Electric vehicle charging and discharging system and method based on elastic demand response

Abstract

The invention provides an electric vehicle charging and discharging system and method based on elastic demand response. The method comprises the steps that a day-ahead reservation type user load model, a wind power generation model, a solar power generation model, an energy storage system model and an equivalent load model are constructed; the day-ahead reservation type user load is constrained, and an intra-day demand response type user load model is established; building a user fast and slow charging selection model according to the intraday demand response type user load model, and carrying out charging and discharging power constraint and capacity constraint on the user fast and slow charging selection model; and constructing a target function about the day-ahead reservation type user load model and the intra-day demand response type user load model by taking the minimum user charging price and the maximum charging amount as targets, and solving the target function to obtain an optimal charging and discharging strategy. According to the invention, the charging cost of the user can be effectively reduced, the charging satisfaction degree of the user can be improved, and renewable energy sources can be effectively consumed.

Description

Technical Field

[0001] The present invention relates to the technical field of electric vehicle charging and discharging control, and in particular to an electric vehicle charging and discharging system and method based on elastic demand response. Background Art

[0002] As energy and environmental issues become increasingly prominent, new energy has attracted widespread attention. Developing the electric vehicle industry is one of the effective ways to alleviate the shortage of traditional energy and reduce environmental pollution. Electric vehicles are green, environmentally friendly, energy-saving, and have low carbon emissions. In the vehicle-to-grid (V2G) mode, electric vehicles have the dual attributes of source and load. Different from the traditional energy and grid interaction mode, V2G realizes the two-way flow of energy and information, which can better serve the grid side and the user side.

[0003] However, the disorderly access of large-scale electric vehicles to the power grid will bring a new round of challenges to the safe and stable operation of the power grid. In the absence of an active and effective dispatching strategy, it will further bring more negative impacts on the safe and stable operation of the power grid. Electric vehicles, because of their dual attributes of source and load, can be used as a load to promote the consumption of new energy, and as a power source to reduce the load pressure of the power grid. The essence of the optimal dispatching of electric vehicle charging and discharging is to control the charging behavior of electric vehicles in the power grid when the power grid is under low load and the discharging behavior of electric vehicles in the power grid when the power grid is under high load. However, directly integrating EVs into the power grid cannot achieve the real low-carbon goal, and widespread access to the grid may also lead to an increase in peak load, which will have a negative impact on the safety and stability of the system. Summary of the invention

[0004] The purpose of the present invention is to provide an electric vehicle charging and discharging system and method based on elastic demand response, aiming to solve the problem that the traditional charging and discharging strategy has an increased peak load and is likely to have a negative impact on the safety and stability of the system.

[0005] In a first aspect, the present invention provides an electric vehicle charging and discharging method based on elastic demand response, the method comprising:

[0006] Construct a load model for day-ahead reservation users, a wind power generation model, a solar power generation model, an energy storage system model, and an equivalent load model;

[0007] Constrain the load of day-ahead reservation users and establish a load model for intraday demand response users;

[0008] A user fast or slow charging selection model is constructed according to the intraday demand response user load model, and charging and discharging power constraints and capacity constraints are imposed on the user fast or slow charging selection model;

[0009] The objective functions of the day-ahead reservation user load model and the intraday demand response user load model are constructed with the goal of minimizing the user charging price and maximizing the charging amount, and the objective functions are solved to obtain the optimal charging and discharging strategy.

[0010] In summary, according to the above-mentioned electric vehicle charging and discharging method based on elastic demand response, the orderly charging and discharging optimization model of electric vehicles in two stages of the day before and within the day considering the elastic demand response can give full play to the scheduling role of electric vehicles and energy storage systems, and cooperate with the peak-valley time-of-use electricity price and the elastic demand response model, which can effectively reduce the user's charging cost and improve the user's charging satisfaction, and can also effectively consume renewable energy. First, a day-ahead reservation user model is established. Through the user's reservation the day before, the start charging time and the end charging time are reported. The power grid obtains the total load of this type of user charging by reading the user's initial SOC, and then obtains the equivalent load predicted by the day before according to the wind and solar output. Through the electricity price response model, the user load that changes with the change of electricity price after the implementation of the peak-valley electricity price is calculated. After establishing the user weight selection model, the elastic demand response model is used to realize the two-way flow of energy between the intraday demand response EV and the power grid. Finally, under the condition of ensuring user charging satisfaction and peak-valley difference, the optimal model of EV charging and discharging power in the power grid is calculated.

[0011] Furthermore, the steps of constructing a day-ahead reservation user load model, a wind power generation model, a solar power generation model, an energy storage system model, and an equivalent load model include:

[0012] The steps to construct a day-ahead reservation user load model include:

[0013] In the day-ahead stage, the EV charging start time and charging end time reported by the user to the charging station are obtained, and the charging load of the day-ahead reservation users is calculated based on the charging power, so as to simulate the dynamic behavior of EV charging selection based on the charging start time, charging end time and charge state of multiple day-ahead reservation users, specifically:

[0014] EV starts charging at the station T s It obeys normal distribution, and its probability density function is:

[0015]

[0016] in, Indicates about T s The probability density function, μ c,s , σ c,s T s The mean and standard deviation of

[0017] EV charging end time T eIt obeys normal distribution, and its probability density function is:

[0018]

[0019] in, Indicates about T e The probability density function, μ c,e , σ c,e T e The mean and standard deviation of

[0020] Initial state of charge of an EV when it enters a charging station It obeys uniform distribution, and its probability density function is:

[0021]

[0022] in, Indicates about The probability density function of is, a and b are The lower and upper limits of

[0023] A solar power generation model is constructed based on light intensity and photovoltaic output, where:

[0024] The light intensity r follows the Beta distribution, and its probability density function is:

[0025]

[0026] Among them, Γ() is the Gamma function, p is an integer variable, f r (r) represents the probability density function of the light intensity r, r max Indicates the limit value of light intensity;

[0027] and are all shape parameters, and their expressions are:

[0028]

[0029] u pv and σ pv Respectively represent the expectation and standard deviation of the ratio of actual data to the maximum value;

[0030] About photovoltaic output P PV The probability density function of is:

[0031]

[0032] P PV = rA PV η PV

[0033] Among them, A PV and η PV represent the photovoltaic radiation area and conversion efficiency respectively, Indicates the maximum power output of the photovoltaic panel;

[0034] The steps to build a wind power generation model include:

[0035] The wind speed follows the Weibull distribution, and the probability density function of the wind speed is:

[0036]

[0037] f w (v) represents the probability density function of wind speed, v, k, c are the actual wind speed, shape factor and scale factor respectively, and the power output of the wind turbine P WT for:

[0038]

[0039] The power output of the wind turbine is P WT The probability density function of fever is:

[0040]

[0041] Among them, v rated and P rated are rated wind speed and WT rated power output, v in and v out are the cut-in wind speed and cut-out wind speed respectively, h=(v rated / v in -1);

[0042] The energy storage system model is constructed according to the following formula:

[0043]

[0044] Among them, C t+1 , C t Respectively represent the capacity of the energy storage device at time t+1 and time t, k loss represents the loss rate factor of the energy storage device, η ch and η dc They represent the charging efficiency and discharging efficiency of the energy storage device respectively. and They represent the charging power and discharging power of the energy storage device at time t, respectively, and Δt represents a time scale;

[0045] The equivalent load model is constructed according to the following formula:

[0046] P t eq =Pt FEV +P t load -P t PV -P t WT

[0047] Among them, P t load represents the basic load of the line at time t, P t eq represents the equivalent load at time t, P t FEV represents the sum of the charging loads of EVs at time t, represents the photovoltaic output at time t, represents the power output by the wind turbine at time t.

[0048] Furthermore, the charging power constraint for the day ahead is performed according to the following formula:

[0049]

[0050] in, is the charging power of the i-th vehicle at time t, is the maximum value of EV charging power, P t FEV is the sum of EV charging loads at time t, represents the charging status of the i-th vehicle of the reservation user at time t. When charging, the value is 1, otherwise the value is 0, η EV is the charging power of the EV, is the charging power expected by the user of the i-th EV; S EV is the rated capacity of the EV battery, is the power of the i-th EV when it leaves the charging station among the users who respond to charging and discharging during the day, ω i is the travel power consumption of the i-th EV, kWh / km, l i is the driving distance of the next trip of the i-th EV;

[0051] The daily demand response user load model is constructed according to the following formula:

[0052]

[0053]

[0054] Among them, R f,i is the remaining power satisfaction of the i-th EV, is the power of the i-th EV when it arrives at the charging station, F cd,i (R f,i) is R f,i The membership function has a value range of [0, 1], l and u are the lower and upper limits of the fuzzy charging demand set, respectively. is the change in vehicle SOC before and after the i-th EV participates in the coordinated charging and discharging optimization, is the change in payment before and after the i-th EV participates in the coordinated charging and discharging optimization, The maximum amount of money paid for EVs not participating in coordinated charging, SOC max is the upper limit of EV state of charge, Participate in the coordination of charging and discharging for the i-th EV to optimize the SOC when off-grid. The SOC of the i-th EV participating in the coordinated charging and discharging optimization when entering the grid, S f For fixed electricity prices, is the charge and discharge control coefficient of the response user, C b This is the maximum charge capacity.

[0055] Furthermore, the step of constructing a user fast or slow charging selection model according to the intra-day demand response user load model, and performing charging and discharging power constraints and capacity constraints on the user fast or slow charging selection model includes:

[0056] The SOC of EV at any time is within the specified range [ε, 1], satisfying the following equation:

[0057]

[0058] Among them, P i c and Respectively represent charging power and charging time, and Respectively represent the charging power of slow charging and fast charging, and Respectively represent the charging time of slow charging and fast charging;

[0059] The daily EV charging power is calculated using the following formula:

[0060]

[0061] in, represents the parking time, t y1 Indicates fast charging time;

[0062] The daily charging and discharging power constraints are performed according to the following formula:

[0063]

[0064] The capacity and state of charge constraints within the day are calculated according to the following formula:

[0065]

[0066] in, and They represent the charging and discharging power of the i-th EV at time t, and Respectively represent the maximum value of EV charging and discharging power, represents the SOC of the i-th EV at time t, SOC max and SOC min Respectively represent the upper and lower limits of EV battery capacity.

[0067] Furthermore, the step of constructing an objective function of the day-ahead reservation user load model and the intraday demand response user load model with the goal of minimizing the user charging price and maximizing the charging amount includes:

[0068] The objective function 1 is constructed according to the following formula:

[0069]

[0070] The objective function 2 is constructed according to the following formula:

[0071]

[0072] in, is the peak and valley price of charging and discharging during period t, in yuan / kWh, They are the peak, flat and valley charging prices, in yuan / kWh. are the discharge electricity prices during peak, flat and valley periods, in yuan / kWh. and They are the time of EV accessing the grid and the time of leaving the grid, minF1 represents minimizing the user charging price, and maxF2 represents maximizing the charging amount;

[0073] The objective functions F1 and F2 are normalized using the linear weighted sum method:

[0074]

[0075] Among them, F is the multi-objective optimization function of electric vehicle charging and discharging, F1 max 、F2 max and F1 min 、F2 min are the maximum and minimum values ​​of the single objective functions F1 and F2, respectively, i,1 , i,2 are the optimization weights of the single objective functions F1 and F2 respectively;

[0076] The optimization weight is calculated according to the following formula:

[0077]

[0078] λ2=1-λ1

[0079] Among them, floor means rounding down.

[0080] Furthermore, the step of solving the objective function to obtain the optimal charging and discharging strategy includes:

[0081] Step 1: sort the grid loads in each period of EV grid connection in ascending order from large to small;

[0082] Step 2: EV peak-valley period division: The proportion of peak-valley period, flat-valley period and low-valley period of each EV in a day is the same;

[0083] Step 3: Set the EV to charge at rated power during the off-peak period, as shown in the following formula:

[0084]

[0085] During the peak and valley periods when optimizing the charge and discharge control coefficient, the particle swarm is initialized as a group of random particles, and then the optimal solution is found through iteration. In each iteration of the n-iterative optimization process, each population consists of N particles, and the initial position and velocity of the i-th particle are vectors of dimension NS:

[0086]

[0087] In the formula and are the position and velocity of the i-th particle during the optimization process;

[0088] Step 4: Charge and discharge control coefficients initialized according to the electric vehicle Calculate the virtual SOC after the charging and discharging behavior of each period is completed:

[0089]

[0090] When the virtual SOC in a given time period exceeds the upper limit SOC max , and when the time period is a peak or flat time period, the time period is corrected to rated power discharge; when the cycle is a valley cycle, the charge and discharge control coefficient is corrected to 0:

[0091]

[0092] When the virtual SOC in a given cycle is less than the lower limit SOC min When the cycle is peak, flat or valley, the charge and discharge control coefficient is corrected and charged at rated power:

[0093]

[0094] After the charge and discharge control coefficient is corrected, the particle swarm performs iterative optimization, and the optimal position found by the i-th particle is called the individual extreme value, which is recorded as:

[0095]

[0096] Afterwards, the position and velocity of each particle will be updated according to the following formula:

[0097]

[0098] in, and are the speed and position of the ith particle in the tth period during the optimization process, ω is the inertia weight, c1 and c2 are learning factors, also known as acceleration constants, r1 and r2 are two acceleration weight coefficients, varying between [0, 1]. After updating the position and speed of the particle, the next iteration is performed, and the final optimal fitness value is the multi-objective function value of the optimal particle.

[0099] In a second aspect, the present invention provides an electric vehicle charging and discharging system based on elastic demand response, the system comprising:

[0100] The day-ahead model building module is used to build the day-ahead reservation user load model, wind power generation model, solar power generation model, energy storage system model, and equivalent load model;

[0101] The intraday model building module is used to constrain the load of day-ahead reservation users and establish a load model for intraday demand response users;

[0102] A selection model building module is used to build a user fast or slow charging selection model according to the intraday demand response user load model, and to impose charging and discharging power constraints and capacity constraints on the user fast or slow charging selection model;

[0103] The objective function construction module is used to construct the objective function of the day-ahead reservation user load model and the day-time demand response user load model with the goal of minimizing the user charging price and maximizing the charging amount, and solve the objective function to obtain the optimal charging and discharging strategy.

[0104] In a third aspect, the present invention provides a storage medium storing one or more programs, which, when executed by a processor, implement the above-mentioned electric vehicle charging and discharging method based on elastic demand response.

[0105] In a fourth aspect, the present invention provides an electronic device, the electronic device comprising a memory and a processor, wherein:

[0106] The memory is used to store computer programs;

[0107] When the processor is used to execute the computer program stored in the memory, the above-mentioned electric vehicle charging and discharging method based on flexible demand response is implemented.

[0108] In addition, compared with the prior art, the present invention also has the following advantages:

[0109] The present invention proposes a two-stage optimization strategy for coordinated charging and discharging of electric vehicles that takes flexible demand response into consideration, and fully implements the optimal scheduling of charging loads for electric vehicles of different types of users based on their charging habits. The charging pattern of uncoordinated charging is roughly the same as the daily electricity usage habits in the area. Due to this charging habit, the uncoordinated charging load and base load are superimposed, increasing the peak-to-valley difference of the distribution network load in the area. Through the optimization strategy proposed by the present invention, the user's charging cost, the peak-to-valley difference and variance of the grid load are significantly reduced, which is beneficial to both the grid and electric vehicle users. BRIEF DESCRIPTION OF THE DRAWINGS

[0110] Figure 1 A flow chart of an electric vehicle charging and discharging method based on elastic demand response proposed in one embodiment of the present invention;

[0111] Figure 2 It is a schematic diagram of a two-stage demand response model within the day before the present invention in an embodiment;

[0112] Figure 3 A schematic diagram of the day-ahead reservation user load, basic load, and wind and solar output in one embodiment of the present invention;

[0113] Figure 4 is a schematic diagram of equivalent load in one embodiment of the present invention;

[0114] Figure 5 A comparison diagram of the base load and the intraday demand response load before and after coordination in one embodiment of the present invention;

[0115] Figure 6 A comparison diagram of charging control coefficients under several different optimized specific gravities in one embodiment of the present invention;

[0116] Figure 7 This is a schematic diagram of the structure of an electric vehicle charging and discharging system based on elastic demand response proposed in one embodiment of the present invention.

[0117] The following specific implementation manner will further illustrate the present invention in conjunction with the above-mentioned drawings. DETAILED DESCRIPTION

[0118] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention. Unless otherwise defined, the technical terms or scientific terms used herein should be understood by people with general skills in the field to which the present invention belongs. "Including" and similar words used in this article mean that the elements or objects appearing before the word include the elements or objects listed after the word and their equivalents, without excluding other elements or objects.

[0119] like Figure 1 and Figure 2 The present invention provides an electric vehicle charging and discharging method based on elastic demand response, the method comprising steps S101 to S104, wherein:

[0120] Step S101: construct a load model for day-ahead reservation users, a wind power generation model, a solar power generation model, an energy storage system model, and an equivalent load model.

[0121] It should be noted that in the day-ahead stage, users report the time when EVs start charging and the time when they end charging to the charging station, and then calculate the charging load of users who have made reservations the day before based on the charging power. Since the data on the start and end time of charging for each EV at the charging station, as well as the state of charge, have certain disorder and uncertainty, the Monte Carlo method is used to simulate the dynamic behavior of EV charging selection. By performing a large number of random checks on EV-related data and estimating statistical characteristic parameters based on the sampling results, a probability model can be established to simulate EV charging load demand:

[0122] EV starts charging at the station T s It obeys normal distribution, and its probability density function is:

[0123]

[0124] In the formula, μ c,s , σ c,s T s The mean and standard deviation of .

[0125] EV charging end time T e It obeys normal distribution, and its probability density function is:

[0126]

[0127] In the formula, μ c,e , σ c,eT e The mean and standard deviation of .

[0128] Initial state of charge of an EV when it enters a charging station It obeys uniform distribution, and its probability density function is:

[0129]

[0130] In the formula, a and b are lower and upper limits.

[0131] The light intensity r follows the Beta distribution. In addition, the photovoltaic output mainly depends on the solar radiation, ambient temperature and the characteristics of the photovoltaic module itself, as shown in the following formula:

[0132]

[0133] Where Γ() is the Gamma function, and its expression is: p is an integer variable, λ1 pv and is the shape parameter, and its expression is:

[0134]

[0135]

[0136] In the formula, u pv and σ pv They represent the expectation and standard deviation of the ratio of actual data to the maximum value.

[0137] Photovoltaic output P PV The expression is:

[0138] P PV = rA PV η PV

[0139] In the formula, A PV and η PV Represent the photovoltaic radiation area and conversion efficiency respectively. The probability density function of photovoltaic (PV) output is:

[0140]

[0141] In the formula, Indicates the maximum power output of the photovoltaic panel.

[0142] The wind speed follows the Weibull distribution, and the probability density function of the wind speed is:

[0143]

[0144] Where v, k, and c are the actual wind speed, shape factor, and scale factor, respectively. The power output of a wind turbine (WT) is P WT It can be expressed as a piecewise function:

[0145]

[0146] In the formula, v rated and P rated are rated wind speed and WT rated power output respectively; v in and v out are the cut-in wind speed and cut-out wind speed respectively. The probability density function of WT output is:

[0147]

[0148] Where h = (v rated / v in -1).

[0149] The expression of the energy storage system model is as follows:

[0150]

[0151] In the formula, C t represents the capacity of the energy storage device at time t; k loss Represents the loss rate factor of the energy storage device; η ch and η dc They represent the charging efficiency and discharging efficiency of energy storage equipment respectively; and They represent the charging power and discharging power of the energy storage device at time t respectively; Δt represents a time scale.

[0152] The expression of the equivalent load model is as follows:

[0153] P t eq =P t FEV +P t load -P t PV -P t WT

[0154] Where P t load represents the basic load of the line at time t; P t eq represents the equivalent load at time t; P t FEV Represents the sum of the charging loads of EVs at time t.

[0155] Step S102: constraining the load of day-ahead reservation users and establishing a load model for intra-day demand response users.

[0156] It should be pointed out that for users who make a reservation for charging a few days ago, their charging power is subject to the maximum charging power limit. The total charging load at any time is equal to the sum of the charging powers of all EVs at that moment. The amount of electricity charged by the EV during the charging time meets the user's charging needs. The expression is as follows:

[0157]

[0158] In the formula, is the charging power of the i-th vehicle at time t, i = 1, 2, ..., NF, t = 1, 2, ..., 24; is the maximum value of EV charging power; P t FEV is the sum of the charging loads of EVs at time t, t = 1, 2, ..., 24; is an integer variable, indicating the charging status of the i-th vehicle of the reservation user at time t. When charging, the value is 1, otherwise the value is 0, i = 1, 2, ..., NF, t = 1, 2, ..., 24; η EV The charging power of the EV; is the charging power expected by the user of the i-th EV, i = 1, 2, ..., NF; S EV is the rated capacity of the EV battery.

[0159] For users who respond to charging and discharging within a day, the intraday user response model consists of rigid demand response and elastic demand response. Rigid charging demand refers to a behavior in which users must charge under specific circumstances. The user's rigid charging demand is usually affected by two factors: subjective factors and objective factors, both of which are closely related to whether the SOC of the electric vehicle can meet the needs of the next trip. When the electric vehicle arrives at the next destination, the SOC of the battery must not be lower than the minimum threshold SOCmin to ensure the service life of the battery. If the SOC is lower than the minimum threshold after the next trip, the user will charge the EV before the next trip. This is an objective factor that affects the user's charging demand, and the expression is as follows:

[0160]

[0161] In the formula, is the power of the i-th EV among the users who respond to charging and discharging during the day when it leaves the charging station, i = 1, 2, ..., NS; ω i is the travel power consumption of the i-th EV, i = 1, 2, ..., NS, kWh / km; l iis the distance of the next trip of the i-th EV, i = 1, 2, ..., NS. EV mileage l i It obeys the log-normal distribution, and its probability density function formula is:

[0162]

[0163] In the formula, μ s and σ s represent expected value and standard deviation respectively.

[0164] In addition, electric vehicle users also face "range anxiety", that is, they charge their electric vehicles before the SOC reaches the warning level. Since the subjective factors that lead to this rigid charging demand vary from person to person and cannot be calculated using precise mathematical expressions, fuzzy logic is used to describe it. The remaining power satisfaction Rf is used to describe the user's satisfaction with the remaining power and the travel demand when the electric vehicle goes to the next location. The expression is as follows:

[0165]

[0166] In the formula, R f,i is the remaining power satisfaction of the i-th EV, i = 1, 2, ..., NS,; is the power of the i-th EV when it arrives at the charging station. The membership function expression of the fuzzy charging demand set is as follows:

[0167]

[0168] F cd,i (R f,i ) is R f,i The membership function has a value range of [0, 1], i = 1, 2, ..., NS, so it is the probability of a vehicle that the user is subjectively willing to charge, and l and u are the lower and upper limits of the fuzzy charging demand set, respectively.

[0169] Users connect their vehicles to the grid for vehicle-grid interaction to participate in the coordinated charging and discharging optimization scheme given by the system. The payment change rate and SOC before and after the optimization are the two major factors affecting the flexible charging demand. Therefore, the elastic charge demand coefficient is defined as:

[0170]

[0171] In the formula, is the change in vehicle SOC before and after the i-th EV participates in the coordinated charge-discharge optimization, i = 1, 2, ..., NS; is the change in payment before and after the i-th EV participates in the coordinated charging and discharging optimization, that is, the peak and valley electricity price at the grid connection time t With charging power P icf Sum of products; The maximum amount of money paid for EVs not participating in coordinated charging; SOC max is the upper limit of EV charge state; For the i-th EV participating in coordinated charging and discharging to optimize the SOC when off-grid, i = 1, 2, ..., NS, The SOC of the i-th EV participating in the coordinated charging and discharging optimization when it enters the grid, i = 1, 2, ..., NS; S f For fixed electricity prices; is the charge and discharge control coefficient of the response user, i=1, 2, ..., NS; C b The maximum charging capacity, which is the amount of energy required to fully charge an EV from zero.

[0172] Step S103: constructing a user fast or slow charging selection model according to the intra-day demand response user load model, and performing charging and discharging power constraints and capacity constraints on the user fast or slow charging selection model.

[0173] In this step, it is assumed that there are sufficient fast and slow charging stations in public places in the context of large-scale grid connection of electric vehicles. When deciding whether to charge quickly or slowly, users will consider factors such as parking time at the current location, the amount of power required for the next trip, and battery consumption. Since fast charging has a greater impact on battery life, users will choose slow charging when the electric vehicle has enough time to charge for the next trip during parking, and will choose fast charging when the parking time of the electric vehicle is not enough to charge for the next trip. Affected by the psychological activity of "range anxiety", users always have a minimum expected charging state ε for each charge. Therefore, the SOC of the EV at any time should be within the specified range [ε, 1], satisfying the following formula:

[0174]

[0175] In the formula, and Respectively represent charging power and charging time, and Respectively represent the charging power of slow charging and fast charging, and They represent the charging time of slow charging and fast charging respectively. Therefore, the relationship between charging method and charging time is expressed as follows:

[0176]

[0177] Because the charging power of fast charging is greater than that of slow charging, it can be assumed that the charging power of fast charging is λ times that of slow charging, that is, make

[0178]

[0179] So we have:

[0180]

[0181] Therefore, the user's charging power P i c Parking time Decide:

[0182]

[0183] When users choose the two optimization weights, they will consider the charging cost and the current SOC. Generally speaking, when the current SOC of the vehicle is large, users tend to choose a larger charging cost optimization weight λ1 to minimize the charging cost, and when the current SOC of the vehicle is small, users tend to choose a larger charging power optimization weight λ2 to maximize the battery power. The user's selection behavior of the optimization weight is expressed by the following formula:

[0184]

[0185] Among them, floor means rounding down. It can be seen from the formula that when the state of charge of the electric vehicle is less than 0.3, the user selects the maximum charging power optimization weight λ2 as 0.9 (the charging cost optimization weight λ1 is 0.1). When the state of charge is between 0.3 and 1, the user's charging cost optimization weight λ1 depends on the state of charge of the electric vehicle. The larger the state of charge, the larger λ1.

[0186] Charge and discharge power constraints:

[0187]

[0188] Where: and They represent the charging and discharging power of the i-th EV at time t, and They represent the maximum value of EV charging and discharging power respectively. When EV is charging and discharging, its state of charge is subject to capacity constraints and state of charge constraints during the charging and discharging process:

[0189]

[0190] In the formula, represents the SOC of the i-th EV at time t, SOC max and SOC min Respectively represent the upper and lower limits of EV battery capacity.

[0191] Step S104: constructing objective functions for the day-ahead reservation user load model and the day-time demand response user load model with the goal of minimizing the user charging price and maximizing the charging amount, and solving the objective functions to obtain the optimal charging and discharging strategy.

[0192] It should be noted that the objective function 1 for minimizing the user charging price is:

[0193]

[0194] In the formula, is the peak and valley price of charging and discharging during period t, in yuan / kWh; They are the peak, average and valley charging prices respectively, in yuan / kWh; are the discharge electricity prices during peak, flat and valley periods, respectively, in yuan / kWh; and are the time when EV enters the grid and the time when it leaves the grid, respectively.

[0195] In order to meet the travel needs of users, the maximum amount of electricity charged by electric vehicles is used as objective function 2:

[0196]

[0197] The objective functions F1 and F2 are normalized using the linear weighted sum method:

[0198]

[0199] Where, F is the multi-objective optimization function of electric vehicle charging and discharging; F1 max , and F1 min , are the maximum and minimum values ​​of the single objective functions F1 and F2 respectively; where λ i,1 , i,2 are the optimization weights of the single objective functions F1 and F2 respectively.

[0200] In addition, this step also needs to use the particle swarm algorithm to solve the model, minimize the user's charging cost and maximize the user's charging power, and verify the effect of the invention. The specific steps are as follows:

[0201] When an electric vehicle is connected to the grid, the system obtains the battery capacity C of the electric vehicle through the battery management system. b and current SOC, as well as the time when the electric vehicle was connected to the grid. In order to formulate a reasonable charging and discharging plan, the user needs to input the time when the electric vehicle exits the grid and the optimization weights λ1 and λ2. Subsequently, the system prepares the electric vehicle for the first stage of coordinated charging. The specific steps are as follows:

[0202] (1) Sort the grid load in ascending order from large to small during each period of EV grid connection.

[0203] (2) EV peak-valley period division: The proportion of peak-valley period, flat-valley period and low-valley period of each EV in a day is the same, as shown in the following formula:

[0204]

[0205] Where T p , T f , T v Represents the time set corresponding to the peak period, flat period, and trough period.

[0206] (3) Set the EV to charge at rated power during the off-peak period, as shown in the following formula:

[0207]

[0208] When optimizing the charge and discharge control coefficient, the particle swarm is initialized as a set of random particles during the peak and valley periods, and then the optimal solution is found through iteration. In each iteration of the n-iterative optimization process, each population consists of N particles, and the initial position and velocity of the i-th particle are vectors of dimension NS:

[0209]

[0210] In the formula, and are the position and velocity of the i-th particle during the optimization process.

[0211] Due to the limitation of battery capacity, the initial charge and discharge control coefficient needs to be modified. The virtual state of charge is introduced to calculate the optimization results. The virtual state of charge is defined as a variable that can calculate the state of charge of the electric vehicle after charging and discharging in advance. After the user selects the optimization weight, before the electric vehicle starts to execute the charge and discharge plan (the electric vehicle charge and discharge control coefficient is calculated at this time), the virtual SOC is used for calculation to obtain the optimization results of the charging capacity and charging cost. According to the charge and discharge control coefficient initialized by the electric vehicle Calculate the virtual SOC after the charging and discharging behavior of each period is completed:

[0212]

[0213] The charge and discharge control coefficient is corrected as follows:

[0214] (1) When the virtual SOC in a given time period exceeds the upper limit SOC max , and when the time period is a peak or flat time period, the time period is corrected to rated power discharge; when the cycle is a valley cycle, the charge and discharge control coefficient is corrected to 0:

[0215]

[0216] (2) When the virtual SOC in a given cycle is less than the lower limit SOC min When the cycle is peak, flat or valley, the charge and discharge control coefficient is corrected and charged at rated power:

[0217]

[0218] After the charge and discharge control coefficient is corrected, the particle swarm performs iterative optimization, and the optimal position found by the i-th particle is called the individual extreme value, which is recorded as:

[0219]

[0220] The global extremum is the best solution for the entire population found so far in this iteration, denoted by:

[0221]

[0222] Afterwards, the position and velocity of each particle will be updated according to the following formula:

[0223]

[0224] In the formula, and are the speed and position of the ith particle in the tth period of the optimization process; ω is the inertial weight; c1 and c2 are learning factors, also known as acceleration constants; r1 and r2 are two acceleration weight coefficients, varying between [0, 1]. After updating the position and speed of the particle, the next iteration is performed, and the best fitness value finally obtained is the multi-objective function value of the optimal particle.

[0225] In this embodiment, the electric vehicle charging and discharging strategy based on elastic demand response of the present invention is simulated in the MATLAB2022b environment. The model involved in this embodiment is divided into day-ahead reservation users and day-demand response users according to the user's charging habits and the charging distribution of different functional areas. The characteristics of the two loads are compared, and the changes in user loads before and after the two-stage optimization are compared. The simulation parameters are as follows:

[0226] Wind turbine parameters: total rated power 600 kW, rated wind speed 15 m / s, cut-in wind speed 3 m / s, cut-out wind speed 25 m / s, wind scaling factor, wind shape factor k=10.

[0227] Photovoltaic equipment parameters: total rated power in standard environment is 400 kW, attenuation coefficient fpv = 0.8, light intensity in standard environment is 1 kW / m2, power supply temperature coefficient k = -0.0045, standard ambient temperature is 25 degrees Celsius, total radiation area of ​​photovoltaic panels is 3200 m2, photovoltaic shape parameter λ1 pv =3; =5, the conversion efficiency is 0.093, and the photovoltaic radiation area is 1300 square meters.

[0228] Electric vehicle parameters: The number of EVs for both types of users is 200, the battery capacity follows a uniform distribution of 60 to 70 kWh, the charging power follows a uniform distribution of 3.5 to 7 kW, the time when EV starts charging follows a normal distribution with a mean of 17.6 and a standard deviation of 3.4, the time when EV ends charging follows a normal distribution with a mean of 6.4 and a standard deviation of 3.4, and the daily mileage follows a log-normal distribution with a mean of 3.2 and a standard deviation of 0.88.

[0229] Based on the data reported by the users who made reservations the day before and the wind and solar data predicted during the day, the equivalent load can be obtained, such as Figure 3 As shown. According to the peak and valley period of this equivalent load and the power market situation, the electricity price of the intraday demand response is reasonably obtained, the electricity price is obtained by the day-ahead reservation user model, and the charging load change and basic load data of the intraday reservation user before and after coordination are obtained according to the elastic demand response model, as shown in Table 1 and Table 3:

[0230] Table 1

[0231]

[0232] Table 2

[0233]

[0234] Table 3

[0235]

[0236] Result description:

[0237] By coordinating the changes in the peak-to-valley difference before and after, it can be seen that the model of the present invention can effectively reduce the peak-to-valley difference of demand response users and better maintain the stability of the power grid.

[0238] In the present invention, the day-ahead reservation type users are mainly applicable to users in residential areas, because their travel time is relatively fixed, so they can charge by making an appointment. The day-ahead demand response type users are mainly applicable to users around commercial areas, and the charging price guidance method is used to attract users to charge during the off-peak period. Due to the influence of people's lifestyles and travel patterns, the uncoordinated charging demand and the basic load of each functional area seem to be roughly the same, such as Figure 3 and Figure 5 ,in Figure 3 The load distribution in residential areas is shown. The charging demand in this area is mainly concentrated at night, which is consistent with the habit of users charging at night when they are resting in residential areas. Figure 5 The charging demand will be concentrated between 11:30 and 14:30 in the morning, which is the main time when users park their electric vehicles in commercial areas. And the peak demand for charging is mainly concentrated in the afternoon, which is also consistent with the time when users are entertaining in commercial areas. Since this charging habit is the same as people's daily electricity consumption pattern, the charging load of uncoordinated charging is superimposed on the original base load of each functional area, thereby increasing the peak-to-valley difference of the load curve. The load curve of two-stage coordinated optimization charging in commercial areas is generally lower than the load curve of uncoordinated charging, but if the coordinated charging method is adopted in residential areas, the load curve of coordinated charging is not much different from that of uncoordinated charging, and the volatility is very small. This is because the time when users can charge in residential areas is generally concentrated in the evening after get off work to the next day when they go to work. If the charging time of users is coordinated, it may not meet the charging needs of users. The results show that by using the model proposed in this paper, part of the charging load during the peak load period is transferred to the load valley period of this functional area or other functional areas, thereby utilizing the characteristics of electric vehicle time and space transfer to realize the time and space transfer of charging load.

[0239] Figure 5 This is a comparison chart of the load changes of demand response users in the simulation model of the present invention before and after coordination. It can be seen from the figure that, affected by people's daily electricity consumption habits, the daily electricity consumption peak is mainly concentrated in the daytime and night, and the charging load before coordination is in the early morning with a low load period. This shows that the charging behavior of electric vehicles is roughly consistent with the daily electricity consumption behavior. The uncoordinated charging load will superimpose the charging load peak on the original base load peak, resulting in the phenomenon of "peak-to-peak superposition" (such as between the 18th and 20th hours), which further increases the peak-to-valley difference of the load curve. After coordination, most of the charging load is transferred from the peak load period during the day to the low load period in the early morning (such as between the 1st and 7th hours), effectively reducing the peak-to-valley difference of the load curve.

[0240] Taking a user of intraday demand response as an example, the user's charging time is 12 hours, the off-grid time is 16 hours, and the initial SOC is 0.5. Through different optimization weights λ1 / λ2=0.1 / 0.9, λ1 / λ2=0.3 / 0.7, λ1 / λ2=0.5 / 0.5, λ1 / λ2=0.7 / 0.3 and λ1 / λ2=0.9 / 0.1, the user's response to V2G discharge selection is analyzed, and the results are shown in Table 4:

[0241] Table 4

[0242]

[0243]

[0244] Figure 6 The distribution of the charge and discharge control coefficients under different optimization weights selected by the user is shown in Table 4, and the optimization results of different optimization weights are shown in Table 4. It shows that the larger the charging weight λ1 selected by the user, the less the fee paid, but the lower the battery charging level. When the charging weight λ1 is 0.7, the fee paid is negative, and the SOC of the EV is 0.57, which means that when the user selects λ1 / λ2=0.7 / 0.3, not only can the charging and discharging profit be obtained, but the SOC of the electric vehicle when it is off the grid can also be improved, and the user responds. In addition, Table 4 shows that when the weight λ1 is less than 0.5, the coordinated charging fee is higher than the non-coordinated charging fee. The reason is that the smaller the weight λ1 is, the shorter the optimization time of the system is at this time.

[0245] In summary, according to the above-mentioned electric vehicle charging and discharging method based on elastic demand response, the orderly charging and discharging optimization model of electric vehicles in two stages of the day before and within the day considering the elastic demand response can give full play to the scheduling role of electric vehicles and energy storage systems, and cooperate with the peak-valley time-of-use electricity price and the elastic demand response model, which can effectively reduce the user's charging cost and improve the user's charging satisfaction, and can also effectively consume renewable energy. First, a day-ahead reservation user model is established. Through the user's reservation the day before, the start charging time and the end charging time are reported. The power grid obtains the total load of this type of user charging by reading the user's initial SOC, and then obtains the equivalent load predicted by the day before according to the wind and solar output. Through the electricity price response model, the user load that changes with the change of electricity price after the implementation of the peak-valley electricity price is calculated. After establishing the user weight selection model, the elastic demand response model is used to realize the two-way flow of energy between the intraday demand response EV and the power grid. Finally, under the condition of ensuring user charging satisfaction and peak-valley difference, the optimal model of EV charging and discharging power in the power grid is calculated.

[0246] like Figure 7 As shown, an embodiment of the present invention provides an electric vehicle charging and discharging system based on elastic demand response, the system comprising:

[0247] The day-ahead model building module 10 is used to build a day-ahead reservation user load model, a wind power generation model, a solar power generation model, an energy storage system model, and an equivalent load model;

[0248] The intraday model building module 20 is used to constrain the load of the day-ahead reservation type users and establish the intraday demand response type user load model;

[0249] A selection model building module 30 is used to build a user fast or slow charging selection model according to the intra-day demand response user load model, and to impose charging and discharging power constraints and capacity constraints on the user fast or slow charging selection model;

[0250] The objective function construction module 40 is used to construct the objective function of the day-ahead reservation user load model and the day-time demand response user load model with the goal of minimizing the user charging price and maximizing the charging amount, and solve the objective function to obtain the optimal charging and discharging strategy.

[0251] Another aspect of the present invention further provides a storage medium on which one or more programs are stored, and when the programs are executed by a processor, the above-mentioned electric vehicle charging and discharging method based on flexible demand response is implemented.

[0252] On the other hand, the present invention further proposes an electronic device, including a memory and a processor, wherein the memory is used to store computer programs, and the processor is used to execute the computer programs stored in the memory to implement the above-mentioned electric vehicle charging and discharging method based on elastic demand response.

[0253] Those skilled in the art will appreciate that the logic and / or steps represented in the flowchart or otherwise described herein, for example, may be considered as an ordered list of executable instructions for implementing logical functions, and may be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For purposes of this specification, "computer-readable medium" may be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0254] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering or, if necessary, processing in another suitable manner, and then stored in a computer memory.

[0255] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, a plurality of steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or a combination thereof: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0256] Although the embodiments of the present invention are described in detail above, it is obvious to those skilled in the art that various modifications and variations can be made to these embodiments. However, it should be understood that such modifications and variations are within the scope and spirit of the present invention as described in the claims. Moreover, the present invention described herein may have other embodiments and may be implemented or realized in a variety of ways.

Claims

1. A method for charging and discharging an electric vehicle based on elastic demand response, characterized in that: The method comprises: Construct a load model for day-ahead reservation users, a wind power generation model, a solar power generation model, an energy storage system model, and an equivalent load model; Constrain the load of day-ahead reservation users and establish a load model for intraday demand response users; A user fast or slow charging selection model is constructed according to the intraday demand response user load model, and charging and discharging power constraints and capacity constraints are imposed on the user fast or slow charging selection model; The objective functions of the day-ahead reservation user load model and the intraday demand response user load model are constructed with the goal of minimizing the user charging price and maximizing the charging amount, and the objective functions are solved to obtain the optimal charging and discharging strategy.

2. The electric vehicle charging and discharging method based on elastic demand response according to claim 1 is characterized in that: The steps of constructing a day-ahead reservation user load model, a wind power generation model, a solar power generation model, an energy storage system model, and an equivalent load model include: The steps to construct a day-ahead reservation user load model include: In the day-ahead stage, the EV charging start time and charging end time reported by the user to the charging station are obtained, and the charging load of the day-ahead reservation users is calculated based on the charging power, so as to simulate the dynamic behavior of EV charging selection based on the charging start time, charging end time and charge state of multiple day-ahead reservation users, specifically: EV starts charging at the station T s It obeys normal distribution, and its probability density function is: in, Indicates about T s The probability density function, μ c,s , σ c,s T s The mean and standard deviation of EV charging end time T e It obeys normal distribution, and its probability density function is: in, Indicates about T e The probability density function, μ c,e , σ c,e T e The mean and standard deviation of Initial state of charge of an EV when it enters a charging station It obeys uniform distribution, and its probability density function is: in, Indicates about The probability density function of is, a and b are The lower and upper limits of A solar power generation model is constructed based on light intensity and photovoltaic output, where: The light intensity r follows the Beta distribution, and its probability density function is: Among them, Γ() is the Gamma function, p is an integer variable, f r (r) represents the probability density function of the light intensity r, r max Indicates the limit value of light intensity; λ1 pv and λ2 pv are all shape parameters, and their expressions are: u pv and σ pv Respectively represent the expectation and standard deviation of the ratio of actual data to the maximum value; About photovoltaic output P PV The probability density function of is: P PV =rA PV η PV Among them, A PV and η PV represent the photovoltaic radiation area and conversion efficiency respectively, Indicates the maximum power output of the photovoltaic panel; The steps to build a wind power generation model include: The wind speed follows the Weibull distribution, and the probability density function of the wind speed is: f w (v) represents the probability density function of wind speed, v, k, c are the actual wind speed, shape factor and scale factor respectively, and the power output of the wind turbine P WT for: The power output of the wind turbine is P WT The probability density function of fever is: Among them, v rated and P rated are rated wind speed and WT rated power output, v in and v out are the cut-in wind speed and cut-out wind speed respectively, h=(v rated / v in -1); The energy storage system model is constructed according to the following formula: Among them, C t+1 , C t Respectively represent the capacity of the energy storage device at time t+1 and time t, k loss represents the loss rate factor of the energy storage device, η ch and η dc They represent the charging efficiency and discharging efficiency of the energy storage device respectively. and They represent the charging power and discharging power of the energy storage device at time t, respectively, and Δt represents a time scale; The equivalent load model is constructed according to the following formula: P t eq =P t FEV +P t load -P t PV -P t WT Among them, P t load represents the basic load of the line at time t, P t eq represents the equivalent load at time t, P t FEV represents the sum of the charging loads of EVs at time t, P t PV represents the photovoltaic output at time t, P t WT represents the power output by the wind turbine at time t.

3. The electric vehicle charging and discharging method based on elastic demand response according to claim 2 is characterized in that: The charging power constraint for the day ahead is calculated according to the following formula: in, is the charging power of the i-th vehicle at time t, is the maximum value of EV charging power, P t FEV is the sum of EV charging loads at time t, represents the charging status of the i-th vehicle of the reservation user at time t. When charging, the value is 1, otherwise the value is 0, η EV is the charging power of EV, E i exp is the charging power expected by the user of the i-th EV; S EV is the rated capacity of the EV battery, is the power of the i-th EV when it leaves the charging station among the users who respond to charging and discharging during the day, ω i is the travel power consumption of the i-th EV, kWh / km, l i is the driving distance of the next trip of the i-th EV; The daily demand response user load model is constructed according to the following formula: Among them, R f,i is the remaining power satisfaction of the i-th EV, is the power of the i-th EV when it arrives at the charging station, F cd,i (R f,i ) is R f,i The membership function has a value range of [0, 1], l and u are the lower and upper limits of the fuzzy charging demand set, respectively. is the change in vehicle SOC before and after the i-th EV participates in the coordinated charging and discharging optimization, is the change in payment before and after the i-th EV participates in the coordinated charging and discharging optimization, The maximum amount of money paid for EVs not participating in coordinated charging, SOC max is the upper limit of EV state of charge, Participate in the coordination of charging and discharging for the i-th EV to optimize the SOC when off-grid. The SOC of the i-th EV participating in the coordinated charging and discharging optimization when entering the grid, S f For fixed electricity prices, is the charge and discharge control coefficient of the response user, C b This is the maximum charge capacity.

4. The electric vehicle charging and discharging method based on elastic demand response according to claim 3 is characterized in that: The step of constructing a user fast or slow charging selection model according to the intra-day demand response user load model, and performing charging and discharging power constraints and capacity constraints on the user fast or slow charging selection model includes: The SOC of EV at any time is within the specified range [ε, 1], satisfying the following equation: Among them, P i c and Represent the charging power and charging time respectively, P s c and Respectively represent the charging power of slow charging and fast charging, and Respectively represent the charging time of slow charging and fast charging; The daily EV charging power is calculated using the following formula: in, represents the parking time, t y1 Indicates fast charging time; The daily charging and discharging power constraints are performed according to the following formula: The capacity and state of charge constraints within the day are calculated according to the following formula: in, and They represent the charging and discharging power of the i-th EV at time t, and Respectively represent the maximum value of EV charging and discharging power, represents the SOC of the i-th EV at time t, SOC max and SOC min Respectively represent the upper and lower limits of EV battery capacity.

5. The electric vehicle charging and discharging method based on elastic demand response according to claim 4 is characterized in that: The step of constructing an objective function of the day-ahead reservation user load model and the intraday demand response user load model with the goal of minimizing the user charging price and maximizing the charging amount includes: The objective function 1 is constructed according to the following formula: The objective function 2 is constructed according to the following formula: in, is the peak and valley price of charging and discharging during period t, in yuan / kWh, They are the peak, flat and valley charging prices, in yuan / kWh. are the discharge electricity prices during peak, flat and valley periods, in yuan / kWh. and They are the time of EV accessing the grid and the time of leaving the grid, minF1 represents minimizing the user charging price, and maxF2 represents maximizing the charging amount; The objective functions F1 and F2 are normalized using the linear weighted sum method: Among them, F is the multi-objective optimization function of electric vehicle charging and discharging, F1 max 、F2 max and F1 min 、F2 min are the maximum and minimum values ​​of the single objective functions F1 and F2, respectively, i,1 , i,2 are the optimization weights of the single objective functions F1 and F2 respectively; The optimization weight is calculated according to the following formula: λ2=1-λ1 Among them, floor means rounding down.

6. The electric vehicle charging and discharging method based on elastic demand response according to claim 5 is characterized in that: The step of solving the objective function to obtain the optimal charging and discharging strategy includes: Step 1: sort the grid loads in each period of EV grid connection in ascending order from large to small; Step 2: EV peak-valley period division: The proportion of peak-valley period, flat-valley period and low-valley period of each EV in a day is the same; Step 3: Set the EV to charge at rated power during the off-peak period, as shown in the following formula: During the peak and valley periods when optimizing the charge and discharge control coefficient, the particle swarm is initialized as a group of random particles, and then the optimal solution is found through iteration. In each iteration of the n-iterative optimization process, each population consists of N particles, and the initial position and velocity of the i-th particle are vectors of dimension NS: In the formula and are the position and velocity of the i-th particle during the optimization process; Step 4: Charge and discharge control coefficients initialized according to the electric vehicle Calculate the virtual SOC after the charging and discharging behavior of each period is completed: When the virtual SOC in a given time period exceeds the upper limit SOC max , and when the time period is a peak or flat time period, the time period is corrected to rated power discharge; when the cycle is a valley cycle, the charge and discharge control coefficient is corrected to 0: When the virtual SOC in a given cycle is less than the lower limit SOC min When the cycle is peak, flat or valley, the charge and discharge control coefficient is corrected and charged at rated power: After the charge and discharge control coefficient is corrected, the particle swarm performs iterative optimization, and the optimal position found by the i-th particle is called the individual extreme value, which is recorded as: Afterwards, the position and velocity of each particle will be updated according to the following formula: in, and are the speed and position of the ith particle in the tth period during the optimization process, ω is the inertia weight, c1 and c2 are learning factors, also known as acceleration constants, r1 and r2 are two acceleration weight coefficients, varying between [0, 1]. After updating the position and speed of the particle, the next iteration is performed, and the final optimal fitness value is the multi-objective function value of the optimal particle.

7. An electric vehicle charging and discharging system based on elastic demand response, characterized in that: The system comprises: The day-ahead model building module is used to build the day-ahead reservation user load model, wind power generation model, solar power generation model, energy storage system model, and equivalent load model; The intraday model building module is used to constrain the load of day-ahead reservation users and establish a load model for intraday demand response users; A selection model building module is used to build a user fast or slow charging selection model according to the intraday demand response user load model, and to impose charging and discharging power constraints and capacity constraints on the user fast or slow charging selection model; The objective function construction module is used to construct the objective function of the day-ahead reservation user load model and the day-time demand response user load model with the goal of minimizing the user charging price and maximizing the charging amount, and solve the objective function to obtain the optimal charging and discharging strategy.

8. A storage medium, characterized in that: The storage medium stores one or more programs, which, when executed by the processor, implement the electric vehicle charging and discharging method based on flexible demand response as described in any one of claims 1 to 6.

9. An electronic device, comprising a memory and a processor, wherein: The memory is used to store computer programs; When the processor is used to execute the computer program stored in the memory, it implements the electric vehicle charging and discharging method based on flexible demand response as described in any one of claims 1-6.

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