Electric vehicle demand response potential assessment method, system and device and medium

Abstract

The invention discloses an electric vehicle demand response potential assessment method, system and device, and a medium. The method comprises the following steps: establishing a demand side response time-of-use electricity price model; establishing a user objective response capability constraint model; calculating a charging response rate and a discharging response rate of the user according to the incentive electricity price and the current residual electricity quantity of the user; calculating an objective index coefficient and a subjective index coefficient according to the objective response capability value and the subjective response potential value of the user; and collecting user satisfaction feedback data according to the demand response potential evaluation value, and adjusting the time-of-use electricity price decision result according to the user satisfaction feedback data. According to the method, the demand side response time-of-use electricity price model and the user objective response capability constraint model are established, the battery safety electric quantity, the charging demand and the limitation of the charging pile rated power of the electric vehicle and the subjective response intention of the user are comprehensively considered, and the demand response potential of the electric vehicle user can be evaluated.

Description

Technical Field

[0001] This invention relates to the field of smart grid technology, and in particular to a method, system, device and medium for assessing the demand response potential of electric vehicles. Background Technology

[0002] With the increasing prevalence of electric vehicles (EVs), their impact on the power grid is becoming increasingly significant. The large-scale integration of EVs not only increases the load on the grid but may also trigger problems such as localized grid overload and voltage instability. To effectively manage EV charging and improve the operational efficiency and reliability of the power grid, demand response technology has been widely researched and applied. Demand response technology uses various incentive measures to guide users to reduce charging demand during peak grid load periods and increase charging demand during off-peak periods, thereby achieving load smoothing and optimization.

[0003] However, most existing methods neglect the objective responsiveness of electric vehicle users. This objective responsiveness is affected by various factors such as the physical characteristics of the battery, the user's travel plans, and the distribution and availability of charging stations. When the battery charge is low or the charging station's power is limited, the user may not be able to adjust the charging time or charge according to the grid dispatch requirements. Existing methods also fail to fully consider the subjective responsiveness of electric vehicle users. Users' willingness to participate in demand response is not only affected by economic incentives but also by factors such as charging convenience and time flexibility, resulting in a significant deviation between the actual demand response effect and the expectation. Summary of the Invention

[0004] In view of the aforementioned existing problems, the present invention is proposed.

[0005] Therefore, the present invention provides a method, system, device and medium for assessing the demand response potential of electric vehicles to solve the problem that in existing methods, users cannot adjust charging time or power according to the requirements of power grid dispatch, and are also affected by factors such as charging convenience and time flexibility, resulting in a large deviation between the actual demand response effect and the expectation.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution: In a first aspect, the present invention provides a method for assessing the demand response potential of electric vehicles, comprising the following steps: establishing a demand-side response time-of-use (TOT) pricing model; calculating the TOT model to obtain a TOT decision result; establishing a user objective response capability constraint model; combining the user objective response capability constraint model with the TOT decision result to calculate the objective response capability value of each user at each time point; calculating the user's charging response rate and discharging response rate based on the incentive price and the user's current remaining electricity, and obtaining the user's subjective response potential value through the charging response rate and discharging response rate; calculating objective index coefficients and subjective index coefficients based on the objective response capability value and the user's subjective response potential value, and calculating the demand response potential assessment value of each user based on the objective index coefficients and subjective index coefficients; collecting user satisfaction feedback data based on the demand response potential assessment value, and adjusting the TOT decision result based on the user satisfaction feedback data.

[0007] As a preferred embodiment of the electric vehicle demand response potential assessment method described in this invention, the step of calculating the demand-side response time-of-use pricing model includes: dividing the load curve within the operating cycle into peak periods, peak-surge periods, normal periods, and valley periods using fuzzy clustering; establishing a load price elasticity matrix, which includes self-elasticity coefficients and cross-elasticity coefficients, wherein the self-elasticity coefficients represent the impact of price changes on the load of the same period, and the cross-elasticity coefficients represent the impact of price changes in one period on the load of other periods; calculating the load changes in peak periods, peak-surge periods, normal periods, and valley periods after price adjustments based on the load price elasticity matrix; and, under preset constraints, solving for the time-of-use pricing decision result with the objective of minimizing the total load of peak periods and the peak-valley load difference.

[0008] The beneficial effects of this preferred technical solution are as follows: by using fuzzy clustering to divide peak and valley periods and establishing a load price elasticity matrix that includes self-elasticity coefficient and cross-elasticity coefficient, it can comprehensively reflect the mutual influence of price changes between different periods, making the time-of-use pricing decision results more reasonable.

[0009] As a preferred embodiment of the electric vehicle demand response potential assessment method described in this invention, the establishment of the user objective response capability constraint model includes the following steps: establishing grid-side safety constraints, which include node voltage qualification rate constraints, average voltage deviation constraints, safe current-carrying line qualification rate constraints, and load rate constraints; establishing battery safety constraints, which include battery lifespan safety constraints, charge / discharge capacity constraints, and charging pile power constraints, wherein the battery lifespan safety constraints require the electric vehicle battery's state of charge to remain between a preset upper and lower limit during demand response; determining the user's natural charging power based on the comparison between the user's current state of charge and the expected charging capacity, wherein the natural charging power is zero when the user's current state of charge is greater than or equal to the expected charging capacity, and equal to the charging power when the user's current state of charge is less than the expected charging capacity; calculating the objective response capability value of each user at each time point based on the difference between the natural charging power and the minimum charge / discharge power, wherein the objective response capability value is the sum of the charging response potential and the discharging response potential when the minimum charge / discharge power is less than zero.

[0010] As a preferred embodiment of the electric vehicle demand response potential assessment method described in this invention, the step of obtaining the user's subjective response potential value includes: classifying the electric vehicle group; calculating the Euclidean distance from each electric vehicle charging behavior sequence to each cluster center, and assigning each electric vehicle to the group corresponding to the nearest cluster center; determining the number of clusters using the silhouette coefficient method, evaluating the classification effect by calculating the mean of the silhouette coefficients of each sample, and using the number of clusters corresponding to the maximum mean silhouette coefficient as the final number of group divisions; and setting charging incentive response thresholds and discharging incentive response thresholds for users within each group based on the incentive electricity price. A critical value is defined: when the incentive price is lower than the charging incentive response critical value, the charging response rate is zero; when the incentive price is lower than the discharging incentive response critical value, the discharging response rate is zero. For the same incentive price, the positive response rate and the negative response rate are calculated separately, and the average of the positive response rate and the negative response rate is taken as the user's overall response rate. When the incentive price reaches the charging saturation incentive price, the charging response rate remains at the maximum charging response rate; when the incentive price reaches the discharging saturation incentive price, the discharging response rate remains at the maximum discharging response rate. The charging response rate and the discharging response rate are added together to obtain the user's subjective response potential value.

[0011] The beneficial effects of this preferred technical solution are as follows: setting charging excitation response thresholds and discharging excitation response thresholds for each group, and calculating the overall response rate by averaging the positive response rate and the negative response rate, can reflect the differentiated response characteristics of different user groups and improve the accuracy of subjective response potential value assessment.

[0012] As a preferred embodiment of the electric vehicle demand response potential assessment method of the present invention, the steps of calculating objective index coefficients and subjective index coefficients include: performing dimensionless processing on the objective response capability value and the user's subjective response potential value; using the minimum value normalization method for positive indicators and the maximum value normalization method for negative indicators to obtain an evaluation index matrix; calculating the objective index coefficients using the entropy weight method; standardizing the evaluation index matrix; calculating the information entropy of each evaluation index; calculating the objective index coefficients based on the information entropy of each evaluation index; calculating the subjective index coefficients; using the nine-scale method to compare the importance of each evaluation index pairwise to construct a judgment matrix; normalizing the judgment matrix by column; calculating the subjective index coefficients of each evaluation index using the arithmetic mean method; calculating the maximum eigenvalue of the judgment matrix; calculating the consistency index and consistency ratio based on the maximum eigenvalue; and when the consistency ratio is less than 0.1, the judgment matrix passes the consistency test.

[0013] The beneficial effects of this preferred technical solution are as follows: it uses the entropy weight method to calculate the objective index coefficients, uses the nine-scale method to construct the judgment matrix to calculate the subjective index coefficients, and ensures the rationality of the judgment matrix through consistency verification. It combines objective weighting with subjective weighting, avoiding the limitations of a single weighting method.

[0014] As a preferred embodiment of the electric vehicle demand response potential assessment method described in this invention, the objective index coefficients are calculated using the entropy weight method, including the following steps: For positive indicators The following formula is used for dimensionless processing: ; For negative indicators The following formula is used for dimensionless processing: ; in, Let j be the evaluation index for the i-th evaluation object. and These represent the maximum and minimum values ​​for different objects under the same evaluation index; Calculate the information entropy of each evaluation indicator using the following formula. : ; in, The evaluation index value is the standardized value, and n is the number of evaluation objects; Calculate the objective index coefficient using the following formula. : ; in, Let be the objective index coefficient of the j-th evaluation indicator, and m be the total number of evaluation indicators.

[0015] As a preferred embodiment of the electric vehicle demand response potential assessment method of the present invention, the step of calculating the demand response potential assessment value for each user includes: The comprehensive index coefficient is obtained by multiplying the objective index coefficient by the subjective index coefficient and then normalizing the result, and then calculated according to the following formula: ; in, Let be the comprehensive index coefficient of the j-th evaluation index. For objective index coefficients, For subjective index coefficients; Positive indicators are ranked in ascending order, and negative indicators are ranked in descending order. The weighted rank-sum ratio is calculated as the assessment value of demand response potential using the following formula: ; in, Let be the assessment value of the demand response potential for the i-th user, and n be the total number of evaluation indicators. Let be the rank of the j-th metric for the i-th user.

[0016] The beneficial effects of this preferred technical solution are as follows:

[0017] Secondly, the present invention provides an electric vehicle demand response potential assessment system, comprising: a time-of-use electricity price model establishment module, which calculates the demand-side response time-of-use electricity price model to obtain time-of-use electricity price decision results; The objective response capability calculation module is used to combine the user objective response capability constraint model with the time-of-use electricity price decision results to calculate the objective response capability value of each user at each time. The group classification module is used to classify electric vehicle groups and output the group classification results; The subjective response willingness assessment module calculates the user's charging response rate and discharging response rate based on the incentive electricity price and the user's current remaining electricity, and obtains the user's subjective response potential value through the charging response rate and discharging response rate; The comprehensive evaluation module calculates objective index coefficients and subjective index coefficients based on the objective response capability value and the user's subjective response potential value, and calculates the demand response potential evaluation value for each user based on the objective index coefficients and subjective index coefficients. The feedback adjustment module collects user satisfaction feedback data based on the demand response potential assessment value, and adjusts the time-of-use electricity pricing decision results based on the user satisfaction feedback data.

[0018] Thirdly, the present invention provides an electronic device, comprising: Memory and processor; The memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions, which, when executed by the processor, implement the steps of the electric vehicle demand response potential assessment method.

[0019] Fourthly, the present invention provides a computer-readable storage medium storing computer-executable instructions that, when executed by a processor, implement the steps of the electric vehicle demand response potential assessment method.

[0020] Compared with the prior art, the beneficial effects of the present invention are as follows: By establishing a demand-side response time-of-use pricing model and a user objective response capability constraint model, the electric vehicle's battery safety capacity, charging demand, charging pile rated power limitations, and user subjective response willingness are comprehensively considered. This allows for a comprehensive and accurate assessment of the electric vehicle user's demand response potential, solving the problem of inaccurate assessment results caused by considering only a single factor in existing technologies.

[0021] This invention optimizes the power supply and demand relationship of the power distribution network by adjusting the charging load of electric vehicles at different times, combined with users' objective response capabilities and subjective willingness to respond, thereby improving energy utilization efficiency and power quality, and achieving load balance and stable grid operation. At the same time, by adjusting the time-of-use pricing decision results through a user satisfaction feedback mechanism, a closed-loop optimization is formed, which can continuously improve users' enthusiasm and satisfaction in participating in demand response. Attached Figure Description

[0022] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0023] Figure 1 This is a schematic diagram of the overall process of the electric vehicle demand response potential assessment method according to an embodiment of the present invention.

[0024] Figure 2 This is a graph showing the relationship between power supply and elastic demand in an electric vehicle demand response potential assessment method according to an embodiment of the present invention. Detailed Implementation

[0025] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of the present invention.

[0026] Example 1, referring to Figure 1 As an embodiment of the present invention, a method for assessing the demand response potential of electric vehicles is provided, comprising the following steps: S100. Establish a demand-side response time-of-use pricing model, calculate the time-of-use pricing model, and obtain the time-of-use pricing decision results.

[0027] S200. Establish a user objective response capability constraint model, and combine the user objective response capability constraint model with the time-of-use electricity price decision results to calculate the objective response capability value of each user at each time.

[0028] S300. Based on the incentive electricity price and the user's current remaining electricity, calculate the user's charging response rate and discharging response rate, and obtain the user's subjective response potential value through the charging response rate and discharging response rate.

[0029] S400. Based on the objective response capability value and the user's subjective response potential value, calculate the objective index coefficient and the subjective index coefficient, and calculate the demand response potential assessment value of each user based on the objective index coefficient and the subjective index coefficient.

[0030] S500. Collect user satisfaction feedback data based on the demand response potential assessment value, and adjust the time-of-use electricity pricing decision result based on the user satisfaction feedback data.

[0031] It should be noted that with the widespread adoption of electric vehicles (EVs), the impact of EV charging load on the power distribution network is becoming increasingly significant. Concentrated charging of EVs leads to increased peak loads on the power grid, causing localized grid overloads, voltage fluctuations, and other problems, affecting the safe and stable operation of the grid. Demand response technology, as an effective load management tool, can guide users to adjust their charging behavior through incentives such as time-of-use pricing, achieving peak shaving and valley filling. However, the responsiveness of EV users is constrained by various factors: on the one hand, objective conditions such as battery state of charge, the rated power of charging piles, and user travel needs limit users' charging and discharging capabilities during specific periods; on the other hand, subjective factors such as users' sensitivity to incentive pricing and their requirements for charging convenience also affect their willingness to participate in demand response. Existing demand response methods often consider only a single factor, resulting in inaccurate assessments of user response potential and difficulty in effectively matching grid dispatch with user demand.

[0032] Therefore, to address the aforementioned issue of inaccurate user response potential assessment, the following steps (S100-S500) are employed: First, a demand-side response time-of-use pricing model is established. This model uses peak-valley time periods and load price elasticity matrix calculations to obtain reasonable time-of-use pricing decisions, providing economic incentives for user response. Second, an objective user response capability constraint model is established, comprehensively considering grid-side safety constraints and battery safety constraints to accurately calculate the objective response capability value for each user while meeting travel needs and equipment safety. Then, based on the incentive price and the user's current remaining electricity, the user's subjective response potential value is calculated, reflecting the user's subjective willingness to participate in demand response. Next, the objective response capability value is combined with the user's subjective response potential value, and the entropy weight method and analytic hierarchy process are used to calculate the comprehensive index coefficient. The rank-sum ratio method is then used to obtain the demand response potential assessment value for each user. Finally, a user satisfaction feedback mechanism is used to adjust the time-of-use pricing decision results, forming a closed-loop optimization to achieve accurate assessment and continuous optimization of the demand response potential of electric vehicle users.

[0033] Example 2, refer to Figures 1-2 As an embodiment of the present invention, based on the above embodiment, a method for assessing the demand response potential of electric vehicles is provided.

[0034] In this embodiment of the application, S100, a demand-side response time-of-use pricing model is established, and the time-of-use pricing decision result is obtained by calculating the demand-side response time-of-use pricing model.

[0035] The steps for calculating the demand-side response time-of-use electricity pricing model include A1 to A4: A1. The load curve within the operating cycle is divided into peak period, peak period, normal period and valley period using fuzzy clustering method.

[0036] Specifically, historical load data of the distribution network within one operating cycle T is obtained, and load curves are plotted. Fuzzy clustering is used to divide the load curves into time periods, with the following criteria: the maximum value of the load curve has a 100% probability of belonging to the peak period, the minimum value of the load curve has a 100% probability of belonging to the valley period, the maximum value of the load curve has a 0% probability of belonging to the valley period, and the minimum value of the load curve has a 0% probability of belonging to the peak period. For other points on the load curve, the membership degree of each time period is calculated using membership functions, and the point is assigned to the time period with the highest membership degree. Finally, the operating cycle T is divided into four time periods: peak period, peak period, normal period, and valley period, and the sum of the four time periods equals the operating cycle T.

[0037] A2. Establish a load price elasticity matrix, which includes self-elasticity coefficients and cross-elasticity coefficients. The self-elasticity coefficients represent the impact of price changes on the load during the same period, and the cross-elasticity coefficients represent the impact of price changes in a certain period on the load in other periods.

[0038] Specifically, the concept of price elasticity from economics is introduced, with the price elasticity coefficient representing the ratio of the rate of change in demand to the rate of change in price. Based on the characteristics of electricity load, the load is divided into three categories: time-adjustable load, time-unadjustable load, and directly reduced load. Time-adjustable load is affected by electricity prices in the current time period and other time periods; time-unadjustable load's usage time is unaffected by price changes; and directly reduced load may be directly reduced due to price changes in the current time period. Through this classification, the elasticity coefficient is divided into self-elasticity coefficient and cross-elasticity coefficient. The self-elasticity coefficient reflects the impact of electricity price changes within time period i on the load within time period i, while the cross-elasticity coefficient reflects the impact of electricity price changes within time period i on the load within time period j. Based on the division of peak and off-peak periods, the period T is divided into N time periods, establishing an N×N load-price elasticity matrix S. The diagonal elements of the matrix are the self-elasticity coefficients for each time period, and the off-diagonal elements are the cross-elasticity coefficients between time periods.

[0039] A3. Calculate the load changes during peak, mid-peak, normal, and valley periods after the electricity price adjustment based on the load price elasticity matrix.

[0040] Specifically, by analyzing the supply and demand curves of the electricity market, it is assumed that the electricity price and load approximately satisfy a linear relationship near the market equilibrium point. Linear relationships between electricity price and load are established for peak, peak-spot, normal, and valley periods, respectively. The self-elasticity coefficient for each period is calculated based on the initial electricity price and load values. Assuming that the daily load of users remains relatively stable, the cross-elasticity coefficient between each period is calculated in conjunction with the daily load conservation condition. The electricity price adjustment is substituted into the load-price elasticity matrix, and matrix operations are used to calculate the load changes during peak, peak-spot, normal, and valley periods after the price adjustment, thus obtaining the load values ​​for each period after the price adjustment.

[0041] The relationship between electricity supply and elastic demand is as follows: Figure 2 As shown. By Figure 2 It can be seen that the relationship between electricity supply and elastic demand can only reflect the relationship between changes in electricity price and changes in electricity consumption during the current period, and cannot reflect the mutual influence of price changes between different periods. Therefore, it cannot fully and accurately represent the user's response to electricity prices.

[0042] A4. Under the preset constraints, with the objectives of minimizing the total load during peak hours and minimizing the difference between peak and valley loads, the time-of-use electricity price decision result is obtained by solving the problem.

[0043] Specifically, a dual-objective optimization function is constructed. Objective function one is to minimize the total load during peak hours and peak hours, and objective function two is to minimize the difference between peak and valley loads. A linear weighting method is used to process the dual-objective function into a single-objective function, and the two optimization objectives are balanced by setting weight coefficients for the objective functions. Pre-set constraints include: price fluctuation constraints, requiring that the electricity price during peak hours, peak hours, and valley hours be no less than a preset fluctuation coefficient multiple of the electricity price during normal hours; marginal cost constraints, requiring that the electricity price during valley hours be no less than the marginal cost of supplying electricity during valley hours; peak electricity price constraints, requiring that the electricity price during peak hours be no more than the generation cost of small generator sets; electricity cost constraints, requiring that the electricity cost for users after the implementation of time-of-use pricing be no more than the electricity cost before implementation; and total load constraints, requiring that the fluctuation coefficient of the total load before and after the implementation of time-of-use pricing be within a preset range. Under the above constraints, an optimization algorithm is used to solve the optimal solution of the single-objective function to obtain the time-of-use pricing decision result.

[0044] In one optional implementation, fuzzy clustering is used in step A1 to divide peak and valley periods. Alternatively, K-means clustering or hierarchical clustering can be used to divide the load curve into periods. By calculating the distance between the load value at each moment and the cluster center, the load curve is automatically divided into a preset number of period categories. This method does not require pre-setting a membership function and can cluster according to the actual distribution characteristics of the load data. It is suitable for distribution network scenarios where load fluctuation patterns are not obvious or seasonal changes are significant.

[0045] In another optional implementation, the time-of-use pricing decision result in step A4 can also be solved using a multi-objective genetic algorithm or a particle swarm optimization algorithm. By setting parameters such as population size, number of iterations, and crossover / mutation probability, the Pareto optimal solution set is searched within the feasible region that satisfies the constraints, providing decision-makers with multiple time-of-use pricing schemes to choose from. This method avoids the subjectivity of weight coefficient selection in traditional weighted methods, obtains more comprehensive optimization results, and is suitable for complex decision-making scenarios that require consideration of the interests of multiple parties.

[0046] In this embodiment of the application, S200, an objective response capability constraint model for users is established, and the objective response capability value of each user at each time is calculated by combining the objective response capability constraint model with the time-of-use electricity price decision result.

[0047] The establishment of the user objective response capability constraint model includes the following steps B1 to B4: B1. Establish grid-side security constraints, which include node voltage qualification rate constraints, average voltage deviation constraints, safe current-carrying line qualification rate constraints, and load rate constraints.

[0048] Specifically, the node voltage qualification rate constraint is the probability distribution of the voltage amplitude of a node in the distribution network at a certain moment. Taking node i as an example, the node voltage qualification rate at time t is equal to the difference between the value of the probability distribution function of the node voltage amplitude at the upper limit of the qualified voltage and the value at the lower limit of the qualified voltage. The average voltage deviation constraint is the difference between the voltage of the node and the voltage of the root node of the line. The voltage rise deviation index and voltage drop deviation index of node i at time t are calculated respectively, and the average of the two is taken as the average voltage deviation of the node. The safe current-carrying line qualification rate constraint is the ratio of the number of lines in the distribution network that exceed the maximum current safe operating range to the total number of lines. The load factor constraint is the ratio of the short-time average load in the distribution network to the maximum load generated. The above four constraints together constitute the grid-side safety constraints to ensure the safety and rationality of grid operation during the participation of electric vehicles in demand response.

[0049] B2. Establish battery safety constraints, which include battery life safety constraints, charge / discharge capacity constraints, and charging pile power constraints. The battery life safety constraints require that the state of charge of the electric vehicle battery be maintained between a preset upper limit and a lower limit during the demand response process.

[0050] Specifically, the battery lifespan safety constraint considers that the electric vehicle's charge level will change due to external factors such as temperature, humidity, and driving environment during demand response. It requires that at time t, the user i's state of charge (SOC) plus the SOC change caused by demand response should still be between the upper and lower limits of the battery's SOC. Simultaneously, the response power should be between the rated discharge power and rated charging power, and the response time should be between the user's arrival time and expected departure time. The charging and discharging capacity constraint considers the randomness of the electric vehicle's remaining charge upon entering the network. If charging response is selected, the electric vehicle's charge level must reach the user's desired charging level before leaving the station. If discharging response is selected, the electric vehicle's charge level must not be lower than the lower limit of its SOC. The charging pile power constraint determines the charging and discharging power based on the electric vehicle's remaining charge upon entering the station. The higher the remaining charge, the lower the initial charging power and the higher the initial discharging power; conversely, the lower the remaining charge, the higher the initial charging power and the lower the initial discharging power. As the electric vehicle's charge level increases, the charging power decreases; conversely, as the electric vehicle's charge level decreases, the discharging power decreases.

[0051] B3. Determine the user's natural charging power based on the comparison between the user's current state of charge and the expected charging capacity. When the user's current state of charge is greater than or equal to the expected charging capacity, the natural charging power is zero. When the user's current state of charge is less than the expected charging capacity, the natural charging power is equal to the charging power.

[0052] Specifically, natural charging power represents the charging power when a user is not participating in the demand response process. The current state of charge (SOC) of user i at time t and the user i's desired charging capacity are obtained, and the current SOC is compared with the desired charging capacity. When the user's current SOC is greater than or equal to the desired charging capacity, it indicates that the user's electric vehicle battery level meets their travel needs and no charging is required; in this case, the natural charging power is zero. When the user's current SOC is less than the desired charging capacity, it indicates that the user's electric vehicle battery level does not yet meet their travel needs and charging is required; in this case, the natural charging power is equal to the charging power calculated based on the charging pile's power constraints.

[0053] B4. The objective response capability value of each user at each time moment is calculated based on the difference between the natural charging power and the minimum charging and discharging power. When the minimum charging and discharging power is less than zero, the objective response capability value is the sum of the charging response potential and the discharging response potential.

[0054] Specifically, at time t, the objective response capability value of user i is equal to the difference between the natural charging power and the minimum charging / discharging power. When the user does not have the discharge response capability, i.e., the minimum charging / discharging power is greater than or equal to zero, the objective response capability value is the difference between the natural charging power and the minimum charging / discharging power. When the user has the discharge response capability, i.e., the minimum charging / discharging power is less than zero, the charging response potential and the discharging response potential are calculated separately. The charging response potential is equal to the natural charging power, and the discharging response potential is equal to the absolute value of the minimum charging / discharging power. The objective response capability value is the sum of the charging response potential and the discharging response potential. The time-of-use electricity price decision result obtained in step S100 is used as the incentive signal. Combined with the objective response capability value of each user at each time, the maximum power range that each user can participate in demand response in each time period is determined.

[0055] In one optional implementation, the grid-side security constraints established in step B1 can also be validated using distribution network power flow calculation methods. By establishing the node admittance matrix of the distribution network, the voltage of each node and the power of each branch are calculated using the forward-backward substitution method or the Newton-Raphson method. This verifies the impact of electric vehicle charging and discharging power on the distribution network power flow distribution, ensuring that the objective response capability value of users is determined under the premise of satisfying the grid-side security constraints. This method can more accurately reflect the impact of electric vehicle access on the operating status of the distribution network.

[0056] In another optional implementation, the battery safety constraints established in step B2 can also incorporate a battery health status assessment model. By collecting historical charge and discharge data of electric vehicle batteries, a battery capacity decay model is established. The battery health status is assessed based on factors such as the number of battery cycles, depth of charge and discharge, and ambient temperature. Based on this, the upper and lower limits of the state of charge are adjusted to ensure that the battery safety constraints can adapt to batteries in different health states, extending battery life while improving the accuracy of objective response capability calculations.

[0057] In this embodiment of the application, S300, the user's charging response rate and discharging response rate are calculated based on the incentive electricity price and the user's current remaining electricity, and the user's subjective response potential value is obtained through the charging response rate and discharging response rate.

[0058] The steps for obtaining the user's subjective response potential value include C1 to C7: C1. Classify electric vehicle groups.

[0059] Specifically, based on the similarity of electric vehicles in their mobility behavior, the k-means clustering algorithm is used to classify electric vehicle groups. The input parameters for classification include three dimensions: the time of entry into the grid, the time of exit from the grid, and the initial remaining battery power upon entering the station. Given a set of electric vehicle samples, each sample is a sequence containing the above three dimensions. Several cluster centers of electric vehicle groups are initialized, transforming the evaluation of a single electric vehicle into the evaluation of vehicles with the same travel behavior, thereby reducing the computational workload.

[0060] C2. Calculate the Euclidean distance from each electric vehicle charging behavior sequence to each cluster center, and assign each electric vehicle to the group corresponding to the nearest cluster center.

[0061] Specifically, for each electric vehicle charging behavior sequence, the Euclidean distance to each cluster center is calculated. The Euclidean distance is equal to the square root of the sum of the squares of the differences between the electric vehicle's dimensional indicators and the corresponding dimensional indicators of the cluster center. The Euclidean distances from the electric vehicle to each cluster center are compared, and the electric vehicle is assigned to the group corresponding to the cluster center with the smallest Euclidean distance. After all electric vehicles are assigned, the average value of each dimensional indicator of all electric vehicles in each group is recalculated, and this average value is used as the new cluster center. The above distance calculation, sample assignment, and cluster center update process is repeated until the cluster centers no longer change or the preset number of iterations is reached.

[0062] C3. The silhouette coefficient method is used to determine the number of clusters. The classification effect is evaluated by calculating the mean of the silhouette coefficients of each sample. The number of clusters corresponding to the maximum mean silhouette coefficient is taken as the final number of population divisions.

[0063] Specifically, the silhouette coefficient method determines the optimal number of taxa by comparing the similarity between taxa and the differences between individuals. For each sample, the average distance from the sample to other samples in the same taxa is calculated; the smaller the value, the more similar the sample is to other samples in the same taxa. The average distance from the sample to all samples in other taxa is also calculated; the larger the value, the less similar the sample is to other taxa. Based on these two distance values, the silhouette coefficient of the sample is calculated. The silhouette coefficient ranges from -1 to 1; the closer to 1, the more similar the samples are within the taxa and the less similar the samples are between taxa; the closer to zero, the similar the samples are within the taxa and the samples are between taxa; and the closer to -1, the more similar the samples are between taxa and the samples are within taxa. The method iterates through different numbers of taxa and calculates the mean silhouette coefficient of all samples for each number of taxa. The number of taxa corresponding to the largest mean silhouette coefficient is taken as the final number of taxa for the taxa.

[0064] C4. For users within each group, set charging incentive response thresholds and discharging incentive response thresholds based on the incentive electricity price. When the incentive electricity price is lower than the charging incentive response threshold, the charging response rate is zero; when the incentive electricity price is lower than the discharging incentive response threshold, the discharging response rate is zero.

[0065] Specifically, based on the consumer psychology model, for users, price has a minimum perceptible difference threshold; when the incentive price is within the threshold range, users are not sensitive to the incentive price and have little or no response; for users within each group, charging incentive response thresholds and discharging incentive response thresholds are set based on the historical response data and behavioral characteristics of that group of users; when the incentive price offered by the electric vehicle load aggregator to electric vehicle users for demand response is lower than the charging incentive response threshold, the user's charging response rate is zero; when the incentive price is lower than the discharging incentive response threshold, the user's discharging response rate is zero.

[0066] C5. Calculate the positive response rate and negative response rate of users for the same incentive electricity price, and take the average of the positive response rate and the negative response rate as the overall response rate of users.

[0067] Specifically, under the same incentive conditions, users' willingness to respond is not constant but is influenced by various factors and exhibits a certain degree of fluctuation. Therefore, for user willingness under the same incentive level, two scenarios are set: positive response and negative response. The positive charging response rate increases linearly from zero to the maximum charging response rate as the incentive price increases from zero to the charging saturation incentive price. The negative charging response rate is zero when the incentive price is below the charging incentive response threshold, and increases linearly from zero to the maximum charging response rate as the incentive price increases from the charging incentive response threshold to the charging saturation incentive price. The average of the positive charging response rate and the negative charging response rate is taken as the user's overall charging response rate. The calculation methods for the positive discharging response rate and the negative discharging response rate are similar to those for the charging response rate, and the average of the positive discharging response rate and the negative discharging response rate is taken as the user's overall discharging response rate.

[0068] C6. When the incentive price reaches the charging saturation incentive price, the charging response rate remains at the maximum charging response rate; when the incentive price reaches the discharging saturation incentive price, the discharging response rate remains at the maximum discharging response rate.

[0069] Specifically, once the threshold range is exceeded, users begin to respond. As the incentive price increases, the user response gradually strengthens. When the incentive price reaches a certain level, the user response no longer increases, i.e., it reaches the response saturation zone. When the incentive price reaches the charging saturation incentive price, the user's charging response rate remains unchanged at the maximum charging response rate, and even if the incentive price continues to increase, the charging response rate will no longer improve. When the incentive price reaches the discharging saturation incentive price, the user's discharging response rate remains unchanged at the maximum discharging response rate, and even if the incentive price continues to increase, the discharging response rate will no longer improve. The above response rate model reflects the charging and discharging behavior characteristics of users under different incentive prices.

[0070] C7. Add the charging response rate to the discharging response rate to obtain the user's subjective response potential value.

[0071] Specifically, by combining the overall user charging response rate and the overall user discharging response rate calculated in steps C4 to C6, the two are added together to obtain the user subjective response potential value. The user subjective response potential value reflects the degree of user's subjective willingness to participate in demand response under the current incentive electricity price and current remaining electricity conditions. By combining the objective response capability value calculated in step S200 with the user subjective response potential value, the comprehensive potential of users to participate in demand response can be comprehensively evaluated.

[0072] In one optional implementation, the classification of the electric vehicle group in step C1 can also be performed using a density-based clustering algorithm. By setting two parameters—neighborhood radius and minimum number of samples—density-connected samples are grouped into the same group, enabling the identification of group distributions of arbitrary shapes while automatically identifying and removing noisy samples. This method does not require pre-specifying the number of clusters and is suitable for scenarios where electric vehicle charging behavior is irregular or abnormal.

[0073] In another optional implementation, step C5, which calculates the user's positive response rate and negative response rate, can also incorporate historical user response data for correction. By collecting data on the actual response power and duration of users' historical participation in demand response, the user's historical response completion rate is calculated. This historical response completion rate is then used as a correction coefficient to weight and correct the positive and negative response rates. This method can reflect users' true response behavior habits and improve the accuracy of predicting users' subjective response potential.

[0074] In this embodiment of the application, S400, based on the objective response capability value and the user's subjective response potential value, an objective index coefficient and a subjective index coefficient are calculated, and an assessment value of each user's demand response potential is calculated based on the objective index coefficient and the subjective index coefficient.

[0075] The steps for calculating the objective index coefficients and subjective index coefficients include D1 to D4: D1. The objective response capability value and the user's subjective response potential value are processed to be dimensionless. For positive indicators, the minimum value normalization method is used, and for negative indicators, the maximum value normalization method is used to obtain the evaluation index matrix.

[0076] Specifically, since objective response capability values ​​and user subjective response potential values ​​have different dimensions and orders of magnitude, the initial data needs to be dimensionless to eliminate the influence of dimensions. For positive indicators, i.e., the larger the indicator value, the better the demand response potential, the minimum value normalization method is used to map the indicator value to the range of zero to one. The closer the processed value is to one, the better the indicator's performance. For negative indicators, i.e., the smaller the indicator value, the better the demand response potential, the maximum value normalization method is used to map the indicator value to the range of zero to one. The closer the processed value is to one, the better the indicator's performance. The dimensionless processed evaluation indicator values ​​of each evaluation object are used to form the evaluation indicator matrix Y, where the rows of the matrix represent different evaluation objects and the columns of the matrix represent different evaluation indicators.

[0077] D2. Calculate the objective index coefficients using the entropy weight method, standardize the evaluation index matrix, calculate the information entropy of each evaluation index, and calculate the objective index coefficients based on the information entropy of each evaluation index.

[0078] The objective index coefficients are calculated using the entropy weight method, including the following steps D2.1 to D2.4: D2.1, For positive indicators The following formula is used for dimensionless processing: ; Specifically, obtain the j-th positive evaluation index value of the i-th evaluation object. To obtain the maximum value of all evaluation objects under the same evaluation indicator. and minimum value The positive index value is obtained by subtracting the minimum value from the index value and then dividing by the difference between the maximum and minimum values.

[0079] D2.2, For negative indicators The following formula is used for dimensionless processing: ; in, Let j be the evaluation index for the i-th evaluation object. and These represent the maximum and minimum values ​​for different objects under the same evaluation index.

[0080] Specifically, obtain the j-th negative evaluation index value of the i-th evaluation object. To obtain the maximum value of all evaluation objects under the same evaluation indicator. and minimum value Subtracting the index value from the maximum value and then dividing by the difference between the maximum and minimum values ​​yields the dimensionless negative index value.

[0081] D2.3 Calculate the information entropy of each evaluation indicator according to the following formula. : ; in, is the standardized evaluation index value, and n is the number of evaluation objects.

[0082] Specifically, the evaluation index matrix Y is standardized to address the limitations of the traditional entropy weight method. To address the situation where the natural logarithm becomes meaningless when the value equals zero, the evaluation index matrix is ​​modified; the information entropy of each evaluation index is calculated. Information entropy reflects the degree of dispersion of the indicator across all evaluation objects. The smaller the information entropy, the greater the difference of the indicator among different evaluation objects, and the greater its contribution to decision-making.

[0083] D2.4 Calculate the objective index coefficient according to the following formula. : ; in, Let be the objective index coefficient of the j-th evaluation indicator, and m be the total number of evaluation indicators.

[0084] Specifically, the entropy weight method assigns coefficients based on the amount of information each indicator conveys to the decision-maker, which is an objective method of assigning values. When an evaluation indicator plays a relatively small role in the system, its information entropy is relatively large, and its impact on decision-making is also relatively small compared to other indicators. Objective indicator coefficients are calculated based on the information entropy of each evaluation indicator, and the smaller the information entropy of an indicator, the larger the objective indicator coefficient it obtains.

[0085] D3. Calculate the subjective index coefficients. Use the nine-scale method to compare the importance of each evaluation index pairwise, construct a judgment matrix, normalize the judgment matrix by column, and use the arithmetic mean method to calculate the subjective index coefficients of each evaluation index.

[0086] Specifically, the Analytic Hierarchy Process (AHP) is a subjective evaluation method that reasonably combines qualitative and quantitative decision-making. This method uses expert subjective experience and evaluation needs to construct a judgment matrix based on evaluation factors to calculate coefficients. A nine-scale method is used to compare the importance of each evaluation indicator pairwise. Scale 1 indicates that two indicators are equally important; scale 3 indicates that the former is slightly more important than the latter; scale 5 indicates that the former is quite important than the latter; scale 7 indicates that the former is strongly important than the latter; scale 9 indicates that the former is extremely important than the latter; scales 2, 4, 6, and 8 are the median values ​​of the above adjacent judgments; and reciprocal scales indicate the degree of importance of the latter compared to the former. Based on expert opinions, each evaluation indicator is scored, constructing a judgment matrix A. The judgment matrix A is then normalized column-wise by dividing each column element by the sum of its elements to obtain the normalized matrix. The arithmetic mean method is used to calculate the average of the elements in each row of the normalized matrix, yielding the subjective index coefficient θ for each evaluation indicator.

[0087] D4. Calculate the largest eigenvalue of the judgment matrix, and calculate the consistency index and consistency ratio based on the largest eigenvalue. When the consistency ratio is less than 0.1, the judgment matrix passes the consistency test.

[0088] Specifically, the product of the judgment matrix A and the subjective index coefficient vector θ is calculated. Each element of the product vector is divided by the corresponding element of the subjective index coefficient vector, and the average of the results is calculated to obtain the largest eigenvalue of the judgment matrix. ; Calculate the consistency index based on the largest eigenvalue. The consistency index is equal to the largest eigenvalue minus the order of the judgment matrix, divided by the order of the judgment matrix minus one; the average consistency index of the judgment matrix is ​​obtained by looking up a table. The average consistency index is related to the order of the judgment matrix; the consistency ratio is calculated based on the consistency index and the average consistency index. The consistency ratio is equal to the consistency index divided by the average consistency index. When the calculated consistency ratio is less than 0.1, the judgment matrix passes the consistency test, indicating that the judgment matrix A and the subjective index coefficient vector θ are reasonable. When the consistency ratio is greater than or equal to 0.1, each evaluation index needs to be re-scored and a new judgment matrix A needs to be constructed until the consistency test is passed.

[0089] In one optional implementation, step D2 uses the entropy weight method to calculate the objective index coefficients. Alternatively, the coefficient of variation method or the CRITIC method can be used. The coefficient of variation method measures the dispersion of an index by calculating the ratio of its standard deviation to its mean; the larger the coefficient of variation, the larger the coefficient obtained. The CRITIC method considers both the comparative strength and the conflict between indicators, thus providing a more comprehensive reflection of the information content of each indicator. All of the above methods are objective value assignment methods, and the appropriate method can be selected based on the actual application scenario.

[0090] In another optional implementation, step D3 uses the nine-scale method to construct the judgment matrix, or a group decision-making method can be used to obtain the judgment matrix of multiple experts. By inviting multiple domain experts to compare the importance of each evaluation indicator pairwise, multiple judgment matrices are obtained. The geometric mean or arithmetic mean method is used to combine the multiple judgment matrices into a comprehensive judgment matrix. This method can reduce the bias of subjective judgment by a single expert and improve the rationality and reliability of subjective indicator coefficients.

[0091] In this embodiment of the application, S500, user satisfaction feedback data is collected based on the demand response potential assessment value, and the time-of-use electricity price decision result is adjusted based on the user satisfaction feedback data.

[0092] The steps for calculating the demand response potential assessment value for each user include E1 to E3: E1. Multiply the objective index coefficient by the subjective index coefficient and normalize the result to obtain the comprehensive index coefficient, which is then calculated according to the following formula: ; in, Let be the comprehensive index coefficient of the j-th evaluation index. For objective index coefficients, This refers to the subjective indicator coefficient.

[0093] Specifically, to address the issues of the entropy weight method's excessive objectivity leading to inaccurate assessment indicators in reflecting the demand response potential of electric vehicles, and the analytic hierarchy process's excessive subjectivity lacking objective basis, the objective indicator coefficients calculated by the entropy weight method are combined with the subjective indicator coefficients calculated by the analytic hierarchy process. For the j-th evaluation indicator, its objective indicator coefficient is... With subjective index coefficient Multiply them to obtain the product value of the indicator; calculate the sum of the product values ​​of all evaluation indicators, divide the product value of the j-th evaluation indicator by the sum of the product values ​​of all evaluation indicators, and obtain the comprehensive index coefficient λ_j of the j-th evaluation indicator; the comprehensive index coefficient takes into account both the information content of objective data and the judgment of subjective experience, and can more reasonably reflect the importance of each evaluation indicator in the assessment of demand response potential.

[0094] E2. Rank positive indicators in ascending order and negative indicators in descending order.

[0095] Specifically, the rank-sum ratio comprehensive evaluation method eliminates the influence of the dimensions and orders of magnitude of the evaluation indicators by ranking them. For positive indicators, i.e., the larger the indicator value, the better the demand response potential, the indicators are ranked in ascending order of value, with the evaluation object with the smallest value assigned to rank 1 and the evaluation object with the largest value assigned to rank n, where n is the number of evaluation objects. For negative indicators, i.e., the smaller the indicator value, the better the demand response potential, the indicators are ranked in descending order of value, with the evaluation object with the largest value assigned to rank 1 and the evaluation object with the smallest value assigned to rank n. If multiple evaluation objects have the same indicator value, they are assigned the same rank, which is the average of the positions they occupy when arranged in order. Through the above ranking method, the evaluation indicator values ​​of each evaluation object are converted into ranks. ,in, Let represent the rank of the j-th indicator of the i-th evaluation object.

[0096] E3. Calculate the weighted rank sum ratio as the assessment value of demand response potential using the following formula: ; in, Let be the assessment value of the demand response potential for the i-th user, and n be the total number of evaluation indicators. Let be the rank of the j-th metric for the i-th user.

[0097] Specifically, for the i-th user, the rank of each of its evaluation metrics is... Multiply by the corresponding comprehensive index coefficient respectively The weighted rank values ​​of each evaluation indicator are obtained; the sum of the weighted rank values ​​of all evaluation indicators is then divided by the total number of evaluation indicators n to obtain the weighted rank ratio of the i-th user. Weighted rank ratio This is the demand response potential assessment value for the i-th user. The larger the value, the higher the user's demand response potential. The demand response potential assessment value is calculated for all users, and users are ranked according to the size of the demand response potential assessment value, providing a basis for the power grid dispatching department to formulate demand response strategies. Users with higher demand response potential assessment values ​​can be given priority to participate in demand response, while users with lower demand response potential assessment values ​​can have their willingness to participate increased by adjusting incentive measures.

[0098] In one optional implementation, the calculation of the comprehensive index coefficient in step E1 can also employ a game theory-based combinational assignment method. By constructing a game model between the objective and subjective index coefficients, and aiming to minimize the deviation between the two coefficients, the optimal linear combination coefficient is solved, achieving a balance between objectivity and subjectivity in the comprehensive index coefficient. This method can determine the optimal combination ratio of objective and subjective index coefficients based on actual data characteristics, avoiding the potential amplification of deviations caused by simple multiplication.

[0099] In another optional implementation, the calculation of the demand response potential assessment value in step E3 can also employ the TOPSIS method for comprehensive evaluation. By determining the positive and negative ideal solutions for each evaluation index, the distances of each user to the positive and negative ideal solutions are calculated. The relative proximity of each user is then calculated based on the distance ratio as the demand response potential assessment value. This method can intuitively reflect the gap between each user and the ideal state, facilitating the identification of user groups with high and low demand response potential.

[0100] In summary, by establishing a demand-side response time-of-use pricing model and a user objective response capability constraint model, and by comprehensively considering the electric vehicle's battery safety capacity, charging demand, charging pile rated power limitations, and user subjective response willingness, the demand response potential of electric vehicle users can be comprehensively and accurately assessed, solving the problem of inaccurate assessment results caused by considering only a single factor in existing technologies.

[0101] This invention optimizes the power supply and demand relationship of the power distribution network by adjusting the charging load of electric vehicles at different times, combined with users' objective response capabilities and subjective willingness to respond, thereby improving energy utilization efficiency and power quality, and achieving load balance and stable grid operation. At the same time, by adjusting the time-of-use pricing decision results through a user satisfaction feedback mechanism, a closed-loop optimization is formed, which can continuously improve users' enthusiasm and satisfaction in participating in demand response.

[0102] Example 3 illustrates a schematic scheme for an electric vehicle demand response potential assessment method. It should be noted that the technical solution of this electric vehicle demand response potential assessment system belongs to the same concept as the technical solution of the aforementioned electric vehicle demand response potential assessment method. Details not described in detail in this embodiment can be found in the description of the technical solution of the aforementioned electric vehicle demand response potential assessment method.

[0103] This embodiment also provides an electric vehicle demand response potential assessment system, including: The time-of-use pricing model building module calculates the demand-side response time-of-use pricing model to obtain the time-of-use pricing decision results. The objective response capability calculation module is used to combine the user objective response capability constraint model with the time-of-use electricity price decision results to calculate the objective response capability value of each user at each time. The group classification module is used to classify electric vehicle groups using the k-means clustering algorithm and output the group classification results; The subjective response willingness assessment module calculates the user's charging response rate and discharging response rate based on the incentive electricity price and the user's current remaining electricity, and obtains the user's subjective response potential value through the charging response rate and discharging response rate; The comprehensive evaluation module calculates objective index coefficients and subjective index coefficients based on the objective response capability value and the user's subjective response potential value, and calculates the demand response potential evaluation value for each user based on the objective index coefficients and subjective index coefficients. The feedback adjustment module collects user satisfaction feedback data based on the demand response potential assessment value, and adjusts the time-of-use electricity pricing decision results based on the user satisfaction feedback data.

[0104] This embodiment also provides an electronic device suitable for assessing the demand response potential of electric vehicles, comprising: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to implement the method for assessing the demand response potential of electric vehicles as proposed in the above embodiment.

[0105] This embodiment also provides a storage medium storing a computer program that, when executed by a processor, implements the method for assessing the demand response potential of electric vehicles as proposed in the above embodiments.

[0106] The storage medium proposed in this embodiment and the method for assessing the demand response potential of electric vehicles proposed in the above embodiments belong to the same inventive concept. Technical details not described in detail in this embodiment can be found in the above embodiments, and this embodiment has the same beneficial effects as the above embodiments.

[0107] Based on the above description of the implementation methods, those skilled in the art can clearly understand that the present invention can be implemented using software and necessary general-purpose hardware, and of course, it can also be implemented using hardware. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as a computer floppy disk, read-only memory (ROM), random access memory (RAM), flash memory, hard disk, or optical disk, etc., including several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods of the various embodiments of the present invention.

[0108] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for assessing the demand response potential of electric vehicles, characterized in that, Includes the following steps: Establish a demand-side response time-of-use pricing model, calculate the time-of-use pricing result based on the demand-side response time-of-use pricing model; Establish a user objective response capability constraint model, and combine the user objective response capability constraint model with the time-of-use electricity price decision results to calculate the objective response capability value of each user at each time. Based on the incentive electricity price and the user's current remaining electricity, calculate the user's charging response rate and discharging response rate, and obtain the user's subjective response potential value through the charging response rate and discharging response rate; Based on the objective response capability value and the user's subjective response potential value, calculate the objective index coefficient and the subjective index coefficient, and calculate the demand response potential assessment value of each user based on the objective index coefficient and the subjective index coefficient. User satisfaction feedback data is collected based on the demand response potential assessment value, and the time-of-use pricing decision is adjusted based on the user satisfaction feedback data.

2. The method for assessing the demand response potential of electric vehicles as described in claim 1, characterized in that, The steps for calculating the demand-side response time-of-use pricing model include: The load curve within the operating cycle is divided into peak periods, peak periods, normal periods, and valley periods using fuzzy clustering. Establish a load-price elasticity matrix; Calculate the load changes during peak, peak-hour, normal, and valley periods after the electricity price adjustment based on the load price elasticity matrix. Under the preset constraints, the time-of-use pricing decision is obtained by minimizing the total load during peak hours and the difference between peak and valley loads, with the goal of minimizing the total load difference between peak and valley periods.

3. The method for assessing the demand response potential of electric vehicles as described in claim 2, characterized in that, Establishing the user objective response capability constraint model includes the following steps: Establish grid-side security constraints, which include node voltage qualification rate constraints, average voltage deviation constraints, safe current-carrying line qualification rate constraints, and load rate constraints. Establish battery safety constraints, which include battery life safety constraints, charge and discharge capacity constraints, and charging pile power constraints. The battery life safety constraints require that the state of charge of the electric vehicle battery be maintained between a preset upper limit and a lower limit during the participation of demand response. The user's natural charging power is determined by comparing the user's current state of charge with the expected charging capacity. When the user's current state of charge is greater than or equal to the expected charging capacity, the natural charging power is zero. When the user's current state of charge is less than the expected charging capacity, the natural charging power is equal to the charging power. The objective response capability value of each user at each time moment is calculated based on the difference between the natural charging power and the minimum charging and discharging power. When the minimum charging and discharging power is less than zero, the objective response capability value is the sum of the charging response potential and the discharging response potential.

4. The method for assessing the demand response potential of electric vehicles as described in claim 3, characterized in that, The steps to obtain the user's subjective response potential value include: Classify electric vehicle users; Calculate the Euclidean distance from each electric vehicle charging behavior sequence to each cluster center, and assign each electric vehicle to the group corresponding to the nearest cluster center; The silhouette coefficient method is used to determine the number of clusters. The classification effect is evaluated by calculating the mean silhouette coefficient of each sample. The number of clusters corresponding to the maximum mean silhouette coefficient is taken as the final number of population divisions. For users within each group, charging incentive response thresholds and discharging incentive response thresholds are set based on the incentive electricity price. When the incentive electricity price is lower than the charging incentive response threshold, the charging response rate is zero; when the incentive electricity price is lower than the discharging incentive response threshold, the discharging response rate is zero. For the same incentive electricity price, calculate the positive response rate and negative response rate of users respectively, and take the average of the positive response rate and the negative response rate as the overall response rate of users; When the incentive price reaches the charging saturation incentive price, the charging response rate remains at the maximum charging response rate; when the incentive price reaches the discharging saturation incentive price, the discharging response rate remains at the maximum discharging response rate. The user's subjective response potential value is obtained by adding the charging response rate and the discharging response rate.

5. The method for assessing the demand response potential of electric vehicles as described in claim 4, characterized in that, The steps for calculating the coefficients of objective indicators and subjective indicators include: The objective response capability value and the user's subjective response potential value are processed to be dimensionless. For positive indicators, the minimum value normalization method is used, and for negative indicators, the maximum value normalization method is used to obtain the evaluation index matrix. The objective index coefficients are calculated using the entropy weight method. The evaluation index matrix is ​​standardized, the information entropy of each evaluation index is calculated, and the objective index coefficients are calculated based on the information entropy of each evaluation index. The subjective index coefficients are calculated by comparing the importance of each evaluation index pairwise using the nine-scale method, constructing a judgment matrix, normalizing the judgment matrix by column, and calculating the subjective index coefficients of each evaluation index using the arithmetic mean method. Calculate the largest eigenvalue of the judgment matrix, and calculate the consistency index and consistency ratio based on the largest eigenvalue. When the consistency ratio is less than 0.1, the judgment matrix passes the consistency test.

6. The method for assessing the demand response potential of electric vehicles as described in claim 5, characterized in that, The calculation of objective index coefficients using the entropy weight method includes the following steps: For positive indicators The following formula is used for dimensionless processing: ； For negative indicators The following formula is used for dimensionless processing: ； in, Let j be the evaluation index for the i-th evaluation object. and These represent the maximum and minimum values ​​for different objects under the same evaluation index; Calculate the information entropy of each evaluation indicator using the following formula. : ； in, The evaluation index value is the standardized value, and n is the number of evaluation objects; Calculate the objective index coefficient using the following formula. : ； in, Let be the objective index coefficient of the j-th evaluation indicator, and m be the total number of evaluation indicators.

7. The method for assessing the demand response potential of electric vehicles as described in claim 6, characterized in that, The steps for calculating the demand response potential assessment value for each user include: The comprehensive index coefficient is obtained by multiplying the objective index coefficient by the subjective index coefficient and then normalizing the result, and then calculated according to the following formula: ； in, Let be the comprehensive index coefficient of the j-th evaluation index. For objective index coefficients, For subjective index coefficients; Positive indicators are ranked in ascending order, and negative indicators are ranked in descending order. The weighted rank-sum ratio is calculated as the assessment value of demand response potential using the following formula: ； in, Let be the assessment value of the demand response potential for the i-th user, and n be the total number of evaluation indicators. Let be the rank of the j-th metric for the i-th user.

8. An electric vehicle demand response potential assessment system, employing the method described in any one of claims 1-7, characterized in that, include: The time-of-use pricing model building module calculates the demand-side response time-of-use pricing model to obtain the time-of-use pricing decision results. The objective response capability calculation module is used to combine the user objective response capability constraint model with the time-of-use electricity price decision results to calculate the objective response capability value of each user at each time. The group classification module is used to classify electric vehicle groups and output the group classification results; The subjective response willingness assessment module calculates the user's charging response rate and discharging response rate based on the incentive electricity price and the user's current remaining electricity, and obtains the user's subjective response potential value through the charging response rate and discharging response rate; The comprehensive evaluation module calculates objective index coefficients and subjective index coefficients based on the objective response capability value and the user's subjective response potential value, and calculates the demand response potential evaluation value for each user based on the objective index coefficients and subjective index coefficients. The feedback adjustment module collects user satisfaction feedback data based on the demand response potential assessment value, and adjusts the time-of-use electricity pricing decision results based on the user satisfaction feedback data.

9. An electronic device, comprising: Memory and processor; The memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions, which, when executed by the processor, implement the steps of the electric vehicle demand response potential assessment method according to any one of claims 1 to 7.

10. A computer-readable storage medium storing computer-executable instructions that, when executed by a processor, implement the steps of the electric vehicle demand response potential assessment method according to any one of claims 1 to 7.

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