to simulate the historical data to calculate the average traffic flow index; and the number, longitude and latitude coordinates and daily mileage of each electric vehicle are generated to provide basic data for the site selection of charging stations; S32: Based on the basic data, the location of the charging station is selected using Voronoi graph theory, and the charging station set is used as the growth point set H = {H1, H2, H3, ..., H L },3≤L≤∞, then the distance from the demand point in the charging station service area to the charging station is less than or equal to the distance from the electric vehicle user to other charging stations, then the constraint condition of the multi-objective charging station location and capacity optimization model must satisfy Vor(H b )={u∈Vor(H b )d(u,H b )≤d(u,H l )}; In the formula, H L represents the different points on the plane, L represents the number of points Vor(H b ) is H b V polygon with vertices, d(u,H l ) represents the point u and H l The distance between.

9. The multi-objective charging station site selection and capacity determination method according to claim 1, characterized in that: In step S40, the site selection and capacity determination model is iteratively solved by a simulated annealing intelligent algorithm to obtain a multi-objective optimal charging station site selection and capacity determination solution, including: S41: The CRITIC method in the objective weighting method is used to assign weights to each objective function. The objective function can be dimensionless for negative indicators and positive indicators respectively. And calculate the final weight w j ; In the formula, x i ' j represents the dimensionless value of a certain objective function, x ij represents the value of the function before it is dimensionless, max(x j ) and min(x j ) represent the maximum and minimum values ​​of the function before non-dimensionalization; S42: Based on the final weight w j , solving it through simulated annealing intelligent algorithm, initializing it through the data of electric vehicle users and the initial selected location of the charging station generated by the Voronoi diagram theory, and weighting the objective function value through the CRITIC method, solving the objective function value under the initial conditions, and generating a domain solution; S43: By comparing the difference between the domain solution objective function and the initial objective function, an iterative solution is performed with the help of the temperature factor update mechanism of the simulated annealing algorithm, and the quality of the solution is gradually adjusted and optimized until the convergence condition is met, thereby obtaining the optimal method for the location and capacity of the final charging station.

10. A multi-objective charging station location and capacity determination device based on failure rate influence and queuing theory, characterized in that: Use of the method according to any one of claims 1 to 9, comprising: A queue model calculation module is used to calculate the average queue length, average waiting time and stay time of electric vehicles at the charging station based on the M / M / c queue theory model and the probability distribution of electric vehicle users arriving at the charging station and using the charging station, and determine the preliminary configuration of charging equipment in the charging station; A failure rate modeling and cost calculation module is used to model the failure rate of charging piles in the charging station based on the preliminary configuration of the charging equipment in the charging station, and to build a failure cost model that takes into account the failure rate of charging pile equipment based on the average maintenance time, the guarantee delay time and the component repair rate, and to form a multi-objective charging station location and capacity optimization model in combination with the satisfaction of electric vehicle users and the construction and operation costs of the charging station; A service range determination module is used to determine the service range of each charging station based on the Voronoi diagram theory, determine the initial coordinates of the charging station by planning the charging needs of electric vehicle users in the area, adjust the location of the charging station by combining the multi-objective charging station site selection and capacity optimization model with the Voronoi diagram theory, and determine the final charging station site selection and capacity solution; The capacity model generation module is used to determine the total demand for charging capacity within the service range of each charging station, determine the demand for charging piles installed at the charging station, and establish the capacity model of each charging station; The multi-objective optimization solution module is used to construct a comprehensive evaluation index based on the capacity model of each charging station, combined with the satisfaction of the electric vehicle users and the construction and operation costs of the charging station, and iteratively solve the site selection and capacity determination model through a simulated annealing intelligent algorithm to obtain a multi-objective optimal charging station site selection and capacity determination plan.

11. A computer 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 method according to any one of claims 1 to 9 is implemented.

12. A storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 9 is implemented.

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