Method for Characterizing Regulation Power of Electric Vehicle Considering User Characteristics and Will and Frequency Support

Abstract

The present application relates to the technical field of electric vehicles, and discloses a method for characterizing the regulation power of electric vehicles considering user characteristics and willingness and frequency support. The method includes: describing the uncertainty factors related to electric vehicles based on the Gaussian mixture distribution method; wherein, the uncertainty factors include the user behavior pattern, battery capacity, charging and discharging rate of the electric vehicle; predicting the dispatchable willingness of the electric vehicle by using the Logit model; characterizing the regulation boundary of the electric vehicle considering user characteristics and user willingness, determining the user's regulation method by changing the upper and lower limits of the power fluctuation of the electric vehicle, and determining the frequency modulation strategy of the electric vehicle. The method of the present application solves the problem that the frequency modulation strategy for electric vehicles (EVs) rarely takes into account the uncertainty of charging behavior, resulting in an over-advancing frequency modulation ability.

Description

Technical Field

[0001] The present application relates to the technical field of electric vehicles, and in particular to a method for regulating power characterization and frequency support of electric vehicles taking into account user characteristics and intentions. Background Art

[0002] Electric vehicles (EVs) use electricity to replace traditional carbon-based fossil fuels, thereby significantly reducing fossil fuel consumption and greenhouse gas emissions. However, the charging and discharging behavior of EV users themselves is random and uncertain. When EVs are connected to the power grid at a high penetration rate, large-scale EV charging and discharging behavior will pose a huge threat to the power grid. Adopting a control strategy that takes EV uncertainty into account is the key to solving the problem.

[0003] With the popularization of electric vehicles, the real-time power supply and power conservation pressure of the power grid are also increasing. Accurate load forecasting helps balance the load of the power grid, promote the use of clean energy, and reduce dependence on fossil fuels. The literature "Zhu Lei, Huang He, Gao Song, et al. Research on optimal configuration of electric vehicle load considering wind power consumption [J]. Proceedings of the CSEE, 2021, 41(S1): 194-203." uses normal distribution to describe the disordered charging and discharging curves of EVs. The literature "Dou Xun, Wang Jun, Yang Zhihong, et al. Electric vehicle clustering control strategy for integrated energy system with AC / DC hybrid distribution network [J]. Proceedings of the CSEE, 2021, 41(14): 4829-4844." uses log-normal distribution to characterize the mileage of EVs. The literature "Zhang Shaoxing. Optimal operation of distribution network considering electricity price incentives after large-scale electric vehicle access [D]. North China Electric Power University, 2022." uses normal distribution to express EV travel and return time. In general, in terms of load forecasting for EVs participating in grid regulation, most of the user characteristics of EV users in relevant literature are approximately normally distributed, and fail to fully release the surplus energy of EVs to participate in grid regulation while ensuring user satisfaction by specifying reasonable control rules.

[0004] In recent years, the scale of EV clusters has continued to increase, and the grid connection of a large number of EV clusters will bring about the problem of sharp frequency fluctuations. To this end, the document "Wu Juai, Xue Yusheng, Xie Dongliang, et al. Joint risk dispatch of electric vehicles participating in the electricity market and the reserve market [J / OL]. Transactions of the Chinese Society of Electrical Engineering: 1-11 [2023-06-14]. "Based on the information interaction between EVA and EV users, the decoupling of EV normal charging behavior and the provision of reserve is realized, and a load frequency control model for aggregators to participate in the joint dispatch of the day-ahead energy market and the reserve market under multiple scenarios is constructed. The document "Huang Xiaoqing, Li Longyi, Xu Pengxin, et al. A multi-agent game-win-win method for sharing electric vehicle charging piles [J]. Transactions of the Chinese Society of Electrical Engineering, 2023, 38 (11): 2945-2961." proposes a method for electric vehicles to participate in load frequency control based on the master-slave game double-layer iteration of the Ford-Fulkerson maximum flow algorithm. The document "Zhang Qian, Deng Xiaosong, Yue Huanzhan, et al. Collaborative optimization strategy for electric vehicles participating in the energy-frequency regulation market taking into account battery life loss [J]. Transactions of the Chinese Society of Electrical Engineering, 2022, 37(1):72-81." proposes a load frequency control method for electric vehicles participating in the energy-frequency regulation market taking into account battery life loss. The document "Zhang Liang, Sun Chenglong, Cai Guowei, et al. Orderly charging and discharging two-stage optimization strategy for electric vehicles based on PSO algorithm [J]. Proceedings of the CSEE, 2022, 42(5):1837-1852." proposes a two-stage optimization strategy for orderly charging and discharging of electric vehicles based on particle swarm algorithm to analyze load frequency regulation. The above documents adopt different methods to optimize the grid connection of EV clusters, but when the number of EVs increases and the charging and discharging power fluctuates greatly, the load frequency regulation strategy will also cause new frequency spikes during the frequency regulation period or it will be difficult to effectively improve the frequency peak-to-average ratio. That is, the frequency regulation characteristics in the proposed control strategy are aggressive, there is no regulatory constraint on the upper and lower limits of frequency regulation, and no consideration is given to the satisfaction of individual users. Summary of the invention

[0005] The purpose of this application is to provide a method for characterizing the power regulation and frequency support of electric vehicles that takes into account user characteristics and intentions. Modeling and analysis are performed based on the travel behavior characteristics of EVs, including the time of entering and leaving the grid, the initial state of charge, and the user's intentions. Conventional EVs participate in the grid forecast load curve and the peak and valley charging price of the load curve generated by user characteristics, and then the initial state of charge, dispatch subsidy, and the time spent at the charging pile are normalized. A Logit model is established to analyze the user's behavioral characteristics, and the EV's adjustable power boundary is characterized according to the user's intention. A real-time power regulation strategy and frequency support strategy for EVs are proposed that takes into account the user's uncertainty characteristics and the user's dispatchable intentions while ensuring that the overall trend of the user's state of charge is rising.

[0006] To achieve the above object, the technical solution adopted in this application is as follows:

[0007] This application provides a method for characterizing the regulation power of electric vehicles considering user characteristics and willingness and frequency support. The method includes:

[0008] Describing the uncertainty factors related to electric vehicles based on the Gaussian mixture distribution method; wherein, the uncertainty factors include the user behavior pattern, battery capacity, charging and discharging rate of electric vehicles;

[0009] Using the Logit model to predict the dispatchable willingness of electric vehicles;

[0010] Characterize the regulation boundary of electric vehicles considering user characteristics and willingness, determine the user's regulation method by changing the upper and lower limits of the power fluctuation of electric vehicles, and determine the frequency regulation strategy of electric vehicles.

[0011] The beneficial effects of this application are:

[0012] 1) This application combines the consideration of user characteristics and willingness, characterizes the EV regulation boundary, restricts the real-time frequency regulation power of EVs, determines the user's frequency regulation method by changing the upper and lower limits of the EV power fluctuation, and accurately characterizes the EV frequency regulation boundary.

[0013] 2) This application conducts a significance analysis on the influencing factors of EV participation in power grid frequency regulation based on SPSS. It is found that the initial SOC of EV users, the waiting time at the charging pile, and the electricity price incentive are the three most important decision variables affecting the behavior of EVs participating in power grid frequency regulation. From an overall perspective, the influence of the perturbation electricity price on the boundary of the EV's ability to participate in power grid frequency regulation is studied; from an individual perspective, and combined with the correlation coefficient, it is found that the initial SOC has a stronger correlation with the proportion of the EV individual's frequency regulation time compared to the waiting time.

[0014] 3) The frequency regulation strategy of electric vehicles proposed in this application, which ensures that the overall trend of the user's state of charge shows an upward trend and takes into account the user's uncertainty characteristics and dispatchable willingness, ensures the overall upward trend of the SOC of EV users during the participation in regulation. By considering the user's willingness, the fast frequency improvement control strategy is optimized, effectively reducing the fluctuation of the load power, and making the frequency deviation as concentrated as possible in the range of ±0.033Hz. Description of the Drawings

[0015] Figure 1 It is a flowchart of a method for characterizing the regulation power of electric vehicles considering user characteristics and willingness and frequency support provided by an embodiment of this application;

[0016] Figure 2 It is an EV charging load curve diagram considering user characteristics provided by an embodiment of this application;

[0017] Figure 3 The droop characteristic diagram when the EV provided by the embodiment of the present application participates in frequency modulation;

[0018] Figure 4 The EV output power characterization diagram under different scenarios provided by the embodiment of the present application;

[0019] Figure 5 The adjustable power capacity characterization diagram of EVs under Scenario 2 and Scenario 3 provided by the embodiment of the present application;

[0020] Figure 6 The typical SOC curve diagram of EVs in different scenarios provided by the embodiment of the present application;

[0021] Figure 7 The comparison diagram of the influence effect of three different electricity prices on the EV charging and discharging power boundary under Scenario 3 provided by the embodiment of the present application;

[0022] Figure 8 The relationship diagram between the initial soc and the ratio of the EV participation in frequency modulation time to the docking time provided by the embodiment of the present application;

[0023] Figure 9 The relationship diagram between different docking times and the ratio of the EV participation in frequency modulation time to the docking time provided by the embodiment of the present application;

[0024] Figure 10 The variable scatter plot matrix diagram provided by the embodiment of the present application;

[0025] Figure 11 The matrix correlation heat map provided by the embodiment of the present application;

[0026] Figure 12 The schematic diagram of the EV auxiliary frequency modulation control analysis under Scenario 2 and Scenario 3 provided by the embodiment of the present application. Detailed implementation manners

[0027] The following uses specific specific examples to illustrate the implementation manners of the present application. Those skilled in the art can easily understand other advantages and effects of the present application from the content disclosed in this specification. The present application can also be implemented or applied through other different specific implementation manners. Various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of the present application. It should be noted that, without conflict, the following embodiments and the features in the embodiments can be combined with each other.

[0028] The following further describes in detail the specific implementation manners of the present application in conjunction with the drawings and embodiments.

[0029] Please refer to Figure 1, which is a flowchart of a method for characterizing the regulated power of electric vehicles and frequency support considering user characteristics and intentions provided by an embodiment of the present application. An embodiment of the present application provides a method for characterizing the regulated power of electric vehicles and frequency support considering user characteristics and intentions, and this method includes the following steps S1 to S3.

[0030] S1: Describe the uncertainty factors related to electric vehicles based on the Gaussian mixture distribution method; among them, the uncertainty factors include the user behavior patterns of electric vehicles, battery capacity, charging and discharging rates.

[0031] The charging power of an individual electric vehicle user connected to the grid is determined by the charging pile, but the grid-connected power of an electric vehicle cluster (EVs) is often determined by the user characteristics of EVs. There are usually the following several methods to characterize the user characteristics of EVs: regression analysis, clustering analysis, decision tree, and machine learning algorithms. Currently, the research mainly uses normal probability distribution fitting. However, the user travel distribution has morning and evening peaks, and the grid-connected distribution also varies according to the peak-valley electricity price. Therefore, the normal distribution cannot accurately describe the multi-peak and peak-valley electricity price incentive phenomena, while the Gaussian mixture model (GMM) can accurately describe the multi-peak distribution. Therefore, in this embodiment, GMM is used to classify and fit according to the probability density function (P _arival_i ) established based on the actual data of the arrival time (T d ) and the initial state of charge (SOC) of EVs. Then, the specific probability distribution function P(T arrive_i |Θ) of the arrival time of the mth EV at the charging pile is:

[0032]

[0033] In the formula: N θ (T arrive_i ; μ θ , D θ ) is the θ th Gaussian component function, μ θ is the mean value, D θ is the variance; J is the total number of Gaussian components; α θ is the weight occupied by this Gaussian component; the expression of one-dimensional GMM: Θ = (θ1,..., θ J ). By determining T arrive_i , μ θ , D θ to fit the Gaussian mixture distribution, and J is set by the EV user himself before estimation and adjusted according to the actual fitting effect after estimation.;

[0034] Determine the probability density function of the initial energy of the electric vehicle Expressed as:

[0035]

[0036] Where: E i0 and SOC i0 are respectively the initial energy and initial SOC of the i th EV; is the mean of the i-th distribution; D θ is the variance of the probability density distribution function; τ i is the number of days that the EV can travel after charging; d iR is the driving range of the EV;

[0037] Estimate the parameters of the initial energy probability density function of the electric vehicle through historical data, and derive the density functions in SOC i0 and T arrive_i During simulation, randomly generate numbers according to each probability density through the Monte Carlo method, and obtain the departure time T _deprive (for a total of EV _Number units), depicting the complete user characteristics.

[0038] S2: Use the Logit model to predict the dispatch willingness of electric vehicles.

[0039] The Logit model is mainly used to analyze and predict the selection behavior of EV users when facing different charging strategies and grid demands in the electricity market environment, judge their willingness to accept grid dispatch according to the user characteristics, and determine their participation mode in grid regulation according to the magnitude of this willingness.

[0040] The Logit model is suitable for characterizing scenarios with binary outcomes (accept, reject), and can effectively present the influence of multiple variables such as charging status, dispatch subsidy incentives, and control flexibility on the decision-making process. To clearly understand how different factors affect the decision. Using the Logit probability model to characterize the dispatch willingness of EVs mainly includes the following steps:

[0041] S21: Consider the state of charge of the EV, the dispatch subsidy for each time period of the grid, and the residence time of the EV user in the charging area as multiple factors affecting the user's willingness. Therefore, the latent variable y i in the probability model of the user's dispatch willingness can be expressed as:

[0042] y i =α1 + β1SOC i0 +β2K EV_i +β3T on_i +u i (4)

[0043] where: y i is the latent variable of the decision-making willingness of i th ; α i is the coefficient of the reference probability distribution function; β1, β2, and β3 are the coefficients of each explanatory variable respectively; SOC i , K Ev_i , T on_i respectively represent the initial state of charge, dispatching subsidy incentive, and the residence time of the charging pile input by the user of the electric vehicle i th ; u i is the random error variable of the decision-making willingness;

[0044] In practical applications, the coefficients of each explanatory variable are usually obtained by performing Logit regression analysis on historical data using the mnrfit function. This function is specifically used to fit a multinomial logistic regression model and is applicable to parameter estimation with binary outcomes.

[0045] S22: Use the Logit function to transform the latent variable y i into the selection probability of the user. The probability functions for the EV to accept and not accept the grid dispatching are represented by Equation (5) and Equation (6) respectively. Among them, the latent variable y i in the dispatching willingness probability model has a value range of (-∞, +∞), and the value range of the dispatching willingness probability F(y i ) is (0, 1).

[0046]

[0047] where: Y i is the decision-making willingness variable of i th . Y i = 1 indicates that the user chooses to accept the grid dispatching, and Y i = 0 indicates that the user chooses to be idle. P i (Y i = 1) represents the probability of the selection decision Y made by the user, with a value range of (0, 1). P i (Y i = 0) represents the probability that the i-th electric vehicle user chooses not to accept the grid dispatching. F(Y i ) represents the cumulative distribution function of the decision-making willingness, and e represents the base of the natural logarithm.

[0048] This decision variable is affected by factors such as the state of charge, the dispatching subsidy at each time period of the grid, and the residence time of the EV user in the charging area. Among them, the dispatching subsidy has the highest weight in the willingness selection. It affects the willingness of users to participate in regulation in the logit model according to the different peak-valley electricity prices of each vehicle in EVs during each hour of their respective residence periods. Therefore, this embodiment is first based on Figure 2Considering the daily EV charging load curve of user characteristics, the peak and valley membership is divided using the trapezoidal membership function calculation method. p is the valley load threshold, T v is the peak load threshold, and the daily load curve is a set of data L = {l1,l2,...,l n}, where each l i Represents the grid load at a specific time point. The trapezoidal valley membership function is usually defined as four parameters, T pa ,T p ,T p ' and T pd , where [T p ,T p '] represents the core area with a membership degree of 1, and [T pa ,T p ] and [T p ',T pd ] is the rising edge and falling edge, and the membership increases from 0 to 1, or decreases from 1 to 0. The four parameters of the trapezoidal peak membership function are T va ,T v ,T v ' and T vd For peak-valley division, the peak load and valley load membership functions are shown in equations (7) and (8) respectively.

[0049]

[0050] Where M v (l) is the valley membership function representing the load curve at a certain moment. It is used to measure the closeness between the current load and the valley load. p (l i ) represents a certain moment l in the load curve i The peak membership function is used to measure the current load l i The degree of proximity to the peak load, l is the load value at a specific moment in the power grid load curve.

[0051] The peak-valley membership of the load in each period of the day is calculated using the trapezoidal peak-valley membership function as shown in Table 1.

[0052] Table 1 Peak-valley membership distribution based on EVs daily load curve

[0053] Time Peak membership degree Valley membership degree Time Peak membership degree Valley membership degree 1 0.64 0.14 13 0.77 0.96 2 0.54 0.24 14 0.76 0.95 3 0.48 0.32 15 0.76 0.96 4 0.44 0.39 16 0.85 0.93 5 0.40 0.45 17 0.94 0.89 6 0.41 0.52 18 0.98 0.79 7 0.44 0.55 19 0.98 0.66 8 0.51 0.63 20 0.98 0.54 9 0.55 0.71 21 0.93 0.42 10 0.61 0.77 22 0.91 0.32 11 0.69 0.87 23 0.84 0.23 12 0.76 0.96 24 0.72 0.14

[0054] S3: Characterize the electric vehicle regulation boundary considering user characteristics and user willingness, determine the user's regulation method by changing the upper and lower limits of electric vehicle power fluctuation, and determine the electric vehicle frequency regulation strategy.

[0055] In this embodiment, step S3 specifically includes:

[0056] S31: Determine the improved control strategy for EVs to participate in fast frequency regulation.

[0057] S32: Based on the improved control strategy for EVs to participate in fast frequency regulation, determine the rules (or strategies) for EVs to participate in power grid frequency regulation.

[0058] Among them, step S31 can be specifically implemented in the following manner:

[0059] Considering that the response time of the bidirectional converter in actual operation is usually in the millisecond level, and the electromagnetic transient process of electric vehicles is often ignored, the EV frequency regulation power expression can be expressed as:

[0060] ΔP EV =Δf·K EV (9)

[0061] In the formula: ΔP EVi represents the change in the charging and discharging power of the i-th electric vehicle, Δf represents the frequency deviation of the system, and K EVi represents the frequency response coefficient of the i-th electric vehicle;

[0062] According to formula (9), there is a linear relationship between the output power fluctuation of EV grid connection and the system frequency. The power-frequency droop characteristic curve of electric vehicles is as Figure 3 shown.

[0063] Figure 3 In, the black line is the droop characteristic of EV grid connection frequency regulation in the bidirectional V2G (bidirectional charging, BC) mode. In this mode, EVs can both charge and discharge, and its power fluctuation limit is shown in formulas (10) and (11). The red line represents the droop characteristic of EV grid connection frequency regulation in the unidirectional V2G (unidirectional charging, UC) mode. Both of them use K EV as the EV system frequency response coefficient for grid connection frequency regulation when the grid frequency fluctuates. However, the adjustment power required for grid frequency fluctuation is often greater than the power fluctuation range of EVs themselves. Therefore, the description of the adjustment power constraint directly affects the ability of EVs to participate in grid regulation. The upper and lower limits of the EV adjustment power constraint are:

[0064]

[0065]

[0066] In the formula: and are the upper limits of power fluctuation in the UC mode and the BC mode respectively, which are determined by the maximum charging and discharging power The charge and discharge power P of the previous sampling time BC , P UC The difference between the BC mode and the UC mode and the upper limit of the FM power The minimum value between the two determines is the lower limit of power fluctuation in BC mode and UC mode, which is calculated by the charge and discharge power P at the previous sampling time. BC , P UC Minimum charge and discharge power The difference between the BC mode and the UC mode and the lower limit of the FM power The maximum value between the two determines;

[0067] In BC mode and Also in UC mode The absolute value of is the maximum value of the charging and discharging power of the electric vehicle. The absolute value of is half of the maximum value of the electric vehicle charging and discharging power, and is 0;

[0068] According to the upper and lower limits of the electric vehicle power regulation, the expression of the change in the charging and discharging power of the electric vehicle is obtained:

[0069]

[0070] Where: ΔP EV K is the change in charging and discharging power of electric vehicles; EV is the response coefficient of the system frequency response of electric vehicles;

[0071] Taking into account user characteristics and user willingness, the EV regulation boundary is characterized, the real-time frequency regulation power of EV is constrained, and the user's frequency regulation method is determined by changing the upper and lower limits of EV power fluctuation, thereby accurately characterizing the EV frequency regulation characteristics.

[0072] Step S32 can be specifically implemented in the following manner:

[0073] In order to improve EV frequency regulation participation and its frequency regulation capability, this embodiment proposes a frequency regulation strategy that takes into account different user characteristics and intentions while ensuring that the overall trend of user charge status is on the rise.

[0074] EV participates in grid frequency regulation and reaches the end charging amount set by the user (SOC e )The required charging time is shown in formula (14).

[0075]

[0076] Where: T UC_i for i th The set SOC is achieved in UC mode eRequired charging time; ΔT UC_i For i th The charging time margin (in minutes) in the UC mode for i. This margin is intended to reduce the error that occurs when using the open circuit voltage (OCV) to replace the EV battery terminal voltage, and can compensate for the fluctuations caused by the mean deviation of the actual grid load fluctuations, so as to ensure that the calculated charging time is not underestimated. Finding the appropriate ΔT UC_i value needs to be obtained by the grid dispatching center based on long-term operation data analysis and calculation;

[0077] To accurately evaluate the charging willingness demand of EV users, considering the initial SOC (SOC i0 ), EV battery capacity, terminal SOC (SOC ie ), and the type of EV battery pack, etc., the calculation formula for the battery energy that needs to be supplemented is as follows:

[0078]

[0079] In the formula: E i represents the energy (in kWh) that needs to be supplemented for i th , S i is the number of power batteries connected in series for i th , OCV i is the open circuit voltage of a single battery for i th . The functional relationship between SOC i and OCV i is expressed as an example of lithium manganese oxide battery:

[0080]

[0081] Based on the charging time of the EV's required supplementary energy obtained from Equation (14), the actual grid connection start time, and the set termination time, the rules for the vehicle to participate in grid frequency modulation are determined. These rules aim to establish a model that improves user satisfaction, which can not only provide more user-friendly and reliable services for EV users, but also provide more accurate frequency modulation resources for grid operators.

[0082] Table 2 Rules for EVs to participate in grid frequency modulation

[0083]

[0084] In Table 2, T on_i is the frequency modulation control time of electric vehicle i th , that is, the difference between the termination time t e_i set by this EV user and the grid connection start time t b_i . When mode = 1, it means working in the unidirectional V2G mode, and when mode = 2, it means working in the bidirectional V2G mode.

[0085] To verify the reliability of the service provided to individual users by the EV participation in the grid regulation rules, the incentive effect of the Logit model on user willingness, the accuracy of the GMM model in characterizing user features, and the effect of the regulation strategy on controlling the load frequency fluctuation, this embodiment uses matlab / simulink to conduct simulation verification with a single-area system as an example. Predict the total load peak P of the system LOAD_max Obtained by the GMM method based on actual data, with 3000 simulated electric vehicles. The EV vehicle type for grid-connected frequency regulation control is a pure electric vehicle, and the power battery type is a lithium manganate battery. The relevant parameters of the remaining EVs participating in the grid frequency control are shown in Table 3, where the real-time regulation power characterization parameters of the electric vehicles are typical values of a single-area power system (please refer to the literature "Prabha K. Power System Stability and Control [M]. Beijing: China Electric Power Press, 2002."). The electric vehicle battery parameters are provided by the literature "Bao Yan, Jiang Jiuchun, Zhang Weige, etc. Research on the Model and Control Strategy of Electric Vehicle Mobile Energy Storage System [J]. Automation of Electric Power Systems. 2012, 36(22): 3643." The electricity prices during the peak period (11:00 - 24:00), valley period (3:00 - 9:00), and normal period (24:00 - 3:00, 9:00 - 11:00) are: peak period (1.0044), valley period (0.6950), normal period (0.4532).

[0086] Table 3 Relevant parameters of EVs participating in grid frequency control

[0087] Parameter Parameter <![CDATA[System power generation P G / MW]]> 300 <![CDATA[Reference power P base / MW]]> 300 <![CDATA[Reference frequency f base / Hz]]> 50 <![CDATA[Governor time constant T g / s]]> 0.2 <![CDATA[Prime mover time constant T ch / s]]> 0.3 Inertia time constant M / s 10 Load-damping constant D 1 Unit regulation rate R / (%Hz / MW(pu)) 5 Regional frequency response coefficient B / (MW / Hz(pu)) 21 EV battery capacity Q / A·h 80 Number of series-connected power batteries S 80 Internal resistance of single battery cell R / mΩ 0.8 <![CDATA[SOC maximum value SOC max (%)]]> 100 <![CDATA[SOC minimum value SOC min (%)]]> 20 <![CDATA[Absolute value of the maximum charge and discharge power of the EV, P max / MW (pu)]]> 3×10－5 <![CDATA[Upper limit of power grid frequency fluctuation value Δf max / Hz]]> 0.1 <![CDATA[Lower limit of power grid frequency fluctuation value Δf min / Hz]]> －0.1 <![CDATA[EV frequency response coefficient K EV / (MW / Hz(pu))]]> －3×10－3 <![CDATA[EV time constant T EV / ms]]> 0.3

[0088] The real-time regulation power characterization of EVs is mainly divided into 3 scenarios:

[0089] Scenario 1: Characterization of EV charging power considering user characteristics;

[0090] Scenario 2: Characterization of EV charging and discharging power considering the charge and discharge control strategy;

[0091] Scenario 3: Characterization of EV charging and discharging power considering user willingness and the charge and discharge control strategy.

[0092] Figure 4 It is the EV real-time power output diagram under different scenarios. Combining the foregoing Figure 1 The load curve of normal power output and Figure 4 Compared with the (a) EV charge and discharge power curve considering the charge and discharge control strategy in it, the load power generated by the EV cluster participating in the grid frequency modulation is significantly smaller, reducing the load power intensity by 41%, and the load rate is increased by 5.23%. Figure 4(b) is the EV charging and discharging power curve that takes into account user willingness and charging and discharging control strategy. The data results show that the charging and discharging control method of scenario 3 effectively reduces the load power generated by the EV cluster. At the same time, based on the electricity price incentive provided by the peak and valley charging of EVs daily load curve, scenario 3 provides reverse power when the grid demand is peak.

[0093] Figure 5 Schematic diagram of the adjustable power capability of EVs in scenarios 2 and 3. Figure 5 (a) and Figure 5 (c) shows that the EV regulation power in scenario 2 can provide upward adjustable power during the period of excess renewable energy production from about 11 noon to 12 midnight, absorbing the excess electricity generated when the wind and solar radiation are strong. By characterizing the upward regulation power reserve, the electricity during the off-peak period can be absorbed in a planned manner and released during the peak period. Therefore, this is also a characterization of the frequency regulation capability of EVs. It helps to reduce the high-priced peak electricity that needs to be purchased, thereby reducing the overall operating cost.

[0094] Figure 5 (b) and Figure 5 (d) shows that its downward adjustment power curve has two peaks, namely the noon peak and the evening peak, which can increase the EV cluster to quickly release the stored power to the grid during the period when the grid load demand is high and the fluctuation is large. Therefore, the EV cluster can be regarded as a distributed load buffer device, providing power support within the difference between the real-time power and the upper and lower boundaries, balancing the supply and demand relationship, and thus improving the stability of the grid.

[0095] Figure 5 (c) and Figure 5 (d) and Figure 5 (a) and Figure 5 The difference in (d) lies mainly in the different electricity price incentives. According to the simulation results of the Logit model, users are more willing to participate in grid regulation during periods with higher peak-valley membership, which not only has a good effect on real-time power regulation, but also improves the reserve reserve for upward and downward power regulation.

[0096] Figure 6 The SOC change curve of a typical EV during the stay time in scenario 2 and scenario 3. Figure 6 As can be seen in (a), the control strategy proposed in this embodiment can effectively utilize the surplus EV residence time to flexibly increase or decrease the EV charging power to participate in grid frequency regulation without affecting user travel. Figure 6In Figure (b), it is the change curve of the SOC of the EV within the residence time considering the control strategy and the schedulable willingness of users. On the premise of meeting the basic vehicle - using needs of users, through the logit model, users are encouraged to participate in reverse charging of the power grid as much as possible to obtain relevant subsidies. After users participate in grid regulation, they can not only reduce expenses, but even obtain relevant economic benefits. This not only meets the frequency - modulation requirements of the power grid, but also rewards users, getting rid of the game between the main body and individuals in the traditional method and achieving a win - win situation.

[0097] This embodiment also conducts a visual analysis of the main decision - making factors affecting the participation of electric vehicles in power - grid frequency modulation. First, from an overall perspective, the impact of different electricity prices on the boundary of the EV's ability to participate in power - grid frequency modulation is analyzed; second, from an individual perspective, the correlations between the different initial SOCs and waiting times of EV users and the proportion of their time participating in power - grid frequency modulation are studied respectively. Specifically, it includes the following three aspects.

[0098] The first aspect is the impact of different electricity - price incentives on the participation of EVs in frequency modulation.

[0099] Figure 7 It is a comparison chart of the impact effects of three different electricity prices on the charging and discharging power boundaries of EVs in Scenario 3. From the lines in the figure, it can be seen that the electricity - price incentive, which is the weight decision variable of the Logit model proposed in this embodiment, is of great significance for depicting the charging and discharging power boundaries of EVs. When the electricity price is low, the charging demand is strong, the upper power boundary is higher and the lower boundary is lower. When the electricity price is high, the charging demand is weak, and the EV discharges electricity back to the power grid. The lower power boundary is higher and the upper boundary is lower. The time - of - use electricity price obtained by solving the peak - valley membership degree takes into account the advantages of both high and low electricity prices, ensuring the economy of power - grid power supply while reducing the power fluctuation amount.

[0100] The second aspect is the impact of the initial SOC of EVs on their participation in power - grid frequency modulation.

[0101] As Figure 8 shown, its abscissa is the percentage of the initial SOC, and the ordinate is the weighted average of the ratio of the time that all EV individuals in this percentage interval participate in frequency modulation to the waiting time of this individual at the charging pile. Figure 8 In Figure, the sampling of EV individuals with an initial SOC of 100% is less, and the characteristic of the participation - in - frequency - modulation ratio is obvious. The trend of the remaining data also reflects that there is a positive correlation between the size of the initial SOC and the proportion of the time that EVs participate in power - grid frequency modulation, that is, as the initial SOC of electric vehicles increases, the ability of EVs to participate in power - grid frequency modulation increases significantly.

[0102] The third aspect is the impact of the residence time of EVs on their participation in frequency modulation.

[0103] As Figure 9As shown, EVs with a longer residence time are more likely to be included in the frequency regulation service. A larger time window is provided to participate in the demand response and frequency regulation service of the power grid. This means that the battery has sufficient time to reach the ideal charging state, thereby maximizing its effectiveness when responding to the frequency regulation signal.

[0104] In this embodiment, a correlation analysis is also performed.

[0105] Based on SPSS, the correlation analysis results are as Figure 10 and Figure 11 shown. Figure 10 is a variable scatter plot matrix. The main diagonal is a bar chart showing the proportion of the quantity distribution of 3,000 EV individuals in their respective decision variables, and the non-diagonal elements are the scatter plots of their mutual correlations. Through the first two plots in the third column, it can be intuitively analyzed that the initial SOC and waiting time are positively correlated with the proportion of participating in frequency regulation. Figure 11 is a matrix correlation heat map. Through the color scale analysis, it can be known that although both the initial SOC and waiting time are positively correlated with the proportion of participating in frequency regulation. However, the correlation coefficient of the initial SOC is 0.6024, which is higher than 0.3937 of the waiting time, showing a stronger correlation.

[0106] Combined with the above examples and corresponding analysis, it can be seen that when adopting the EV-assisted frequency regulation control strategy, although the system frequency and active power output fluctuations are improved to a certain extent compared with the conventional charging scenario, compared with the control strategy considering user willingness, its frequency regulation effect is poor. The reason is that the simple EV-assisted control scenario does not consider the influence of electricity price incentives and does not take into account that the benefits obtained by users during charging will greatly increase user willingness, restricting the frequency regulation potential of the EV cluster. In the design of the EV frequency regulation control strategy in this embodiment, through the real-time state feedback of user characteristics and user willingness and the compensation of external disturbances, the EV cluster can respond more flexibly to the changes in the power grid frequency, reducing the power grid frequency deviation range and suppressing the fluctuations of the active power output of the prime mover Figure 12 In (a), it shows the comparison curve of the regulation power provided by the power grid for Scenario 2 and Scenario 3. It can be seen from this that the difference in the regulation capabilities provided by Scenario 3 and Scenario 2 during the peak period from 11 am to 11 pm is significantly higher than that during the valley period and normal period. Figure 12 In (b), it gives the influence of the active power output of three scenarios, namely conventional charging, considering EV-assisted frequency regulation, and considering the user willingness model, on the system frequency change characteristics.

[0107] Combined with the calculation results of the simulation indicators, it can be seen that Scenario 3 shows excellent frequency regulation performance in terms of various frequency indicators. When the improved method is adopted for the EV cluster, Δf max and Δf min are reduced by 13.91% and 29.27% respectively compared with Scenario 2 and Scenario 1. In addition, from the system Δf RMSIn terms of the angle, the improved method reduces by 11.13% and 29.59% respectively compared with the traditional EV auxiliary control and the non-frequency modulation control; from the time scale of Δf dtime [±0.033], in terms of the time scale, the improved method shows the shortest duration and shortens the frequency deviation time by 18.21% and 42.69% respectively compared with the other two control methods; from the perspective of COVAR(f sys ,P EV ), the degree of system frequency fluctuation caused by the active power fluctuation of the EV cluster under the improved method is reduced by 17.76% and 41.30% respectively; in addition, from the Figure 12 frequency deviation PDF shown in (c) of, the probability that the frequency deviation of the improved method is concentrated in the range of ±0.033Hz is greater than that of the other two control methods. It effectively prevents the generation of new frequency spikes during the frequency modulation period.

[0108] In summary, based on the premise of considering different user characteristics and user needs, this application proposes a method for characterizing the real-time regulation power of EVs and frequency support that takes into account the uncertain characteristics of users and the willingness of users to be dispatched. Through the Gaussian mixture distribution and the Logit model, a charging scheme that takes into account both auxiliary frequency control and user satisfaction is formulated, and the following conclusions are obtained:

[0109] 1) By combining the consideration of user characteristics and user willingness, the regulation boundary of EVs is characterized, the real-time frequency modulation power of EVs is constrained, and the user frequency modulation method is determined by changing the upper and lower limits of the EV power fluctuation, so as to accurately characterize the EV frequency modulation boundary.

[0110] 2) Based on SPSS, a significance analysis is carried out on the influencing factors of EVs participating in power grid frequency modulation. It is found that the initial SOC of EV users, the waiting time at the charging pile, and the electricity price incentive are the three most important decision variables affecting the behavior of EVs participating in power grid frequency modulation. From an overall perspective, this paper studies the influence of the perturbation electricity price on the boundary of the EV's ability to participate in power grid frequency modulation; from an individual perspective, and combined with the correlation coefficient, it is found that the initial SOC has a stronger correlation with the proportion of the EV individual's participation in frequency modulation time compared with the waiting time.

[0111] 3) The proposed electric vehicle frequency modulation strategy that ensures the overall trend of the user's state of charge shows an upward trend and takes into account the uncertain characteristics of users and the willingness of users to be dispatched ensures the overall upward trend of the SOC of EV users during the participation in the regulation process. By considering the user's willingness, the fast frequency improvement control strategy is optimized, effectively reducing the fluctuation of the load power, and making the frequency deviation as concentrated as possible in the range of ±0.033Hz.

[0112] The above embodiments are only used to illustrate the present application and are not intended to limit the present application. Those of ordinary skill in the relevant technical field can also make various changes and modifications without departing from the spirit and scope of the present application. Therefore, all equivalent technical solutions also fall within the scope of the present application, and the patent protection scope of the present application shall be defined by the claims.

Claims

1. A method for regulating power characterization and frequency support of electric vehicles considering user characteristics and intentions, characterized in that: The method includes: Describing the uncertainty factors related to electric vehicles based on the Gaussian mixture distribution method; wherein, the uncertainty factors include the user behavior pattern, battery capacity, charging and discharging rate of electric vehicles; Predicting the dispatchable willingness of electric vehicles using the Logit model; Characterizing the regulation boundary of electric vehicles considering user characteristics and user willingness, determining the user's regulation method by changing the upper and lower limits of the power fluctuation of electric vehicles, and determining the frequency modulation strategy of electric vehicles, including: Establishing an expression for the frequency modulation power of electric vehicles, expressed as: ΔP EVi = Δf·K EVi (9) Where: ΔP EVi represents the change in the charging and discharging power of the i-th electric vehicle, Δf represents the frequency deviation of the system, and K EVi represents the frequency response coefficient of the i-th electric vehicle; Establishing the upper and lower limit constraints of the regulation power of electric vehicles, expressed as: Wherein: and are respectively the upper limits of power fluctuations in the UC mode and the BC mode, which are determined by the maximum charging and discharging power and the difference between the charging and discharging power P BC , P UC at the previous sampling time, and the minimum value between the upper limit values of frequency modulation power in the BC mode and the UC mode ; is the lower limit of power fluctuations in the BC mode and the UC mode, which is determined by the difference between the charging and discharging power P BC , P UC at the previous sampling time and the minimum charging and discharging power , and the maximum value between the lower limit values of frequency modulation power in the BC mode and the UC mode ; Under the BC mode and also under the UC mode The absolute value of is the maximum value of the charging and discharging power of the electric vehicle, The absolute value of is half of the maximum value of the charging and discharging power of the electric vehicle, and is 0; Obtaining an expression for the change in the charging and discharging power of electric vehicles according to the upper and lower limit constraints of the regulation power of electric vehicles: where: ΔP EV is the change in the charging and discharging power of the electric vehicle; K EV is the response coefficient of the electric vehicle participating in the system frequency response; Combining the consideration of user characteristics and user willingness, characterizing the regulation boundary of electric vehicles, constraining the real-time frequency modulation power of electric vehicles, and determining the frequency modulation method of users by changing the upper and lower limits of the power fluctuation of electric vehicles to characterize the frequency modulation characteristics of electric vehicles; Determining the frequency modulation strategy of electric vehicles based on the frequency modulation characteristics of electric vehicles.

2. The electric vehicle power regulation characterization and frequency support method considering user characteristics and intentions as claimed in claim 1 is characterized in that: Describing the uncertainty factors related to electric vehicles based on the Gaussian mixture distribution method, including: Based on the Gaussian mixture model, classify and fit according to the probability density function established from the actual data of the arrival time T of electric vehicles _arival_i and the initial state of charge. Then the specific probability distribution function P(T arrive_i |Θ) of the arrival time of the i-th electric vehicle at the charging pile is as follows: Where: N θ (T arrive_i ; μ θ , D θ ) is the θ th Gaussian component function, μ θ is the mean value, D θ is the variance; J is the total number of Gaussian components; α θ is the weight of this Gaussian component; The expression of one-dimensional GMM: Θ = (θ1,..., θ J ), by determining T arrive_i , μ θ , D θ to fit the Gaussian mixture distribution, and J is set by the EV user himself before estimation and adjusted according to the actual fitting effect after estimation; Determine the probability density function of the initial energy of an electric vehicle It is expressed as: Where: E i0 and SOC i0 are the initial energy and initial SOC of the i th th EV, respectively; is the mean of the i θ th distribution; D i is the variance of the probability density distribution function; τ i is the number of days the EV can travel after charging; d iR is the driving range of the EV. Estimate the parameters of the probability density function of the initial energy of the electric vehicle through historical data, and fit to obtain the SOC i0 and T arrive_i Derive the density function in. During simulation, draw random numbers according to each probability density through the Monte Carlo method, and at the same time obtain the departure time T according to the dwell time set by the user _deprive , depicting the complete user characteristics.

3. The electric vehicle power regulation characterization and frequency support method considering user characteristics and intentions as claimed in claim 2 is characterized in that: Predicting the dispatchable willingness of electric vehicles using the Logit model, including: Determining the latent variables in the probability model of user dispatch willingness based on the state of charge of electric vehicles, the dispatching subsidies in each period of the power grid, and the residence time of electric vehicle users in the charging area; Converting the latent variables into the selection probabilities of users using the Logit function; wherein, the selection probabilities of users are characterized by the probability functions of electric vehicles accepting and not accepting grid dispatching.

4. The electric vehicle power regulation characterization and frequency support method considering user characteristics and intentions as claimed in claim 3 is characterized in that: Based on the state of charge of electric vehicles, the dispatching subsidies in each period of the power grid, and the residence time of electric vehicle users in the charging area, determining the latent variables in the probability model of user dispatch willingness through the following formula: y i = α1 + β1SOC i0 + β2K EV_i + β3T on_i + u i (4) where: y i is the latent variable of the decision-making willingness of i th , and i th is the electric vehicle; α i is the coefficient of the reference probability distribution function; β1, β2, and β3 are the coefficients of each explanatory variable; SOC i , K Ev_i , T on_i respectively represent the initial state of charge, dispatching subsidy incentive, and the residence time of the charging pile input by the user of the electric vehicle i th . u i is a random error variable for the decision-making willingness.

5. The electric vehicle power regulation characterization and frequency support method considering user characteristics and intentions as claimed in claim 4 is characterized in that: The selection probabilities of users are respectively expressed by the probability functions of electric vehicles accepting and not accepting grid dispatching as: Where: Y i is the decision willingness variable for i th , Y i = 1 indicates that the user chooses to accept the grid dispatching, Y i = 0 indicates that the user chooses to be idle, P i (Y i = 1) represents the probability of the choice decision Y made by the user, and the value range is (0, 1), P i (Y i = 0) represents the probability that the i-th electric vehicle user chooses not to accept the grid dispatching, F(Y i ) represents the cumulative distribution function of the decision willingness, and e represents the base of the natural logarithm; The decision willingness variable is affected by the state of charge, the dispatching subsidies in each period of the power grid, and the residence time of electric vehicle users in the charging area; among them, the dispatching subsidy has the highest proportion of the willingness selection weight, and affects the willingness of users to participate in regulation in the Logit model according to the different peak-valley electricity prices of each vehicle in the electric vehicle cluster during each hour period of their respective residence periods.

6. The electric vehicle power regulation characterization and frequency support method considering user characteristics and intentions as claimed in claim 5 is characterized in that: Affecting the willingness of users to participate in regulation in the Logit model according to the different peak-valley electricity prices of each vehicle in the electric vehicle cluster during each hour period of their respective residence periods, including: First, based on the daily load curve of electric vehicle charging considering user characteristics, the peak-valley membership degree is divided using the method of calculating with the trapezoidal membership function; Taking T p as the valley load threshold value and T v as the peak load threshold value, the daily load curve is a set of data L = {l1, l2,..., l i ..., l n}, where each l i represents the grid load at a set time point; Define the trapezoidal valley membership function with four parameters, namely T pa , T p , T p ’, and T pd ; where [T p , T p ’] represents the core region with a membership degree of 1, and [T pa , T p and [T p ’, T pd are the rising edge and the falling edge, and the membership degree increases from 0 to 1, or decreases from 1 to 0; The four parameters for setting the trapezoidal peak membership function are T va , T v , T v ’, and T vd ; For the peak-valley division, the membership functions of the peak load and the valley load are respectively shown in Equations (7) and (8): Where M v (l) is the membership function of the valley value at a certain moment in the load curve, which is used to measure the proximity of the current load to the valley load. M p (l i ) is the membership function of the peak value at a certain moment l i in the load curve; it is used to measure the proximity of the current load l i to the peak load, and l is the load value at a specific moment in the power grid load curve.

7. The electric vehicle power regulation characterization and frequency support method considering user characteristics and intentions as claimed in claim 1 is characterized in that: Determining the frequency modulation strategy of electric vehicles based on the frequency modulation characteristics of electric vehicles, including: Calculating the charging time required for electric vehicles to participate in grid frequency modulation to reach the set termination charging amount through the following formula: Where: T UC_i is the charging time required for electric vehicle i th to reach the set termination charging amount SOC ie under the UC mode; ΔT UC_i is the charging time margin of the i-th electric vehicle under the UC mode; E i is the energy to be supplemented for i th ; Taking into account the initial state of charge (SOC) i0 , the battery capacity of the electric vehicle, and the end state of charge (SOC) ie , the type of the electric vehicle battery pack, calculate the battery energy to be supplemented through the following formula: Where: S i is the number of power batteries connected in series, and OCV th is the open circuit voltage of a single battery of i i th ; Determining the strategy of the electric vehicle participating in grid frequency modulation according to the charging time required for the electric vehicle to supplement the required energy obtained from Equation (14), the actual grid connection start time, and the set termination time.

8. The electric vehicle power regulation characterization and frequency support method considering user characteristics and intentions as claimed in claim 7 is characterized in that: The strategy of the electric vehicle participating in grid frequency modulation is: If T UC_i ≥ T on_i , then mode = 2 until t e_i ; where T on_i is the frequency modulation control time of electric vehicle i th , and when mode = 2, it means working in the BC mode, and t e_i is the termination time; If SOC0 < 50% and T UC_i < T on_i , then mode = 1 until SOC = 50%, then mode = 2 for T on_i - T UC_i , then mode = 1 until t e_i moment; where SOC represents the state of charge of the electric vehicle battery; where mode = 1 indicates working in the UC mode; If T UC_i <T on_i and SOC0 ≥ 50%, then compare whether the time used from SOC = 50% to SOC0 is greater than the time taken to charge from SOC = 50% to SOC0 at the charging power P max / 2 If T ≥ T pmax / 2 , then mode = 2; where T represents the actual time required for the electric vehicle to reach the target SOC from the current SOC, and T pmax / 2 represents the theoretical time required to reach the target SOC from SOC = 50% when charging at 50% of the peak charging power - P max / 2 ; If SOC0≥50%, T≥T pmax / 2 and T UC_i <T on_i , then mode = 2 for a duration of T on_i -T u , and then mode = 1 until time t e ; where t e represents the termination time of the electric vehicle in participating in the grid frequency regulation or charging / discharging task.

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