Vehicle network interaction comprehensive effect evaluation method and device, computer equipment and storage medium

Abstract

The invention relates to a comprehensive effect evaluation method and device for vehicle network interaction, computer equipment and a storage medium, and relates to the technical field of comprehensive effect evaluation. The method comprises the following steps: acquiring charging characteristics corresponding to various types of electric vehicles in vehicle-network interaction, and acquiring power grid index comparison information of each region before and after the vehicle-network interaction; determining a comprehensive effect evaluation index according to the charging characteristics and the power grid index comparison information; the comprehensive effect evaluation index comprises a plurality of evaluation parameters determined from multiple dimensions; determining a subjective weight of each evaluation parameter in the comprehensive effect evaluation index based on an analytic hierarchy process, and determining an objective weight of each evaluation parameter in the comprehensive effect evaluation index based on an entropy weight method; based on the subjective weight and the objective weight, determining a comprehensive index vector corresponding to the comprehensive effect evaluation index; and for the comprehensive index vectors of the various types of electric vehicles in the various areas, fusing the comprehensive index vectors based on a D-S evidence theory to obtain a comprehensive evaluation result of the various types of electric vehicles.

Description

Technical Field

[0001] The present application relates to the technical field of comprehensive effect evaluation, and in particular to a comprehensive effect evaluation method, device, computer equipment, storage medium and computer program product for vehicle-network interaction. Background Art

[0002] With the rapid development of social economy, new energy vehicles represented by electric vehicles have become the development direction. However, with the surge in the number of electric vehicles, the large-scale access of their load to the power grid will inevitably bring many unstable factors to the power grid in some areas. The uncertainty of electric vehicles will affect the comprehensive level of the power grid and have an impact on the stability of the power grid. For example, when using fast charging and ultra-fast charging technology, the high-power charging demand will have a greater impact on the stability of the power grid voltage.

[0003] In view of the impact of the influx of a large number of electric vehicles on the power grid, it is necessary to evaluate the comprehensive effect of the interaction between electric vehicles and distribution networks, and formulate charging strategies based on the evaluation results to avoid the negative impact of electric vehicles on the power grid. In related technologies, the comprehensive effect of vehicle-grid interaction can usually be evaluated through various indicators of the distribution network under the condition of electric vehicle charging, and the charging strategy can be determined based on the evaluation results. However, this evaluation process does not take into account the impact of electric vehicles on multiple dimensions in multiple interaction scenarios, resulting in insufficient accuracy of the comprehensive effect obtained by the evaluation. Summary of the invention

[0004] Based on this, it is necessary to provide a comprehensive effect evaluation method, device, computer equipment, computer-readable storage medium and computer program product for vehicle-grid interaction that can improve the evaluation accuracy in response to the above-mentioned technical problems.

[0005] In a first aspect, the present application provides a comprehensive effect evaluation method for vehicle-grid interaction. The method comprises:

[0006] Obtain the charging characteristics of various types of electric vehicles in vehicle-grid interaction, and obtain the grid indicator comparison information of each region before and after vehicle-grid interaction;

[0007] Determine a comprehensive effect evaluation index according to the charging characteristics and the grid index comparison information; the comprehensive effect evaluation index includes multiple evaluation parameters determined from multiple dimensions; one dimension corresponds to at least one evaluation parameter;

[0008] Determine the subjective weight of each evaluation parameter in the comprehensive effect evaluation index based on the hierarchical analysis method, and determine the objective weight of each evaluation parameter in the comprehensive effect evaluation index based on the entropy weight method;

[0009] Based on the subjective weight and the objective weight, determining a comprehensive indicator vector corresponding to the comprehensive effect evaluation indicator;

[0010] For the comprehensive index vectors of each type of electric vehicles in each region, the comprehensive index vectors are fused based on the DS evidence theory to obtain the comprehensive evaluation results of each type of electric vehicles.

[0011] In one embodiment, the dimensions include at least the user side, the grid side, the operator and the society; the evaluation parameters of the user side include the degree of battery loss, user credibility, degree of utilization of adjustable potential, and user satisfaction; the evaluation parameters of the grid side include load regulation capability, network loss optimization capability, vehicle-grid interaction safety, and vehicle-grid interaction flexibility; the evaluation parameters of the operator include operator revenue; and the evaluation parameters of the society include green electricity consumption;

[0012] The charging characteristics include at least battery discharge rate, total battery ampere-hours, discharge time, expected start charging time, expected off-grid time, necessary charging time, battery capacity, charging power, discharge power, electricity sales revenue and electricity sales;

[0013] The grid index comparison information at least includes the starting charging time, the actual off-grid time, the maximum load of the grid, the minimum load of the grid, the dischargeable capacity, and the distribution network voltage;

[0014] Determining a comprehensive effect evaluation index according to the charging characteristics and the grid index comparison information includes:

[0015] Calculating the degree of battery loss of the electric vehicle based on the battery discharge rate and the total ampere-hours of the battery;

[0016] Calculating user credibility based on the expected charging start time, the expected off-grid time and the actual off-grid time;

[0017] Calculating an adjustable potential utilization degree based on the dischargeable amount, the discharge time, the actual off-grid time, the necessary charging time, the battery capacity, the charging power, and the discharge power;

[0018] Calculating user satisfaction based on the discharge time, the expected start charging time, the expected off-grid time, the actual off-grid time, the necessary charging time, the battery capacity, the charging power, and the discharge power;

[0019] Calculating a load regulation capability based on the maximum load of the power grid and the minimum load of the power grid;

[0020] Calculate and optimize network loss capabilities based on network loss during vehicle-network interaction and network loss during non-vehicle-network interaction;

[0021] Calculating vehicle-grid interaction safety based on the distribution network voltage;

[0022] Calculate the flexibility of vehicle-grid interaction based on the corresponding grid load before and after vehicle-grid interaction;

[0023] Calculate operator revenue based on electric vehicle discharge revenue, non-vehicle-grid interaction revenue, and carbon revenue;

[0024] The green electricity consumption is calculated based on the additional green electricity consumption through load transfer during vehicle-grid interaction, and the corresponding power of wind and solar power abandonment when there is no vehicle-grid interaction.

[0025] In one embodiment, the determining of the subjective weight of each evaluation parameter in the comprehensive effect evaluation index based on the hierarchical analysis method includes:

[0026] Based on the multiple evaluation parameters in the comprehensive effect evaluation index and the nine-scale method, a judgment matrix is ​​constructed;

[0027] Based on the geometric mean method and the judgment matrix, determining the initial weight corresponding to each of the evaluation parameters and the maximum eigenvalue corresponding to the judgment matrix;

[0028] Based on the maximum eigenvalue, a consistency index of the judgment matrix is ​​determined. If the consistency index fails to pass the consistency check, the step of constructing the judgment matrix is ​​repeated until the consistency index passes the consistency check and the initial weight is determined to be the subjective weight.

[0029] In one embodiment, the method of determining the objective weight of each evaluation parameter in the comprehensive effect evaluation index based on the entropy weight method includes:

[0030] Performing standardization processing on the judgment matrix to obtain a standardized judgment matrix;

[0031] For each evaluation parameter in the comprehensive effect evaluation index, mapping the elements in the judgment matrix corresponding to the evaluation parameter to obtain a mapping value of the evaluation parameter;

[0032] The objective weight of the evaluation parameter is determined based on the ratio of the mapping value to the total mapping values ​​corresponding to all evaluation parameters.

[0033] In one embodiment, determining the comprehensive indicator vector corresponding to the comprehensive effect evaluation indicator based on the subjective weight and the objective weight includes:

[0034] For each evaluation parameter in the comprehensive effect evaluation index, determine the product value of the subjective weight and the objective weight corresponding to each evaluation parameter, and determine the comprehensive weight corresponding to each evaluation parameter based on the ratio of the product value to the total value of the products corresponding to all evaluation parameters;

[0035] For each type of electric vehicle participating in the vehicle-grid interaction, a weighted sum is performed on the comprehensive weights of the evaluation parameters corresponding to each type of electric vehicle to obtain a comprehensive indicator vector corresponding to the comprehensive effect evaluation indicator.

[0036] In one embodiment, the comprehensive indicator vector is fused based on the DS evidence theory to obtain a comprehensive evaluation result of each type of electric vehicle, including:

[0037] Based on the comprehensive indicator vectors corresponding to each type of vehicle in each region, the identification framework of the DS evidence theory is determined; the identification framework includes evaluation events for each type of electric vehicle in each region; the evaluation events are used to indicate whether each type of electric vehicle has the best interaction effect in different regions;

[0038] Probability distribution is performed on the evaluation events under the recognition framework to obtain the mass function corresponding to each evaluation event;

[0039] The mass functions are fused by Dempster synthesis rule to obtain comprehensive evaluation results for various types of electric vehicles.

[0040] In a second aspect, the present application also provides a comprehensive effect evaluation device for vehicle-grid interaction. The device comprises:

[0041] The data acquisition module is used to obtain the charging characteristics of various types of electric vehicles in vehicle-grid interaction and to obtain the grid indicator comparison information of each region before and after vehicle-grid interaction;

[0042] An evaluation index determination module, configured to determine a comprehensive effect evaluation index according to the charging characteristics and the grid index comparison information; the comprehensive effect evaluation index comprises a plurality of evaluation parameters determined from a plurality of dimensions; one dimension corresponds to at least one evaluation parameter;

[0043] A weight determination module, used to determine the subjective weight of each evaluation parameter in the comprehensive effect evaluation index based on the hierarchical analysis method, and to determine the objective weight of each evaluation parameter in the comprehensive effect evaluation index based on the entropy weight method and the subjective weight;

[0044] An indicator vector determination module, used to determine the comprehensive indicator vector corresponding to the comprehensive effect evaluation indicator based on the subjective weight and the objective weight;

[0045] The comprehensive effect evaluation module is used to fuse the comprehensive indicator vectors of various types of electric vehicles in various regions based on the DS evidence theory to obtain comprehensive evaluation results of various types of electric vehicles.

[0046] In a third aspect, the present application further provides a computer device, wherein the computer device comprises a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the steps of the method described in the first aspect are implemented.

[0047] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the steps of the method described in the first aspect are implemented.

[0048] In a fifth aspect, the present application further provides a computer program product, wherein the computer program product comprises a computer program, and when the computer program is executed by a processor, the steps of the method described in the first aspect are implemented.

[0049] The above-mentioned comprehensive effect evaluation method, device, computer equipment, storage medium and computer program product of vehicle-grid interaction obtain the charging characteristics corresponding to each type of electric vehicle in vehicle-grid interaction, obtain the grid index comparison information of each region before and after vehicle-grid interaction, and determine the comprehensive effect evaluation index according to the charging characteristics and the grid index comparison information, so as to obtain multiple evaluation parameters determined from multiple dimensions, thereby determining the evaluation parameters of multiple dimensions in multiple interaction scenarios. Based on this, the server can determine the subjective weight of each evaluation parameter in the comprehensive effect evaluation index based on the hierarchical analysis method, determine the objective weight of each evaluation parameter in the comprehensive effect evaluation index based on the entropy weight method, and determine the comprehensive index vector corresponding to the comprehensive effect evaluation index through the subjective weight and the objective weight, so as to sort the importance of each evaluation parameter, accurately obtain the weight of each evaluation parameter, and thus improve the accuracy of the comprehensive index vector. Finally, for the comprehensive index vectors of each type of electric vehicle in each region, the comprehensive index vectors are fused based on the DS evidence theory to obtain the comprehensive evaluation results of each type of electric vehicle, and when determining the comprehensive evaluation results, the comprehensive index vectors of multiple application scenarios can be used as influencing factors, thereby improving the accuracy of the comprehensive evaluation results of each type of electric vehicle. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the related technologies, the drawings required for use in the embodiments or the related technical descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0051] Figure 1 A diagram of an application environment of a comprehensive effect evaluation method for vehicle-grid interaction in one embodiment;

[0052] Figure 2A schematic diagram of a flow chart of a comprehensive effect evaluation method of vehicle-grid interaction in one embodiment;

[0053] Figure 3 A schematic diagram of a flow chart of a step of determining a subjective weight in one embodiment;

[0054] Figure 4 A schematic diagram of a flow chart of a step of determining an objective weight in one embodiment;

[0055] Figure 5 It is a flowchart of the steps of fusing comprehensive indicator vectors based on DS evidence theory in one embodiment;

[0056] Figure 6 A schematic diagram of a flow chart of a comprehensive effect evaluation method of vehicle-grid interaction in another embodiment;

[0057] Figure 7 A schematic diagram of the main division of vehicle-grid interaction effect evaluation indicators in one embodiment;

[0058] Figure 8 It is a schematic diagram of a comprehensive evaluation process of vehicle-network interaction based on DS evidence theory in one embodiment;

[0059] Fig. 9 It is a structural block diagram of a comprehensive effect evaluation device for vehicle-grid interaction in one embodiment;

[0060] Fig.10 FIG. 4 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0061] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0062] The comprehensive effect evaluation method of vehicle-grid interaction provided in the embodiment of the present application can be applied to Figure 1In the application environment shown. Among them, the terminal 102 communicates with the server 104 through the network. The data storage system can store the data that the server 104 needs to process. The data storage system can be integrated on the server 104, or it can be placed on the cloud or other network servers. Each terminal 102 can transmit the charging characteristics corresponding to each type of electric vehicle for vehicle-grid interaction to the server 104, and send the grid index comparison information of each area before and after the vehicle-grid interaction to the server 104. Therefore, the terminal can be a data terminal corresponding to different types of electric vehicles, or it can be a grid terminal that stores grid index data. The server 104 can determine the pre-configured comprehensive effect evaluation index based on the charging characteristics and the grid index comparison information. The comprehensive effect evaluation index can include evaluation parameters corresponding to multiple dimensions, and each dimension includes at least one evaluation parameter. Based on this, the server 104 can determine the subjective weight of each evaluation parameter in the comprehensive effect evaluation index through the hierarchical analysis method, and determine the objective weight of each evaluation parameter in the comprehensive effect evaluation index through the entropy weight method, and determine the comprehensive index vector of the comprehensive effect evaluation index based on the subjective weight and the objective weight. At this time, the server 104 may fuse the comprehensive index vectors according to each type of electric vehicle, for example, fuse the comprehensive index vectors for each type of electric vehicle in each area to obtain a comprehensive evaluation result for each type of electric vehicle.

[0063] The terminal 102 may be, but is not limited to, various personal computers, laptop computers, smart phones, tablet computers, electric vehicles, power grid equipment, etc. The server 104 may be implemented as an independent server or a server cluster consisting of multiple servers.

[0064] In an exemplary embodiment, Figure 2 As shown in the figure, a comprehensive effect evaluation method for vehicle-grid interaction is provided. Figure 1 The server 104 in the example is used as an example to illustrate the method, which includes the following steps S202 to S210. Among them:

[0065] Step S202, obtaining charging characteristics corresponding to various types of electric vehicles in vehicle-grid interaction, and obtaining grid indicator comparison information of each region before and after the vehicle-grid interaction.

[0066] Among them, vehicle-grid interaction is used to represent the interaction process between electric vehicles and distribution networks, such as the charging and discharging of electric vehicles through distribution networks. Various types of electric vehicles can be electric vehicles of different models and brands, or electric vehicles of multiple users divided according to their uses. Charging characteristics include various parameters that exist when electric vehicles are charging and discharging, such as charging time, discharging time, charging power, etc. The grid indicator comparison information can represent the difference between various grid indicators before and after the electric vehicle performs vehicle-grid interaction, as well as the difference between various grid indicators and charging characteristics. The grid indicator comparison information can also represent various grid indicators of the distribution network under preset scenarios.

[0067] Specifically, the server can receive the charging characteristics sent by each terminal. The charging characteristics can be first collected and sent to the terminal by each type of electric vehicle, and then transmitted to the server through the terminal. The server can receive the grid indicator comparison information of multiple pre-configured areas before and after the vehicle-grid interaction. The grid indicator comparison information in an area can be determined by at least one vehicle included in at least one type of electric vehicle. Through the impact of electric vehicles on the distribution network before and after the vehicle-grid interaction, the grid indicator comparison information can be collected by the terminal on the grid side, and the grid indicator comparison information can be sent to the server through the terminal on the grid side.

[0068] Step S204, determining comprehensive effect evaluation indicators based on charging characteristics and grid indicator comparison information.

[0069] The comprehensive effect evaluation index includes multiple evaluation parameters determined from multiple dimensions; one dimension corresponds to at least one evaluation parameter. In one example, the dimensions may include user side, grid side, etc.

[0070] Specifically, the server can calculate multiple evaluation parameters included in the comprehensive effect evaluation index according to a pre-configured calculation strategy. For each dimension, the server can determine at least one evaluation parameter for each dimension based on the charging characteristics of each type of electric vehicle and / or the grid indicator comparison information of each region. After determining the evaluation parameters of all dimensions, the comprehensive effect evaluation index is obtained. In one example, a single type of electric vehicle may have a corresponding comprehensive effect evaluation index in a single region, or a single type of electric vehicle may have a corresponding comprehensive effect evaluation index in all regions. The embodiments of the present application are not specifically limited.

[0071] Step S206, determining the subjective weight of each evaluation parameter in the comprehensive effect evaluation index based on the hierarchical analysis method, and determining the objective weight of each evaluation parameter in the comprehensive effect evaluation index based on the entropy weight method.

[0072] Among them, the hierarchical analysis method is a method that decomposes complex problems into multiple levels through a hierarchical structure, thereby conducting systematic analysis and decision-making. The entropy weight method is a weight allocation method based on information entropy, which is mainly used in multi-index comprehensive evaluation. The entropy weight method determines the weight by analyzing the amount of information of each indicator, which is relatively objective.

[0073] Specifically, the server can calculate each evaluation parameter in the comprehensive effect evaluation index according to the hierarchical analysis method to obtain the subjective weight corresponding to each evaluation parameter. The server can calculate each evaluation parameter in the comprehensive effect evaluation index according to the entropy weight method to obtain the objective weight corresponding to each evaluation parameter.

[0074] Step S208: Determine the comprehensive indicator vector corresponding to the comprehensive effect evaluation indicator based on the subjective weight and the objective weight.

[0075] Among them, the comprehensive indicator vector is determined based on the comprehensive weight; the comprehensive weight can be obtained by multiplying and fusing the subjective weight vector and the objective vector.

[0076] Specifically, the server may perform multiplication fusion on the subjective weight and the objective weight to obtain a comprehensive weight, and determine each evaluation index for processing according to the comprehensive weight, thereby obtaining a comprehensive index vector corresponding to the comprehensive effect evaluation index.

[0077] Step S210 , for the comprehensive index vectors of each type of electric vehicles in each region, the comprehensive index vectors are merged based on the DS evidence theory to obtain a comprehensive evaluation result of each type of electric vehicles.

[0078] Among them, DS evidence theory (Dempster-Shafer Theory) is an information fusion and decision-making theory used to deal with uncertainty and ambiguity.

[0079] Specifically, for each type of electric vehicle, the server may fuse the comprehensive index vectors of the same type of electric vehicles in each region through the DS evidence theory to obtain comprehensive evaluation results of each type of electric vehicles.

[0080] In the above-mentioned comprehensive effect evaluation method of vehicle-grid interaction, by obtaining the charging characteristics corresponding to each type of electric vehicle in vehicle-grid interaction, obtaining the grid index comparison information of each region before and after vehicle-grid interaction, and determining the comprehensive effect evaluation index according to the charging characteristics and grid index comparison information, multiple evaluation parameters determined from multiple dimensions can be obtained, thereby determining the evaluation parameters of multiple dimensions in multiple interaction scenarios. Based on this, the server can determine the subjective weight of each evaluation parameter in the comprehensive effect evaluation index based on the hierarchical analysis method, determine the objective weight of each evaluation parameter in the comprehensive effect evaluation index based on the entropy weight method, and determine the comprehensive index vector corresponding to the comprehensive effect evaluation index through the subjective weight and the objective weight, and can rank each evaluation parameter according to its importance, accurately obtain the weight of each evaluation parameter, and thus improve the accuracy of the comprehensive index vector. Finally, for the comprehensive index vectors of each type of electric vehicle in each region, the comprehensive index vectors are fused based on the DS evidence theory to obtain the comprehensive evaluation results of each type of electric vehicle. When determining the comprehensive evaluation results, the comprehensive index vectors of multiple application scenarios can be used as influencing factors, thereby improving the accuracy of the comprehensive evaluation results of each type of electric vehicle.

[0081] In an exemplary embodiment, the dimensions include at least the user side, the grid side, the operator and society; the evaluation parameters on the user side include battery loss degree, user credibility, adjustable potential utilization degree, and user satisfaction; the evaluation parameters on the grid side include load regulation capability, network loss optimization capability, vehicle-grid interaction safety, and vehicle-grid interaction flexibility; the evaluation parameters of the operator include operator revenue; and the evaluation parameters of society include green electricity consumption.

[0082] Among them, the charging characteristics at least include the battery discharge rate, total battery ampere-hours, discharge time, expected start charging time, expected off-grid time, necessary charging time, battery capacity, charging power, discharge power, electricity sales revenue and electricity sales. The grid index comparison information at least includes the start charging time, actual off-grid time, maximum grid load, minimum grid load, dischargeable capacity, and distribution network voltage.

[0083] The specific implementation process of the step "determining comprehensive effect evaluation indicators based on charging characteristics and grid indicator comparison information" includes:

[0084] 1. The server can calculate the battery loss degree of the electric vehicle based on the battery discharge rate and the total battery ampere-hours; the battery loss degree represents the actual battery capacity in percentage form.

[0085] Specifically, the battery loss of a single electric vehicle It can be expressed as:

[0086]

[0087] In the formula, , B is the exponential fitting coefficient, is the discharge rate, usually n×C, C corresponds to 2A, R and T are the gas constant and absolute temperature values, is the total amount of ampere-hours flowing through the battery, is the number of cycles, and DOD is the discharge window, i.e. the discharge time.

[0088] Therefore, the overall EV battery loss index for:

[0089]

[0090] 2. The server can calculate the user credibility based on the expected start charging time, the expected off-grid time and the actual off-grid time; the user credibility represents the integrity of the charging process in the form of a percentage.

[0091] Specifically, the user credibility index value is calculated to describe the integrity of the charging process in percentage form.

[0092] Among them, credibility It can be expressed as:

[0093]

[0094] In the formula, and are the expected starting charging time and expected off-grid time of the i-th vehicle respectively; is the time when the i-th vehicle actually goes off the grid.

[0095] 3. The server can calculate the degree of utilization of the adjustable potential based on the dischargeable amount, discharge time, actual off-grid time, necessary charging time, battery capacity, charging power and discharge power; the degree of utilization of the adjustable potential is expressed in percentage form.

[0096] Specifically, the adjustable potential utilization index value is calculated to describe the adjustable potential usage in percentage.

[0097] Among them, the degree of potential utilization can be adjusted It can be expressed as:

[0098]

[0099] In the formula, is the dischargeable capacity, is the actual discharge time. If the number of adjustments is 0, the vehicle has no adjustable potential utilization, and the actual discharge time is 0. Considering that the actual number of adjustments is 0, there is no vehicle-grid interaction, this paper does not consider this situation. For adjustable time, , They are the off-grid time and the starting charging time. is the necessary charging time, that is, the time it takes for the vehicle's SOC to be charged to 90%. The charging state of the grid. is the battery capacity, They are discharge power and charge power respectively.

[0100] Among them, the discharge capacity , adjustable time , Necessary charging time It can be expressed as:

[0101]

[0102]

[0103]

[0104] 4. The server can calculate user satisfaction based on the discharge time, expected start charging time, expected off-grid time, actual off-grid time, necessary charging time, battery capacity, charging power and discharge power.

[0105] Specifically, the user satisfaction index value is calculated to describe the degree of user satisfaction in percentage form.

[0106] Among them, user satisfaction It can be expressed as:

[0107] p e 4 = 1 n ∑ i =1 n ( T d i - T in i ) P c i + SOC i C i - 1+ P c i P d i + P c i [ ( T out i - T in i ) P c i - 0.9- SOC i C i ] 0.9 C i × T d i - T in i T out i - T in i

[0108] All the parameters in the formula have been explained in the calculation steps of the above-mentioned other indicators. This process describes the SOC loss caused by the user's premature termination of the later charging and discharging process. Since the user himself also has a certain understanding of this, his satisfaction should be measured by the ratio of the actual SOC to the planned SOC when the gun is pulled out in advance.

[0109] 5. The server can calculate the load regulation capability based on the maximum load and the minimum load of the power grid; the load regulation capability describes the vehicle-grid interactive regulation capability in the form of a percentage of load smoothness.

[0110] Specifically, the load regulation capability index value is calculated to describe the vehicle-grid interactive regulation capability in the form of a percentage of load smoothness.

[0111] Among them, the load regulation capability It can be expressed as:

[0112]

[0113] In the formula, is the maximum load of the power grid in this regulation interval, It is the minimum load of the power grid in this regulation interval. The smaller the peak-to-valley difference is, the larger the value is.

[0114] 6. The server can calculate and optimize the network loss capability based on the network loss during vehicle-network interaction and the network loss during non-vehicle-network interaction.

[0115] Specifically, the network loss optimization capability index value is calculated to describe the network loss optimization capability of vehicle-grid interaction in percentage form.

[0116] Among them, optimizing network loss capability It can be expressed as:

[0117]

[0118] In the formula, Refers to the network loss during the period t when the vehicle network interacts, is the network loss during the period t when there is no vehicle-grid interaction, and It can be expressed as:

[0119]

[0120]

[0121] Where R is the line resistance, is the voltage of the distribution network in period t, and is the active power and reactive power during vehicle-grid interaction, and It is the active power and reactive power during non-vehicle-grid interaction.

[0122] 7. The server can calculate the security of vehicle-grid interaction based on the distribution network voltage; the security of vehicle-grid interaction is described in the form of node voltage fluctuation rate.

[0123] Specifically, the vehicle-grid interaction safety index value is calculated, and the vehicle-grid interaction safety is described in the form of node voltage fluctuation rate.

[0124] Among them, the security of vehicle-network interaction It can be expressed as:

[0125]

[0126] In the formula, is the maximum or minimum voltage of the node within a period of time, is the rated voltage.

[0127] 8. The server can calculate the flexibility of vehicle-grid interaction based on the corresponding grid load before and after the vehicle-grid interaction; the flexibility of vehicle-grid interaction is described in the form of peak shaving and valley filling rate.

[0128] Specifically, the vehicle-grid interaction flexibility index value is calculated, and the vehicle-grid interaction flexibility is described in the form of peak shaving and valley filling rate.

[0129] Among them, the flexibility of vehicle-network interaction It can be expressed as:

[0130]

[0131] In the formula, is the maximum load before vehicle-grid interaction, is the maximum load after vehicle-grid interaction, is the minimum load before vehicle-grid interaction, is the minimum load after vehicle-grid interaction, is the rated capacity of the transformer.

[0132] 9. The server can calculate the operator's revenue based on electric vehicle discharge revenue, non-vehicle-grid interaction revenue and carbon revenue.

[0133] Specifically, the operator profit index value is calculated to describe the operator profit in the form of rate of return.

[0134] Among them, the operator's revenue It can be expressed as:

[0135]

[0136] In the formula Profits from discharging electric vehicles; Normal income for those who do not participate in vehicle-network interaction; For carbon benefits.

[0137] Among them, the discharge income during the vehicle-grid interaction period j is Carbon benefits It can be expressed as:

[0138]

[0139]

[0140] In the formula, is the unit revenue of selling electricity to the grid during the vehicle-grid interaction period j, which includes the costs of load transfer, The amount of electricity sold during vehicle-grid interaction during this period; is the carbon emission factor of period j; is the cost per kilogram of carbon dioxide emitted in period j.

[0141] 10. The server can calculate the green electricity consumption based on the additional green electricity consumption through load transfer during vehicle-grid interaction, and the corresponding power of wind and solar power abandonment when not participating in vehicle-grid interaction; green electricity consumption is described in the form of a percentage change in consumption.

[0142] Specifically, the green electricity consumption index value is calculated, and the green electricity consumption effect is described in the form of a percentage change in the consumption amount.

[0143] Among them, green electricity consumption It can be expressed as:

[0144]

[0145] In the formula, is the amount of green electricity consumed by transferring load when the i-th vehicle participates in the vehicle-grid interaction in time period t, It is the corresponding power of wind and solar power abandoned when the vehicle-grid interaction is not involved in this period.

[0146] In this embodiment, the comprehensive effect evaluation indicators determined by comparing the charging characteristics and power grid indicators may include battery loss degree, user credibility, adjustable potential utilization degree, user satisfaction, load regulation capability, network loss optimization capability, vehicle-grid interaction safety, vehicle-grid interaction flexibility, operator revenue, and green electricity consumption, which can improve the dimensions of the comprehensive effect evaluation indicators and improve the accuracy and efficiency of determining the evaluation parameters.

[0147] In an exemplary embodiment, Figure 3 As shown, the specific implementation process of the step "determining the subjective weight of each evaluation parameter in the comprehensive effect evaluation index based on the hierarchical analysis method" includes steps S302 to S306. Among them:

[0148] Step S302: construct a judgment matrix based on multiple evaluation parameters in the comprehensive effect evaluation index and the nine-scale method.

[0149] Among them, the nine-scale method is an evaluation and decision support technique, which is usually used in the analytic hierarchy process. The nine-scale method helps decision makers to score and compare different options or evaluation criteria through a set of predefined scales.

[0150] Specifically, the server can construct a judgment matrix F by comparing each indicator in the vehicle-grid interaction, and using the nine-scale method to obtain a comparison matrix, thereby obtaining a judgment matrix. The meaning of each scale value in the nine-scale method is shown in Table 1:

[0151] Table 1 Nine-scale method

[0152]

[0153] Table , That is, the comparison index, the judgment matrix of each index can be expressed as:

[0154]

[0155] Among them, the indicators in each row of the judgment matrix are, from left to right, safety, flexibility, ability to optimize network losses, load regulation capability, utilization of adjustable potential, green electricity consumption, battery loss, operator revenue, user satisfaction, and user credibility.

[0156] Step S304: Based on the geometric mean method and the judgment matrix, determine the initial weight corresponding to each evaluation parameter and the maximum eigenvalue corresponding to the judgment matrix.

[0157] Specifically, the geometric mean method is used to obtain the subjective relative weight , combine the weights to form the subjective weight vector W, and find the maximum eigenvalue of the judgment matrix F .

[0158] Among them, the geometric mean method is used to calculate the subjective relative weight It can be expressed as:

[0159]

[0160] In the formula, i and j are the row and column numbers of F respectively, and n is the number of indicators, which is 10 here.

[0161] Among them, the subjective weight vector W can be expressed as:

[0162]

[0163] Among them, the maximum eigenvalue of the judgment matrix F The calculation of can be expressed as:

[0164]

[0165] Step S306, based on the maximum eigenvalue, determine the consistency index of the judgment matrix. If the consistency index fails the consistency check, repeat the step of constructing the judgment matrix until the consistency index passes the consistency check and the initial weight is determined to be the subjective weight.

[0166] Specifically, the judgment matrix F is checked for consistency and its consistency ratio is calculated. When the judgment matrix consistency ratio CR>0.1, it means that the judgment matrix cannot pass the consistency check, and the judgment matrix F needs to be reset until it passes the consistency check.

[0167] Among them, the consistency ratio It can be expressed as:

[0168]

[0169] In the formula, is the average consistency index of a general matrix of a specific order, which is uniquely determined by the order of the judgment matrix. In a specific embodiment provided in this application, the order is 10, then 1.49 is taken. is the consistency index of the specific judgment matrix.

[0170] Among them, the consistency index of the specific judgment matrix F It can be expressed as:

[0171]

[0172] In this embodiment, a judgment matrix is ​​constructed by the nine-scale method, and the maximum eigenvalue of the judgment matrix is ​​determined by the geometric mean method, and the consistency index corresponding to the maximum eigenvalue is determined. While ensuring that the consistency index passes the consistency check, the subjective weight is obtained, which can improve the accuracy of determining the subjective weight.

[0173] In an exemplary embodiment, Figure 4 As shown, the specific implementation process of the step "determining the objective weight of each evaluation parameter in the comprehensive effect evaluation index based on the entropy weight method" includes steps S402 to S406. Among them:

[0174] Step S402, normalizing the judgment matrix to obtain a standardized judgment matrix.

[0175] Specifically, the process of standardizing the judgment matrix F can be expressed as:

[0176]

[0177] In the formula, is the value of the i-th row and j-th column in the judgment matrix F, is the value of the i-th row and j-th column after standardization.

[0178] Step S404, for each evaluation parameter in the comprehensive effect evaluation index, mapping the elements corresponding to the evaluation parameters in the judgment matrix to obtain the mapping values ​​of the evaluation parameters.

[0179] Step S406: determining the objective weight of the evaluation parameter based on the ratio of the mapping value to the total mapping value corresponding to all evaluation parameters.

[0180] Specifically, the judgment matrix F is modified to obtain the entropy weight matrix as the objective weight vector.

[0181] Among them, the process of correcting the judgment matrix F can be expressed as:

[0182]

[0183]

[0184] In the formula, is the objective weight of the jth indicator.

[0185] Among them, the entropy weight matrix It can be expressed as:

[0186]

[0187] In this embodiment, the judgment matrix is ​​standardized and the elements in the judgment matrix corresponding to the evaluation parameters are mapped to obtain the mapping value. For example, the mapping value is , and determining the ratio of each mapping value to the total mapping value as the objective weight can improve the accuracy of determining the objective weight.

[0188] In an exemplary embodiment, the specific implementation process of the step of "determining the comprehensive indicator vector corresponding to the comprehensive effect evaluation indicator based on the subjective weight and the objective weight" includes:

[0189] For each evaluation parameter in the comprehensive effect evaluation index, the product value of the subjective weight and the objective weight corresponding to each evaluation parameter is determined, and the comprehensive weight corresponding to each evaluation parameter is determined based on the ratio of the product value to the total value of the product corresponding to all evaluation parameters; for each type of electric vehicle participating in the vehicle-grid interaction, the comprehensive weights of each evaluation parameter corresponding to each type of electric vehicle are weighted and summed to obtain the comprehensive indicator vector corresponding to the comprehensive effect evaluation index.

[0190] Specifically, the subjective weights and objective weights obtained in the first two sections are integrated to obtain a comprehensive weight vector S.

[0191] Among them, the comprehensive weight vector S can be expressed as:

[0192]

[0193] In the formula, is the comprehensive weight of the ith indicator, specifically:

[0194]

[0195] Afterwards, the comprehensive evaluation index of vehicle-grid interaction effect is evaluated based on the index values ​​of various evaluation parameters.

[0196] Among them, assuming that there are l types of electric vehicles participating in the vehicle-grid interaction, the comprehensive evaluation formula for the vehicle-grid interaction effect of the e-th type of vehicle can be expressed as:

[0197]

[0198] In the formula, It represents the comprehensive effect of the e-type vehicle participating in the vehicle-network interaction. is the g-th item in the comprehensive weight vector S, It is the dimensionless value of the gth indicator of the eth type electric vehicle.

[0199] Among them, the dimensionless value It can be expressed as:

[0200]

[0201]

[0202] It should be noted that when the total number of electric vehicle types is l and the number of evaluation indicators is n, the evaluation indicator values ​​can form an l×n-order matrix P, which can be expressed as:

[0203]

[0204] Then in the above formula, for The modulus of the g-th column vector. In an example provided in an embodiment of the present application, the total number of electric vehicle types is 4 and the number of evaluation indicators is 10.

[0205] In this embodiment, the subjective weight and the objective weight can be integrated through the above process to obtain a comprehensive weight, and the comprehensive weight is weighted and summed for each evaluation parameter to obtain a comprehensive indicator vector, which can improve the accuracy of determining the comprehensive indicator vector.

[0206] In an exemplary embodiment, Figure 5 As shown, the specific implementation process of the step "fusing the comprehensive index vector based on the DS evidence theory to obtain the comprehensive evaluation results of various types of electric vehicles" includes steps S502 to S506. Among them:

[0207] Step S502: Determine the identification framework of the DS evidence theory based on the comprehensive indicator vector corresponding to each type of vehicle model in each region.

[0208] Among them, the identification framework includes evaluation events for each type of electric vehicle in each area; the evaluation events are used to indicate whether each type of electric vehicle interacts best in different areas.

[0209] Specifically, a DS evidence theory recognition framework is established, and the set of evaluation results of different vehicle model evaluation indicators in each area is used as the recognition framework Θ. Definition , where e is the type of car corresponding event, E is the type set, and the full set of Θ 2Θ is defined. In a specific embodiment provided in this application, E is 4. Among them, the full set of Θ 2Θ can be expressed as:

[0210]

[0211] Among them, the definition After comparing the comprehensive evaluation of various types of vehicles in each area, the evaluators felt that private cars had the best vehicle-network interaction effect. After comparing the comprehensive evaluation of various types of vehicles in each area, the evaluators felt that the vehicle-network interaction effect of official vehicles was the best. After comparing the comprehensive evaluation of various types of vehicles in each area, the evaluators felt that buses had the best vehicle-network interaction effect. It means that after comparing the comprehensive evaluations of various types of vehicles in each area, the evaluators felt that taxis had the best vehicle-network interaction effect.

[0212] It should be noted that the probability that the vehicle-network interaction effects of various types of vehicles are considered to be the same and the best by the evaluation party is small, so in a specific embodiment provided in this application, similar It means that after comparing the comprehensive evaluation of various types of vehicles in each area, it is found that the vehicle-network interaction effects of private cars and official cars are the same and the best events are set to zero.

[0213] Step S504: Probability distribution is performed on the evaluation events under the recognition framework to obtain the mass function corresponding to each evaluation event.

[0214] Specifically, the basic probability distribution of events under the recognition framework is also called mass function distribution. The specific process of mass function distribution can be expressed as:

[0215]

[0216] In the formula, Assign a set of functions to the confidence of the cth region, is the normalized set of evaluation results. It can be expressed as:

[0217]

[0218] In a specific embodiment provided in the present application, the result obtained by processing step S504 is shown in the following table:

[0219] Table 2 Mass function allocation

[0220]

[0221] In particular, in DS evidence theory, we can calculate the belief function and the suspicion function To determine the confidence interval of the event, it indicates the three states of the event: true, uncertain, and false. This interval reflects the degree of confirmation of the event, which can be expressed as:

[0222]

[0223] Step S506: various mass functions are merged by using the Dempster synthesis rule to obtain comprehensive evaluation results for various types of electric vehicles.

[0224] The Dempster synthesis rule is used for data fusion, and the mass functions of multiple independent evidences are fused into a new evidence. In a specific embodiment provided in the present application, this process will fuse 16 evaluation results of multiple regions for multiple types of electric vehicles to form 4 comprehensive evaluation results for vehicle types.

[0225] Among them, Dempster's synthesis rule can be expressed as:

[0226]

[0227] In the formula, c is the number of the area, k is the vehicle type, and in a specific embodiment provided in the present application, c and k are equal and both are 4. K is the conflict coefficient, is the evaluation result of the evaluation party c on the vehicle type k, and is the value assigned by the mass function. Among them, the conflict coefficient K can be expressed as:

[0228]

[0229] In one embodiment provided in this application, the final result is a vector , which can be specifically expressed as:

[0230] m 1234 =[ m 1234 1 , m 1234 2 , m 1234 3 , m 1234 4 ]

[0231] In the formula, is the comprehensive evaluation result of the kth type of vehicle.

[0232] In this embodiment, by fusing the comprehensive index vectors through the DS evidence theory, the comprehensive index vectors of the same type of electric vehicles in various regions can be fused to obtain a comprehensive evaluation result of each type of electric vehicle, thereby improving the accuracy of the evaluation of this type of electric vehicles.

[0233] The specific implementation process of the above-mentioned comprehensive effect evaluation method of vehicle-grid interaction is described in detail below in conjunction with a specific embodiment.

[0234] The embodiment of the present application provides a comprehensive effect evaluation method for vehicle-grid interaction, comprising the following steps: selecting multiple parameters as indicators for comprehensive effect evaluation of vehicle-grid interaction, obtaining charging characteristics of at least one electric vehicle of multiple types participating in the vehicle-grid interaction, and obtaining comparative information of various indicators of power grids before and after the vehicle-grid interaction for at least one day in each area participating in the vehicle-grid interaction; calculating the selected multiple indicator values ​​(evaluation parameters) based on the above-mentioned acquired data; comparing the above-mentioned multiple indicators in pairs, using the nine-scale method, and sorting them according to their importance to obtain a judgment matrix, based on this judgment matrix, using the geometric mean method to normalize and obtain the subjective relative weight vector of each indicator, obtaining the maximum eigenvalue of the judgment matrix, and performing consistency check on the judgment matrix; standardizing the judgment matrix, using the entropy weight method to correct the standardized judgment matrix, obtaining the entropy weight matrix as the objective weight vector, multiplying the subjective weight vector with the objective weight vector to obtain a comprehensive weight vector, and multiplying the dimensionless value of the corresponding indicator according to the judgment matrix sequence of the nine-scale method to obtain a comprehensive indicator vector.

[0235] In a specific embodiment provided in the present application, 10 parameters including battery loss degree, user credibility, adjustable potential utilization degree, user satisfaction, load regulation capability, network loss optimization capability, vehicle-grid interaction safety, flexibility, operator revenue, and green electricity consumption are selected from the user side, grid side, operator, and social perspectives as indicators for evaluating the comprehensive effect of vehicle-grid interaction; the charging characteristics of at least one private car, official car, bus, and taxi are obtained, and the comparison information before and after the vehicle-grid interaction of at least one day in each of the residential, commercial, industrial, and comprehensive areas is obtained; the user-side index values ​​of the four types of electric vehicles, namely private cars, official cars, buses, and taxis, are calculated based on the charging characteristics of the four types of vehicles; the grid-side index values, operator revenue, and green electricity consumption of the four types of areas, namely residential areas, commercial areas, industrial areas, and comprehensive areas, are calculated based on the load characteristics of the four types of areas.

[0236] In a specific embodiment provided in the present application, the user-side index values ​​of four types of electric vehicles, namely private cars, official cars, buses and taxis, are calculated based on the charging characteristics of the four types of vehicles, and the grid-side index values, operator revenues and green electricity consumption of the four types of areas, namely residential areas, commercial areas, industrial areas and comprehensive areas, are calculated based on the load characteristics of the four types of areas, namely residential areas, commercial areas, industrial areas and comprehensive areas. Specifically, the following are included:

[0237] According to the charging characteristics of the four types of vehicles, the electric vehicle battery loss percentage is calculated based on the electric vehicle lithium iron phosphate battery loss model, the user credibility percentage is calculated based on the actual time when the user is off the grid, the adjustable potential utilization percentage is calculated based on the actual discharge capacity, and the user satisfaction percentage is calculated based on the deviation between the actual battery state of charge at the time of off-grid and the target; according to the power supply and load characteristics of the four types of areas, the load regulation capability is calculated based on the load smoothness, the optimized network loss capability is calculated based on the network loss, the vehicle-grid interaction safety is calculated based on the voltage fluctuation on the grid side before and after the vehicle-grid interaction, the flexibility is calculated based on the peak shaving and valley filling rate, the operator's benefit is calculated based on the additional benefit of the operator participating in the vehicle-grid interaction, and the green electricity consumption effect is calculated based on the abandoned wind and solar power before and after the vehicle-grid interaction.

[0238] Optionally, in one embodiment of the present application, a nine-scale method is used to sort by importance to obtain a judgment matrix, including: indicators in each row of the judgment matrix are, from left to right, safety, flexibility, ability to optimize network losses, load regulation capability, degree of utilization of adjustable potential, green electricity consumption, battery loss, operator revenue, user satisfaction, and user credibility.

[0239] An embodiment of the present application provides a comprehensive evaluation method for vehicle-grid interaction effects, comprising the following steps: based on the DS evidence theory framework, taking the best vehicle-grid interaction effects of each vehicle as multiple events, taking multiple regions as different evaluation parties, and taking a comprehensive indicator vector as a basic probability distribution, obtaining the vehicle-grid interaction effect evaluation results of each type of vehicle in each region, and using the Dempster synthesis rule to fuse the data to obtain the final synthesized probability distribution as the comprehensive vehicle-grid interaction effect evaluation result of multiple types of vehicles.

[0240] In a specific example provided in the present application, the best vehicle-grid interaction effect of each vehicle is taken as multiple events, and multiple areas are taken as different evaluation parties, specifically including: residential areas, commercial areas, industrial areas, and comprehensive areas are taken as vehicle-grid interaction evaluation parties, and four types of electric vehicles, namely private cars, official cars, buses, and taxis, are taken as evaluated parties for the vehicle-grid interaction effect evaluation.

[0241] It can be seen from the above technical solutions that the embodiments of the present application have the following advantages: the present invention selects multi-subject side parameters as vehicle-grid interaction evaluation indicators, takes into account the characteristics of multi-regional and multi-category electric vehicles, and adopts subjective and objective weight fusion and DS evidence theory to construct a vehicle-grid interaction evaluation method. It considers multiple scenarios and multiple subjects, and evaluates in detail the vehicle-grid interaction effects of multiple types of electric vehicles in different regions, thereby improving the comprehensiveness of the vehicle-grid interaction effect evaluation.

[0242] like Figure 6 As shown, an embodiment of the present application provides a comprehensive effect evaluation method for vehicle-grid interaction, in which the charging characteristics of at least one private car, official car, bus, and taxi are obtained, and comparative information of various power grid indicators before and after vehicle-grid interaction in residential areas, commercial areas, industrial areas, and comprehensive areas for at least one day is obtained. According to the results of electric vehicles participating in vehicle-grid interaction, this application uses the analytic hierarchy process and the entropy weight method to fuse the selected key indicators, and conducts a comprehensive evaluation of the effect of vehicle-grid interaction based on the DS evidence theory, providing support for the analysis of the results of electric vehicles participating in vehicle-grid interaction. Thereby, the problem that the prior art lacks multi-scenario and multi-subject analysis of the effect of vehicle-grid interaction is solved. A comprehensive dynamic evaluation method for vehicle-grid interaction provided in an embodiment of the present application includes the following steps:

[0243] Step S610, select 10 parameters including battery loss degree, user credibility, adjustable potential utilization degree, user satisfaction, load regulation capability, network loss optimization capability, vehicle-grid interaction safety, flexibility, operator revenue, and green power consumption from the user side, grid side, operator, and social perspectives as indicators for comprehensive evaluation of vehicle-grid interaction, obtain charging characteristics of at least one private car, official car, bus, and taxi, obtain comparison information of various grid indicators before and after vehicle-grid interaction in residential areas, commercial areas, industrial areas, and comprehensive areas for at least one day, and calculate the value of each indicator based on the above information. Figure 7 As shown, Figure 7 It illustrates the specific division of 10 evaluation parameters.

[0244] Step S620, constructing a judgment matrix by using the hierarchical analysis method, normalizing it by the geometric mean method to obtain the subjective relativity weight, and obtaining the maximum eigenvalue of the judgment matrix, and performing a consistency check on the judgment matrix.

[0245] Step S630, standardize the judgment matrix based on the entropy weight method, modify the judgment matrix to calculate the entropy weight matrix, use the entropy weight matrix as the objective weight vector, and merge it with the subjective relative weight to form a comprehensive weight vector, and merge the comprehensive weight vector with the dimensionless index values.

[0246] Step S640, establish the DS evidence theory recognition framework, take the best vehicle-network interaction effect of each type of vehicle as the event, take the residential area, commercial area, industrial area, and comprehensive area as the evaluator, and make basic probability distribution for these events based on the fused indicators.

[0247] Step S650, using Dempster synthesis rule to fuse the independent evidence of multiple events into a new evidence, and obtain the final result after the comprehensive evaluation of each region.

[0248] like Figure 8 As shown, Figure 8 The diagram shows the comprehensive evaluation process of vehicle-network interaction based on DS evidence theory. Specifically, it can include: establishing a DS evidence theory recognition framework, and taking the set of evaluation results of different vehicle model evaluation indicators in each area as the recognition framework Θ. Performing basic probability distribution on events under the recognition framework, this process is also called mass function distribution. Using Dempster synthesis rule for data fusion, the mass functions of multiple independent evidences are fused into a new evidence.

[0249] It should be understood that, although the various steps in the flowcharts involved in the above-mentioned embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-mentioned embodiments can include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

[0250] Based on the same inventive concept, the embodiment of the present application also provides a vehicle-grid interaction comprehensive effect evaluation device for implementing the vehicle-grid interaction comprehensive effect evaluation method involved above. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above method, so the specific limitations in one or more vehicle-grid interaction comprehensive effect evaluation device embodiments provided below can refer to the limitations of the vehicle-grid interaction comprehensive effect evaluation method above, and will not be repeated here.

[0251] In an exemplary embodiment, Fig. 9 As shown, a comprehensive effect evaluation device 900 for vehicle-grid interaction is provided, comprising: a data acquisition module 901, an evaluation index determination module 902, a weight determination module 903, an index vector determination module 904 and a comprehensive effect evaluation module 905, wherein:

[0252] The data acquisition module 901 is used to obtain the charging characteristics of various types of electric vehicles in vehicle-grid interaction, and obtain the grid indicator comparison information of each area before and after the vehicle-grid interaction;

[0253] The evaluation index determination module 902 is used to determine the comprehensive effect evaluation index according to the charging characteristics and the grid index comparison information; the comprehensive effect evaluation index includes multiple evaluation parameters determined from multiple dimensions; one dimension corresponds to at least one evaluation parameter;

[0254] The weight determination module 903 is used to determine the subjective weight of each evaluation parameter in the comprehensive effect evaluation index based on the hierarchical analysis method, and to determine the objective weight of each evaluation parameter in the comprehensive effect evaluation index based on the entropy weight method and the subjective weight;

[0255] An indicator vector determination module 904 is used to determine a comprehensive indicator vector corresponding to the comprehensive effect evaluation indicator based on the subjective weight and the objective weight;

[0256] The comprehensive effect evaluation module 905 is used to fuse the comprehensive index vectors of each type of electric vehicles in each region based on the DS evidence theory to obtain comprehensive evaluation results of each type of electric vehicles.

[0257] Furthermore, the dimensions include at least the user side, the grid side, the operator and the society; the evaluation parameters on the user side include at least the degree of battery loss, user credibility, the degree of utilization of adjustable potential, and user satisfaction; the evaluation parameters on the grid side include load regulation capability, network loss optimization capability, vehicle-grid interaction safety, and vehicle-grid interaction flexibility; the evaluation parameters of the operator include operator revenue; the evaluation parameters of the society include green electricity consumption; among them, the charging characteristics include at least the battery discharge rate, the total battery ampere-hour, the discharge time, the expected start charging time, the expected off-grid time, the necessary charging time, the battery capacity, the charging power, the discharge power, the electricity sales revenue and the electricity sales amount; the grid indicator comparison information includes at least the start charging time, the actual off-grid time, the maximum load of the grid, the minimum load of the grid, the dischargeable amount, and the distribution network voltage.

[0258] The evaluation index determination module 902 is specifically used to: calculate the battery loss degree of the electric vehicle based on the battery discharge rate and the total battery ampere-hour; the battery loss degree is expressed as a percentage of the actual battery capacity; calculate the user credibility based on the expected starting charging time, the expected off-grid time and the actual off-grid time; calculate the adjustable potential utilization degree based on the dischargeable amount, the discharge time, the actual off-grid time, the necessary charging time, the battery capacity, the charging power and the discharge power; calculate the user satisfaction based on the discharge time, the expected starting charging time, the expected off-grid time, the actual off-grid time, the necessary charging time, the battery capacity, the charging power and the discharge power; calculate the load regulation capacity based on the maximum load of the power grid and the minimum load of the power grid; calculate the optimization network loss capacity based on the network loss during vehicle-grid interaction and the network loss during non-vehicle-grid interaction; calculate the vehicle-grid interaction safety based on the distribution network voltage; calculate the vehicle-grid interaction flexibility based on the corresponding grid load before and after the vehicle-grid interaction; calculate the operator's income based on the electric vehicle discharge income, the non-vehicle-grid interaction income and the carbon income; calculate the green electricity consumption based on the additional green electricity consumption through load transfer during vehicle-grid interaction and the corresponding power of wind and solar power abandonment when not participating in vehicle-grid interaction.

[0259] Furthermore, the weight determination module 903 is specifically used to: construct a judgment matrix based on multiple evaluation parameters in the comprehensive effect evaluation index and the nine-scale method; determine the initial weights corresponding to each evaluation parameter and the maximum eigenvalue corresponding to the judgment matrix based on the geometric mean method and the judgment matrix; determine the consistency index of the judgment matrix based on the maximum eigenvalue, and if the consistency index fails to pass the consistency check, repeat the steps of constructing the judgment matrix until the consistency index passes the consistency check and the initial weight is determined to be the subjective weight.

[0260] Furthermore, the weight determination module 903 is specifically used to: standardize the judgment matrix to obtain a standardized judgment matrix; for each evaluation parameter in the comprehensive effect evaluation index, map the elements corresponding to the evaluation parameters in the judgment matrix to obtain the mapping value of the evaluation parameter; determine the objective weight of the evaluation parameter based on the ratio of the mapping value to the total mapping value corresponding to all evaluation parameters.

[0261] Furthermore, the indicator vector determination module 904 is specifically used to: determine the product value of the subjective weight and the objective weight corresponding to each evaluation parameter in the comprehensive effect evaluation index, and determine the comprehensive weight corresponding to each evaluation parameter based on the ratio of the product value to the total value of the products corresponding to all evaluation parameters; for each type of electric vehicle participating in the vehicle-grid interaction, perform weighted summation on the comprehensive weights of each evaluation parameter corresponding to each type of electric vehicle to obtain a comprehensive indicator vector corresponding to the comprehensive effect evaluation index.

[0262] Furthermore, the comprehensive effect evaluation module 905 is specifically used to: determine the recognition framework of the DS evidence theory based on the comprehensive indicator vector corresponding to each type of vehicle model in each region; the recognition framework includes evaluation events for each type of electric vehicle in each region; the evaluation events are used to indicate whether each type of electric vehicle has the best interaction effect in different regions; perform probability distribution on the evaluation events under the recognition framework to obtain the mass function corresponding to each evaluation event; and fuse the mass functions through the Dempster synthesis rule to obtain the comprehensive evaluation results for each type of electric vehicle.

[0263] Each module in the above-mentioned comprehensive effect evaluation device can be implemented in whole or in part by software, hardware and a combination thereof. Each of the above-mentioned modules can be embedded in or independent of a processor in a computer device in the form of hardware, or can be stored in a memory in a computer device in the form of software, so that the processor can call and execute the operations corresponding to each of the above modules.

[0264] In an exemplary embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as shown in FIG. Fig.10 As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. Among them, the processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store the charging characteristics corresponding to each type of electric vehicle and the grid indicator comparison information of each area before and after the vehicle-grid interaction. The input / output interface of the computer device is used to exchange information between the processor and the external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a comprehensive effect evaluation method of vehicle-grid interaction is realized.

[0265] Those skilled in the art will understand that Fig.10 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0266] In an exemplary embodiment, a computer device is further provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above-mentioned method embodiments when executing the computer program.

[0267] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps in the above-mentioned method embodiments are implemented.

[0268] In one embodiment, a computer program product is provided, including a computer program, which implements the steps in the above method embodiments when executed by a processor.

[0269] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0270] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but are not limited to this.

[0271] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0272] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A comprehensive effect evaluation method for vehicle-grid interaction, characterized in that: The method comprises: Obtain the charging characteristics of various types of electric vehicles in vehicle-grid interaction, and obtain the grid indicator comparison information of each region before and after vehicle-grid interaction; Determine a comprehensive effect evaluation index according to the charging characteristics and the grid index comparison information; the comprehensive effect evaluation index includes multiple evaluation parameters determined from multiple dimensions; one dimension corresponds to at least one evaluation parameter; Determine the subjective weight of each evaluation parameter in the comprehensive effect evaluation index based on the hierarchical analysis method, and determine the objective weight of each evaluation parameter in the comprehensive effect evaluation index based on the entropy weight method; Based on the subjective weight and the objective weight, determining a comprehensive indicator vector corresponding to the comprehensive effect evaluation indicator; For the comprehensive index vectors of each type of electric vehicles in each region, the comprehensive index vectors are fused based on the DS evidence theory to obtain the comprehensive evaluation results of each type of electric vehicles.

2. The method according to claim 1, characterized in that The dimensions include at least the user side, the grid side, the operator and the society; the evaluation parameters of the user side include the degree of battery loss, user credibility, degree of utilization of adjustable potential and user satisfaction; the evaluation parameters of the grid side include load regulation capability, network loss optimization capability, vehicle-grid interaction safety and vehicle-grid interaction flexibility; the evaluation parameters of the operator include operator revenue; the evaluation parameters of the society include green electricity consumption; The charging characteristics include at least battery discharge rate, total battery ampere-hours, discharge time, expected start charging time, expected off-grid time, necessary charging time, battery capacity, charging power, discharge power, electricity sales revenue and electricity sales; The grid index comparison information at least includes the starting charging time, the actual off-grid time, the maximum load of the grid, the minimum load of the grid, the dischargeable capacity, and the distribution network voltage; Determining a comprehensive effect evaluation index according to the charging characteristics and the grid index comparison information includes: Calculating the degree of battery loss of the electric vehicle based on the battery discharge rate and the total ampere-hours of the battery; Calculating user credibility based on the expected charging start time, the expected off-grid time and the actual off-grid time; Calculating an adjustable potential utilization degree based on the dischargeable amount, the discharge time, the actual off-grid time, the necessary charging time, the battery capacity, the charging power, and the discharge power; Calculating user satisfaction based on the discharge time, the expected start charging time, the expected off-grid time, the actual off-grid time, the necessary charging time, the battery capacity, the charging power, and the discharge power; Calculating a load regulation capability based on the maximum load of the power grid and the minimum load of the power grid; Calculate and optimize network loss capabilities based on network loss during vehicle-network interaction and network loss during non-vehicle-network interaction; Based on the distribution network voltage, the vehicle-grid interaction security is calculated; the vehicle-grid interaction security is described in the form of node voltage fluctuation rate; Calculate the flexibility of vehicle-grid interaction based on the corresponding grid load before and after vehicle-grid interaction; Calculate operator revenue based on electric vehicle discharge revenue, non-vehicle-grid interaction revenue, and carbon revenue; The green electricity consumption is calculated based on the additional green electricity consumption through load transfer during vehicle-grid interaction, and the corresponding power of wind and solar power abandonment when there is no vehicle-grid interaction.

3. The method according to claim 1, characterized in that The method of determining the subjective weight of each evaluation parameter in the comprehensive effect evaluation index based on the hierarchical analysis method includes: Based on the multiple evaluation parameters in the comprehensive effect evaluation index and the nine-scale method, a judgment matrix is ​​constructed; Based on the geometric mean method and the judgment matrix, determining the initial weight corresponding to each of the evaluation parameters and the maximum eigenvalue corresponding to the judgment matrix; Based on the maximum eigenvalue, a consistency index of the judgment matrix is ​​determined. If the consistency index fails to pass the consistency check, the step of constructing the judgment matrix is ​​repeated until the consistency index passes the consistency check and the initial weight is determined to be the subjective weight.

4. The method according to claim 3, characterized in that The objective weight of each evaluation parameter in the comprehensive effect evaluation index is determined based on the entropy weight method, including: Performing standardization processing on the judgment matrix to obtain a standardized judgment matrix; For each evaluation parameter in the comprehensive effect evaluation index, mapping the elements in the judgment matrix corresponding to the evaluation parameter to obtain a mapping value of the evaluation parameter; The objective weight of the evaluation parameter is determined based on the ratio of the mapping value to the total mapping values ​​corresponding to all evaluation parameters.

5. The method according to claim 1, characterized in that The step of determining the comprehensive indicator vector corresponding to the comprehensive effect evaluation indicator based on the subjective weight and the objective weight includes: For each evaluation parameter in the comprehensive effect evaluation index, determine the product value of the subjective weight and the objective weight corresponding to each evaluation parameter, and determine the comprehensive weight corresponding to each evaluation parameter based on the ratio of the product value to the total value of the products corresponding to all evaluation parameters; For each type of electric vehicle participating in the vehicle-grid interaction, a weighted sum is performed on the comprehensive weights of the evaluation parameters corresponding to each type of electric vehicle to obtain a comprehensive indicator vector corresponding to the comprehensive effect evaluation indicator.

6. The method according to claim 1, characterized in that The comprehensive index vector is fused based on the DS evidence theory to obtain comprehensive evaluation results of various types of electric vehicles, including: Based on the comprehensive indicator vectors corresponding to each type of vehicle in each region, the identification framework of the DS evidence theory is determined; the identification framework includes evaluation events for each type of electric vehicle in each region; the evaluation events are used to indicate whether each type of electric vehicle has the best interaction effect in different regions; Probability distribution is performed on the evaluation events under the recognition framework to obtain the mass function corresponding to each evaluation event; The mass functions are fused by Dempster synthesis rule to obtain comprehensive evaluation results for various types of electric vehicles.

7. A comprehensive effect evaluation device for vehicle-grid interaction, characterized in that: The device comprises: The data acquisition module is used to obtain the charging characteristics of various types of electric vehicles in vehicle-grid interaction and to obtain the grid indicator comparison information of each region before and after vehicle-grid interaction; An evaluation index determination module, used to determine a comprehensive effect evaluation index according to the charging characteristics and the grid index comparison information; the comprehensive effect evaluation index includes multiple evaluation parameters determined from multiple dimensions; one dimension corresponds to at least one evaluation parameter; A weight determination module, used to determine the subjective weight of each evaluation parameter in the comprehensive effect evaluation index based on the hierarchical analysis method, and to determine the objective weight of each evaluation parameter in the comprehensive effect evaluation index based on the entropy weight method and the subjective weight; An indicator vector determination module, used to determine the comprehensive indicator vector corresponding to the comprehensive effect evaluation indicator based on the subjective weight and the objective weight; The comprehensive effect evaluation module is used to fuse the comprehensive indicator vectors of various types of electric vehicles in various regions based on the DS evidence theory to obtain comprehensive evaluation results of various types of electric vehicles.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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