Construction method of car charging station based on quantum genetic algorithm and without alternative station site

Abstract

The application discloses a kind of based on quantum genetic algorithm and no alternative station site's automobile charging station construction method, comprising the following steps: step S1. build electric vehicle charging station site selection and capacity determination model;Step S2. obtain the relevant data of the region to be planned;Step S3. the region to be planned is divided into m charging planning service sub-region;Step S4. site selection and capacity determination of the electric vehicle charging station to be built in charging planning service area;Step S5. determine the optimal charging station site selection and capacity determination scheme in the region to be planned.The present application combines quantum computing theory and genetic algorithm, solves the problem that iteration number is many, convergence speed is slow and is easy to fall into local extreme value in genetic algorithm due to improper selection, crossing and variation mode, finally realizes electric vehicle low-cost high-efficiency planning, and the present application planning scheme considers the influence of existing charging station, as far as possible reduces the service range overlap between each station, avoids resource waste.

Description

Technical Field

[0001] This invention relates to the field of electric vehicle charging station expansion planning technology, and specifically to a method for constructing an electric vehicle charging station that takes into account existing electric vehicle charging stations. Background Technology

[0002] With the global shortage of traditional fossil fuels becoming increasingly severe and environmental pollution problems intensifying, the dual pressures of energy crisis and environmental protection are becoming increasingly prominent. Vigorously developing electric vehicles is a key measure to promote energy transformation in the transportation sector. It can not only effectively reduce dependence on oil and ensure energy security, but also directly reduce exhaust emissions at the source, which is of profound significance for achieving the goals of energy conservation, emission reduction, and air pollution control. However, the limited driving range and relatively slow charging speed of electric vehicles remain key bottlenecks restricting their large-scale promotion and application. Therefore, building a comprehensive charging infrastructure system is an important guarantee for promoting the popularization of electric vehicles. Further accelerating the layout and construction of charging infrastructure is not only an urgent need for promoting the application of electric vehicles, but also a key strategic measure to deeply advance the energy consumption revolution.

[0003] However, existing technologies are mostly limited to research on charging infrastructure with alternative sites and initial planning studies, with limited research on charging station expansion without alternative sites or under existing charging facilities. This makes it difficult for planning schemes to meet practical application needs. Specifically, on the one hand, the quality of solutions obtained by existing alternative site models is limited by the quality of the alternative site set. If the quality of the alternative site set itself is low, even if the subsequent optimization algorithm is sophisticated, the result will be difficult to be optimal. At the same time, the process of formulating the alternative site set is complex and subjective, usually relying on expert scoring, decision-maker preferences, or comprehensive evaluation based on multiple criteria, which is difficult to fully quantify and is easily influenced by personal experience and stakeholders. On the other hand, the electric vehicle industry has moved from the explosive introduction phase to the large-scale growth phase, and the problems it faces have fundamentally changed. A preliminary charging network has been formed, but the charging demand is explicit and growing explosively, urgently requiring optimization of the existing network and improvement of its service capacity.

[0004] Meanwhile, solving high-dimensional nonlinear charging station site selection and sizing models is prone to getting stuck in local optima and slow iteration speed. If the charging station planning model has high data dimensionality and many constraints, ordinary charging station planning models may fail due to data complexity. Summary of the Invention

[0005] The purpose of this invention is to provide a method for constructing electric vehicle charging stations based on quantum genetic algorithms and without alternative sites. This method combines quantum computing theory and genetic algorithms to solve the problems of excessive iterations, slow convergence speed, and susceptibility to local extrema caused by improper selection, crossover, and mutation methods in genetic algorithms.

[0006] The objective of this invention is achieved through the following technical solution: A method for constructing an electric vehicle charging station based on a quantum genetic algorithm and without alternative sites includes the following steps: Step S1. Constructing a site selection and capacity determination model for electric vehicle charging stations: By balancing the interests of charging station operators, electric vehicle users, and power grid companies, a site selection and capacity determination model for electric vehicle charging stations is constructed on the basis of no alternative sites; the so-called no alternative sites means that the number of charging stations is first determined, and then the final site is determined by optimizing the objective function across the entire range. Step S2. Obtain relevant data for the area to be planned: Obtain relevant data for the area to be planned, including basic data on charging demand points and charging stations, charging station construction and operation cost data, and land cost data; Step S3. Divide the area to be planned into m charging planning service sub-regions: There are m electric vehicle charging stations to be built in the area to be planned. Set a preset site for each electric vehicle charging station. Using the m preset sites as base points, use the Voronoi diagram algorithm to divide the area to be planned into m sub-regions. In each sub-region, use the Floyd algorithm to dynamically update the path length between nodes and gradually solve the shortest path between the charging demand point and the preset site of the charging station in its sub-region. At the same time, use the Floyd algorithm to calculate the shortest distance between each charging demand point and the preset site of the charging station in the adjacent sub-region. By comparing the path distance from the charging demand point to each preset site of the charging station, determine the charging planning service sub-regions of the m electric vehicle charging stations to be built in the area to be planned. Step S4. Site selection and capacity determination for electric vehicle charging stations within the charging planning service area: A charging station site selection and capacity determination scheme includes a charging station site selection scheme and a charging station scale scheme; within the charging planning service area, search for actual unconstructable locations for charging stations, setting the land price of these locations to positive infinity to ensure that subsequent planning schemes automatically discard unconstructable locations; solve the electric vehicle charging station site selection and capacity determination model using a quantum genetic algorithm to obtain the social cost of the charging station site selection and capacity determination scheme, and the charging station site selection and capacity determination scheme with the minimum social cost is the final determined site selection and capacity determination planning scheme; the social cost includes the grid dispatch loss cost, the charging station construction cost, the charging station operation cost, and the time cost incurred by electric vehicle users during the entire charging process; Step S5. Determine the optimal charging station site selection and capacity allocation scheme within the planned area: The charging station scale scheme refers to the number of ordinary charging stations and fast charging stations within the charging station area. The number of ordinary charging stations is set to a range of [a, b], and the number of fast charging stations is set to a range of [s, h]. Therefore, the number of charging station scale schemes is (b-a+1)×(h-s+1). Within the charging planning service area, the number of charging station scale schemes is (b-a+1)×(h-s+1). Input these (b-a+1)×(h-s+1) charging station scale schemes into the electric vehicle charging station site selection model to obtain the minimum social cost under each scheme. By comparing and selecting the scale scheme corresponding to the minimum social cost and its location, the optimal site selection and capacity allocation scheme is determined.

[0007] Furthermore, in step 1, the objective function of the electric vehicle charging station site selection and sizing model is as follows:

[0008] in This is the cost of charging losses; It is the construction cost of charging stations; It is the operating cost of the charging station; It represents the time cost incurred by electric vehicle users during the entire charging process; m is the number of existing charging stations; n is the number of load demand points within the planned area; This refers to the number of ordinary charging stations built. It refers to the number of fast charging stations built; This represents the average electricity price for the entire year. The average effective working time per day for existing charging stations, ordinary charging stations, or fast charging stations; It is the number of charging piles installed at the i-th charging station; where This refers to the number of charging piles built at ordinary charging stations. It refers to the number of charging piles built at fast charging stations; These are the line loss and charging loss of a single charger in an existing charging station, respectively. These are the line loss and charging loss of a single charger in a typical charging station, respectively. These are the line loss and charging loss of a single charger in a fast charging station, respectively. It is the distribution loss of the power grid system; , These are the prices of the j-th charging pile at the i-th ordinary charging station and the j-th charging pile at the i-th fast charging station, respectively. and These are the land area and land acquisition unit price of the i-th charging station, respectively; This is the infrastructure construction cost of the i-th charging station; It is the distance between the i-th charging station and the nearest charging demand point; This is the price for laying power transmission lines for the unit; It is a conversion factor that calculates the equipment's annual operating costs, depreciation costs, and employee wages back to the initial investment cost; It is the distance from the i-th demand point to its nearest charging station; The average speed at which charging users travel to charging stations; It is the sum of the loads at all demand points served by the i-th charging station; , These are the power outputs of a single charging pile in a regular charging station and a fast charging station, respectively. It is a conversion factor that converts the time spent by users during the charging process, including travel time and charging time, into economic losses.

[0009] Furthermore, the electric vehicle charging station site selection and capacity model includes constraints on the service range of existing charging stations: the optimization of the charging network layout should follow two core principles: first, to avoid internal competition and resource utilization efficiency losses, the location of new stations should avoid overlap with existing stations in terms of customer groups; second, new stations should be prioritized in service blind spots to expand the effective coverage of the network, rather than further clustering in already saturated hotspot areas; the ultimate goal of this strategy is to ensure that scarce land and electricity resources can generate the greatest marginal benefits, serving a wider range of regions and populations, that is:

[0010] In the formula, This indicates the total number of charging demand points whose distance to the nearest charging station has been shortened after the construction of the charging station. This is the charging station impact factor, used to characterize the degree of impact of charging stations on charging demand points within the planning area; =0.8, which means that at least 80% of the charging demand points are shortened in distance to the nearest charging station.

[0011] Furthermore, the electric vehicle charging station site selection and sizing model also includes constraints on the service range of the charging station: While ensuring high charging accessibility for users at any node, it is necessary to fully consider the service overlap range, namely:

[0012] In the formula For the first From the nearest charging station to the demand point The distance length; The first , A set of service demand points for each charging station; Set a threshold for charging distance; Set a threshold for the overlap of charging stations.

[0013] Furthermore, the electric vehicle charging station site selection and capacity determination model also includes a total power constraint for the planning area: when multiple charging stations exist within the planning area, to ensure stable grid operation, the sum of the total charging power of the charging stations within the area and the maximum power of other basic loads within the area should always be controlled within the maximum power limit allowed by the grid, i.e.:

[0014] In the formula, For the first The first ordinary charging station The power of charging piles with different serial numbers For the first The first fast charging station The power of charging piles with different serial numbers For the planned load power other than charging power within the sub-region, This refers to the maximum power that substations within the planned sub-region are allowed to connect to.

[0015] Furthermore, the electric vehicle charging station site selection and capacity determination model also includes electric vehicle charging time constraints: the site selection and capacity determination of charging stations should fully consider the charging time costs for electric vehicle users, that is:

[0016] In the formula This refers to the maximum time cost that electric vehicle users can afford.

[0017] Furthermore, in step 2, the basic data of the charging demand points and charging stations includes the coordinates of the charging demand points, the charging demand load, the actual road path distance of the charging demand points, the number of existing charging stations, the number of electric vehicle charging stations to be built, the annual average electricity price, the average daily effective working time of existing charging stations and electric vehicle charging stations to be built, the number of ordinary charging piles and fast charging piles installed, the power of the charging piles, the line loss and charging loss of the charging piles, the distribution loss of the power grid system, and the average speed of charging users to the charging station; the charging station construction and operation cost data includes the price of charging piles, the cost of charging station infrastructure construction, the price of laying a unit of transmission line, the conversion factor for converting the annual operating cost, depreciation cost, and employee wages of the equipment into the initial investment cost, and the conversion factor for converting the time spent by users in the charging process, including travel time and charging time, into economic losses; the land cost data includes the land area of ​​the charging station and the land acquisition unit price.

[0018] Furthermore, in step 4, the quantum genetic algorithm adopts a genetic algorithm based on the principle of improved quantum computing, abbreviated as IQGA. The IQGA dynamically adjusts the rotation angle of the quantum gate according to the evolution process and introduces a population catastrophe operation to enhance the global optimization ability of the quantum genetic algorithm in the later stage of iteration.

[0019] Furthermore, the specific steps of the IQGA are as follows: (1) Initialize the population : All chromosome genes in the population ( All are initialized to ( This allows the chromosome to be represented as a superposition of all possible states with equal probability, i.e.:

[0020] in The first chromosome A state, which is represented by a length of binary string ( ); (2) Testing the initial population Individual: Measure the initial population to obtain a set of initial solutions. ,in For the t-th generation population, the th There are solutions, each represented as a solution of length . The binary string, where each binary digit is obtained by the probability selection of the qubit; (3) Calculation Fitness of each individual: In this model, the optimized objective function can be directly used as the fitness function. The smaller the objective value, the higher the fitness of the function, and the better the individual is in the population. Then, the fitness of this set of solutions is evaluated, and the individual with the best fitness is recorded as the target value for the next evolution. (4) Iterative phase: As the iteration proceeds, the solutions of the population gradually converge toward the optimal solution; in each iteration, the population is first measured to obtain a set of definite solutions. Then, the fitness value of each solution is calculated, and the individuals in the population are adjusted using a quantum rotation gate according to the current evolutionary goal and the predetermined adjustment strategy to obtain an updated population. (5) Quantum catastrophe operation: Record the current optimal solution and its fitness, and compare it with the current target value. If the optimal individual has not changed for several consecutive generations, the algorithm will perform a quantum catastrophe operation: except for the current optimal individual, all other individuals in the population are reinitialized. This operation introduces a significant perturbation to make the population leave the current local optimum, thereby restarting the effective search. (6) Determine whether the algorithm termination condition is met. If it is met, stop the search and output the calculation result; otherwise, go to step 4.

[0021] The beneficial effects of this invention are: This application combines quantum computing theory with genetic algorithms to solve the problems of excessive iterations, slow convergence speed, and susceptibility to local optima caused by improper selection, crossover, and mutation methods in genetic algorithms. Ultimately, it achieves low-cost and high-efficiency planning for electric vehicles. The planning scheme of this application takes into account the impact of existing charging stations and minimizes the overlap of service areas between stations to avoid resource waste.

[0022] This application balances the interests of charging station operators, electric vehicle users, and power grid companies, and constructs a site selection and capacity determination model for electric vehicle charging stations without alternative sites. This not only improves the efficiency of electric vehicle charging station planning, but also makes the construction plan more reasonable, avoiding the subjectivity of traditional expert scoring. Attached Figure Description

[0023] Figure 1 This is a flowchart of the method for constructing a car charging station according to the present invention. Detailed Implementation

[0024] The following specific examples illustrate the implementation of this disclosure. Those skilled in the art can easily understand the advantages and effects of this disclosure from the content disclosed in this specification. Obviously, the described embodiments are only a part of the embodiments of this disclosure, and not all of them. This disclosure can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of this disclosure. It should be noted that, in the absence of conflict, the following embodiments and features in the embodiments can be combined with each other. Based on the embodiments in this disclosure, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this disclosure.

[0025] like Figure 1 As shown, a method for constructing an electric vehicle charging station based on quantum genetic algorithm and no alternative site includes the following steps: Step S1. Constructing an Electric Vehicle Charging Station Site Selection and Capacity Determination Model: By balancing the interests of charging station operators, electric vehicle users, and power grid companies, an electric vehicle charging station site selection and capacity determination model is constructed based on the absence of alternative sites. The absence of alternative sites means that the number of charging stations is first determined, and then the final site is determined through a full-range optimization using an objective function, thus freeing the user from the constraints of a candidate set and avoiding subjective bias and early decision-making errors. When conducting top-level charging network planning for large areas (such as urban or provincial highway networks), the absence of alternative sites demonstrates its unique value because it is difficult to exhaustively list all potential sites; it is more likely to find a globally optimal or near-globally optimal solution.

[0026] Specifically, taking into account the construction and operation costs of charging stations, user time costs, and grid loss costs, and comprehensively considering factors such as service range and charging losses, this technology proposes a site selection and capacity grading model for electric vehicle charging stations that minimizes the overall social cost, as shown below: The objective function of this model is given by equation (3), which aims to minimize the charging costs of all stakeholders while considering existing charging stations. The first term of equation (3) The calculation method for charging loss costs is described in detail, and can be determined by equation (4). The second term of equation (3) The detailed composition of the construction cost of charging stations is explained, and it can be determined by equation (5). The third term of equation (3)... and the fourth item The charging station operating cost and the time cost of the entire charging process for electric vehicle users are described respectively, and are determined by equations (6) and (7) respectively.

[0027]

[0028] in This is the cost of charging losses; It is the construction cost of charging stations; It is the operating cost of the charging station; It represents the time cost incurred by electric vehicle users during the entire charging process; m is the number of existing charging stations; n is the number of load demand points within the planned area; This refers to the number of ordinary charging stations built. It refers to the number of fast charging stations built; This represents the average electricity price for the entire year. The average effective working time per day for existing charging stations, ordinary charging stations, or fast charging stations; It is the number of charging piles installed at the i-th charging station; These are the line loss and charging loss of a single charger in an existing charging station, respectively. These are the line loss and charging loss of a single charger in a typical charging station, respectively. These are the line loss and charging loss of a single charger in a fast charging station, respectively. It is the distribution loss of the power grid system; , These are the prices of the j-th charging pile at the i-th ordinary charging station and the j-th charging pile at the i-th fast charging station, respectively. and These are the land area and land acquisition unit price of the i-th charging station, respectively; This is the infrastructure construction cost of the i-th charging station; It is the distance between the i-th charging station and the nearest charging demand point; This is the price for laying power transmission lines for the unit; It is a conversion factor that calculates the equipment's annual operating costs, depreciation costs, and employee wages back to the initial investment cost; It is the distance from the i-th demand point to its nearest charging station; The average speed at which charging users travel to charging stations; It is the sum of the loads at all demand points served by the i-th charging station; , These are the power outputs of a single charging pile in a regular charging station and a fast charging station, respectively. It is a conversion factor that converts the time spent by the user during the charging process (including travel time and charging time) into economic losses.

[0029] The site selection and sizing model for electric vehicle charging stations includes the following constraints: First, the service range constraints of existing charging stations: Optimizing the charging network layout should follow two core principles: First, to avoid internal competition and resource utilization inefficiency, the location of new stations should avoid overlap with existing stations in terms of customer base; second, new stations should be prioritized in service blind spots to expand the effective coverage of the network, rather than further clustering in already saturated hotspot areas; the ultimate goal of this strategy is to ensure that scarce land and electricity resources can generate the greatest marginal benefits, serving a wider range of regions and populations, that is:

[0030] In the formula, This indicates the total number of charging demand points whose distance to the nearest charging station has been shortened after the construction of the charging station. This is the charging station impact factor, used to characterize the degree of impact of charging stations on charging demand points within the planning area; = 0.8, which means that at least 80% of the charging demand points will have their distance to the nearest charging station shortened.

[0031] Second, charging station service range constraints: The total service range of charging stations should cover all demand points within the planned area, ensuring that users at any demand point within the planned area can conveniently charge within a reasonable distance. At the same time, to ensure the effective utilization of charging stations and avoid waste of human and material resources, it is essential to minimize the overlap of service ranges between stations. Therefore, while ensuring high charging accessibility for users at any node, the scope of service overlap needs to be fully considered, namely:

[0032] In the formula For the first From the nearest charging station to the demand point The distance length; The first , A set of service demand points for each charging station; Set a threshold for charging distance; Set a threshold for the overlap of charging stations.

[0033] Third, total power constraint of the planning area: When there are multiple charging stations in the planning area, in order to ensure the stable operation of the power grid, the sum of the total charging power of the charging stations in the area and the maximum power of other basic loads in the area should always be controlled within the maximum power limit allowed by the power grid, that is:

[0034] In the formula, For the first The first ordinary charging station The power of charging piles with different serial numbers For the first The first fast charging station The power of charging piles with different serial numbers For the planned load power other than charging power within the sub-region, This refers to the maximum power that substations within the planned sub-region are allowed to connect to.

[0035] Fourth, electric vehicle charging time constraints: The site selection and capacity determination of charging stations should fully consider the charging time costs for electric vehicle users, that is:

[0036] In the formula This refers to the maximum time cost that electric vehicle users can afford.

[0037] Step S2. Obtain relevant data for the area to be planned: Obtain relevant data for the area to be planned, including basic data on charging demand points and charging stations, data on charging station construction and operation costs, and land cost data.

[0038] Specifically, the basic data on charging demand points and charging stations includes the coordinates of charging demand points, charging demand load, actual road path distance to charging demand points, number of existing charging stations, number of electric vehicle charging stations to be built, annual average electricity price, average daily effective working hours of existing and planned electric vehicle charging stations, number of ordinary and fast charging piles installed, charging pile power, line loss and charging loss of charging piles, power distribution loss of the power grid system, and average speed of charging users traveling to charging stations; the data on charging station construction and operation costs includes the price of charging piles, the cost of charging station infrastructure construction, the price of laying a unit of transmission line, the conversion factor for converting the annual operating costs, depreciation costs, and employee wages of the equipment into the initial investment cost, and the conversion factor for converting the time spent by users during the charging process (including travel time and charging time) into economic losses; the data on land costs includes the land area of ​​the charging station and the unit price of land acquisition.

[0039] Step S3. Divide the area to be planned into m charging planning service sub-regions: There are m electric vehicle charging stations to be built in the area to be planned. Set a preset site for each electric vehicle charging station. Using the m preset sites as base points, use the Voronoi diagram algorithm to divide the area to be planned into m sub-regions. In each sub-region, use the Floyd algorithm to dynamically update the path length between nodes and gradually solve the shortest path between the charging demand point and the preset site of the charging station in its sub-region. At the same time, use the Floyd algorithm to calculate the shortest distance between each charging demand point and the preset site of the charging station in the adjacent sub-region. By comparing the path distance from the charging demand point to each preset site of the charging station, determine the charging planning service sub-regions of the m electric vehicle charging stations to be built in the area to be planned.

[0040] The Voronoi diagram algorithm consists of a set of continuous polygons formed by the perpendicular bisectors of line segments connecting two adjacent points. Points within each polygon are shorter than their distances to other generators. It has a well-structured, simple data structure, high storage efficiency, and can represent linear features and the boundaries of areas to be planned with arbitrary shapes. It can effectively calculate and represent the service range of existing charging stations. The specific process is as follows: Step 1: Construct a Delaunay triangle from the discrete points of several charging station sites, and number the discrete points and the resulting triangle.

[0041] Step 2: Calculate and record the circumcenter of each triangle, traverse the triangle linked list, and find the adjacent triangles that share the same three sides as the current triangle.

[0042] Step 3: After the traversal is complete, all Vino edges have been found. Draw the Vino graph based on the edges to obtain m sub-regions.

[0043] The steps to determine the charging planning service areas by comparing the path distances from charging demand points to each charging station are as follows: Step 1: Initialize the path matrix: For the first sub-region, input the actual road path distance from the charging demand point i directly to the charging station in that region; if there is no direct actual road path between them, initialize the distance to infinity to obtain the initial distance. .

[0044] Step 2: Iteratively solve for the shortest path within the region: Use the Floyd algorithm to iteratively enumerate the paths, calculating the shortest path distance from charging demand point i through k intermediate nodes to the charging station. Finally, the shortest path distance from the demand point i to the charging station in its sub-region is determined by equation (1).

[0045]

[0046] Step 3: Cross-regional shortest path calculation and service allocation: For charging demand point i, calculate the shortest path distance between it and each adjacent sub-region's candidate charging station. By comparing the path distances from demand point i to all charging stations, determine the shortest distance from demand point i to a charging station. Furthermore, considering the uncertainty of users' charging, equation (2) is used to define the service range of the charging station.

[0047]

[0048] In the formula, Let δ represent the shortest distance from charging demand point i to the j-th charging station, and δ be the service range boundary value of the charging station. If the above inequality holds, then the service range of the j-th charging station includes charging demand point i; otherwise, it does not.

[0049] Step 4: Determine the global service relationship: Repeat the above steps to solve the distance between the charging demand point and the nearest charging station in all sub-regions in turn, and finally determine the charging planning service sub-regions of all charging stations by combining equation (2).

[0050] Step S4. Site selection and capacity determination for electric vehicle charging stations within the charging planning service area: A charging station site selection and capacity determination scheme includes a charging station site selection scheme and a charging station scale scheme; within the charging planning service area, search for actual unconstructable locations for charging stations, setting the land price of these locations to positive infinity to ensure that subsequent planning schemes automatically discard unconstructable locations; solve the electric vehicle charging station site selection and capacity determination model using a quantum genetic algorithm to obtain the social cost of the charging station site selection and capacity determination scheme, and the charging station site selection and capacity determination scheme with the minimum social cost is the final determined site selection and capacity determination planning scheme; the social cost includes the grid dispatch loss cost, the charging station construction cost, the charging station operation cost, and the time cost incurred by electric vehicle users during the entire charging process; To address the issues of excessive iterations, poor adaptive adjustment capabilities, and convergence to local optima in the later stages of traditional quantum genetic optimization algorithms, this invention proposes an improved quantum computing-based genetic algorithm (IQGA). This algorithm dynamically adjusts the rotation angle of the quantum gates according to the evolutionary process and employs quantum catastrophe operations to escape local optima, achieving a wider search range and faster iteration speed than conventional quantum genetic algorithms. The specific steps of IQGA are as follows: (1) Initialize the population : All chromosome genes in the population ( All are initialized to ( This allows the chromosome to be represented as a superposition of all possible states with equal probability, i.e.:

[0051] in The first chromosome A state, which is represented by a length of binary string ( ).

[0052] (2) Testing the initial population Individual: Measure the initial population to obtain a set of initial solutions. ,in For the t-th generation population, the th There are solutions, each represented as a solution of length . The binary string, where each binary digit is obtained by probabilistic selection of qubits.

[0053] (3) Calculation Individual fitness: In this model, the optimized objective function can be directly used as the fitness function. The smaller the objective value, the higher the fitness of the function, and the better the individual is in the population. Then, the fitness of this set of solutions is evaluated, and the individual with the best fitness is recorded as the target value for the next evolutionary step.

[0054] (4) Iterative Phase: As the iteration progresses, the solutions of the population gradually converge towards the optimal solution. In each iteration, the population is first measured to obtain a set of definite solutions. Then, the fitness value of each solution is calculated, and based on the current evolutionary goal and the predetermined adjustment strategy, the individuals in the population are adjusted using a quantum rotation gate to obtain an updated population.

[0055] (5) Quantum catastrophe operation: Record the current optimal solution and its fitness, and compare it with the current target value. If the optimal individual has not changed for several consecutive generations, the algorithm will perform a quantum catastrophe operation: except for the current optimal individual, all other individuals in the population are reinitialized. This operation introduces a significant perturbation, causing the population to deviate from the current local optimum, thereby restarting the effective search.

[0056] (6) Determine whether the algorithm termination condition is met. If it is met, stop the search and output the calculation result; otherwise, go to step 4.

[0057] In quantum genetic algorithms, the rotation gate is the final execution mechanism for the evolutionary operation, and the method for determining the rotation angle is shown in Table 1. For the current chromosome number Bit; The current optimal chromosome number Bit; The fitness function; The direction of rotation angle; To determine the magnitude of the rotation angle, and incorporating the concept of dynamically adjusting the quantum gate rotation angle, this invention proposes... One specific implementation mechanism:

[0058] In the formula, n is the current iteration number; MAXGEN is the maximum iteration number; and r is a random number between [0,1]. This mechanism adopts a large-scale exploration in the early stage of iteration to improve convergence efficiency, and then switches to fine-grained mining in the later stage to achieve precise positioning. This dynamic strategy not only effectively balances convergence speed and solution accuracy, but also significantly enhances the algorithm's ability to locate high-quality global optimal solutions. The specific rotation angle selection strategy is shown in Table 1 below.

[0059]

[0060] Step S5. Determine the optimal charging station site selection and capacity allocation scheme within the planned area: The charging station scale scheme refers to the number of ordinary charging stations and fast charging stations within the charging station area. The number of ordinary charging stations is set to a range of [a, b], and the number of fast charging stations is set to a range of [s, h]. Therefore, the number of charging station scale schemes is (b-a+1)×(h-s+1). Within the charging planning service area, the number of charging station scale schemes is (b-a+1)×(h-s+1). Input these (b-a+1)×(h-s+1) charging station scale schemes into the electric vehicle charging station site selection model to obtain the minimum social cost under each scheme. By comparing and selecting the scale scheme corresponding to the minimum social cost and its location, the optimal site selection and capacity allocation scheme is determined.

[0061] This invention has the following technical features: First, this application combines quantum computing theory with genetic algorithms to solve the problems of excessive iterations, slow convergence speed, and susceptibility to local extrema caused by improper selection, crossover, and mutation methods in genetic algorithms, ultimately achieving low-cost and high-efficiency planning for electric vehicles.

[0062] Secondly, this application balances the interests of charging station operators, electric vehicle users, and power grid companies, and constructs a site selection and capacity determination model for electric vehicle charging stations on the basis of no alternative sites. This not only improves the efficiency of electric vehicle charging station planning, but also makes the construction plan more reasonable and avoids the subjectivity of traditional expert scoring.

[0063] Third, this application improves the traditional quantum genetic algorithm by adopting a genetic algorithm based on improved quantum computing principles, referred to as IQGA. The IQGA dynamically adjusts the rotation angle of the quantum gate according to the evolution process and introduces a population catastrophe operation, which can greatly enhance the global optimization ability of the quantum genetic algorithm in the later stages of iteration.

[0064] Fourth, it proposes a planning scheme for charging stations under the condition of existing charging facilities. The planning scheme of this application takes into account the impact of existing charging stations, minimizes the overlap of service areas between stations, avoids waste of resources, and can provide effective data support for the safe and stable operation of urban transportation systems with a high proportion of new energy electric vehicles.

[0065] The above description is merely illustrative of the embodiments of the present invention and is not intended to limit the present invention. For those skilled in the art, any modifications, equivalent substitutions, improvements, etc., made without creative effort within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for constructing a charging station for an automobile based on a quantum genetic algorithm and without a standby site, characterized by, Includes the following steps: Step S1. Constructing a site selection and capacity determination model for electric vehicle charging stations: By balancing the interests of charging station operators, electric vehicle users, and power grid companies, a site selection and capacity determination model for electric vehicle charging stations is constructed on the basis of no alternative sites; the so-called no alternative sites means that the number of charging stations is first determined, and then the final site is determined by optimizing the objective function across the entire range. Step S2. Obtain relevant data for the area to be planned: Obtain relevant data for the area to be planned, including basic data on charging demand points and charging stations, charging station construction and operation cost data, and land cost data; Step S3. Divide the area to be planned into m charging planning service sub-regions: There are m electric vehicle charging stations to be built in the area to be planned. Set a preset site for each electric vehicle charging station. Using the m preset sites as base points, use the Voronoi diagram algorithm to divide the area to be planned into m sub-regions. In each sub-region, use the Floyd algorithm to dynamically update the path length between nodes and gradually solve the shortest path between the charging demand point and the preset site of the charging station in its sub-region. At the same time, use the Floyd algorithm to calculate the shortest distance between each charging demand point and the preset site of the charging station in the adjacent sub-region. By comparing the path distance from the charging demand point to each preset site of the charging station, determine the charging planning service sub-regions of the m electric vehicle charging stations to be built in the area to be planned. Step S4. Site selection and capacity determination for electric vehicle charging stations to be built within the charging planning service area: A charging station site selection and capacity determination scheme includes a charging station site selection scheme and a charging station scale scheme; Within the planned charging service area, locations where charging stations cannot be built are searched extensively. The land price for these locations is set to positive infinity to ensure that subsequent planning schemes automatically discard such locations. A quantum genetic algorithm is used to solve the electric vehicle charging station site selection and capacity quantization model to obtain the social cost of the charging station site selection and capacity quantization scheme. The charging station site selection and capacity quantization scheme with the minimum social cost is the final determined site selection and capacity quantization planning scheme. The social cost includes the grid dispatch loss cost, charging station construction cost, charging station operation cost, and the time cost incurred by electric vehicle users during the entire charging process. The quantum genetic algorithm uses a genetic algorithm based on the principle of improved quantum computing, abbreviated as IQGA. IQGA dynamically adjusts the rotation angle of the quantum gate according to the evolution process and introduces a swarm catastrophe operation to enhance the global optimization capability of the quantum genetic algorithm in the later stages of iteration. Step S5. Determine the optimal charging station site selection and capacity allocation scheme within the planned area: The charging station scale scheme refers to the number of ordinary charging stations and fast charging stations within the charging station area. The number of ordinary charging stations is set to a range of [a, b], and the number of fast charging stations is set to a range of [s, h]. Therefore, the number of charging station scale schemes is (b-a+1)×(h-s+1). Within the charging planning service area, the number of charging station scale schemes is (b-a+1)×(h-s+1). Input these (b-a+1)×(h-s+1) charging station scale schemes into the electric vehicle charging station site selection model to obtain the minimum social cost under each scheme. By comparing and selecting the scale scheme corresponding to the minimum social cost and its location, the optimal site selection and capacity allocation scheme is determined.

2. The quantum genetic algorithm and non-alternative site-based car charging station construction method according to claim 1, characterized in that: In step 1, the objective function of the electric vehicle charging station site selection and sizing model is as follows: wherein is the charging loss cost; is the charging station construction cost; is the charging station operation cost; is the time cost generated by the electric vehicle user throughout the charging process; m is the number of existing charging stations; n is the number of load demand points within the planning area; This refers to the number of ordinary charging stations built. It refers to the number of fast charging stations built; This represents the average electricity price for the entire year. The average effective working time per day for existing charging stations, ordinary charging stations, or fast charging stations; It is the number of charging piles installed at the i-th charging station; where This refers to the number of charging piles built at ordinary charging stations. It refers to the number of charging piles built at fast charging stations; These are the line loss and charging loss of a single charger in an existing charging station, respectively. These are the line loss and charging loss of a single charger in a typical charging station, respectively. These are the line loss and charging loss of a single charger in a fast charging station, respectively. It is the distribution loss of the power grid system; , These are the prices of the j-th charging pile at the i-th ordinary charging station and the j-th charging pile at the i-th fast charging station, respectively. and These are the land area and land acquisition unit price of the i-th charging station, respectively; This is the infrastructure construction cost of the i-th charging station; It is the distance between the i-th charging station and the nearest charging demand point; This is the price for laying power transmission lines for the unit; It is a conversion factor that calculates the equipment's annual operating costs, depreciation costs, and employee wages back to the initial investment cost; It is the distance from the i-th demand point to its nearest charging station; The average speed at which charging users travel to charging stations; It is the sum of the loads at all demand points served by the i-th charging station; , These are the power outputs of a single charging pile in a regular charging station and a fast charging station, respectively. It is a conversion factor that converts the time spent by users during the charging process, including travel time and charging time, into economic losses.

3. The method for constructing an electric vehicle charging station based on quantum genetic algorithm and without alternative sites as described in claim 2, characterized in that: The electric vehicle charging station site selection and capacity determination model includes constraints on the service range of existing charging stations. The optimization of the charging network layout should follow two core principles: First, to avoid internal competition and resource utilization inefficiency, the location of new stations should avoid overlap with existing stations in terms of customer base; second, new stations should be prioritized in service blind spots to expand the effective coverage of the network, rather than further clustering in already saturated hotspot areas. The ultimate goal of this strategy is to ensure that scarce land and electricity resources can generate the greatest marginal benefits, serving a wider range of regions and populations, i.e.: In the formula, This indicates the total number of charging demand points whose distance to the nearest charging station has been shortened after the construction of the charging station. This is the charging station impact factor, used to characterize the degree of impact of charging stations on charging demand points within the planning area; = 0.8, which means that at least 80% of the charging demand points will have their distance to the nearest charging station shortened.

4. The method for constructing an electric vehicle charging station based on quantum genetic algorithm and without alternative sites as described in claim 3, characterized in that: The electric vehicle charging station site selection and sizing model also includes constraints on the service range of the charging station: While ensuring high charging accessibility for users at any node, it is necessary to fully consider the service overlap range, namely: In the formula For the first From the nearest charging station to the demand point The distance length; The first , A set of service demand points for each charging station; Set a threshold for charging distance; Set a threshold for the overlap of charging stations.

5. The method for constructing an electric vehicle charging station based on quantum genetic algorithm and without alternative sites as described in claim 4, characterized in that: The electric vehicle charging station site selection and capacity determination model also includes a total power constraint for the planning area: when multiple charging stations exist within the planning area, to ensure stable grid operation, the sum of the total charging power of the charging stations within the area and the maximum power of other basic loads within the area should always be controlled within the maximum power limit allowed by the grid, i.e.: In the formula, For the first The first ordinary charging station The power of charging piles with different serial numbers For the first The first fast charging station The power of charging piles with different serial numbers For the planned load power other than charging power within the sub-region, This refers to the maximum power that substations within the planned sub-region are allowed to access.

6. The method for constructing an electric vehicle charging station based on quantum genetic algorithm and without alternative sites as described in claim 5, characterized in that: The electric vehicle charging station site selection and capacity determination model also includes electric vehicle charging time constraints: the site selection and capacity determination of charging stations should fully consider the charging time costs for electric vehicle users, that is: In the formula This refers to the maximum time cost that electric vehicle users can afford.

7. The method for constructing an electric vehicle charging station based on quantum genetic algorithm and without alternative sites as described in claim 6, characterized in that: In step 2, the basic data of the charging demand points and charging stations includes the coordinates of the charging demand points, the charging demand load, the actual road path distance of the charging demand points, the number of existing charging stations, the number of electric vehicle charging stations to be built, the annual average electricity price, the average daily effective working time of existing and planned electric vehicle charging stations, the number of ordinary and fast charging piles installed, the power of the charging piles, the line loss and charging loss of the charging piles, the distribution loss of the power grid system, and the average speed of charging users to the charging station; the charging station construction and operation cost data includes the price of charging piles, the cost of charging station infrastructure construction, the price of laying a unit of transmission line, the conversion factor for converting the annual operating cost, depreciation cost, and employee wages of the equipment into the initial investment cost, and the conversion factor for converting the time spent by users during the charging process, including travel time and charging time, into economic losses; the land cost data includes the land area of ​​the charging station and the land acquisition unit price.

8. The method for constructing an electric vehicle charging station based on quantum genetic algorithm and without alternative sites as described in claim 1, characterized in that... The specific steps of the IQGA are as follows: (1) Initialize the population : All chromosome genes in the population ( All are initialized to ( This allows the chromosome to be represented as a superposition of all possible states with equal probability, i.e.: in The first chromosome A state, which is represented by a length of binary string ( ); (2) Testing the initial population Individual: Measure the initial population to obtain a set of initial solutions. ,in For the t-th generation population, the th There are solutions, each represented as a solution of length . The binary string, where each binary digit is obtained by the probability selection of the qubit; (3) Calculation Fitness of each individual: In this model, the optimized objective function can be directly used as the fitness function. The smaller the objective value, the higher the fitness of the function, and the better the individual is in the population. Then, the fitness of this set of solutions is evaluated, and the individual with the best fitness is recorded as the target value for the next evolution. (4) Iterative phase: As the iteration proceeds, the solutions of the population gradually converge toward the optimal solution; in each iteration, the population is first measured to obtain a set of definite solutions. Then, the fitness value of each solution is calculated, and the individuals in the population are adjusted using a quantum rotation gate according to the current evolutionary goal and the predetermined adjustment strategy to obtain an updated population. (5) Quantum catastrophe operation: Record the current optimal solution and its fitness, and compare it with the current target value. If the optimal individual has not changed for several consecutive generations, the algorithm will perform a quantum catastrophe operation: except for the current optimal individual, all other individuals in the population are reinitialized. This operation introduces a significant perturbation to make the population leave the current local optimum, thereby restarting the effective search. (6) Determine whether the algorithm termination condition is met. If it is met, stop the search and output the calculation result; otherwise, go to step 4.

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