Method for site selection and capacity determination of electric vehicle charging station integrating charging experience and operation benefit

Abstract

This invention discloses a method for site selection and capacity determination of electric vehicle charging stations that integrates charging experience and operational efficiency. The method includes: acquiring charging station demand information in the area to be sited and determined; based on the location of the demand points, finding the optimal K value and determining the number of clusters by plotting a broken line showing the sum of squared errors within clusters as a function of the number of clusters, and using the elbow method; determining the initial center of each cluster using the K-means++ algorithm; clustering the charging station demand points using the K-means algorithm, iterating based on an improved weighted distance to obtain several clusters, and using the center of each cluster as a candidate charging station point; establishing an objective function that optimizes both user charging experience and charging station operational efficiency based on each candidate charging station point, and solving this function using a genetic algorithm based on constraints to obtain the final site selection and capacity determination scheme for the charging station. This invention optimizes the user's charging experience throughout the entire charging process from start to finish while ensuring the profitability of the charging station operator.

Description

Technical Field

[0001] This invention relates to a method for determining the location and capacity of electric vehicle charging stations that integrates charging experience and operational efficiency, and belongs to the field of electric vehicle charging station location and capacity planning technology. Background Technology

[0002] In recent years, the new energy vehicle industry has developed rapidly, and the market has experienced explosive growth. Driven by the dual carbon goals of "carbon peaking" and "carbon neutrality," as well as various incentive measures, new energy vehicles have not only become increasingly technologically mature, but their prices have also decreased significantly due to subsidies for vehicle purchases and tax reductions. Consequently, the number of new energy vehicles on the road continues to grow.

[0003] Meanwhile, charging infrastructure, represented by charging piles, has not kept pace with the growth of new energy vehicles, leading to a tight supply-demand relationship for charging. In addition, the existing charging infrastructure also suffers from numerous problems. On the one hand, due to varying levels of regional development, first-tier cities have relatively abundant charging resources, while the supply-demand imbalance is more pronounced in third- and fourth-tier cities. On the other hand, daily life and work needs, such as commuting, also result in varying utilization rates of charging piles at different times.

[0004] Currently, most methods for determining the location and capacity of electric vehicle charging stations fall into two categories: those that consider operator costs and those that consider user needs. The former typically prioritizes construction and equipment costs as key factors in site selection and capacity determination, neglecting the impact of population and other factors on revenue. The latter, due to the difficulty in balancing cost considerations, often favors lower-cost solutions or solutions offering a better user charging experience at a higher cost. Summary of the Invention

[0005] The technical problem to be solved by this invention is to provide a site selection and capacity determination method for electric vehicle charging stations that integrates charging experience and operational efficiency. This method combines the user's charging experience with the benefits of the charging station operator and obtains the site selection point and capacity determination scheme of the charging station through an iterative algorithm.

[0006] To solve the above-mentioned technical problems, the present invention adopts the following technical solution: A site selection and capacity determination method for electric vehicle charging stations that integrates charging experience and operational efficiency includes the following steps: Step 1: Obtain charging station demand information for the area to be selected, including the location of the demand point, the number of users at the demand point, and the land use type of the demand point. Step 2: Based on the location of the required points, find the optimal cluster sum of squared errors as a function of the number of clusters by plotting a line graph. This can be done using the elbow method. The value determines the number of clusters; Step 3: Based on the number of clusters, use the K-means++ algorithm to determine the initial center of each cluster; Step 4: Based on the number of clusters and the initial center of each cluster, the K-means algorithm is used to cluster the charging station demand points. Several clusters are obtained by iterating according to the improved weighted distance, and the center of each cluster is used as the candidate charging station point. Step 5: Based on each candidate charging station, establish an objective function that optimizes the user charging experience and the operational efficiency of the charging station, and solve it using a genetic algorithm based on the constraints to obtain the final location and capacity scheme of the charging station.

[0007] Compared with the prior art, the present invention, employing the above technical solution, has the following technical effects: 1. This invention addresses the electric vehicle charging scenario in daily life. Through continuous iteration, it not only meets users' charging needs but also optimizes the entire charging experience from start to finish while ensuring the profitability of charging station operators.

[0008] 2. By rationally arranging the location and scale of charging stations, this invention can prevent land resource encroachment caused by excessively dense stations and overcapacity, and avoid charging resource shortages caused by sparse stations and insufficient capacity, ultimately achieving synergistic optimization of land resource utilization, charging service efficiency and grid carrying capacity. Attached Figure Description

[0009] Figure 1 This is a flowchart of the electric vehicle charging station site selection and capacity determination method that integrates charging experience and operational efficiency according to the present invention; Figure 2 This is a diagram showing the distribution and classification of demand points in an embodiment of the present invention; Figure 3 This is a line graph showing the sum of squares within a cluster as a function of the K value in an embodiment of the present invention; Figure 4 This is a diagram showing the K-means clustering results in an embodiment of the present invention; Figure 5 This is a diagram of the optimal decision-making scheme for charging station site selection and capacity determination in an embodiment of the present invention. Detailed Implementation

[0010] Embodiments of the present invention are described in detail below, examples of which are illustrated in the accompanying drawings. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and should not be construed as limiting the present invention.

[0011] like Figure 1As shown, this invention proposes a site selection and capacity determination method for electric vehicle charging stations that integrates charging experience and operational efficiency. This method can obtain a better site selection and capacity determination scheme for electric vehicle charging stations based on given information, and specifically includes the following process: Step 1: Collect and organize information (including location, population, etc.) for each demand point in the demand network. Step 2: Based on the location of the user's required points, draw a broken line showing the sum of squared errors within the cluster as a function of the K value. Find the elbow-like inflection point in the broken line and use twice its K value as the number of cluster centers in the pre-selection site. The formula for calculating the sum of squared errors within a cluster is as follows: , in, This represents the sum of squared errors within the cluster. Indicates the number of clusters. Indicates the first Clusters, Indicates clustering to Demand points, Indicates the first The center of each cluster, Indicate demand points and Manhattan distance.

[0012] Step 3: Use the K-means++ algorithm to determine the initial centers; details are as follows: Randomly select a point as the cluster center, and then for each data point, calculate its distance to the nearest cluster center. ,by Based on the probability of selection, select the next cluster center, and continue until all cluster centers have been selected.

[0013] Step 4: Using the K-means algorithm, based on the collected information such as land costs, and considering the user's charging experience and the benefits of the charging station operator, calculate the weighted distance from each demand point to the cluster center, assign each demand point to the nearest cluster center, and then calculate the cluster center of each new cluster. This process is iterated until a stable clustering result is obtained, which is the location of several candidate charging station points. Consider a set of points ,in To represent a point in a set of points, This represents the cluster center of a point set. This indicates the weight given to spatial distance; Indicates the weight of the population; Indicates the weight of land costs; Point of Coordinate values; Point of Coordinate values; Representing cluster centers of Coordinate values; Representing cluster centers of Coordinate values; Point Population size; Representing cluster centers Land costs.

[0014] Based on the above symbol definitions, the formula for calculating the weighted distance used in clustering is as follows: (1)

[0015] Step 5: Based on the collected information such as cost and user tolerance for queue length, use a genetic algorithm to combine the user's charging experience with the benefits of the charging station operator, and iterate multiple times to obtain the site selection and capacity plan for the charging station. Consider a set of user needs. Define a set of candidate charging station locations. , Indicates the first The demand point to the first Manhattan distance of each candidate charging station location; This represents the average speed of the electric vehicle; Indicates the first Average queuing time per charging station; This indicates the power consumption of an electric vehicle per unit distance. This indicates the power output of a single charging station. Indicates cost per unit of time; Indicates the battery capacity of an electric vehicle; This indicates the hourly difference that a charging station earns when providing charging services; Indicates the first The number of charging piles purchased for each charging station; This indicates the service rate of the charging station; Indicates the first User reach rate at each charging station; This indicates the penalties awarded based on user reviews; Indicates land use cost; Indicates the cost of purchasing equipment; Indicates operating and maintenance costs; Indicates the first The investment and service life of each charging station; Indicates the first The land price per unit area where each charging station is located; This represents the average area of ​​a single parking space in a charging station; Indicates the equipment discount rate; Indicates the first The number of transformers purchased for each charging station; This indicates the unit price of the transformer; This indicates the unit price of the charging station; This represents a percentage factor used to calculate operating and maintenance costs; Indicates the first Average queue length per charging station; This indicates the maximum acceptable queue length for the user. Indicates the first Average queuing time per charging station; This indicates the maximum queuing time that a user can tolerate; Indicates the first The utilization rate of charging piles at each charging station; This indicates the total number of charging stations required to be built, i.e., the limit on the number of charging stations; Represents an extremely large number; This indicates the maximum power that the local power grid can handle; Indicates the working efficiency of the charging pile; This indicates the output power of a single transformer; For 0-1 variables, if the first... If any candidate site is selected as a charging station construction site, then The value is 1 if it is 1, otherwise it is 0. For 0-1 variables, if the first... The demand point was assigned to the first Each charging station, The value is 1 if it is 1, otherwise it is 0.

[0016] Based on the above symbol definitions, the method for determining the location and capacity of electric vehicle charging stations that integrates charging experience and operational efficiency can be expressed as the following optimization problem, with the objective function being the optimal user charging experience. The objective function is to maximize operational efficiency. : (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (13) (14) (15) (16) (17) (18) (19) Equation (2) represents the quantified user time cost, used to represent the user charging experience. Equation (3) represents the operating efficiency of the charging station operator. Equations (4), (5) and (6) respectively represent the calculation methods of land use cost, equipment purchase cost and operation and maintenance cost. Equation (7) represents the relationship between the penalty term and queue length and queuing time. Equations (8) and (9) respectively represent the calculation methods of average queue length and average queuing time. Equation (10) represents the limit on the number of charging stations. Equations (11), (12) and (13) respectively represent the user arrival rate of the charging station, the number of charging piles and the number of transformers. Equations (14) and (15) respectively represent the impact of local charge load on the number of charging piles and transformers. Equation (16) represents the limit on system utilization to prevent infinite queuing. Equation (17) represents the limit on queuing time. Equations (18) and (19) represent the limit on decision variables to prevent duplicate allocation.

[0017] The genetic algorithm is as follows: 1) Generate an initial population containing a certain number of individuals. Each individual in the population represents a decision-making scheme, including a location decision value. The value of , and the number of charging piles required for each charging station. and number of transformers ; 2) Pre-validate the initial population, such as in the decision-making process. Does the sum relate to the limit on the number of charging stations? If the pre-verification passes, proceed to the next step; otherwise, return to step 1 to regenerate individuals until the initial population size reaches the preset target. 3) Calculate the fitness of each individual in the initial population. : , in, and This is a coefficient, which can be set to better reflect the actual situation based on specific problems. If the user's charging experience is considered the primary influencing factor, then let... , If the operational efficiency of charging stations is considered the primary influencing factor, then... , If both are major influencing factors, then and The value can be determined by other methods such as AHP (Analog-Philosophy of Hierarchy).

[0018] 4) A roulette wheel selection method is used to select parent individuals based on their fitness ratio; individuals with higher fitness have a greater probability of being selected. After selecting the parent individuals, the accompanying variables are exchanged or linearly combined to obtain offspring. Then, the variable values ​​of individuals are randomly flipped, increased, or decreased according to the mutation probability to obtain new offspring. After verifying that the individuals are valid, the next iteration begins. 5) During iteration, record the optimal decision scheme and its fitness for each generation of the population. After reaching the maximum number of iterations, the iteration terminates, and the decision scheme with the highest fitness is selected as the optimal decision scheme.

[0019] This invention selects some residential areas in a city, as well as some non-residential areas such as companies, schools, and shopping malls that may have charging needs, as user demand points. It then uses the model and algorithm constructed in this invention for analysis, such as... Figure 2 As shown.

[0020] The three weights for the weighted distance are set as follows: , , By combining data on demand point coordinates, population, and land costs, a broken line graph showing the sum of squares within a cluster as a function of the K value is calculated, as shown below. Figure 3 As shown.

[0021] Based on the number of clusters obtained, the demand points should be divided into six parts in the pre-selection process. The results of K-means clustering using the Python programming language are as follows: Figure 4 As shown.

[0022] The set of requirements is comprised of all the aforementioned requirements. All cluster centers form a set of candidate charging station locations. Combining data such as cost and tolerance, a genetic algorithm was used in Python programming language to solve the problem. The population size was set to 50, and the maximum number of iterations was 100. The optimal decision scheme obtained is as follows: Figure 5 As shown.

[0023] Based on the same inventive concept, embodiments of this application provide a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps of the aforementioned method for determining the location and capacity of electric vehicle charging stations that integrates charging experience and operational benefits.

[0024] Based on the same inventive concept, embodiments of this application provide a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the aforementioned method for determining the location and capacity of electric vehicle charging stations that integrates charging experience and operational benefits.

[0025] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0026] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0027] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0028] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0029] The above embodiments are merely illustrative of the technical concept of the present invention and should not be construed as limiting the scope of protection of the present invention. Any modifications made to the technical solutions based on the technical concept proposed in this invention shall fall within the scope of protection of this invention.

Claims

1. A method for site selection and capacity determination of electric vehicle charging stations that integrates charging experience and operational efficiency, characterized in that, Includes the following steps: Step 1: Obtain charging station demand information for the area to be selected, including the location of the demand point, the number of users at the demand point, and the land use type of the demand point. Step 2: Based on the location of the required points, find the optimal cluster sum of squared errors as a function of the number of clusters by plotting a line graph. This can be done using the elbow method. The value determines the number of clusters; Step 3: Based on the number of clusters, use the K-means++ algorithm to determine the initial center of each cluster; Step 4: Based on the number of clusters and the initial center of each cluster, the K-means algorithm is used to cluster the charging station demand points. Several clusters are obtained by iterating according to the improved weighted distance, and the center of each cluster is used as the candidate charging station point. Step 5: Based on each candidate charging station, establish an objective function that optimizes the user charging experience and the operational efficiency of the charging station, and solve it using a genetic algorithm based on the constraints to obtain the final location and capacity scheme of the charging station.

2. The method for site selection and capacity determination of electric vehicle charging stations that integrates charging experience and operational efficiency as described in claim 1, characterized in that, In step 2, the formula for calculating the sum of squared errors within a cluster is as follows: ， in, This represents the sum of squared errors within the cluster. Indicates the number of clusters. Indicates the first Clusters, Indicates clustering to Demand points, Indicates the first The center of each cluster, Indicate demand points and Manhattan distance; get Follow After plotting the value changes on a line graph, the optimal value is obtained using the elbow method. The value will be optimal. The value is twice the number of clusters.

3. The method for site selection and capacity determination of electric vehicle charging stations that integrates charging experience and operational efficiency as described in claim 1, characterized in that, In step 4, the improved weighted distance calculation formula is as follows: ， in, This represents the improved weighted distance. , Demand points x and y coordinates , Cluster centers x and y coordinates For demand points The number of users; Cluster center Land costs; , and The weights for distance, population, and land costs are respectively, and the sum of the three is 1.

4. The method for site selection and capacity determination of electric vehicle charging stations that integrates charging experience and operational efficiency according to claim 1, characterized in that, In step 5, the objective function is to optimize the user charging experience. The objective function is to maximize the operational efficiency of the charging station. The specific formula is as follows: ， ， ， ， ， ， ， ， in, Represents the set of demand points. This represents the set of candidate charging station locations. Indicates the first The demand point to the first Manhattan distance of the candidate charging station locations This represents the average speed of the electric vehicle. Indicates the first Average queuing time for each candidate charging station This indicates the power consumption of an electric vehicle per unit distance. This indicates the power output of a single charging station. Indicates cost per unit of time; For 0-1 variables, if the first... The demand point was assigned to the first There are 10 candidate charging stations. The value is 1 if it is 1, otherwise it is 0. Indicates the battery capacity of an electric vehicle. This represents the hourly difference that a charging station earns when providing charging services. Indicates the first The number of charging piles purchased for each candidate charging station site. This indicates the service rate of the charging station. Indicates the first User arrival rate at each candidate charging station location This indicates the penalties awarded based on user reviews; For site selection decision value, For 0-1 variables, if the first... If a candidate charging station site is selected as a construction site, then The value is 1 if it is 1, otherwise it is 0. Indicates land use cost, This indicates the cost of purchasing the equipment. Indicates operating and maintenance costs, Indicates the first The investment and service life of each candidate charging station site; Indicates the first The land price per unit area of ​​the location of each candidate charging station site This represents the average area of ​​a single parking space at a candidate charging station location. Indicates the equipment discount rate; Indicates the first The number of transformers purchased for each candidate charging station site. This indicates the unit price of the transformer. This indicates the unit price of the charging station; Indicates percentage coefficient; Indicates the first Average queue length of each candidate charging station This represents the maximum acceptable queue length for a user. Indicates the first Average queuing time for each candidate charging station This indicates the maximum queuing time that a user can tolerate; Indicates the first Utilization rate of charging piles at each candidate charging station site; The constraints are set as follows: ， ， ， ， ， ， ， ， ， ， in, This indicates the total number of charging stations required to be built. To represent an extremely large number, This indicates the maximum power that the power grid in the area to be located and rated can carry. This indicates the working efficiency of the charging station. This indicates the output power of a single transformer.

5. The method for site selection and capacity determination of electric vehicle charging stations that integrates charging experience and operational efficiency as described in claim 4, characterized in that, In step 5, the specific process of the genetic algorithm is as follows: An initial population is generated, where each individual represents a decision-making scheme, including a location decision value. The value of , and the number of charging piles required for each charging station. and number of transformers Each decision scheme is pre-validated to check whether it meets the constraints. If it passes the pre-validation, the next step is carried out. If it fails, a new individual is generated until the number of individuals in the initial population meets the requirements. Calculate the fitness of each individual in the population. : ， in, and Let be the coefficient; if we tend to consider user charging experience as the primary influencing factor, then let , If the operational efficiency of charging stations is considered the primary influencing factor, then... , If both are major influencing factors, then and The value of is determined using the analytic hierarchy process (AHP). The roulette wheel selection method is used to select parent individuals based on fitness. After the parent individuals are selected, the parent individuals are... and Offspring are obtained by exchanging or linearly combining them. Then, the variable values ​​of individuals are randomly flipped, added, or removed according to the mutation probability to obtain new offspring. After verifying that the individuals are legal, the next round of iteration begins. During the iteration, the optimal decision scheme and its fitness for each iteration are recorded. After reaching the maximum number of iterations, the iteration terminates, and the decision scheme with the highest fitness is selected from the fitness corresponding to each iteration as the final site selection point and capacity setting scheme for the charging station.

6. A computer device comprising a memory, a processor, and a computer program stored in the memory and capable of running on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the electric vehicle charging station site selection and capacity determination method as described in any one of claims 1 to 5.

7. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of the electric vehicle charging station site selection and capacity determination method as described in any one of claims 1 to 5.

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