Electric vehicle cluster potential evaluation method considering subjective response capability

Abstract

The invention relates to an electric vehicle cluster potential evaluation method considering subjective response capability. According to the method, firstly, an electric vehicle regulation and control feasible region model is constructed, a single charging and discharging power scheduling boundary is solved under constraint conditions, the charging and discharging power scheduling boundary of an electric vehicle cluster is accurately described through a multi-time scale aggregation method, and a basic technical support is provided for potential evaluation. And secondly, an electric vehicle user subjective response model of the electric vehicle user excited electricity price is constructed based on the adjustable excitation electricity price and the charging and discharging power boundary, and the adjustment and control potential of the electric vehicle cluster is fully excavated. And finally, performing electric vehicle response potential evaluation based on the set pair analysis theory of fuzzy optimization, performing fuzzy optimization on the set pair analysis theory, establishing a quantitative model of potential evaluation, and realizing evaluation quantification of the electric vehicle user response potential. And finally, collaborative optimization of the executability of the scheduling instruction and the acceptability of the user is realized.

Description

Technical Field

[0001] This invention relates to the field of electric vehicle cluster demand response potential assessment technology, and in particular to an electric vehicle cluster potential assessment method that takes into account subjective response capabilities. Background Technology

[0002] Currently, under the dual pressures of the irreversible depletion of traditional fossil fuels and the continuous aggravation of ecological and environmental pollution, electric vehicles (EVs) have attracted much attention due to their life-cycle energy efficiency optimization mechanism and zero-emission characteristics. In recent years, the large-scale penetration of EVs has brought opportunities for the low-carbon development of the transportation industry, as EVs possess the dual attributes of both road network transportation vehicles and mobile electrical loads on the power grid. However, the rapid growth in the scale of EVs and the uncertainty of their spatiotemporal distribution have placed enormous pressure on power grid operation. To scientifically plan and optimize the scheduling of the power grid and charging facilities, it is necessary to deduce the regulatory capacity inherent in a large-scale EV fleet.

[0003] When electric vehicles are integrated into the grid on a large scale as distributed energy storage resources, accurate assessment of their cluster scheduling potential is a core technology supporting the flexible regulation of new power systems. Traditional assessment systems mainly construct feasible region models based on battery physical parameters, deriving cluster scheduling capabilities by aggregating constraints such as individual cell charge / discharge power and state of charge, but neglecting the significant impact of dynamic user behavior on actual dispatchable capacity. Existing methods suffer from several bottlenecks: feasible region aggregation technology fails to consider the deviation between scheduling instructions and execution results caused by user operation delays; the equivalent battery model assumes all electric vehicles have full responsiveness, thus overestimating aggregation potential and failing to reflect sudden changes in behavioral preferences under unexpected scenarios. Therefore, current assessment systems suffer from multiple bottlenecks: the disconnect between physical constraints and behavioral parameters leads to an imbalance between scheduling instructions and user acceptance; and single economic incentive strategies lack synergistic optimization with battery health status. There is an urgent need to construct a collaborative assessment framework that integrates battery dynamics and user behavior evolution to improve the accuracy of electric vehicle cluster scheduling potential assessment and actual dispatchable efficiency.

[0004] It should be noted that the information disclosed in the background section above is only used to enhance the understanding of the background of this disclosure, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0005] To address, or at least partially address, the aforementioned technical problems, this invention provides a method for assessing the potential of electric vehicle clusters that takes into account subjective response capabilities.

[0006] This invention provides a method for assessing the potential of electric vehicle clusters that considers subjective response capabilities, comprising: Based on the factors affecting the regulation capability of electric vehicles, a feasible domain model for electric vehicle regulation is constructed without affecting the normal use needs of electric vehicles. The factors affecting the regulation capability of electric vehicles include: the battery's state of charge (SOC), rated capacity, and health status, as well as the time distribution status of the electric vehicle. Based on the preset constraints in the charging and discharging process of electric vehicles, the charging and discharging power regulation capability boundary of a single electric vehicle is obtained from the output of the electric vehicle regulation feasible domain model; the charging and discharging power regulation capability boundary of a single electric vehicle is aggregated to the electric vehicle cluster level to obtain the total charging and discharging power boundary of the electric vehicle cluster. Based on the incentive electricity price and the boundary of charging and discharging power, a subjective response model of electric vehicle users is constructed. The subjective response model of electric vehicle users is used to calculate the subjective charging and discharging response potential of electric vehicle users. We employ fuzzy optimization set pair analysis theory to assess the cluster response potential of electric vehicles based on charge and discharge power boundaries and the subjective charge and discharge response potential of electric vehicle users.

[0007] Furthermore, the charging and discharging process of electric vehicles follows constraints on battery charging and discharging power and battery state of charge, as well as battery safety constraints. Among these, battery safety constraints include: battery lifespan safety constraints, charging and discharging capacity constraints, and charging pile power constraints.

[0008] Furthermore, the battery charge / discharge power and battery state of charge satisfy the following constraints: ; In the formula: S(t) and P(t) represent the state of charge and charge / discharge power of the electric vehicle's battery at time t, respectively; E b For power battery capacity; t0, t end These are the start and end times of charging, respectively. Battery charging and discharging power meets the following requirements: In the formula: , These are the maximum discharge power and maximum charging power of an electric vehicle, respectively. The battery state of charge meets the following requirements: In the formula: , These are the minimum and maximum states of charge (SOC) settings for the power batteries of electric vehicles.

[0009] Furthermore, the safety constraints on battery lifespan are expressed as follows: , In the formula: for Electric vehicle users The state of charge of the power battery in an electric vehicle; , These are the upper and lower limits of the state of charge of the electric vehicle's power battery, respectively. , These are the rated charging and discharging power of the electric vehicle, respectively. , Electric vehicle users Arrival time and expected departure time of electric vehicles; for Time users Electric vehicle power batteries respond to power Changes in battery state of charge caused by charging and discharging: ; in, Charging efficiency when charging electric vehicles; The battery capacity of the power battery for electric vehicles. For time intervals.

[0010] Furthermore, response power Changes in battery state of charge caused by charging and discharging The charge / discharge capacity constraints are as follows: Charging capacity constraints for charging response: , Discharge capacity constraint for discharge response: , , In the formula: For electric vehicle users Expected charging capacity For electric vehicle users The expected departure time.

[0011] Furthermore, the power constraints for charging stations are as follows: , , , in, For users The charging power of the electric vehicle at time t. For users The discharge power of the electric vehicle at time t; The rated charging (discharging) power of user i's electric vehicle at time t; , users respectively The minimum and maximum rated power of the electric vehicle load at time t.

[0012] Furthermore, in the feasible region model for electric vehicle regulation, the boundaries of the maximum charging and discharging operating regions are represented as follows: ; Where AB, BC, CD, AF, FE, and ED are the boundaries of the maximum operating region for charging and discharging; S0 is the battery state of charge at the start of charging; S obj The target battery state of charge value set for electric vehicle users at the end of charging; E b t0 represents the capacity of the power battery; t0 represents the charging start time. , These are the minimum and maximum states of charge (SOC) settings for the power batteries of electric vehicles, respectively. , These represent the maximum discharge power and maximum charging power of an electric vehicle, respectively; t E Let E be the time at which point E is located.

[0013] Furthermore, the subjective response model of electric vehicle users is as follows: Set the user's maximum charge and discharge response rate and User charging incentive response threshold User discharge excitation response critical value User charging saturation incentive electricity price User discharge saturation incentive electricity price ; Calculate the user charging positive response rate based on the scope of the incentive electricity price, the incentive electricity price, and the charging setting parameters. and negative response rate : ; ; The overall charging response rate for users is the average of the positive and negative charging response rates. ; Calculate the user discharge positive response rate based on the scope of the incentive electricity price, the incentive electricity price, and the discharge setting parameters. and negative response rate in, ; ; The overall discharge response rate of the user is the average of the positive and negative discharge response rates of the user: ; The user response potential of a single electric vehicle is obtained by correcting the overall charge and discharge response rate using the upper bound of the charge and discharge power. User charging response potential: ; User discharge response potential: ; in, and These are the upper limits of the charging and discharging power, respectively.

[0014] Furthermore, using fuzzy optimization set pair analysis theory, the assessment of electric vehicle cluster response potential is conducted based on charge / discharge power boundaries and the subjective charge / discharge response potential of electric vehicle users, including: Establish potential assessment grading standards based on charge / discharge power boundaries and the subjective charge / discharge response potential of electric vehicle users. ;in, For the indicator, electric vehicles correspond to the indicator. The index value is denoted as Indicator value It is derived from the normalization of the charging and discharging power boundary and the subjective charging and discharging response potential of electric vehicle users, and then further differentiated into percentages. The correlation between evaluation index values ​​and each evaluation level is calculated using set pair analysis theory. Fuzzy optimization is performed, and the relative membership degree between the evaluation index value and each evaluation level is calculated based on the degree of connection. The maximum membership criterion is used to determine the level of demand response potential.

[0015] Furthermore, the various indicator values ​​of electric vehicle users and their corresponding levels are considered as having... For each set of evaluation indicators, considering the weight of each indicator, a comprehensive judgment is made from three aspects: similarity, difference, and contradiction. The method is as follows: Set potential assessment indicators At the level In the middle, the same degree for: ; In the formula: For the first Individual Indicator Values Normalized weights; Set potential assessment indicators In position and hierarchy In non-adjacent levels, the degree of opposition... for: ; Set potential assessment indicators In position and hierarchy In adjacent levels, a single indicator Difference Single indicator The coefficient of difference is : ; In the formula: , indicating indicators The indicator value falls into the level middle; , indicating indicators The indicator value falls into the level middle; Then the degree of connection for: ; In the formula: The coefficient of opposition. Pick .

[0016] Furthermore, fuzzy optimization is performed, and membership degrees are calculated using the degree of association. : ; In the formula, For a certain indicator relative to the level The degree of difference The value is equal to the degree of connection in step 2. .

[0017] Furthermore, using eigenvalues To quantify the results of potential assessment, The eigenvalues ​​comprehensively reflect the synergistic effect of the physical feasible region and subjective response capability of electric vehicles. The calculation formula is: ; In the formula: As a level; For the number of levels, This represents the degree of membership.

[0018] The technical solutions provided in the embodiments of the present invention have the following advantages compared with the prior art: Most current electric vehicle cluster potential assessment methods rely solely on physical parameters such as battery SOC and charging / discharging power to construct the feasible region, failing to incorporate user subjective response characteristics. This leads to a disconnect between scheduling instructions and actual user behavior, ignoring user operation delays and sudden behavioral preferences in unexpected scenarios, and overestimating schedulable potential. This invention aims to construct a collaborative assessment framework integrating physical constraints and subjective responses to overcome these bottlenecks. By quantifying the dynamic response threshold and saturation range of users to incentive electricity prices, a multi-objective optimization relationship between battery health status and user economic preferences is established, achieving a balance between the executability of scheduling instructions and user acceptance. Simultaneously, fuzzy optimization set pair analysis theory is introduced to transform user response potential assessment into the calculation of similar, dissimilar, and inverse three-state connectivity, improving the robustness of assessment under unexpected scenarios, and ultimately solving the problem of potential assessment distortion caused by the separation of physical constraints and behavioral parameters.

[0019] This invention significantly improves the accuracy and practicality of electric vehicle cluster potential assessment. First, by integrating a dynamic feasible region model with fuzzy optimization set pair analysis, it enhances the accuracy of electric vehicle user response potential assessment. Furthermore, the phased charge / discharge response rate model accurately characterizes user nonlinear behavior, avoiding the overestimation problem of traditional linear models. Second, it generates charge / discharge strategies based on dynamic constraints of battery health status, reducing execution deviations caused by user operation delays. In addition, the technical solution offers advantages in both economy and system compatibility. The incentive price threshold response model optimizes grid-side incentive costs, reducing ineffective investment. Finally, by introducing fuzzy optimization set pair analysis theory, it transforms user response potential assessment into the calculation of similar, dissimilar, and inverse three-state connectivity, ultimately solving the problem of potential assessment distortion caused by the separation of physical constraints and behavioral parameters. It supports large-scale electric vehicle cluster potential assessment, providing high-precision core technical support for flexible regulation of new power systems. Attached Figure Description

[0020] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention.

[0021] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0022] Figure 1 This is a flowchart of a method for assessing the potential of an electric vehicle cluster that takes into account subjective response capabilities, provided as an embodiment of the present invention.

[0023] Figure 2 This is a schematic diagram of the maximum operating region for charging and discharging in the feasible domain model for electric vehicle regulation provided in the embodiments of the present invention.

[0024] Figure 3 This is a schematic diagram illustrating the positive and negative response rates of electric vehicle user participation in demand response, provided in an embodiment of the present invention.

[0025] Figure 4 This is a schematic diagram of the forward process of the information graphic code preprocessing module provided in the embodiments of the present invention.

[0026] Figure 5 This is an architecture diagram of an electric vehicle cluster potential assessment method that considers subjective response capabilities, provided in an embodiment of the present invention.

[0027] Figure 6 This is a schematic diagram of an electric vehicle cluster potential assessment device that takes into account subjective response capabilities, provided in an embodiment of the present invention. Detailed Implementation

[0028] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0029] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0030] The explanation of the meaning of the terms involved in this invention: Feasible domain: refers to the dynamic capability boundary of an electric vehicle cluster that can participate in grid dispatch within a specific time window. It is formed by physical parameters such as battery state of charge, rated capacity, and charging and discharging power, as well as the temporal distribution of electric vehicles.

[0031] Subjective responsiveness: refers to the quantitative indicator of electric vehicle users' actual behavioral tendency to participate in demand response, influenced by subjective factors such as economic incentives, battery status, and personal preferences.

[0032] Example 1 See Figure 1 and Figure 5As shown, this invention provides a method for assessing the potential of electric vehicle clusters considering subjective response capabilities. First, a physical constraint modeling system integrating feasible domain aggregation technology is established: a dynamic feasible domain model for electric vehicle regulation is constructed based on battery state of charge, rated capacity, health status, and charging / discharging power parameters. This model, combined with constraints, aggregates the physical scheduling boundaries of individual electric vehicle clusters across multiple time scales, providing fundamental technical support for potential assessment. Second, a dynamic assessment model of user subjective response characteristics is constructed: subjective attribute information such as regulated electricity prices and individual economic levels is introduced to construct a subjective response rate model for electric vehicle users under incentive-based electricity prices, fully exploring the regulation potential of electric vehicle clusters. Finally, the response potential of electric vehicles is assessed based on fuzzy optimization set pair analysis theory: fuzzy optimization is applied to set pair analysis theory to establish a quantitative model for potential assessment, achieving quantification of the response potential of electric vehicle users. Ultimately, this achieves synergistic optimization of scheduling command executability and user acceptance. The specific process includes: S100: Based on the factors affecting the regulation capability of electric vehicles, a feasible domain model for electric vehicle regulation is constructed without affecting the demand for electric vehicle use.

[0033] Factors affecting the controllability of electric vehicles include: the battery's state of charge (SOC), rated capacity, and state of health, as well as the time distribution of the electric vehicle.

[0034] The state of charge (SOC) of a battery reflects the electrical state of an electric vehicle's power battery. The SOC determines the maximum amount of electrical energy an electric vehicle can provide and absorb during grid dispatch, and is directly related to its dispatchability. As an electric vehicle is used, the power battery degrades, and the actual electrical energy gradually decreases. Combining the battery's health status with its rated capacity determines the actual usable battery capacity. The rated capacity of a battery is specific to different electric vehicle models; different electric vehicle models have different rated battery capacities.

[0035] The temporal distribution of electric vehicles determines the temporal distribution of the corresponding virtual energy storage, i.e., when electric vehicles can participate in the grid regulation process.

[0036] Considering the factors affecting the regulation capability of electric vehicles mentioned above, and without affecting the normal use needs of electric vehicles, a feasible domain model for electric vehicle regulation is constructed to analyze the regulation and operation range of electric vehicles.

[0037] like Figure 2As shown. Point A represents the electric vehicle arriving at the charging location and entering the charging state. Boundary AB represents the electric vehicle charging from point A at maximum power to its highest battery state of charge; boundary BC represents the electric vehicle remaining at its highest battery state of charge until charging ends; boundary CD represents the range of battery state of charge that the electric vehicle must meet at the end of charging; boundary ED represents the electric vehicle charging from point E at maximum power until charging ends; boundary AF represents the electric vehicle discharging from point A at maximum power to its lowest battery state of charge; boundary FE represents the electric vehicle being at its lowest battery state of charge. (The boundary values ​​are...) The area constituted is the maximum operating range for charging and discharging of the electric vehicle. The boundaries of the maximum operating range for charging and discharging are represented as follows: ; S0 represents the battery's state of charge at the start of charging; S obj The target battery state of charge value set for electric vehicle users at the end of charging; E b t0 represents the capacity of the power battery; t0 represents the charging start time. , These are the minimum and maximum states of charge (SOC) settings for the power batteries of electric vehicles, respectively. , These represent the maximum discharge power and maximum charging power of an electric vehicle, respectively; t E This refers to the current time at point E. The charging demand must be met when the electric vehicle leaves to ensure that power adjustments during the charging process do not negatively impact the user's charging experience. For example... Figure 1 In the sequence AabcD, the charging power of each segment is P. Aa P ab P bc P cD The final state of battery charge reaches S. obj .

[0038] S200 obtains the boundary of the charging and discharging power regulation capability of a single electric vehicle, output by the feasible domain model of electric vehicle regulation, based on the preset constraints in the charging and discharging process of electric vehicles.

[0039] During the charging and discharging process of an electric vehicle, the battery charging and discharging power and the battery state of charge satisfy the following constraints: ; In the formula: S(t) and P(t) represent the state of charge and charge / discharge power of the electric vehicle's power battery at time t, respectively; t0 and t... end These represent the start and end times of charging and discharging, respectively. The battery charging and discharging power satisfies the following constraints: The battery state of charge satisfies the following constraints: .

[0040] During the charging and discharging process of electric vehicles, battery safety constraints must also be followed. These constraints include: battery lifespan safety constraints, charging and discharging capacity constraints, and charging pile power constraints.

[0041] Battery lifespan safety constraints: During the demand response process of electric vehicles, the battery capacity of the electric vehicle's power battery will undergo permanent changes due to the influence of external temperature, humidity, and driving environment factors. Therefore, to ensure the lifespan of the electric vehicle battery, the battery's safe capacity limit must be met, i.e., the battery lifespan safety constraint, which is expressed as: , In the formula: for Time users The state of charge of the power battery in an electric vehicle; , These are the upper and lower limits of the state of charge of the electric vehicle's power battery, respectively. for Time users Electric vehicle power batteries respond to power Changes in battery state of charge caused by charging and discharging: ; , These are the rated charging and discharging power of the electric vehicle, respectively. , users respectively Arrival time and expected departure time; Charging efficiency when charging electric vehicles; This refers to the battery capacity of the power battery for electric vehicles.

[0042] Charge and discharge capacity constraints: Because the remaining battery power of an electric vehicle is random when it is registered with the grid, and the electric vehicle can choose to charge or discharge during the demand response process, if it chooses to charge, the battery power must reach the initial expected level before leaving the station, provided that the travel needs are met; if it chooses to discharge, the initial expected level must also be met, as too low a battery level will affect the use of the electric vehicle.

[0043] In the process of participating in demand response, with response power Changes in battery state of charge caused by charging and discharging The charge / discharge capacity constraints are as follows: Charging capacity constraints for charging response: , Discharge capacity constraint for discharge response: , , In the formula: For electric vehicle users The expected charging capacity.

[0044] Charging station power constraints: Since the rated power of a charging pile significantly impacts the responsiveness of an electric vehicle, this invention establishes a charging pile power constraint. Specifically, the charging and discharging power is determined by the electric vehicle's remaining battery level: the higher the remaining battery level of the electric vehicle upon entering the station, the lower the initial charging power and the higher the initial discharging power; conversely, the lower the remaining battery level, the higher the initial charging power and the lower the initial discharging power. Furthermore, the charging power decreases as the electric vehicle's battery level increases, and the discharging power decreases as the electric vehicle's battery level decreases. The specific charging pile power constraints are as follows: , , , in, For users The charging power of the electric vehicle at time t. For users The discharge power of the electric vehicle at time t; The rated charging or discharging power of user i's electric vehicle at time t; , users respectively The minimum and maximum rated power of the electric vehicle load at time t.

[0045] Based on the above constraints, the output of the feasible region model for electric vehicle regulation is obtained, which is the boundary of the charging and discharging power regulation capability of a single electric vehicle: ; in and For users Lower / upper limit of electric vehicle discharge power and user Lower / upper limits for electric vehicle charging power.

[0046] By aggregating the charging and discharging power regulation capability boundary of a single electric vehicle to the level of an electric vehicle cluster, the total charging and discharging power boundary of the electric vehicle cluster is obtained:

[0047] Where N is the total number of electric vehicle clusters.

[0048] S300 constructs a subjective response model for electric vehicle users based on incentive electricity prices and the boundary of charging and discharging power.

[0049] For electric vehicle users, whether or not to participate in demand response depends on their subjective willingness. Based on this, this invention proposes a subjective response model for electric vehicle users that considers user behavior factors.

[0050] The subjective response level of electric vehicle users is influenced by the incentive electricity price. According to the consumer psychology model, the incentive electricity price has a minimum perceptible difference threshold for users. When the incentive electricity price is within the minimum perceptible difference threshold range, users are insensitive to the incentive electricity price and have little or no response. After exceeding the minimum perceptible difference threshold range, users begin to respond, and as the incentive electricity price increases, the user response gradually increases. When the incentive electricity price reaches a certain level, the user response no longer increases, that is, it reaches the response saturation zone. However, under the same incentive electricity price conditions, users' willingness to respond is not constant, but is affected by a variety of factors and has a certain degree of fluctuation. Therefore, for user willingness under the same incentive electricity price level, this application sets two scenarios: positive response and negative response, and takes the half-mean of the response rate of the two scenarios as the user's overall willingness to respond. In addition, considering that users have different levels of acceptance of charging and discharging responses under the same incentive electricity price, this application adjusts the response rate based on charging and discharging power to reflect the charging and discharging behavior of different users.

[0051] Specifically, the maximum charging and discharging response rate for users is set. and User charging incentive response threshold User discharge excitation response critical value User charging saturation incentive electricity price User discharge saturation incentive electricity price ;like Figure 3 As shown: Incentive pricing for electric vehicle users to participate in demand response; and These are the user's charging and discharging response rates, respectively. and These are the user's maximum charge and discharge response rates, respectively. The user's charging incentive response threshold is defined as follows: when the incentive electricity price is higher than the user's charging incentive response threshold, the user's charging response is always greater than zero. Minimum excitation value for user discharge; The user's discharge excitation response threshold is defined as follows: when the excitation price is higher than the user's discharge excitation response threshold, the user's charging response is always greater than zero. A charging saturation incentive price is set for users. When the incentive price reaches the user's charging saturation incentive price, the user's charging response rate remains stable. constant; The user discharge saturation incentive price is set, and when the incentive price reaches the user discharge saturation incentive price, the user discharge response rate remains constant. constant.

[0052] and These are the user's positive and negative response rates to charging, respectively. ; ; The overall charging response rate for users is the average of the positive and negative charging response rates. .

[0053] and These represent the positive and negative response rates of user discharge, respectively. ; ; The overall discharge response rate of the user is the average of the positive and negative discharge response rates of the user: ; The user response potential of a single electric vehicle is obtained by correcting the overall charge and discharge response rate using the upper bound of the charge and discharge power. User charging response potential: ; User discharge response potential: ; in, and These are the upper limits of the charging and discharging power, respectively.

[0054] S400, based on fuzzy optimization set pair analysis theory, assesses the cluster response potential of electric vehicles.

[0055] Combining the previously constructed feasible domain model for electric vehicle regulation and the subjective response model of electric vehicle users, the cluster response potential of electric vehicles is evaluated based on the set pair analysis theory of fuzzy optimization.

[0056] In set pair analysis for fuzzy optimization, the charge / discharge power boundary and the subjective charge / discharge response potential of electric vehicle users are transformed into evaluation index values. It directly participates in the calculation of connectivity.

[0057] The core idea of ​​set pair analysis theory is to treat the evaluation sample and evaluation level as a set pair, and the index values ​​of the charge / discharge power boundary and the subjective charge / discharge response potential of electric vehicle users. With assessment grading standards By comparison, a quantitative analysis of similarities, differences, and antonyms is performed, and the degree of connection is calculated using the following formula: ; In the formula: , , These are respectively: degree of identity, degree of difference, and degree of opposition. , , Both are non-negative, and ; The coefficient of difference. ,along with Gradually approaching zero, The uncertainty increases accordingly; The coefficient of opposition. .

[0058] This application introduces set pair analysis theory based on fuzzy optimization, treating evaluation samples and evaluation levels as set pairs with m evaluation indicators. It analyzes the degree of similarity, difference, and opposition between evaluation samples and evaluation levels, as well as their overall correlation. Then, it utilizes the difference degree from variable fuzzy set theory to describe the closeness between the evaluation samples and evaluation levels from both "good" and "bad" perspectives. By organically combining the shared essence of correlation and difference, the evaluation level is determined.

[0059] like Figure 4 As shown, the specific implementation steps and explanations for evaluating the response potential of electric vehicles based on fuzzy optimization set pair analysis theory are as follows: Step 1: Establish potential assessment level standards for charging and discharging power boundaries and the subjective charging and discharging response potential of electric vehicle users.

[0060] Establish potential assessment grading standards ; in, For the indicator, electric vehicles correspond to the indicator. The index value is denoted as Indicator value Derived from the charging and discharging power boundary and the subjective charging and discharging response potential of electric vehicle users, the maximum value of this indicator cluster for all electric vehicles is used as a reference, normalized, and then percentaged to obtain the indicator value: ; For the indicators ,grade The evaluation standard limits; The number of levels; The number of indicators.

[0061] The level of an indicator is the level corresponding to the range in which its value falls. .

[0062] Step 2: Use set pair analysis theory to calculate the correlation between the evaluation index values ​​and each evaluation level.

[0063] The various indicator values ​​of electric vehicle users and their corresponding levels are considered as having The evaluation indicators are set in pairs, and considering the weight of each indicator, a comprehensive judgment is made from three aspects: similarity, difference, and inverse relationship. The method is as follows. Let potential assessment indicators be... At the level In the middle, the same degree for: ; In the formula: For the first Individual Indicator Values Normalized weights.

[0064] Set potential assessment indicators In position and hierarchy In non-adjacent levels, the degree of opposition... for: ; Set potential assessment indicators At the level Adjacent levels, then single indicator Difference Single indicator The coefficient of difference is : ; In the formula: , indicating indicators The indicator value falls into the level middle; , indicating indicators The indicator value falls into the level In the middle; then the degree of connection for ; In the formula: The number of indicators; The coefficient of opposition. Pick .

[0065] Combining the above formulas with the model mentioned earlier, we can derive the physical meanings of the terms "same," "different," "opposite," and "unlike."

[0066] Same degree a Indicator value Fully meets the rating This means that both the user's physical and behavioral aspects meet the standards.

[0067] Opposition b Indicator value and Complete conflict, meaning that the user response rate is so low that the physical capabilities cannot be utilized.

[0068] Difference c The index value is in a transitional state, that is, the physical boundary is high but the response rate is moderate.

[0069] Step 3: Calculate the relative membership degree between the evaluation index value and each evaluation level based on the degree of connection.

[0070] Clearly, connectivity The less similarity there is between the various electric vehicles and the evaluation level, the better. The closer a value is to -1, the more likely an electric vehicle is to not belong to any evaluation category. The greater the similarity, the opposite is true. This shows the degree of connection. It is also a relative degree of difference.

[0071] To further improve the overall evaluation results, fuzzy optimization is performed here, using the degree of connection to calculate the membership degree. : ; In the formula, For a certain indicator relative to the level The degree of difference The value is equal to the degree of connection in step 2. .

[0072] Step 4: Determine the level of demand response potential using the maximum membership criterion. To avoid distortion, this application uses eigenvalues. To quantify the results of potential assessment, The eigenvalues ​​comprehensively reflect the synergistic effect of the physical feasible region and subjective response capability of electric vehicles. The calculation formula is: ; In the formula As a level; The number of levels.

[0073] Example 2 See Figure 6As shown, the present invention provides an electric vehicle cluster potential assessment device considering subjective response capability, comprising: at least one processing unit, the processing unit being connected to a storage unit via a bus unit, the storage unit storing a computer program, and the computer program being executed by the processing unit to implement the electric vehicle cluster potential assessment method considering subjective response capability, comprising: Based on the factors affecting the regulation capability of electric vehicles, a feasible domain model for electric vehicle regulation is constructed without affecting the normal use needs of electric vehicles. The factors affecting the regulation capability of electric vehicles include: the battery's state of charge (SOC), rated capacity, and health status, as well as the time distribution status of the electric vehicle. Based on the preset constraints in the charging and discharging process of electric vehicles, the charging and discharging power regulation capability boundary of a single electric vehicle is obtained from the output of the electric vehicle regulation feasible domain model; the charging and discharging power regulation capability boundary of a single electric vehicle is aggregated to the electric vehicle cluster level to obtain the total charging and discharging power boundary of the electric vehicle cluster. Based on the incentive electricity price and the boundary of charging and discharging power, a subjective response model of electric vehicle users is constructed. The subjective response model of electric vehicle users is used to calculate the subjective charging and discharging response potential of electric vehicle users. We employ fuzzy optimization set pair analysis theory to assess the cluster response potential of electric vehicles based on charge and discharge power boundaries and the subjective charge and discharge response potential of electric vehicle users.

[0074] Of course, the storage unit in the electric vehicle cluster potential assessment device considering subjective response capability provided in the embodiments of the present invention stores computer programs that are not limited to the method operations described above, and can also execute related operations in the electric vehicle cluster potential assessment method considering subjective response capability provided in any embodiment of the present invention.

[0075] Example 3 This invention provides a computer-readable storage medium storing computer instructions that, when executed by a processor, implement the electric vehicle cluster potential assessment method considering subjective response capabilities, including: Based on the factors affecting the regulation capability of electric vehicles, a feasible domain model for electric vehicle regulation is constructed without affecting the normal use needs of electric vehicles. The factors affecting the regulation capability of electric vehicles include: the battery's state of charge (SOC), rated capacity, and health status, as well as the time distribution status of the electric vehicle. Based on the preset constraints in the charging and discharging process of electric vehicles, the charging and discharging power regulation capability boundary of a single electric vehicle is obtained from the output of the electric vehicle regulation feasible domain model; the charging and discharging power regulation capability boundary of a single electric vehicle is aggregated to the electric vehicle cluster level to obtain the total charging and discharging power boundary of the electric vehicle cluster. Based on the incentive electricity price and the boundary of charging and discharging power, a subjective response model of electric vehicle users is constructed. The subjective response model of electric vehicle users is used to calculate the subjective charging and discharging response potential of electric vehicle users. We employ fuzzy optimization set pair analysis theory to assess the cluster response potential of electric vehicles based on charge and discharge power boundaries and the subjective charge and discharge response potential of electric vehicle users.

[0076] Of course, the computer program stored in the computer-readable storage medium provided in the embodiments of the present invention is not limited to the method operation described above, but can also perform related operations in the electric vehicle cluster potential assessment method considering subjective response capability provided in any embodiment of the present invention.

[0077] In the embodiments provided by this invention, it should be understood that the disclosed structures and methods can be implemented in other ways. For example, the structural embodiments described above are merely illustrative. For instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection through some interfaces, structures, or units, and may be electrical, mechanical, or other forms.

[0078] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0079] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0080] The above description is merely a specific embodiment of the present invention, enabling those skilled in the art to understand or implement the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the present invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features claimed herein.

Claims

1. A method for evaluating potential of an electric vehicle cluster considering subjective response capability, characterized by, The application relates to an electric vehicle (EV) cluster response potential evaluation method, and belongs to the field of smart grid demand response. The application comprises the following steps: According to the influencing factors of the electric vehicle regulation capability, an electric vehicle regulation feasible region model is constructed without affecting the use demand of the electric vehicle, wherein the influencing factors of the electric vehicle regulation capability include the state of charge (SOC), the rated capacity and the health state of the battery, and the time distribution state of the electric vehicle; Based on the preset constraint condition in the charging and discharging process of the electric vehicle, the charging and discharging power regulation capability boundary of a single electric vehicle output by the electric vehicle regulation feasible region model is obtained; the charging and discharging power regulation capability boundary of the single electric vehicle is aggregated to the electric vehicle cluster level, and the total charging and discharging power boundary of the electric vehicle cluster is obtained; According to the incentive price and the utilization charging and discharging power boundary, an electric vehicle user subjective response model is constructed, and the electric vehicle user subjective charging and discharging response potential is calculated by the electric vehicle user subjective response model; 2. The method for evaluating the potential of an electric vehicle cluster considering subjective response capability according to claim 1, wherein, The charging and discharging power boundary and the electric vehicle user subjective charging and discharging response potential are used to evaluate the electric vehicle cluster response potential by adopting the fuzzy optimization set pair analysis theory.

3. The method for evaluating the potential of an electric vehicle cluster considering subjective response capability according to claim 2, characterized by, The charging and discharging process of the electric vehicle follows the pool charging and discharging power and the battery state of charge constraint and the battery safety constraint, wherein the battery safety constraint includes the battery service life safety constraint, the charging electric quantity constraint and the charging pile power constraint. ； In the formula, S(t) and P(t) are the battery state of charge and the charging and discharging power of the power battery of the electric vehicle at time t, respectively; E b is the power battery capacity; t0 and t end are the charging start and end times, respectively. The battery charging and discharging power satisfies: ; in the formula: , respectively are the maximum discharging power and the maximum charging power of the electric vehicle; The battery state of charge satisfies: ; wherein: , are the minimum battery state of charge and the maximum battery state of charge, respectively, set for the traction battery of the electric vehicle.

4. The method for evaluating the potential of an electric vehicle cluster considering subjective response capability according to claim 2, wherein, The battery charging and discharging power and the battery state of charge satisfy the following constraints: ， wherein: is the instant electric vehicle user the state of charge of the electric vehicle power battery; , are respectively the upper and lower limits of the state of charge of the electric vehicle power battery; , are respectively the rated charge and discharge power of the electric vehicle; , are respectively the electric vehicle arrival time and expected departure time of the electric vehicle user . For The user Electric vehicle power battery in response to power Battery state of charge change caused by charging and discharging: ； wherein charging efficiency when charging an electric vehicle; battery capacity of the power battery of the electric vehicle, is a time interval.

5. The method for evaluating the potential of an electric vehicle cluster considering subjective response capability according to claim 2, wherein, Response power Battery state of charge variation amount caused by charging and discharging The charging and discharging power constraints satisfied are as follows: The battery service life safety constraint is expressed as: ， The charging electric quantity constraint of the charging response is: ， ， In the formulae: Qdes is the desired charge amount for the electric vehicle user Qdes is the desired charge amount for the electric vehicle user Qdes is the desired charge amount for the electric vehicle user Qdes is the desired charge amount for the electric vehicle user 6. The method for evaluating the potential of an electric vehicle cluster considering subjective response capability according to claim 2, wherein The discharging electric quantity constraint of the discharging response is: ， ， ， wherein, for a user charging power of the electric vehicle at time t, for a user discharging power of the electric vehicle at time t; for a user i the rated charging (discharging) power of the electric vehicle at time t; , for a user minimum and maximum rated power of the electric vehicle load at time t.

7. The method for evaluating the potential of an electric vehicle cluster considering subjective response capability according to claim 1, wherein, The charging pile power constraint is as follows: ； Wherein, AB, BC, CD, AF, FE, ED are the boundaries of the maximum operating area of charging and discharging; S0 is the battery state of charge at the start of charging; S obj is the target battery state of charge value set for the electric vehicle user at the end of charging; E b is the capacity of the power battery; t0 is the start time of charging; 、 are the minimum battery state of charge and the maximum battery state of charge set for the power battery of the electric vehicle, respectively; 、 are the maximum discharging power and the maximum charging power of the electric vehicle, respectively; t E is the time at which point E is located. 8.The method of claim 1, wherein, In the electric vehicle regulation feasible region model, the boundaries of the maximum operation region of the charging and discharging are expressed as: Setting user maximum charge, discharge response rate and ; user charge incentive response threshold , user discharge incentive response threshold , user charge saturation incentive price , user discharge saturation incentive price ; A user charging positive response rate is calculated according to the range in which the incentive electricity price is located, the incentive electricity price, and charging setting parameters and a negative response rate : ； ； The electric vehicle user subjective response model is as follows: ； A user discharge positive response rate is calculated according to a range in which an incentive electricity price is located, the incentive electricity price, and discharge setting parameters and a negative response rate wherein, ； ； The overall charging response rate of the user is the average value of the positive response rate and the negative response rate of the user charging: ； The overall discharging response rate of the user is the average value of the positive response rate and the negative response rate of the user discharging: User charging response potential: ; User discharge response potential: ; wherein, and are the upper bounds of the charge and discharge power, respectively. 9.The method of claim 1, wherein, The user response potential of a single electric vehicle is obtained by correcting the overall charging and discharging response rate by using the charging and discharging power upper limit: The potential evaluation grade standard is established according to the charging and discharging power boundary and the subjective charging and discharging response potential of the electric vehicle user ; wherein, is an index, and the electric vehicle corresponds to the index The index value of the electric vehicle is denoted as ; the index value is obtained by normalizing the charging and discharging power boundary and the subjective charging and discharging response potential of the electric vehicle user and then percentage. The evaluation of the electric vehicle cluster response potential based on the charging and discharging power boundary and the electric vehicle user subjective charging and discharging response potential by adopting the fuzzy optimization set pair analysis theory comprises the following steps: The connection degree of the evaluation index value and each evaluation grade is calculated by using the set pair analysis theory; Fuzzy optimization is carried out, and the relative membership degree of the evaluation index value and each evaluation grade is calculated according to the connection degree; The grade of the demand response potential is determined by using the maximum membership degree criterion.

10. The method for evaluating the potential of an electric vehicle cluster considering subjective response capability according to claim 9, wherein, The various indicator values ​​of electric vehicle users and their corresponding levels are considered as having For each set of evaluation indicators, considering the weight of each indicator, a comprehensive judgment is made from three aspects: similarity, difference, and contradiction. The method is as follows: Set potential evaluation index In the rank The same degree Is: ； In the formula: is the first index value normalized weight; Let potential assessment index In the grade Not adjacent to the degree, then the degree Is: ； Set potential assessment indicators In position and hierarchy In adjacent levels, a single indicator Difference Single indicator The coefficient of difference is : ； In the formulae: denotes that the index value of the index falls into the grade ; denotes that the index value of the index falls into the grade ; Then the contact degree is: ； In the formulae: is the opposite coefficient, take . 11.The method of claim 9, wherein, Fuzzy optimization is performed, and the membership degree is calculated using the connection degree : ； In the formula, the difference of a certain index with respect to the grade , the value of the contact degree in step 2 . 12.The method of claim 9, wherein, Use eigenvalue to quantify the potential evaluation results, Comprehensive reflects the synergy effect of the physical feasible region of electric vehicles and the subjective response ability, the eigenvalue The calculation formula is: ； wherein: is a rating; is a number of ratings, is a membership grade.

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