Optimal configuration method and device for forced metrological verification terminal of electric vehicle charging pile

Abstract

The invention discloses an optimal configuration method and device for a forced metrological verification terminal of an electric vehicle charging pile. The method comprises the following steps: S1, constructing a user charging selection model; s2, the predicted electric vehicle charging demand is input into a user charging selection model, and electric vehicle user charging selection is obtained; s3, obtaining a charging pile contact rate according to the charging selection of the electric vehicle user; s4, a forced metrological verification terminal optimization configuration model is constructed, the objective function of the forced metrological verification terminal optimization configuration model is that the comprehensive cost is minimum, and the constraint condition is that the charging pile contact rate is larger than a preset value; and S5, solving the forced metrological verification terminal optimization configuration model by adopting an improved shuffled frog leaping algorithm. According to the technical scheme, under the condition that the same verification target is achieved, the charging pile metrological verification cost is greatly reduced, and resource waste is avoided.

Description

Technical Field

[0001] The present invention belongs to the technical field of electric vehicle charging piles, and in particular relates to a method and a device for optimizing the configuration of a mandatory measurement and verification terminal of an electric vehicle charging pile. Background Art

[0002] As electric vehicles are rapidly promoted and popularized, the problem of charging metering has also arisen. As the transaction settlement hub of electric energy metering, charging pile metering devices affect the accuracy of transaction settlement and become an important tool for assessing the economic and technical indicators of the power system. Compared with traditional manual verification methods, adding mandatory metering verification terminals to electric charging piles can save verification costs, improve verification efficiency, and enhance verification accuracy, and has become one of the main verification methods at present.

[0003] There are currently several technical practices in the society for the configuration of mandatory metrology and verification modules for electric vehicle charging piles: First, a configuration method based on a simple estimation of the number of charging piles. This method usually assumes that each charging pile needs to be equipped with a metrology and verification terminal, or equipped in a certain proportion. The disadvantage of this method is that it cannot accurately reflect the actual charging demand, which may lead to resource waste or failure to meet the verification demand. This method does not fully consider the temporal and spatial distribution of charging demand, lacks accurate prediction of the temporal and spatial distribution of charging demand, and ultimately leads to a mismatch between the configuration of the verification terminal and the actual demand; second, a configuration method based on the location relationship of charging stations. This method configures the metrology and verification terminal according to the location relationship and distance between charging stations. It is generally believed that charging stations with a closer distance can share the same metrology and verification terminal to reduce costs. However, this method does not fully consider factors such as the user's selection habits, the temporal and spatial distribution of charging demand, and users may be more inclined to choose charging stations that are farther away but have better services, resulting in a low utilization rate of some verification terminals; third, a simple prediction configuration method based on charging demand. This method refers to configuring the metrology and verification terminal based on the prediction of electric vehicle charging demand through statistical models or empirical formulas. This method has certain limitations and lacks accurate modeling of the temporal and spatial distribution of charging demand. In addition, the prediction based on charging demand generally does not fully consider the relationship between charging demand and verification demand, which will lead to insufficient or unreasonable configuration of verification terminals. Summary of the invention

[0004] The technical problem to be solved by the present invention is to provide a method and device for optimizing the configuration of mandatory metering and calibration terminals for electric vehicle charging piles. Based on charging big data, the method and device fully consider the user's charging habits, user's charging needs, installation costs, and the relationship between vehicles and piles. With the goal of minimizing the installation cost and maximizing the coverage of charging piles to be calibrated by standard vehicles, an optimal configuration model for DC charging pile metering and calibration terminals is constructed. While achieving the same calibration goal, the metering and calibration costs of charging piles are greatly reduced, and waste of resources is avoided.

[0005] To achieve the above object, the present invention adopts the following technical solution:

[0006] A method for optimizing configuration of a mandatory measurement and verification terminal for an electric vehicle charging pile, comprising:

[0007] Step S1, constructing a user charging selection model;

[0008] Step S2, inputting the predicted electric vehicle charging demand into the user charging selection model to obtain the electric vehicle user charging selection;

[0009] Step S3, obtaining the charging pile contact rate according to the charging selection of the electric vehicle user;

[0010] Step S4, constructing a mandatory measurement and verification terminal optimization configuration model, wherein the objective function of the mandatory measurement and verification terminal optimization configuration model is to minimize the comprehensive cost, and the constraint condition is that the charging pile contact rate is greater than a predetermined value;

[0011] Step S5: using an improved hybrid frog leaping algorithm to solve the mandatory metrology verification terminal optimization configuration model.

[0012] Preferably, in step S1, the distance between the parking spot and the target charging station and the charging service price are used to jointly characterize the user's charging station selection habits, so as to select the station with the smallest cost value as the user's target charging station and construct a user charging station selection habit model.

[0013] Preferably, in step S2, based on the electric vehicle type, charging characteristics and driving information, a predicted electric vehicle charging demand is obtained, wherein the predicted electric vehicle charging demand includes: total charging demand, parking point distribution, remaining power and power distribution required for adjacent trips.

[0014] As a preferred option, the optimization configuration model of the mandatory metrology verification terminal is:

[0015] C ALL =min(C1+C2)

[0016]

[0017] Among them, Y j represents a 0-1 decision variable. If the design quantity verification terminal is installed at position j, its value is 1. If the design quantity verification terminal is not installed at position j, its value is 0. Q represents the fixed input cost. v l Indicates the unit price of type l measurement verification terminal; N jl Indicates the number of type l measurement and verification terminals installed at charging station j; n is the number of maintenance times within a predetermined time; L i represents the labor cost of the ith maintenance; C drepresents the transportation cost per unit distance during the maintenance process; D i represents the distance between the ith maintenance point and the starting position; C ALL represents the total cost; C1 represents the construction cost; C2 represents the operation and maintenance cost;

[0018] The charging pile contact rate ρ is used to represent the connection status of charging piles in the region. The constraints of the mandatory measurement and verification terminal optimization configuration model are:

[0019]

[0020] Among them, p k Indicates the connection between the kth charging pile and the charging pile with the measurement verification terminal installed. If the pile has been installed with the measurement verification terminal or is connected to the verified charging pile through the electric vehicle, then p k =1, otherwise, p k =0, n represents the total number of charging piles in the area.

[0021] The present invention also provides an optimized configuration device for a mandatory measurement and verification terminal of an electric vehicle charging pile, comprising:

[0022] The first building module is used to build a user charging selection model;

[0023] A first processing module is used to input the predicted electric vehicle charging demand into the user charging selection model to obtain the electric vehicle user charging selection;

[0024] The second processing module is used to obtain the charging pile contact rate according to the charging selection of the electric vehicle user;

[0025] The second construction module is used to construct a mandatory measurement and verification terminal optimization configuration model, wherein the objective function of the mandatory measurement and verification terminal optimization configuration model is to minimize the comprehensive cost, and the constraint condition is that the charging pile contact rate is greater than a predetermined value;

[0026] The calculation module is used to solve the optimization configuration model of the mandatory metrology verification terminal by adopting the improved hybrid frog leaping algorithm.

[0027] Preferably, the first building module uses the distance between the parking spot and the target charging station and the charging service price to jointly characterize the user's charging station selection habits, so as to select the station with the smallest cost value as the user's target charging station and build a user charging station selection habit model.

[0028] As a preferred option, the optimization configuration model of the mandatory metrology verification terminal is:

[0029] C ALL =min(C1+C2)

[0030]

[0031] Among them, Y j represents a 0-1 decision variable. If the design quantity verification terminal is installed at position j, its value is 1. If the design quantity verification terminal is not installed at position j, its value is 0. Q represents the fixed input cost. v l Indicates the unit price of type l measurement verification terminal; N jl Indicates the number of type l measurement and verification terminals installed at charging station j; n is the number of maintenance times within a predetermined time; L i represents the labor cost of the ith maintenance; C d represents the transportation cost per unit distance during the maintenance process; D i represents the distance between the ith maintenance point and the starting position; C ALL represents the total cost; C1 represents the construction cost; C2 represents the operation and maintenance cost;

[0032] The charging pile contact rate ρ is used to represent the connection status of charging piles in the region. The constraints of the mandatory measurement and verification terminal optimization configuration model are:

[0033]

[0034] Among them, p k Indicates the connection between the kth charging pile and the charging pile with the measurement verification terminal installed. If the pile has been installed with the measurement verification terminal or is connected to the verified charging pile through the electric vehicle, then p k =1, otherwise, p k =0, n represents the total number of charging piles in the area

[0035] Preferably, a prediction module is also included for obtaining a predicted charging demand of the electric vehicle based on the type of electric vehicle, charging characteristics and driving information, wherein the predicted charging demand of the electric vehicle includes: total charging demand, parking point distribution, remaining power and power distribution required for adjacent trips.

[0036] The beneficial effects of the present invention are as follows:

[0037] First, the present invention aims to establish a more accurate charging demand prediction model by comprehensively considering factors such as the spatiotemporal distribution of electric vehicle charging demand, user charging selection habits, and parking point distribution, thereby improving the prediction accuracy of the configuration of the metering and calibration terminal; second, by comprehensively considering various factors, the present invention aims to achieve a reasonable configuration of the metering and calibration terminal, avoid waste of resources, and ensure that the calibration needs are met; third, the present invention takes into account the total cost of the configuration of the metering and calibration terminal, including construction costs and operation and maintenance costs, and aims to reduce the operating costs of the charging station by optimizing the configuration plan; fourth, the present invention takes into account the user's charging selection habits and needs, and aims to improve the user's charging experience by reasonably configuring the metering and calibration terminal. Fifth, the present invention aims to reduce waste of resources and promote the sustainable development of electric vehicle charging stations by optimizing the configuration of the metering and calibration terminal. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] 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 embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying creative work.

[0039] Figure 1 The present invention is a flowchart of a method for optimizing configuration of a mandatory metering and verification terminal for an electric vehicle charging pile according to an embodiment of the present invention. DETAILED DESCRIPTION

[0040] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0041] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0042] Embodiment 1:

[0043] like Figure 1 As shown, an embodiment of the present invention provides a method for optimizing configuration of a mandatory measurement and verification terminal for an electric vehicle charging pile, comprising:

[0044] Step S1, constructing a user charging selection model;

[0045] Step S2, inputting the predicted electric vehicle charging demand into the user charging selection model to obtain the electric vehicle user charging selection;

[0046] Step S3, obtaining the charging pile contact rate according to the charging selection of the electric vehicle user;

[0047] Step S4, constructing a mandatory measurement and verification terminal optimization configuration model, wherein the objective function of the mandatory measurement and verification terminal optimization configuration model is to minimize the comprehensive cost, and the constraint condition is that the charging pile contact rate is greater than a predetermined value;

[0048] Step S5: using an improved hybrid frog leaping algorithm to solve the mandatory metrology verification terminal optimization configuration model.

[0049] As an implementation method of an embodiment of the present invention, in step S1, the distance between the parking point and the target charging station and the charging service price are combined to jointly characterize the user's charging station selection habits, so as to select the station with the smallest cost value as the user's target charging station and construct a user charging station selection habit model.

[0050]

[0051] Where Q represents the total number of candidate sites; q represents the number of selected charging stations; P Q,q represents the set of all site selection solutions f when the total number of candidate sites is Q and the number of charging stations is g, f∈P Q,q ; N Q,q,f represents the set of all proposed additional sites i under any site selection method f, i∈N Q,q,f ; represents the set of charging piles to be installed at site i under any site selection scheme f Where Z represents a set of integers; represents the number of additional metrological verification devices installed at station i under any site selection scheme f, represents the number of users who go to the proposed station i for charging and parking under any site selection scheme f; M ij represents the selection cost of user j for the surrounding proposed site i; c i represents the charging service unit price of the charging station; p i Indicates the unit price of parking service; c f represents the average charging service price of all proposed stations under a site selection scheme; p f represents the average parking service price of all proposed sites; represents the addressing distance of user f to the proposed site i; ω1 and ω2 represent the weights of the addressing distance and service price when the user chooses a charging station; U A represents the set of electric vehicle users with charging demand, j∈U A .

[0052] As an implementation method of an embodiment of the present invention, in step S2, based on the electric vehicle type, charging characteristics, and driving information, a predicted electric vehicle charging demand is obtained, and the predicted electric vehicle charging demand includes: total charging demand, parking point distribution, remaining power, and power distribution required for adjacent trips.

[0053] Electric vehicle types and charging characteristics are as follows:

[0054] According to their functions, electric vehicles can be divided into electric private cars, electric taxis and other types. The two types of vehicles adopt different charging modes based on their own travel patterns and charging characteristics.

[0055] (1) Private cars: These vehicles are mostly used for commuting, with fixed starting and ending points and long stay times.

[0056] (2) Taxis: The travel time and starting and ending points of this type of vehicle are highly random, the driving routes are not fixed, and the charging frequency is high.

[0057] Set different charging demand trigger conditions for different types of electric vehicles.

[0058] 1) Private cars: These vehicles are used for commuting and have fixed parking spots. After arriving at the workplace, the private car updates the vehicle status information and checks whether the charging condition is triggered. When the remaining power cannot meet the needs of the next trip, the charging demand is triggered. i ,Right now:

[0059]

[0060] Among them, e c Indicates the power consumption per kilometer. Indicates the distance between the workplace and the residence, which is triggered when the battery power is insufficient to support the user to return home.

[0061] If the above formula is not satisfied, it means that the vehicle can return to the residence smoothly. Then, the time-space information is updated after returning to the residence, and the private car charging strategy is adopted for charging. The charging start time satisfies the formula:

[0062]

[0063] Among them, μ and σ represent parameters related to the charging start time.

[0064] 2) Taxis: In view of the special working nature of taxis, during their driving, the charging trigger condition is set to enable nearby fast charging when the remaining power reaches a threshold.

[0065]

[0066] Wherein, ρ represents the threshold parameter of battery power.

[0067] Assume that the distance between the electric taxi and the nearest fast charging facility is d fc The charging time is t fc and the remaining capacity at the start of charging C i (t fc ) is calculated by the following formula.

[0068]

[0069] Ci(t fc )=Ci(t)-ec·d fc

[0070] The status information of the electric taxi's fast charging is recorded, and the travel time t is updated. If the time has not reached the simulation deadline T1, the location at this time is used as the travel starting point, and the taxi travel simulation is repeated.

[0071] The total daily charging demand is as follows:

[0072] The number of daily electric vehicle charging demands in cities affects the reasonable configuration of the metering and verification modules in charging stations. The total daily charging demand can be estimated by the number of electric vehicles in the study area and the average number of daily charging times of users.

[0073]

[0074] Among them, A represents the total daily charging demand of the city; SOC h Indicates the total amount of battery power; SOC l Indicates the lowest amount of power that the user can tolerate. When the charge is lower than this amount, charging is required. SOC d represents the average daily mileage and power consumption; N′ represents the number of electric vehicles in the region; Indicates the average number of charging times per vehicle per day.

[0075] The spatial and temporal distribution of parking spots is as follows:

[0076] In order to obtain the probability distribution of parking spots in each sub-area, the study area is divided into N sub-areas, and then the frequency distribution of each sub-area is statistically analyzed based on the actual daily travel chain data of private cars, so as to obtain the spatial probability distribution of parking spots. As shown in the following formula:

[0077]

[0078] Where P(L=i) represents the probability that the parking spot is in the i-th area; n i represents the frequency of parking spots; N represents the total number of sub-areas.

[0079] The parking time from 7 am to 24 pm in a day is divided into M intervals, and the probability of parking time intervals in different sub-areas is calculated.

[0080]

[0081] Where P(ST=k|L=i) represents the probability that a vehicle is parked in sub-area i and is in parking time interval k; m: represents the total number of parking spots in sub-area i that are in parking time interval k. The joint probability distribution of parking spot areas and parking time intervals is obtained by the following formula:

[0082] P(ST=k,L=i)=P(ST=k|L=i)P(L=i)

[0083]

[0084] The remaining power and power required for the next trip are distributed as follows:

[0085] After determining the spatiotemporal distribution of charging demand, it is necessary to further clarify the distribution of the remaining power and the power required for the adjacent trip when the user has the intention to charge. Using Bayesian theory, the joint probability distribution of the remaining power of the vehicle and the power required for the adjacent trip when the user has the intention to charge is inversely calculated.

[0086]

[0087] Among them, SOC i Indicates the current remaining power of the vehicle; SOC j ′ represents the power required for the next trip; CC represents the user's willingness to charge; m represents dividing the remaining power from 0 to 100% into m intervals; n represents dividing the power required for the user's next trip from 0 to 100% into n intervals.

[0088] When the remaining power is less than the power required for the adjacent trip, the user will inevitably choose to charge because the vehicle's range cannot meet the user's travel needs, so the probability is 1; when the remaining power exceeds the power required for the adjacent trip, as the remaining power increases, the probability of the user choosing to charge gradually decreases; when the remaining power is close to full charge, the vehicle can meet most of the adjacent trips, so the probability of the user choosing to charge is almost 0.

[0089] As an implementation of the embodiment of the present invention, step S2, calculating the distance l from the charging process to each charging station based on the predicted charging demand ij , and input it into the user charging selection model. Since the addressing distance from the user's charging demand point to the charging station and the charging and parking prices of the charging station are both cost variables, the charging station corresponding to their weighted minimum value is considered as the user's target charging station.

[0090] Since the addressing distance from the user's parking point to the charging station and the charging and parking prices of the charging station are both cost variables, the charging station corresponding to the weighted minimum value is considered as the user's target charging station. As shown in the formula:

[0091] min{M ij},i∈N Q,q,f ,j∈U A

[0092] Introducing the user's charging station selection habits into the charging station site selection process is in line with the actual situation when users have charging needs during their daily travel. Users make comprehensive considerations based on the addressing distance between themselves and the surrounding charging stations and the charging and parking prices of each charging station, and then choose the charging station with the lowest cost as their target charging station.

[0093] As an implementation method of the embodiment of the present invention, in step S3, a charging pile association matrix is ​​constructed according to the charging selection of the electric vehicle user to obtain the charging pile contact rate, and the specific method is as follows.

[0094] Assume that the candidate charging station node set is N = (n1, n2, n3, ..., n m ), where m is the number of candidate charging stations, and its association matrix L should be of m×m order. Suppose the initial association matrix L (0) is an m×m-order zero matrix, which counts the charging choices of electric vehicle users obtained in step S3. If the k-1th charging choice of a vehicle is n i , the kth charging selection is n j ,but This is continuously updated, and finally the correlation matrix L is obtained.

[0095] As an implementation method of an embodiment of the present invention, in step S4, in order to scientifically and reasonably quantify the total cost of the measurement and verification terminal configuration, the total cost is divided into construction cost and operation and maintenance cost, and the model takes the minimum total cost as the objective function. Among them, the construction cost includes fixed investment and equipment investment; the operation and maintenance cost considers the manpower and transportation costs generated during the operation and maintenance process. The mandatory measurement and verification terminal optimization configuration model is:

[0096] C ALL =min(C1+C2)

[0097]

[0098] Among them, Y j represents a 0-1 decision variable. If the design quantity verification terminal is installed at position j, its value is 1. If the design quantity verification terminal is not installed at position j, its value is 0. Q represents the fixed input cost. v l Indicates the unit price of type l measurement verification terminal; Njl Indicates the number of type l measurement and verification terminals installed at charging station j; n is the number of maintenance times within a predetermined time; L i represents the labor cost of the ith maintenance; C d represents the transportation cost per unit distance during the maintenance process; D i represents the distance between the ith maintenance point and the starting position; C ALL represents the total cost; C1 represents the construction cost; C2 represents the operation and maintenance cost;

[0099] Through the value transfer of electric vehicles, the calibration of charging piles without measurement and verification terminals is realized. The charging pile contact rate ρ represents the connection status of charging piles in the area. The constraints of the mandatory measurement and verification terminal optimization configuration model are:

[0100]

[0101] Among them, p k Indicates the connection between the kth charging pile and the charging pile with the measurement verification terminal installed. If the pile has been installed with the measurement verification terminal or is connected to the verified charging pile through the electric vehicle, then p k =1, otherwise, p k =0, n represents the total number of charging piles in the area.

[0102] As an implementation method of the embodiment of the present invention, in step S5, an improved hybrid frog leaping algorithm is used to solve the mandatory metrology verification terminal optimization configuration model.

[0103] The hybrid frog leaping algorithm is a cooperative search algorithm based on groups. In the local search, each individual has its own changes, and affects other individuals and is affected by other individuals, and evolves with the evolution of the sub-group. When the sub-group evolves to a certain stage, each sub-group communicates with each other to achieve mixed operations between groups until the requirements are met. The process is as follows:

[0104] Step 1 initializes the population and sets the number of electric vehicle users, the number of candidate charging stations, and the maximum number of iterations.

[0105] Step 2 randomly generates initial frog individuals, each of which corresponds to a configuration scheme to force measurement verification of C in the terminal optimization configuration model. ALL is the fitness function, and the fitness values ​​of all individuals are calculated.

[0106] Step 3: Sort the individuals according to their fitness value from large to small, and divide the sub-populations equally.

[0107] Step 4: According to the update formula of the hybrid frog leaping algorithm, frog individuals in each population jump towards the direction of high fitness, that is, the local optimal configuration solution, and the step size changes with the increase in the number of iterations.

[0108] Although the hybrid frog leaping algorithm has shown relatively good performance in solving many optimization problems, it still has some shortcomings. The algorithm converges slowly in the later stage, the optimization accuracy is not high, and it is easy to fall into the local optimum. In response to these problems, the step size of the hybrid frog leaping algorithm will be improved. In order to improve the optimization convergence ability of the algorithm, the step size of the worst solution update of the population in the optimization iteration process of the hybrid frog leaping algorithm is improved, and an adaptive step size search mechanism is set as follows:

[0109]

[0110] Among them, D j and D j-1 are the step sizes of the j-th and j-1-th population update iterations, and δ is the weight coefficient between δ max and δ min rand() represents any number between [0,1]. represents the worst fitness value, Represents the fitness value after the update. The step size of this update and the step size of the previous generation jointly determine the update of the worst solution in the algorithm subpopulation. During the entire algorithm iteration process, each individual updates and iterates towards the global optimal trend. The above update method is conducive to accelerating the iteration of individuals towards the global optimal solution and accelerating the global convergence speed. The size of δ changes with the number of iterations. In the early stage of iteration, a relatively large weight coefficient is conducive to reducing the iteration time of the global search. As the algorithm iterates to the middle and late stages, the gradually decreasing weight coefficient accelerates the individual's local search ability and is conducive to discovering more excellent individuals.

[0111] Step 5: Mix the groups, reorder and divide the individuals in each group, and record the global optimal solution.

[0112] Step 6 checks whether the calculation stop condition is met and determines whether the maximum number of iterations has been reached. If not, return to step 4; if reached, the algorithm stops calculating.

[0113] The present invention can quantify the total cost of the configuration of the metering and verification terminal by comprehensively considering the transformation cost and the operation and maintenance cost, and ensure the minimization of the cost; the present invention realizes the verification of the charging piles without the metering and verification terminal installed through the value transmission of the electric vehicle, and can ensure that all charging piles are verified through the value transmitted by the electric vehicle to meet the verification requirements; the present invention establishes a charging demand prediction model based on factors such as the user's charging station selection habits, the total daily charging demand, and the spatiotemporal distribution of parking points, and reasonably configures the number of metering and verification terminals. The present invention divides the sub-areas and counts the parking frequency of each area to obtain the spatial and temporal probability distribution of the parking points, providing a reference for the selection of charging stations; the present invention uses the Bayesian theory to clarify the remaining power when the user has the intention to charge and the power distribution required for the adjacent trip, so as to predict the user's charging needs.

[0114] Embodiment 2:

[0115] The embodiment of the present invention also provides an optimized configuration device for a mandatory measurement and verification terminal of an electric vehicle charging pile, comprising:

[0116] The first building module is used to build a user charging selection model;

[0117] A first processing module is used to input the predicted electric vehicle charging demand into the user charging selection model to obtain the electric vehicle user charging selection;

[0118] The second processing module is used to obtain the charging pile contact rate according to the charging selection of the electric vehicle user;

[0119] The second construction module is used to construct a mandatory measurement and verification terminal optimization configuration model, wherein the objective function of the mandatory measurement and verification terminal optimization configuration model is to minimize the comprehensive cost, and the constraint condition is that the charging pile contact rate is greater than a predetermined value;

[0120] The calculation module is used to solve the optimization configuration model of the mandatory metrology verification terminal by adopting the improved hybrid frog leaping algorithm.

[0121] As an implementation method of an embodiment of the present invention, the first construction module uses the distance between the parking point and the target charging station and the charging service price to jointly characterize the user's charging station selection habits, so as to select the station with the smallest cost value as the user's target charging station and construct a user charging station selection habit model.

[0122] As an implementation method of an embodiment of the present invention, the optimization configuration model of the mandatory measurement and verification terminal is:

[0123] C ALL =min(C1+C2)

[0124]

[0125] Among them, Yj represents a 0-1 decision variable. If the design quantity verification terminal is installed at position j, its value is 1. If the design quantity verification terminal is not installed at position j, its value is 0. Q represents the fixed input cost. v l Indicates the unit price of type l measurement verification terminal; N jl Indicates the number of type l measurement and verification terminals installed at charging station j; n is the number of maintenance times within a predetermined time; L i represents the labor cost of the ith maintenance; C d represents the transportation cost per unit distance during the maintenance process; D i represents the distance between the ith maintenance point and the starting position; C ALL represents the total cost; C1 represents the construction cost; C2 represents the operation and maintenance cost;

[0126] The charging pile contact rate ρ is used to represent the connection status of charging piles in the region. The constraints of the mandatory measurement and verification terminal optimization configuration model are:

[0127]

[0128] Among them, p k Indicates the connection between the kth charging pile and the charging pile with the measurement verification terminal installed. If the pile has been installed with the measurement verification terminal or is connected to the verified charging pile through the electric vehicle, then p k =1, otherwise, p k =0, n represents the total number of charging piles in the area

[0129] As an implementation mode of an embodiment of the present invention, it also includes a prediction module, which is used to obtain the predicted charging demand of the electric vehicle based on the type and charging characteristics of the electric vehicle and driving information. The predicted charging demand of the electric vehicle includes: the total charging demand, the distribution of parking spots, the remaining power and the distribution of power required for adjacent trips.

[0130] The embodiments described above are only descriptions of the preferred embodiments of the present invention and are not intended to limit the scope of the present invention. Without departing from the design spirit of the present invention, various modifications and improvements made to the technical solutions of the present invention by ordinary technicians in this field should all fall within the protection scope determined by the claims of the present invention.

Claims

1. A method for optimizing the configuration of mandatory measurement and verification terminals for electric vehicle charging piles, characterized in that: include: Step S1, constructing a user charging selection model; Step S2, inputting the predicted electric vehicle charging demand into the user charging selection model to obtain the electric vehicle user charging selection; Step S3, obtaining the charging pile contact rate according to the charging selection of the electric vehicle user; Step S4, constructing a mandatory measurement and verification terminal optimization configuration model, wherein the objective function of the mandatory measurement and verification terminal optimization configuration model is to minimize the comprehensive cost, and the constraint condition is that the charging pile contact rate is greater than a predetermined value; Step S5: using an improved hybrid frog leaping algorithm to solve the mandatory metrology verification terminal optimization configuration model.

2. The method for optimizing the configuration of the mandatory measurement and verification terminal for electric vehicle charging piles according to claim 1, characterized in that: In step S1, the distance between the parking spot and the target charging station and the charging service price are used to jointly characterize the user's charging station selection habit, so as to select the station with the smallest cost value as the user's target charging station and construct a user charging station selection habit model.

3. The method for optimizing the configuration of the mandatory measurement and verification terminal of the electric vehicle charging pile according to claim 2, characterized in that: In step S2, based on the electric vehicle type, charging characteristics, and driving information, a predicted electric vehicle charging demand is obtained, wherein the predicted electric vehicle charging demand includes: total charging demand, parking point distribution, remaining power, and power distribution required for adjacent trips.

4. The method for optimizing the configuration of the mandatory measurement and verification terminal for electric vehicle charging piles according to claim 3, characterized in that: The optimization configuration model of mandatory metrology verification terminal is: C ALL =min(C1+C2) Among them, Y j represents a 0-1 decision variable. If the design quantity verification terminal is installed at position j, its value is 1. If the design quantity verification terminal is not installed at position j, its value is 0. Q represents the fixed input cost. v l Indicates the unit price of type l measurement verification terminal; N jl Indicates the number of type l measurement and verification terminals installed at charging station j; n is the number of maintenance times within a predetermined time; L i represents the labor cost of the ith maintenance; C d represents the transportation cost per unit distance during the maintenance process; D i represents the distance between the ith maintenance point and the starting position; C ALL represents the total cost; C1 represents the construction cost; C2 represents the operation and maintenance cost; The charging pile contact rate ρ is used to represent the connection status of charging piles in the region. The constraints of the mandatory measurement and verification terminal optimization configuration model are: Among them, p k Indicates the connection between the kth charging pile and the charging pile with the measurement verification terminal installed. If the pile has been installed with the measurement verification terminal or is connected to the verified charging pile through the electric vehicle, then p k =1, otherwise, p k =0, n represents the total number of charging piles in the area.

5. An optimized configuration device for mandatory measurement and verification terminal of electric vehicle charging piles, characterized in that: include: The first building module is used to build a user charging selection model; A first processing module is used to input the predicted electric vehicle charging demand into the user charging selection model to obtain the electric vehicle user charging selection; The second processing module is used to obtain the charging pile contact rate according to the charging selection of the electric vehicle user; The second construction module is used to construct a mandatory measurement and verification terminal optimization configuration model, wherein the objective function of the mandatory measurement and verification terminal optimization configuration model is to minimize the comprehensive cost, and the constraint condition is that the charging pile contact rate is greater than a predetermined value; The calculation module is used to solve the optimization configuration model of the mandatory metrology verification terminal by adopting the improved hybrid frog leaping algorithm.

6. The electric vehicle charging pile mandatory measurement and verification terminal optimization configuration device according to claim 5, characterized in that: The first construction module uses the distance between the parking spot and the target charging station and the charging service price to jointly characterize the user's charging station selection habits, so as to select the station with the smallest cost value as the user's target charging station and construct a user charging station selection habit model.

7. The electric vehicle charging pile mandatory measurement and verification terminal optimization configuration device according to claim 6, characterized in that: The optimization configuration model of mandatory metrology verification terminal is: C ALL =min(C1+C2) Among them, Y j represents a 0-1 decision variable. If the design quantity verification terminal is installed at position j, its value is 1. If the design quantity verification terminal is not installed at position j, its value is 0. Q represents the fixed input cost. v l Indicates the unit price of type l measurement verification terminal; N jl Indicates the number of type l measurement and verification terminals installed at charging station j; n is the number of maintenance times within a predetermined time; L i represents the labor cost of the ith maintenance; C d represents the transportation cost per unit distance during the maintenance process; D i represents the distance between the ith maintenance point and the starting position; C ALL represents the total cost; C1 represents the construction cost; C2 represents the operation and maintenance cost; The charging pile contact rate ρ is used to represent the connection status of charging piles in the region. The constraints of the mandatory measurement and verification terminal optimization configuration model are: Among them, p k Indicates the connection between the kth charging pile and the charging pile with the measurement verification terminal installed. If the pile has been installed with the measurement verification terminal or is connected to the verified charging pile through the electric vehicle, then p k =1, otherwise, p k =0, n represents the total number of charging piles in the area.

8. The electric vehicle charging pile mandatory measurement and verification terminal optimization configuration device according to claim 7, characterized in that: It also includes a prediction module for obtaining a predicted charging demand for an electric vehicle based on the type of electric vehicle, charging characteristics, and driving information. The predicted charging demand for an electric vehicle includes: total charging demand, parking point distribution, remaining power, and power distribution required for adjacent trips.

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