A charging station operation strategy optimization method based on vehicle secondary travel data

Abstract

The present application relates to a kind of based on the charging station operation strategy optimization method of vehicle secondary data, comprising the following steps: S1, obtains electric vehicle secondary data, obtains different charging station location information based on electric vehicle secondary data, while determining the parameter in charging and discharging process;S2, based on the parameter obtained in step S1, constructs evaluation index in combination with user and site characteristics;S3, the evaluation index of S2 is fused, and the weight of each index is established in combination with entropy weight method;S4, based on the weight of each index, using the method of approximation ideal point sorting, obtains the score of each charging station, and based on the score optimization operation strategy of corresponding position station site.Compared with prior art, the present application has the advantages.

Description

Technical Field

[0001] This invention relates to the technical field of charging station operation strategy optimization, and in particular to a method for optimizing charging station operation strategy based on vehicle trip data. Background Technology

[0002] In recent years, the market share of new energy electric vehicles has increased significantly, and basic supporting facilities such as charging stations have also been gradually improved. However, with the increasing market share of charging stations and pure electric vehicles, the load on the existing power grid has been exacerbated. Currently, traditional charging stations are unable to effectively alleviate the shortage of power distribution resources. In response to this problem, V2G charging stations can effectively alleviate the imbalance of regional power distribution resources. In addition, V2G charging stations can effectively delay the actual problem of regional power grid expansion, and the value-added discharge service can also increase user participation.

[0003] V2G charging stations, as an important means of balancing regional electricity load, not only have the charging function of traditional charging stations but also the function of receiving discharges from user vehicles and integrating the discharged energy into the power grid. Parameters describing the operation strategy of a V2G charging station include the initial charging state, State of Charge (SOC), the final charging state, and the final SOC. Existing technologies struggle to optimize the operation strategy of V2G charging stations, making it difficult to objectively and rationally improve the service level of charging piles. Furthermore, they cannot use quantitative methods to identify the operational shortcomings of different V2G charging stations, all of which hinder the future promotion and use of V2G charging stations. Summary of the Invention

[0004] The purpose of this invention is to overcome the shortcomings of the existing technology by providing a charging station operation strategy optimization method based on vehicle second-trip data, which can continuously optimize charging station services and supporting facilities and improve the service efficiency of V2G charging stations.

[0005] The objective of this invention can be achieved through the following technical solutions:

[0006] A method for optimizing charging station operation strategies based on vehicle second-trip data includes the following steps:

[0007] S1. Obtain the next-trip data of electric vehicles, and based on the next-trip data of electric vehicles, obtain the location information of different charging stations, and at the same time determine the parameters in the charging and discharging process.

[0008] S2. Combining user and site characteristics, construct evaluation indicators based on the parameters obtained in step S1;

[0009] S3. Integrate the evaluation indicators of S2 and establish the weights of each indicator by combining the entropy weight method.

[0010] S4. Based on the weights of each indicator, use the approximation of ideal point sorting method to obtain the score of each charging station, and optimize the operation strategy of the corresponding location station based on the score.

[0011] Furthermore, the specific steps of S2 are as follows:

[0012] S2.1 Based on the location information of different charging stations, use clustering methods to find the vehicles served by different stations within the evaluation period, determine whether the charging station is a public charging station or a private charging station, and assign a number to the charging station.

[0013] S2.2. Based on the parameters obtained in S1, construct evaluation indicators for public and private charging stations. Each evaluation indicator is either a very large or very small indicator. The evaluation indicators include user experience indicators and charging station service quality indicators corresponding to public and private charging stations, respectively.

[0014] User experience metrics include: station queuing time (index1), station charging cost (index2), station discharge revenue (index3), station fast charging station percentage (index4), station charging time (index5), station discharge time (index6), station peak charging capacity (index7), and station peak discharge capacity (index8).

[0015] The service quality indicators for charging stations include the total number of charging stations served (index 9) and the load variance of charging stations (index 1). 10 Peak-to-valley difference in charging and discharging at charging stations (index) 11 Charging station charging efficiency index 12 Charging station attraction index 13 Charging station service rate index 14 Charging station load smoothness index 15 Traffic disturbance at charging stations (index) 16 Charging station infrastructure status index 17 .

[0016] Furthermore, the specific steps of S3 are as follows:

[0017] S3.1 Construct an initial two-dimensional matrix, the elements of which are x. ij x ij This represents the value of the j-th indicator at the i-th charging station. The extremely small indicators in the indicator values ​​are reverse-processed to be converted into extremely large indicators.

[0018] S3.2, Standardize the transformed two-dimensional matrix obtained in S3.1;

[0019] S3.3 Construct an index probability matrix from the two-dimensional matrix obtained in S3.2;

[0020] S3.4 Calculate the information entropy of each indicator in the indicator probability matrix;

[0021] S3.5 Calculate the entropy weight of each indicator based on the information entropy of each indicator, where the entropy weight is the weight of each indicator.

[0022] Furthermore, the expression for the entropy weight of each indicator is as follows:

[0023]

[0024] Where, ω j h is the entropy weight of the j-th index value. j Let be the information entropy of the j-th indicator value, b be the total number of indicators, and i1 represent the i1-th indicator.

[0025] The expression for the information entropy of the j-th indicator value is:

[0026]

[0027] Among them, z ij Let be the probability of the j-th indicator value of the i-th V2G station, and n be the total number of charging stations;

[0028] The probability of the j-th indicator value for the i-th V2G site forms an indicator probability matrix, and the expression for the probability of the j-th indicator value is:

[0029]

[0030] Among them, y ij The elements are standardized two-dimensional matrices, where n is the total number of charging stations.

[0031] Furthermore, the expression for the elements of a standardized two-dimensional matrix is:

[0032]

[0033] Where, x ij The elements of the transformed two-dimensional matrix obtained in S3.1 are n, where n is the total number of charging stations.

[0034] Furthermore, the specific steps of S4 are as follows:

[0035] S4.1. Using the approximation ideal point sorting method, based on the index weights and elements in the index probability matrix, calculate the distance between each index of each station and the maximum value.

[0036] S4.2. Using the approximation of ideal point sorting method, based on the index weights and elements in the index probability matrix, calculate the distance between each index of each station and the minimum value.

[0037] S4.3. Calculate the score of each charging station based on the distance between each indicator and the maximum value and the distance between each indicator and the minimum value, and optimize the operation strategy of the corresponding location station based on the score.

[0038] Furthermore, the expression for the distance between each indicator and the maximum value for each site is as follows:

[0039]

[0040] Where b is the total number of indicators, z ij z is the probability of the j-th indicator value for the i-th V2G site. j_max For the maximum value of index j, ω j The entropy weight of the j-th index value;

[0041] The expression for the distance between each indicator and the minimum value for each site is:

[0042]

[0043] Among them, z j_min For the minimum value of index j, ω j Let be the entropy weight of the j-th index value.

[0044] Furthermore, the expression for the score of each charging station is as follows:

[0045] score i =D j_min / (D j_min +D j_max )

[0046] Among them, score i Let D be the score of the i-th V2G site. j_min D represents the distance between each indicator and the minimum value. j_max This represents the distance between each indicator and its maximum value.

[0047] Furthermore, the following indicators are considered: station queuing time (index 1) is a very small indicator; station charging cost (index 2) is a very small indicator; station discharge revenue (index 3) is a very large indicator; the percentage of fast charging piles at a station (index 4) is a very large indicator; station charging time (index 5) is a very small indicator; station discharge time (index 6) is a very large indicator; peak charging volume at a station (index 7) is a very small indicator; peak discharge volume at a station (index 8) is a very large indicator; total number of charging stations served (index 9) is a very large indicator; and charging station load variance (index 1) is a very large indicator. 10 For extremely small-scale indicators, the peak-to-valley difference in charging and discharging at charging stations (index) 11 For extremely small metrics, the charging efficiency index of a charging station 12As a very large indicator, the charging station attraction rate (index) 13 As a very large indicator, the charging station service rate index 14 As a very large indicator, the charging station load smoothness index 15 It is a very small indicator, representing the traffic disturbance situation at charging stations. 16 For extremely small-scale indicators, the charging station infrastructure status index 17 This is an extremely large indicator.

[0048] Furthermore, the charging station load variance index 10 The expression is:

[0049]

[0050] in, The cumulative charging energy consumption for the i-th V2G charging period is used for evaluation. To evaluate the cumulative energy of V2G charging and discharging during the i-th V2G period, η dg The energy conversion rate of vehicle-discharged electrical energy into the power grid, n day To evaluate the total number of days in the statistical period;

[0051] charging station load smoothness index 15 The expression is:

[0052]

[0053] in, This represents the total charging power during time period t. N represents the number of vehicles charging during time period t. t This refers to the number of time periods within the evaluation time.

[0054] Compared with the prior art, the present invention has the following beneficial effects:

[0055] (1) The invention adopts an expert scoring method for qualitative subjective indicators and a fusion evaluation method of entropy weight method and the approximation ideal point ranking method (Topsis) for objective indicators. It not only considers the objective data indicators of user experience of charging stations, but also fully considers the subjective indicators of the charging station's own conditions. The fusion of subjective and objective influence indicators can quantify the evaluation of V2G sites from multiple aspects, multiple criteria and multiple angles, making the method highly operable and implementable.

[0056] (2) Using the secondary data of electric vehicles not only reduces the scale of vehicle trajectory data processing, but also allows for faster and more efficient analysis of the status of different V2G stations in the region. The adjustability of the evaluation time enables different charging stations to quickly understand their own operating status, continuously optimize charging station services and supporting facilities, improve the service efficiency of V2G charging stations, and ultimately continuously promote the construction and development of V2G charging stations. Attached Figure Description

[0057] Figure 1 This is a flowchart of the present invention. Detailed Implementation

[0058] The present invention will now be described in detail with reference to the accompanying drawings and specific embodiments. These embodiments are based on the technical solution of the present invention and provide detailed implementation methods and specific operating procedures. However, the scope of protection of the present invention is not limited to the following embodiments.

[0059] Definition: V2G: Electric vehicle network access technology.

[0060] This invention proposes a charging station operation strategy optimization method based on vehicle trip data to address the current problem of being unable to quantitatively evaluate the user experience, site load, and facility status of V2G charging stations, thus affecting user experience and the promotion and development of V2G charging stations. The flowchart of the method is as follows: Figure 1 As shown. The method includes the following steps:

[0061] S1. Obtain the electric vehicle secondary travel data from the new energy data center, obtain the location information and type information of V2G charging piles, and extract important data items and related parameters for charging station users and charging stations during the charging and discharging process.

[0062] S2. Combining the characteristics of users and sites, and based on the variables and parameters obtained in step S1, construct evaluation indicators corresponding to user experience and site service quality.

[0063] Key parameters in S1 include:

[0064] The data includes the vehicle code, the type of the next line, the charging status at the start of the next line, the collection time at the start of the next line, the latitude at the start of the next line, the longitude at the start of the next line, the SOC at the start of the next line, the vehicle status at the start of the next line, the charging status at the end of the next line, the collection time at the end of the next line, the latitude at the end of the next line, the longitude at the end of the next line, the SOC at the end of the next line, the total current at the end of the next line, and the vehicle status at the end of the next line. If the V2G charging station location information includes the station's latitude and longitude, then the charging station is a private V2G charging station. If the number of vehicles entering and leaving each week and the vehicle codes are relatively random, then the charging station is a public V2G charging station.

[0065] The specific steps of S2 are as follows:

[0066] S2.1 Charging pile location information is mainly clustered based on vehicle charging location information. Charging pile types are categorized into public and private charging piles according to the number of vehicles using them. Key data items for charging station users and the charging / discharging process at charging stations are shown in Table 1. Relevant parameters include the charging station service radius and the V2G charging pile grid-connected energy conversion rate. Using a 3km service radius as a baseline, the dbscan clustering method is used to identify vehicles served by different stations within the evaluation period, and the charging stations are numbered {1...i}.

[0067] Table 1 Key Data Items for Electric Vehicle Travel OD

[0068]

[0069] S2.2 User Experience-Related Metrics include: Station Queue Time (index 1), Station Charging Cost (index 2), Station Discharging Revenue (index 3), Station Fast Charging Pile Ratio (index 4), Station Charging Time (index 5), Station Discharging Time (index 6), Station Peak Charging Volume (index 7), and Station Peak Discharging Volume (index 8). Charging Station Service Quality Metrics include: Total Number of Charging Stations Served (index 9), and Charging Station Load Variance (index 1). 10 Peak-to-valley difference in charging and discharging at charging stations (index) 11 Charging station charging efficiency index 12 Charging station attraction index 13 Charging station service rate index 14 Charging station load smoothness index 15 Traffic disturbance at charging stations (index) 16 Charging station infrastructure status index 17 ,in:

[0070] The station queuing time is a very small indicator, expressed by the formula index1 = rt. i -ct i -dt i In the formula rt i Let ct be the dwell time of a vehicle at the i-th V2G station in the OD data. i Charging time for electric vehicles, dt i This refers to the discharge duration of electric vehicles, measured in hours.

[0071] The cost of charging at a site is a very small indicator, index2 = ct i m t In the formula m t The electricity price is under time-of-use pricing, and the unit is yuan.

[0072] The site discharge revenue is a very large indicator, index3 = dti m t The unit is yuan;

[0073] The percentage of fast charging stations at a site is a very large indicator, defined as index4 = num. i,f / num i,all This indicates the proportion of fast charging stations in the overall charging piles at the site;

[0074] Charging time at the station is a very small metric. In the formula It is the time when the vehicle finishes charging at the i-th V2G station, where... It is the start time of vehicle charging at the i-th V2G station. At the same time, the station discharge time is a very large indicator. Both the discharge time and the charging time are obtained by subtracting the start time and the end time. The difference is that the vehicle operating status and the vehicle charging and discharging status in the OD data need to be jointly judged to ensure the accurate distinction of the vehicle charging and discharging operating status.

[0075] Peak charging volume at the station is a very small indicator. In the formula The remaining battery capacity of vehicle k after charging during peak hours. cap represents the remaining battery capacity of the vehicle after charging during peak hours, designated k. k The fixed capacity of the battery for vehicle number k;

[0076] Peak discharge volume at the site is an extremely large indicator. In the formula This represents the remaining battery capacity of vehicle k before it discharges during peak hours. For vehicle number k during peak hours, the remaining battery capacity after discharge is ca□ k The fixed capacity of the battery for vehicle number k;

[0077] The total number of charging stations served is a very large indicator, index9 = ∑i k In the formula, i k The number of vehicles served by V2G charging station i within the analysis period, in units of vehicles;

[0078] The daily load variance of a charging station is a very small indicator:

[0079]

[0080] in The cumulative charging energy consumption for the i-th V2G charging period is used for evaluation. To evaluate the cumulative energy of V2G charging and discharging during the i-th V2G period, η dg The energy conversion rate of vehicle-discharged electrical energy into the power grid, n day To evaluate the total number of days in the statistical period;

[0081] The peak-to-valley difference in charging and discharging at charging stations is a very small indicator. in The energy consumed during peak hours for charging the i-th V2G charging station. The grid-connected power supplied to the i-th V2G charging station during peak hours for discharge. The i-th V2G charging station consumes electricity during off-peak hours. The grid-connected power supplied to the i-th V2G charging station during off-peak hours is expressed in kilowatt-hours (kW).

[0082] Charging efficiency is a very large indicator, index 12 =ct i / (ct i +wt i ), where wt i To evaluate the waiting time for all vehicles to charge and discharge at the depot within a given time period, index 12 ∈(0,1), unit: hour;

[0083] The attraction rate of charging stations is a very large indicator. Where D in D represents the total number of vehicles entering the charging station for charging during the evaluation period. all To evaluate the total number of vehicles with charging needs within the service radius of a charging station during a given time period, a vehicle with a battery level less than 30% is defined as having charging needs. 13 ∈(0,1);

[0084] Charging station service rate is a very large indicator:

[0085]

[0086] Where t represents each time period within the evaluation time, and N... t The number of time periods within the evaluation time. N represents the number of vehicles charging during time period t. charger This refers to the total number of charging piles in the charging station. This indicator is used to describe whether the charging piles in the charging station meet the charging demand.

[0087] Load smoothness of charging stations is a very small-scale indicator:

[0088]

[0089] in This represents the total charging power within time period t. This indicator is the variance of the average charging power across different time periods, used to represent the fluctuation of the average charging power during the V2G charging station evaluation period.

[0090] The charging station infrastructure is a very large indicator. It is determined by organizing experts to score the stations, and the overall index of the infrastructure conditions for each station is calculated using an expert scoring method. 16 The rating scale is as follows: 1 represents unqualified; 2 represents qualified; 3 represents good; and 4 represents excellent. This indicator is used to describe the supporting conditions of various facilities in the charging station.

[0091] Traffic disturbance at charging stations is a minor indicator. The index of the station's infrastructure conditions is determined by organizing experts to score the data. 17 The categories are: 1. Lesser impact; 2. Moderate impact; 3. More severe impact; 4. Severe impact. This indicator is used to describe the impact of charging stations on surrounding traffic.

[0092] S3. Integrate the evaluation metrics of different objects from step S2, and establish the weights of each metric using the entropy weight method, including the following steps:

[0093] S3.1 Since all indicators are positive, the initial two-dimensional matrix elements are x. ij x ij This represents the value of the j-th indicator for the i-th V2G site, and uniformly applies the extremely small indicator x. * The process is reversed to transform it into an extremely large indicator, x * =(x * max -x * ) / (x * max -x * min ), where x * max x is the maximum value of the smallest index. * min This represents the minimum value of an extremely small indicator.

[0094] S3.2 Standardization of the evaluation index matrix, In the formula, n represents the total number of V2G sites being evaluated;

[0095] S3.3 Construct the index probability matrix Z, In the formula z ij This represents the probability of the j-th indicator value for the i-th V2G site;

[0096] S3.4 Calculate the information entropy of each indicator. When z ij When h is 0, j =0;

[0097] S3.5 Calculate the entropy weights of each indicator. In the formula, b is the total number of indicators, and the determined entropy weight is the weight value of each indicator.

[0098] Based on the weights of each indicator determined in step S3.5, S4 uses the Topsis method to obtain the score for each site, including the following steps:

[0099] S4.1 Calculate the distance between the evaluation index and the maximum value for each station:

[0100]

[0101] In the formula z j_max The maximum value of index j;

[0102] S4.2 Calculate the distance between the evaluation index and the minimum value for each station:

[0103]

[0104] In the formula z j_min The minimum value of index j;

[0105] The overall score of the i-th V2G site in S4.3 is (score) i =D j_min / (D j_min +D j_max A higher score indicates a better evaluation result, based on the score. i The score provides an overall result for all V2G charging stations within the evaluation range, allowing for optimization of station operation based on the overall results.

[0106] The following is a practical example analysis:

[0107] This example uses data from 360 test electric vehicles in the Lingang New City area of ​​Pudong, Shanghai, provided by the New Energy Data Center. The data was collected in the first two weeks of June 2021 and includes 8 V2G charging stations that can be converted during the test. First, the location of the V2G charging stations is determined by the charging and discharging data items in the second row of electric vehicle data. Then, the multiple V2G charging points are clustered using the dbscan clustering algorithm to form 8 V2G charging stations with a 3km radius.

[0108] When executing S2, qualitative indicators are scored by experts to obtain relevant indicator values, while other indicators are assigned values ​​using the one-time data items of electric vehicles. This yields various indicators for 8 stations. In this example, the sum of charging and service fees for charging piles is set at 1 yuan / kWh. The electricity price for discharge value-added services is still under testing. The test setting is that the electricity price per kWh for discharge value-added services is the same as the electricity price per kWh for charging. The discharge energy loss test is set at 5%. The indicators are shown in Table 2.

[0109] Table 2 shows the values ​​of various indicators for V2G charging stations.

[0110]

[0111] Execute S3. The initial matrix is ​​reversed and standardized through the minimum index. Combined with the entropy weight method, the weight matrix of each index is obtained as ω=[0.034,0.059,0.069,0.042,0.064,0.160,0.074,0.074,0.069,0.060,0.052,0.002,0.010,0.001,0.052,0.004,0.174].

[0112] Execute S4, approximating the ideal solution (Topsis), and combine it with the index weight matrix in S3 to calculate the evaluation score of each V2G site. The results are shown in Table 3.

[0113] Table 3 Scoring Results

[0114]

[0115] The preferred embodiments of the present invention have been described in detail above. It should be understood that those skilled in the art can make numerous modifications and variations based on the concept of the present invention without creative effort. Therefore, all technical solutions that can be obtained by those skilled in the art based on the concept of the present invention through logical analysis, reasoning, or limited experimentation on the basis of existing technology should be within the scope of protection defined by the claims.

Claims

1. A method for optimizing charging station operation strategies based on vehicle trip data, characterized in that, Includes the following steps: S1. Obtain the next-trip data of electric vehicles, and based on the next-trip data of electric vehicles, obtain the location information of different charging stations, and at the same time determine the parameters in the charging and discharging process. S2. Combining user and site characteristics, construct evaluation indicators based on the parameters obtained in step S1; S3. Integrate the evaluation indicators of S2 and establish the weights of each indicator using the entropy weight method. S4. Based on the weights of each indicator, use the approximation of ideal point sorting method to obtain the score of each charging station, and optimize the operation strategy of the corresponding location station based on the score; the specific steps of S2 are as follows: S2.1 Based on the location information of different charging stations, use clustering methods to find the vehicles served by different stations within the evaluation period, determine whether the charging station is a public charging station or a private charging station, and assign a number to the charging station. S2.2 Based on the parameters obtained in S1, construct evaluation indicators for public and private charging stations. Each evaluation indicator is either a very large or very small indicator. The evaluation indicators include user experience indicators and charging station service quality indicators corresponding to public and private charging stations, respectively. ，： User experience metrics include: station queuing time (index1), station charging cost (index2), station discharge revenue (index3), percentage of fast charging stations (index4), station charging time (index5), station discharge time (index6), peak charging capacity (index7), and peak discharge capacity (index8). The service quality indicators for charging stations include the total number of charging stations served (index 9) and the load variance of charging stations (index 1). 10 Peak-to-valley difference in charging and discharging at charging stations (index) 11 Charging station charging efficiency index 12 Charging station attraction index 13 Charging station service rate index 14 Charging station load smoothness index 15 Traffic disturbance at charging stations (index) 16 Charging station infrastructure status index 17 ; The specific steps of S4 are as follows: S4.

1. Using the approximation ideal point sorting method, based on the index weights and elements in the index probability matrix, calculate the distance between each index of each station and the maximum value. S4.

2. Using the approximation of ideal point sorting method, based on the index weights and elements in the index probability matrix, calculate the distance between each index of each station and the minimum value. S4.

3. Calculate the score of each charging station based on the distance between each indicator and the maximum value and the distance between each indicator and the minimum value, and optimize the operation strategy of the corresponding location station based on the score. The expression for the distance between each indicator and the maximum value for each site is: Where b is the total number of indicators, z ij z is the probability of the j-th indicator value for the i-th V2G site. j_max For the maximum value of index j, ω j The entropy weight of the j-th index value; The expression for the distance between each indicator and the minimum value for each site is: Among them, z j_min For the minimum value of index j, ω j The entropy weight of the j-th index value; The expression for the score of each charging station is: score1=D j_min / (D j_min +D j_max ) Among them, score i Let D be the score of the i-th V2G site. j_min D represents the distance between each indicator and the minimum value. j_max This represents the distance between each indicator and its maximum value.

2. The method for optimizing charging station operation strategy based on vehicle trip data according to claim 1, characterized in that, The specific steps for S3 are as follows: S3.1 Construct an initial two-dimensional matrix, the elements of which are x. ij ,x ij This represents the value of the j-th indicator at the i-th charging station. The extremely small indicators in the indicator values ​​are reverse-processed to be converted into extremely large indicators. S3.2, Standardize the transformed two-dimensional matrix obtained in S3.1; S3.3 Construct an index probability matrix from the two-dimensional matrix obtained in S3.2; S3.4 Calculate the information entropy of each indicator in the indicator probability matrix; S3.5 Calculate the entropy weight of each indicator based on the information entropy of each indicator, where the entropy weight is the weight of each indicator.

3. The method for optimizing charging station operation strategy based on vehicle trip data according to claim 2, characterized in that, The expressions for the entropy weights of each indicator are: Where, ω j h is the entropy weight of the j-th index value. j Let be the information entropy of the j-th indicator value, b be the total number of indicators, and i1 represent the i1-th indicator. The expression for the information entropy of the j-th indicator value is: Among them, z ij Let be the probability of the j-th indicator value of the i-th V2G station, and n be the total number of charging stations; The probability of the j-th indicator value for the i-th V2G site forms an indicator probability matrix, and the expression for the probability of the j-th indicator value is: Among them, y ij The elements are standardized two-dimensional matrices, where n is the total number of charging stations.

4. The method for optimizing charging station operation strategy based on vehicle trip data according to claim 3, characterized in that, The expression for the elements of a standardized two-dimensional matrix is: Where, x ij The elements of the transformed two-dimensional matrix obtained in S3.1 are n, where n is the total number of charging stations.

5. The method for optimizing charging station operation strategy based on vehicle trip data according to claim 1, characterized in that, Queueing time at a charging station (index 1) is a very small indicator; charging cost at a charging station (index 2) is a very small indicator; discharge revenue at a charging station (index 3) is a very large indicator; percentage of fast charging stations at a charging station (index 4) is a very large indicator; charging time at a charging station (index 5) is a very small indicator; discharge time at a charging station (index 6) is a very large indicator; peak charging volume at a charging station (index 7) is a very small indicator; peak discharge volume at a charging station (index 8) is a very large indicator; total number of charging stations served (index 9) is a very large indicator; charging station load variance (index 1) is also a very large indicator. 10 For extremely small-scale indicators, the peak-to-valley difference in charging and discharging at charging stations (index) 11 For extremely small metrics, the charging efficiency index of a charging station 12 As a very large indicator, the charging station attraction rate (index) 13 As a very large indicator, the charging station service rate index 14 As a very large indicator, the charging station load smoothness index 15 It is a very small indicator, representing the traffic disturbance situation at charging stations. 16 For extremely small-scale indicators, the charging station infrastructure status index 17 This is an extremely large indicator.

6. The method for optimizing charging station operation strategy based on vehicle trip data according to claim 5, characterized in that, charging station load variance index 10 The expression is: in, The cumulative charging energy consumption for the i-th V2G charging period is used for evaluation. To evaluate the cumulative energy of V2G charging and discharging during the i-th V2G period, η dg The energy conversion rate of vehicle-discharged electrical energy into the power grid, n day To evaluate the total number of days in the statistical period; Charging station load smoothness index 15 The expression is: in, This represents the total charging power during time period t. N represents the number of vehicles charging during time period t. t This represents the number of time periods within the evaluation time.

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