Electric vehicle charging and discharging method, device and equipment based on fuzzy logic reasoning and price incentive demand response and medium

Abstract

The invention discloses an electric vehicle charging and discharging method, device and equipment based on fuzzy logic reasoning and price excitation demand response and a medium. The method comprises the steps that modeling is conducted on loss of an energy storage battery of an electric vehicle; in combination with a membership function and a fuzzy rule, based on a Mamdani fuzzy logic algorithm, according to the charging time anxiety degree, the charging cost reduction degree and the residual SOC of the user to which the electric vehicle belongs, determining the intention of the user to participate in charging and discharging scheduling; and in consideration of the intention that the user participates in charge and discharge scheduling, combining a price demand response mode and an excitation demand response mode of the electric vehicle to optimize the operation state of a power grid which is used for charging the electric vehicle. The invention belongs to the field of electric vehicle charging and discharging. According to the method, a corresponding charging and discharging strategy scheme can be accurately formulated according to the requirements of an electric vehicle user, the peak-valley difference of a power grid is reduced while the charging cost and the charging time comprehensive cost of the user are reduced, and the load fluctuation is reduced.

Description

Technical Field

[0001] The present invention relates to the field of energy storage, and particularly to a method, device, equipment and medium for charging and discharging an electric vehicle based on fuzzy logic inference and price-incentive demand response. Background Art

[0002] As a movable power load, the charging behavior of an electric vehicle has significant temporality and spatiality. During peak power consumption periods, a large number of electric vehicles connecting to the grid for charging will greatly increase the peak-valley difference of the grid load and exacerbate the volatility of the grid load. Such a sharp change in the load not only increases the risk of grid operation but also may pose a threat to the safe and stable operation of the grid.

[0003] The existing charging and discharging strategies for electric vehicles are not perfect. For electric vehicle owners, there is no effective way to encourage them to discharge the electric vehicle to the grid during peak power consumption periods and then charge it during off-peak power consumption periods when the electricity price is low. Therefore, how to improve the charging and discharging strategy of the energy storage battery of an electric vehicle is an urgent problem to be solved. Summary of the Invention

[0004] By providing a method, device, equipment and medium for charging and discharging an electric vehicle based on fuzzy logic inference and price-incentive demand response, the present invention solves the technical problem of unreasonable charging and discharging strategies for electric vehicles in the prior art and achieves the technical effect of improving the charging and discharging strategy of electric vehicles.

[0005] In a first aspect, the present invention provides a method for charging and discharging an electric vehicle based on fuzzy logic inference and price-incentive demand response, the method including:

[0006] Modeling the loss of the energy storage battery of the electric vehicle;

[0007] Combining the membership function and fuzzy rules, and based on the Mamdani fuzzy logic algorithm, determining the intention of the user to participate in charging and discharging scheduling according to the charging time anxiety level, charging cost reduction level and remaining SOC of the user to whom the electric vehicle belongs;

[0008] Considering the intention of the user to participate in charging and discharging scheduling, optimizing the operation state of the grid by combining the price demand response mode and incentive demand response mode of the electric vehicle, where the grid is used to charge the electric vehicle.

[0009] Further, modeling the loss of the energy storage battery of the electric vehicle includes:

[0010] Modeling the battery loss according to the depth of discharge, including:

[0011] DOD = 1 - SOC dc,end

[0012] Among them, DOD is the depth of discharge of the battery, and SOC dc,end is the state of charge of the electric vehicle at the end of discharge;

[0013] Determine the relationship between the depth of discharge of the battery and the cycle life of the energy storage battery of the electric vehicle;

[0014] Based on this relationship, obtain the number of recyclable times of the energy storage battery after the electric vehicle discharges through V2G, and determine the battery loss cost of the energy storage battery during the discharge process of the electric vehicle through V2G.

[0015] Furthermore, determining the relationship between the depth of discharge of the battery and the cycle life of the energy storage battery of the electric vehicle includes:

[0016] The preliminary relationship between the depth of discharge of the battery and the cycle life of the energy storage battery of the electric vehicle includes:

[0017] L = mDOD n

[0018] where L is the number of cycles of the energy storage battery of the electric vehicle, and both m and n are fitting coefficients;

[0019] Based on V2G technology, the total available capacity of the battery of the energy storage battery when the electric vehicle discharges through V2G includes:

[0020] M c = E cap L N λ dc

[0021]

[0022] where M c is the total available capacity of the battery affected by the number of cycles, L N is the number of cycles of the energy storage battery when DOD = 0.8, λ dc is the influence coefficient after the electric vehicle discharges through V2G, L V2G is the number of recyclable times of the energy storage battery after the electric vehicle discharges through V2G, E cap is the battery capacity of the energy storage battery;

[0023] Determine the influence coefficient of the depth of discharge and the influence coefficient of the depth of discharge interval, including:

[0024]

[0025] where λ dc,dep is the influence coefficient of the depth of discharge, λ dc,sec is the influence coefficient of the depth of discharge interval, DOD refis the reference value of depth of discharge, DOD ini is the depth of discharge at the initial stage of energy storage battery discharge, DOD end is the depth of discharge at the end of energy storage battery discharge, G, F, R, β D are all preset coefficients;

[0026] Obtain the influence on the number of cycles of the energy storage battery when the electric vehicle discharges through V2G, including:

[0027]

[0028] Among them, L dc is the number of cycles lost by the energy storage battery after the electric vehicle discharges through V2G, ψ ini is the influence coefficient of the depth of discharge on the number of cycles of the energy storage battery at the initial stage of energy storage battery discharge; ψ end is the influence coefficient of the depth of discharge on the number of cycles of the energy storage battery at the end of energy storage battery discharge.

[0029] Furthermore, based on this relationship, obtain the number of recyclable cycles of the energy storage battery after the electric vehicle discharges through V2G, and determine the battery loss cost of the energy storage battery during the discharge process of the electric vehicle through V2G, including:

[0030] The number of recyclable cycles of the energy storage battery after the electric vehicle discharges through V2G, including:

[0031] L V2G = L N - L dc

[0032] When the electric vehicle discharges through V2G, the battery loss cost caused by unit discharge amount, including:

[0033]

[0034] Among them, C loss is the loss cost of the energy storage battery caused by unit discharge amount, C buy is the cost of the energy storage battery.

[0035] Furthermore, combine the membership function and fuzzy rules, and based on the Mamdani fuzzy logic algorithm, determine the intention of the user of this electric vehicle to participate in the charge and discharge scheduling according to the charging time anxiety degree, charging cost reduction degree and remaining SOC of the user to which this electric vehicle belongs, including:

[0036] After this electric vehicle is connected to the charging pile, obtain the estimated charging amount and expected staying duration of this electric vehicle;

[0037] Determine the charging time anxiety level, charging cost reduction level, and remaining SOC of the user to whom the electric vehicle belongs based on the expected charging amount and expected stay duration, and use them as the inputs of the Mamdani fuzzy logic algorithm;

[0038] Convert the charging time anxiety level, charging cost reduction level, and remaining SOC into fuzzy subsets respectively, and characterize them based on the membership function;

[0039] Construct a fuzzy logic rule base corresponding to each fuzzy subset;

[0040] Based on the Mamdani fuzzy logic algorithm, perform fuzzy inference to obtain a fuzzy result;

[0041] Based on the centroid method, defuzzify the fuzzy result to obtain the intention of the user to participate in the charge-discharge scheduling.

[0042] Furthermore, considering the intention of the user to participate in the charge-discharge scheduling, combined with the price demand response mode and incentive demand response mode of the electric vehicle, optimize the operation state of the power grid, including:

[0043] Consider different intentions of the user to participate in the charge-discharge scheduling, and construct a price scheduling model for electric vehicles;

[0044] According to the peak shaving target of the power grid, construct an incentive scheduling model;

[0045] Combine the price scheduling model and the incentive scheduling model, and with the goal of minimizing the charging cost of electric vehicles and minimizing the load fluctuation of the power grid, construct an electric vehicle charge-discharge scheduling model based on time-of-use electricity price and incentive mechanism;

[0046] Under several preset constraint conditions, solve the electric vehicle charge-discharge scheduling model to optimize the operation state of the power grid.

[0047] Furthermore, the electric vehicle charge-discharge scheduling model includes:

[0048]

[0049] Among them, minF is the electric vehicle charge-discharge scheduling model, ω1 is the weight coefficient of the charging cost, ω2 is the weight coefficient of the net load variance, F1 is the charging cost of the electric vehicle, F2 is the power grid load fluctuation, F 1,max is the unordered charging cost of the electric vehicle, F 2,max is the net load variance of the power grid under the unordered charging of the electric vehicle.

[0050] Secondly, the present invention provides an electric vehicle charge-discharge device based on fuzzy logic inference and price incentive demand response. The device includes:

[0051] A model construction module for modeling the losses of the energy storage battery of an electric vehicle;

[0052] An intention degree module for combining a membership function and fuzzy rules, and based on the Mamdani fuzzy logic algorithm, determining the intention of the user of the electric vehicle to participate in charge and discharge scheduling according to the charging time anxiety level, the degree of reduction in charging cost, and the remaining SOC of the user to whom the electric vehicle belongs;

[0053] An operation optimization module for optimizing the operation state of the power grid under the intention of the user to participate in charge and discharge scheduling, where the power grid is used to charge the electric vehicle, in combination with the price demand response mode and the incentive demand response mode of the electric vehicle.

[0054] Thirdly, the present invention provides an electronic device, including:

[0055] A processor;

[0056] A memory for storing instructions executable by the processor;

[0057] Wherein, the processor is configured to execute to implement the electric vehicle charge and discharge method based on fuzzy logic inference and price incentive demand response provided in the first aspect.

[0058] Fourthly, the present invention provides a non-transitory computer-readable storage medium, when the instructions in the storage medium are executed by the processor of the electronic device, enabling the electronic device to execute and implement the electric vehicle charge and discharge method based on fuzzy logic inference and price incentive demand response provided in the first aspect.

[0059] One or more technical solutions provided in the present invention have at least the following technical effects or advantages:

[0060] Compared with the existing electric vehicle charge and discharge scheduling technologies, the present invention infers the willingness of electric vehicle users to charge and discharge based on fuzzy logic, accurately calculates the degree of participation of electric vehicles in charge and discharge, and can accurately evaluate the impact of electric vehicle charge and discharge behaviors on the power grid.

[0061] Compared with the existing technologies for motivating electric vehicles to participate in charge and discharge scheduling, the combined price-based and incentive-based demand response proposed in the present invention can analyze the charging time, remaining power, and charging cost according to the needs of electric vehicle users, accurately formulate corresponding charge and discharge strategy plans, reduce the comprehensive cost of user charging time and charging cost, while reducing the peak-valley difference of the power grid and reducing the load volatility.

[0062] Compared with the calculation of the cost of electric vehicle users during the existing charging and discharging process, in the charging and discharging process of the present invention, the influence of the discharge depth of the energy storage battery of the electric vehicle on the cycle life is fully considered, the additional battery loss caused by charging and discharging of the electric vehicle is accurately calculated, and the cost calculation of the electric vehicle is more accurate, which conforms to the engineering reality. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the drawings in the following description are some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0064] Figure 1 It is a schematic flow chart of the electric vehicle charging and discharging method based on fuzzy logic reasoning and price incentive demand response provided by the present invention;

[0065] Figure 2 It is a schematic diagram of the SOC change during the charging and discharging process and only the charging process of the energy storage battery provided by the present invention;

[0066] Figure 3 It is a schematic diagram of the relationship between the number of cycles and the discharge depth of the energy storage battery provided by the present invention;

[0067] Figure 4 It is a schematic flow chart of determining the intention of the user to participate in the charging and discharging scheduling provided by the present invention;

[0068] Figure 5 It is a schematic curve diagram of the membership degree of the input variable provided by the present invention;

[0069] Figure 6 It is a schematic flow chart of the orderly charging scheduling method of electric vehicles based on time-of-use electricity price provided by the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0070] By providing an electric vehicle charging and discharging method based on fuzzy logic reasoning and price incentive demand response, the embodiments of the present invention solve the technical problem of unreasonable charging and discharging strategies for electric vehicles in the prior art.

[0071] The technical solution of the present invention for solving the above technical problem is generally as follows:

[0072] An electric vehicle charging and discharging method based on fuzzy logic reasoning and price incentive demand response, the method comprising: modeling the loss of the energy storage battery of the electric vehicle; combining membership functions and fuzzy rules, and based on the Mamdani fuzzy logic algorithm, determining the intention of the user to participate in charging and discharging scheduling according to the charging time anxiety level, the reduction of charging cost, and the remaining SOC of the user to which the electric vehicle belongs; considering the intention of the user to participate in charging and discharging scheduling, combining the price demand response mode and the incentive demand response mode of the electric vehicle, and optimizing the operating state of the power grid, where the power grid is used to charge the electric vehicle.

[0073] To better understand the above technical solution, the above technical solution will be described in detail below in conjunction with the accompanying drawings of the specification and specific embodiments.

[0074] First, it should be noted that the term "and / or" appearing in this article is only a description of the association relationship between associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist simultaneously, and B exists alone. In addition, the character " / " in this article generally represents an "or" relationship between the preceding and following associated objects.

[0075] The present invention provides an Figure 1 electric vehicle charging and discharging method based on fuzzy logic reasoning and price incentive demand response as shown in

[0076] Step S11, modeling the loss of the energy storage battery of the electric vehicle.

[0077] During the charging and discharging process of the energy storage battery of the electric vehicle, too low SOC (remaining power percentage) will accelerate the capacity decay of the energy storage battery and shorten the service life of the energy storage battery; too high SOC will accelerate the aging process of the energy storage battery and cause permanent damage to the capacity of the energy storage battery. Figure 2 It is a schematic diagram of the change of SOC during the charging and discharging process and only the charging process of the energy storage battery of the electric vehicle.

[0078] Therefore, in order to maximize the service life of the energy storage battery of the electric vehicle, during the charging and discharging process, certain restrictions should be imposed on the maximum SOC and minimum SOC of the energy storage battery.

[0079] Using the classical loss model of the energy storage battery to model the loss of the energy storage battery of the electric vehicle, including:

[0080] Modeling the battery loss according to the depth of discharge, including:

[0081] DOD = 1 - SOC dc,end

[0082] where DOD is the depth of discharge of the battery, SOCdc,end is the state of charge of the electric vehicle at the end of discharging.

[0083] As Figure 3 shown, Figure 3 is a schematic diagram of fitting the discharge data of a certain type of lithium battery (relationship diagram between the number of cycles and discharge depth of the energy storage battery). By fitting the discharge data, the relationship between the discharge depth of the battery and the cycle life of the energy storage battery of the electric vehicle can be obtained, specifically including:

[0084] The preliminary relationship between the discharge depth of the battery and the cycle life of the energy storage battery of the electric vehicle, including:

[0085] L = mDOD n

[0086] Based on the V2G technology, the total available capacity of the battery of the energy storage battery during discharging of the electric vehicle through V2G includes:

[0087] M c = E cap L N λ dc

[0088]

[0089] Among them, M c is the total available capacity of the battery affected by the number of cycles, L N is the number of cycles of the energy storage battery when DOD = 0.8, λ dc is the influence coefficient after discharging of the electric vehicle through V2G, L V2G is the number of recyclable cycles of the energy storage battery after discharging of the electric vehicle through V2G, E cap is the battery capacity of the energy storage battery.

[0090] The V2G technology refers to the technology that enables bidirectional energy transfer between an electric vehicle (EV) and the power system.

[0091] Furthermore, determine the influence coefficient of the discharge depth and the influence coefficient of the discharge depth interval (the number of cycles of the energy storage battery is affected by both the discharge depth and the discharge depth interval), including:

[0092]

[0093] Among them, λ dc,dep is the influence coefficient of the discharge depth, λ dc,sec is the influence coefficient of the discharge depth interval, DOD ref is the reference value of the discharge depth, taking 0.8, DOD ini is the discharge depth at the initial stage of discharging of the energy storage battery, DOD endis the depth of discharge at the end of the energy storage battery discharge, and G, F, R, and β D are all corresponding preset coefficients (which can be determined according to empirical values).

[0094] To obtain the influence of the electric vehicle discharging through V2G on the cycle life of the energy storage battery, including (i.e., considering the influence of DOD ini on the cycle life of the energy storage battery, and giving the influence of the electric vehicle discharging process on the cycle life of the energy storage battery):

[0095]

[0096] where L dc is the number of cycle life losses of the energy storage battery after the electric vehicle discharges through V2G, and ψ ini is the influence coefficient of the depth of discharge on the cycle life of the energy storage battery at the initial stage of the energy storage battery discharge; ψ end is the influence coefficient of the depth of discharge on the cycle life of the energy storage battery at the end of the energy storage battery discharge.

[0097] Based on this relationship, the available cycle life of the energy storage battery after the electric vehicle discharges through V2G is obtained, and the battery loss cost during the electric vehicle discharging through V2G is determined, including:[[]]

[0098] The available cycle life of the energy storage battery after the electric vehicle discharges through V2G, including:[[]]

[0099] L V2G = L N - L dc

[0100] The battery loss cost caused by unit discharge amount when the electric vehicle discharges through V2G, including:[[]]

[0101]

[0102] where C loss is the loss cost of the energy storage battery caused by unit discharge amount, and C buy is the cost of the energy storage battery.

[0103] Regarding L V2G 、L N 、L dc For special explanation, L dc specifically refers to the "lost" number of cycle life of the energy storage battery; L N refers to the total number of cycle life that the energy storage battery can perform at a certain depth of battery discharge; that is to say, L V2G is the number of cycle life that the energy storage battery can still perform after experiencing a certain number of losses.

[0104] Step S12: Combine the membership function and fuzzy rules, and based on the Mamdani fuzzy logic algorithm, determine the intention of the user of this electric vehicle to participate in the charge and discharge scheduling according to the charging time anxiety level, the degree of reduction in charging cost, and the remaining SOC of the user to whom this electric vehicle belongs.

[0105] To determine the intention of the user to participate in the charge and discharge scheduling, reference can also be made to Figure 4 .

[0106] Specifically, it includes:

[0107] After this electric vehicle is connected to the charging pile, obtain the estimated charging amount and the expected staying duration of this electric vehicle. According to the estimated charging amount and the expected staying duration, determine the charging time anxiety level, the degree of reduction in charging cost, and the remaining SOC of the user to whom this electric vehicle belongs, and use them as the inputs of the Mamdani fuzzy logic algorithm; convert the charging time anxiety level, the degree of reduction in charging cost, and the remaining SOC into fuzzy subsets respectively, and characterize them based on the membership function; construct a fuzzy logic rule base corresponding to each fuzzy subset respectively; based on the Mamdani fuzzy logic algorithm, perform fuzzy inference to obtain a fuzzy result; based on the centroid method, defuzzify the fuzzy result to obtain the intention of this user to participate in the charge and discharge scheduling.

[0108] Select the Gaussian function as the membership function, including:

[0109]

[0110] σ and C are the standard deviation and the mean respectively, x is the variable, that is, (charging time anxiety level, degree of reduction in charging cost, and remaining SOC), and the schematic diagram of the membership curve of the input variable is as Figure 5 shown.

[0111] By analyzing the charging time anxiety level, the degree of reduction in charging cost, and the remaining SOC, establish a fuzzy inference rule base.

[0112] Specifically, it includes:

[0113] For example, if the charging time anxiety level is low (below the threshold), the degree of reduction in charging cost is obvious (above the threshold), and the remaining SOC is medium (between 40% - 60%), then the willingness of the electric vehicle to respond to the demand response is high.

[0114] Divide the intention of the user to participate in the charge and discharge scheduling into five fuzzy subsets: low, relatively low, medium, relatively high, and strong, and represent them by L, ML, M, MH, and H respectively. The fuzzy subsets corresponding to low, medium, and high of the input data are represented by S, M, and B respectively.

[0115] To describe the fuzzy rules and determine the corresponding fuzzy implication relationship, the fuzzy implication relationship of the fuzzy controller is represented by the union of all fuzzy relationships R:

[0116]

[0117] Therefore, a fuzzy logic rule base corresponding to the fuzzy subsets can be constructed, as shown in Table 1, including:

[0118] Table 1

[0119]

[0120]

[0121] Use fuzzy rules and membership functions for reasoning, apply Mamdani fuzzy logic reasoning to process fuzzy information, and generate fuzzy output results. Specifically:

[0122] Assume that the sampling step of the fuzzy controller is k (k = 0, 1,... n), and adopt Mamdani fuzzy logic reasoning (max-min composition method). Denote the fuzzy sets of charging time anxiety level, charging cost reduction level, and remaining SOC as The output probability is denoted as where j = 1, 2,... 27, then the membership function corresponding to the fuzzy relationship R can be expressed as:

[0123]

[0124] where, is the comprehensive membership function corresponding to the fuzzy relationship R, μ R is the membership function of a single input variable, and ∧ represents the logical AND operation, which is the minimum value in fuzzy logic.

[0125] Then the fuzzy relationship corresponding to the fuzzy rule can be expressed as:

[0126]

[0127] where, × is the Cartesian product, used to generate all possible combinations of input variables, combine multiple fuzzy input variables with a set of fuzzy rules, U is the fuzzy value (i.e., the fuzzy result), and after defuzzification, a specific output value is obtained. R * is the fuzzy relationship defined in the fuzzy rule base.

[0128] Based on the "centroid method", defuzzify the fuzzy output U (fuzzy result), determine the weighted average of the product of the output membership function and its corresponding output value, and then divide by the sum of the membership degree functions, so as to determine the centroid of the closed area between the membership function curve and the horizontal axis to obtain a specific output value. The specific expression is as follows:

[0129]

[0130] where y k+1 is the output value of the next step size; is the input membership function, for y h is the centroid of X k is the input fuzzy set, is the i-th fuzzy set of the input, y j is the representative output value corresponding to the j-th output fuzzy set, representing the fuzzy description of the output variable, that is, each output fuzzy set itself or the corresponding membership degree distribution.

[0131] After defuzzifying the fuzzy value, the magnitude of the user's intention to participate in the charge and discharge scheduling is obtained.

[0132] Step S13, considering the user's intention to participate in the charge and discharge scheduling, combined with the price demand response mode and incentive demand response mode of the electric vehicle, optimize the operating state of the power grid, and the power grid is used to charge the electric vehicle.

[0133] Specifically include: considering the user's non-intention to participate in the charge and discharge scheduling, constructing a price scheduling model for the electric vehicle; constructing an incentive scheduling model according to the peak shaving target of the power grid; combining the price scheduling model and the incentive scheduling model, and aiming at minimizing the charging cost of the electric vehicle and minimizing the load fluctuation of the power grid, constructing an electric vehicle charge and discharge scheduling model based on time-of-use electricity price and incentive mechanism; solving the electric vehicle charge and discharge scheduling model under several preset constraint conditions to optimize the operating state of the power grid.

[0134] After obtaining the expected charging amount and expected staying duration of the electric vehicle, the expected staying duration of the electric vehicle and the expected charging time can be compared to determine whether the expected staying duration supports the transfer of the charging time.

[0135] Regarding the price demand response mode (price scheduling model):

[0136]

The expected staying duration is less than or equal to the expected charging time (fully charged state)

[0137] t i,dep - t i,in ≤ T i,ch

[0138] t i,dep is the expected departure time of the i-th electric vehicle, t i,ib is the time when the i-th electric vehicle accesses the charging pile, T i,chLet \(T\) be the estimated charging time (fully charged state). When the expected stay duration is less than the estimated charging time, it indicates a short stay. The user has no intention of adjustment, and the electric vehicle starts charging immediately after connecting to the charging pile until leaving.

[0139] Based on time-of-use pricing, determine the charging cost of the electric vehicle when the expected stay duration is less than the estimated charging time, including:

[0140]

[0141] Among them, \(\gamma\) ch (t) is the time-of-use price at time \(t\), \(P\) ch is the charging power, \(\Delta t\) is the time interval, \(\eta\) ch is the charging efficiency, \(C\) i,nod is the charging cost of the \(i\)-th electric vehicle, \(t\) in is the time when the electric vehicle connects to the charging pile, \(t\) dep is the time when the electric vehicle leaves the charging pile.

[0142] Time-of-Use Pricing (TOU) is a billing mechanism that divides 24 hours of a day into different time periods according to the operating conditions of the power system and the electricity demand of users, and sets different electricity prices for each time period. It usually includes peak hours, flat peak, and off-peak hours, and the electricity prices of each period will vary according to the marginal cost of the system and other factors.

[0143]

Expected stay duration is greater than the estimated charging time (fully charged state)

[0144] \(t\) i,dep -\(t\) i,in \(>\) \(T\) i,ch

[0145] When the expected stay duration is greater than the estimated charging time, it indicates a long stay. According to time-of-use pricing, if the electricity price in the next time period is lower within the stay time range, the charging selection will be automatically transferred to the next time period.

[0146] The present invention also provides a method for orderly charging scheduling of electric vehicles based on time-of-use pricing. For details, please refer to Figure 6 .

[0147] After the electric vehicle connects to the charging pile, determine the time period in which the time-of-use price is located at this moment.

[0148] If it is the peak electricity price period, judge the electricity price in the next time period: if it is the off-peak electricity price, transfer to the next time period for charging; if it is the flat peak electricity price, further judge the time period after the flat peak electricity price period. If it is the peak electricity price or flat peak electricity price, transfer to the flat peak electricity price period for charging. If it is the off-peak electricity price period, transfer to the off-peak electricity price period for charging.

[0149] If it is a flat - peak electricity price period, judge the electricity price of the next period: if it is a valley - peak electricity price, then transfer to the next period for charging; if the electricity price of the next period is a peak - electricity price or a flat - peak electricity price, then the electric vehicle starts charging after being connected.

[0150] If it is a valley - peak electricity price period, then the electric vehicle starts charging immediately after being connected.

[0151] The time - of - use electricity price is shown in Table 2.

[0152]

[0153] When the expected stay duration is greater than the estimated charging time, based on the time - of - use electricity price, the charging cost of the electric vehicle is as follows, including:

[0154]

[0155] Among them, γ′ ch (t) is the flat - peak or valley - peak electricity price at time t (i.e., the flat - peak or valley - peak electricity price of the next stage to be adjusted), C i,ord is the charging cost of the i - th electric vehicle, P i,ch (t) is the charging power, and when charging is completed, P i,ch (t)=0.

[0156] Regarding the incentive demand - response mode (incentive dispatching model), it includes:

[0157] According to the power - grid peak - shaving target, set the incentive electricity price for the electric vehicle to discharge. During the peak - electricity consumption period, a higher discharge incentive electricity price is given to encourage the electric vehicle to participate in discharging. In the present invention, a peak - shaving compensation coefficient ξ is introduced, and the electric - vehicle discharge incentive price is set in combination with the time - of - use electricity price, including:

[0158]

[0159] Among them, ξ is the peak - shaving compensation coefficient, γ dc is the discharge incentive price of the electric vehicle, P de is the power - grid peak - shaving demand, P ave is the average value of the net load.

[0160] If the electric - vehicle user is willing to participate in the charging dispatching and discharging dispatching, then the charging cost after the electric vehicle participates in discharging is as follows, including:

[0161]

[0162] Among them, C i,V2G is the charging cost paid by the i - th electric vehicle participating in the charging dispatching and discharging dispatching (i.e., the charging cost that the electric vehicle needs to pay after participating in the incentive - type demand response), Pdc The discharging power at time t is P(t).

[0163] Combining the price scheduling model and the incentive scheduling model, an electric vehicle charging and discharging scheduling model based on time-of-use electricity price and incentive mechanism is constructed.

[0164] It should be particularly emphasized that the present invention discusses from the price side and the incentive side respectively. Therefore, the essence in the patent name refers to the combined incentive considering price demand response and incentive demand response.

[0165] Specifically, on the grid side, in order to ensure the stable operation of the system, the main purposes are to shave peaks and fill valleys and reduce the load fluctuation of the grid, and the optimization variable is the charging and discharging power of electric vehicles within the t period; on the electric vehicle user side, the main purpose is to minimize the charging cost, and the optimization variable is the charging and discharging electricity within the t period.

[0166] The minimum charging cost of electric vehicles includes:

[0167] On the electric vehicle user side, the optimization objective is to minimize the charging cost of electric vehicles, which includes: charging cost, discharging income, and battery loss cost (that is, the loss cost of the energy storage battery obtained in step S11).

[0168]

[0169] Among them, minF1 is to minimize the charging cost of electric vehicles on the electric vehicle user side, n is the number of electric vehicles, and N is the total number of electric vehicles.

[0170] The minimum grid load fluctuation includes:

[0171] When optimizing the scheduling to minimize the charging cost of electric vehicles, it may cause a peak in the charging load during the low electricity price period. To ensure the stability of the grid operation, on the grid side, the optimization objective is to minimize the net load variance.

[0172]

[0173] Among them, min F2 is to minimize the net load variance on the grid side, P L (t) is the basic load of the distribution network in the t period; P ev (t) is the electric vehicle load in the t period, T is the total number of time intervals, and t is the time interval number.

[0174] Normalize the electric vehicle charging cost f1 and the grid load fluctuation f2 to obtain the electric vehicle charging and discharging scheduling model, including:

[0175]

[0176] Among them, min F is the charging and discharging scheduling model of electric vehicles, ω1 is the weight coefficient of the charging cost, ω2 is the weight coefficient of the net load variance, F1 is the charging cost of electric vehicles, F2 is the grid load fluctuation, and F 1,max is the unordered charging cost of electric vehicles, and F 2,max is the net load variance of the power grid under the unordered charging of electric vehicles.

[0177] Solve under the following preset constraint conditions, including:

[0178] Charging and discharging state constraints of electric vehicles:

[0179] 0 ≤ σ i,ch (t) + σ i,dc (t) ≤ 1

[0180] Among them, σ i,c (t) is the charging flag of the i-th electric vehicle at time t, and σ i,dc (t) is the discharging flag of the i-th electric vehicle at time t. When the electric vehicle is in the charging state, σ i,c (t) = 1, σ i,dc (t) = 0; when in the discharging state, σ i,c (t) = 0, σ i,dc (t) = 1.

[0181] State of charge constraint:

[0182] SOC min ≤ SOC i (t) ≤ SOC max

[0183]

[0184] Among them, σ c (t) is the charging decision variable of the electric vehicle, and σ dc (t) is the discharging decision variable of the electric vehicle. When σ ch = 1, it means charging is selected in this period, corresponding to σ dc = 0; when σ dc = 1, it means discharging is selected in this period, corresponding to σ ch = 0, and η dis is the discharging efficiency of the electric vehicle, and P dc is the discharging power of the electric vehicle.

[0185] Charging and discharging power constraints, including:

[0186] 0 ≤ P i,c (t) ≤ P c,max

[0187] 0 ≤ P dc\((t)\leq P\) dc,max

[0188] where \(P\) c,max is the maximum allowable charging power, and \(P\) dc,max is the maximum allowable discharging power.

[0189] The above full process and solution process are described as follows:

[0190] Step 1: Obtain the information when the electric vehicle accesses the charging pile: the arrival time at the charging station, the remaining SOC information, the estimated charging amount, and the staying time;

[0191] Step 2: The power grid issues the peak shaving scheduling target. The charging station operator calculates the estimated charging cost of the electric vehicle participating in orderly charging and the charge-discharge scheduling through V2G according to the user's charging information, and calculates the willingness of the electric vehicle to participate in the charge-discharge scheduling based on fuzzy inference;

[0192] Step 3: Classify the electric vehicles accessing the charging pile into electric vehicles for disorderly charging, electric vehicles for orderly charging based on time-of-use electricity price, and electric vehicles participating in charge-discharge scheduling through V2G, and calculate the schedulable capacity of the electric vehicle;

[0193] Step 4: According to the power grid operation data, and using the constructed electric vehicle charge-discharge scheduling model, use the particle swarm optimization algorithm to solve the optimal charge-discharge scheduling scheme of the electric vehicle for each time period;

[0194] Step 5: Output the electric vehicle scheduling scheme, charging cost, and operation data such as the load fluctuation of the power grid after charge-discharge scheduling.

[0195] In summary, the present invention provides an electric vehicle charging and discharging method based on fuzzy logic reasoning and price incentive demand response, including: modeling the loss of the energy storage battery of the electric vehicle; combining membership functions and fuzzy rules, and based on the Mamdani fuzzy logic algorithm, determining the intention of the user of the electric vehicle to participate in charging and discharging scheduling according to the charging time anxiety level, charging cost reduction level, and remaining SOC of the user to whom the electric vehicle belongs; considering the intention of the user to participate in charging and discharging scheduling, combining the price demand response mode and incentive demand response mode of the electric vehicle, and optimizing the operating state of the power grid, where the power grid is used to charge the electric vehicle. Compared with the existing electric vehicle charging and discharging scheduling technologies, the present invention is based on fuzzy logic reasoning of the charging and discharging willingness of electric vehicle users, accurately calculates the degree of participation of electric vehicles in charging and discharging, and can accurately evaluate the impact of electric vehicle charging and discharging behaviors on the power grid. Compared with the existing technologies that encourage electric vehicles to participate in charging and discharging scheduling, the combined price-based and incentive-based demand response proposed by the present invention can analyze the charging time, remaining power, and charging cost according to the needs of electric vehicle users, accurately formulate corresponding charging and discharging strategy plans, reduce the comprehensive cost of user charging time and charging cost, while reducing the peak-valley difference of the power grid and reducing the load volatility. Compared with the existing calculation of the cost of electric vehicle users during the charging and discharging process, the present invention fully considers the impact of the discharge depth of the energy storage battery of the electric vehicle on the cycle life during the charging and discharging process, accurately calculates the additional battery loss caused by charging and discharging of the electric vehicle, and calculates the cost of the electric vehicle more accurately, which is in line with engineering practice.

[0196] Based on the same inventive concept, the present invention provides an electric vehicle charging and discharging device based on fuzzy logic reasoning and price incentive demand response. The device includes:

[0197] A model construction module for modeling the loss of the energy storage battery of the electric vehicle;

[0198] An intention degree module for combining membership functions and fuzzy rules, and based on the Mamdani fuzzy logic algorithm, determining the intention of the user to participate in charging and discharging scheduling according to the charging time anxiety level, charging cost reduction level, and remaining SOC of the user to whom the electric vehicle belongs;

[0199] An operation optimization module for optimizing the operating state of the power grid under the intention of the user to participate in charging and discharging scheduling, where the power grid is used to charge the electric vehicle.

[0200] Based on the same inventive concept, the present invention also provides an electronic device as shown, including:

[0201] A processor;

[0202] A memory for storing instructions executable by the processor;

[0203] Among them, the processor is configured to execute to implement the electric vehicle charging and discharging method based on fuzzy logic reasoning and price incentive demand response provided as described above.

[0204] Based on the same inventive concept, the present invention also provides a non-transitory computer-readable storage medium. When the instructions in the storage medium are executed by the processor of an electronic device, the electronic device can execute to implement the electric vehicle charging and discharging method based on fuzzy logic reasoning and price incentive demand response provided as described above.

[0205] Since the electronic device introduced in this embodiment is the electronic device adopted for implementing the information processing method in the embodiments of the present invention, based on the information processing method introduced in the embodiments of the present invention, those skilled in the art can understand the specific implementation manners and various variations of the electronic device in this embodiment. Therefore, the specific implementation of how this electronic device implements the method in the embodiments of the present invention will not be described in detail here. As long as the electronic device adopted by those skilled in the art to implement the information processing method in the embodiments of the present invention belongs to the scope protected by the present invention.

[0206] Those skilled in the art should understand that the embodiments of the present invention can be provided as a method, a system, or a computer program product. Therefore, the present invention can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0207] The present invention is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to the embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, and the combination of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing devices generate means for implementing the specified functions in Figure 1 one process or multiple processes and / or blocks Figure 1 one block or multiple blocks.

[0208] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory generate a manufactured article including instruction means, and the instruction means implements the process in Figure 1One or more processes and / or blocks Figure 1 The functions specified in one or more blocks.

[0209] These computer program instructions can also be loaded onto a computer or other programmable data processing device, so that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process. Thus, the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in one Figure 1 One or more processes and / or blocks Figure 1 The steps of the functions specified in one or more blocks.

[0210] Although the preferred embodiments of the present invention have been described, those skilled in the art can make additional changes and modifications once they learn the basic creative concepts. Therefore, the appended claims are intended to be construed to include the preferred embodiments and all changes and modifications falling within the scope of the present invention.

[0211] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalent technologies, the present invention also intends to include these modifications and variations.

Claims

1. An electric vehicle charging and discharging method based on fuzzy logic reasoning and price incentive demand response, characterized in that: The method comprises: Modeling the loss of energy storage batteries in electric vehicles; Combining membership functions and fuzzy rules, and based on the Mamdani fuzzy logic algorithm, the user's intention to participate in charging and discharging scheduling is determined according to the user's charging time anxiety, charging cost reduction, and remaining SOC; Considering the user's intention to participate in charging and discharging scheduling, the price demand response mode and the incentive demand response mode of the electric vehicle are combined to optimize the operating state of the power grid used to charge the electric vehicle.

2. The electric vehicle charging and discharging method based on fuzzy logic reasoning and price incentive demand response as claimed in claim 1, characterized in that: Model the losses of energy storage batteries in electric vehicles, including: Model battery wear based on depth of discharge, including: DOD=1-SOC dc,end Among them, DOD is the depth of discharge of the battery, SOC dc,end It is the charge state of the electric vehicle at the end of discharge; Determine the relationship between battery discharge depth and cycle life of energy storage batteries for electric vehicles; Based on this relationship, the number of cycles of the energy storage battery after the electric vehicle is discharged through V2G is obtained, and the battery loss cost of the energy storage battery during the discharge of the electric vehicle through V2G is determined.

3. The electric vehicle charging and discharging method based on fuzzy logic reasoning and price incentive demand response as claimed in claim 2, characterized in that: Determine the relationship between battery discharge depth and cycle life of energy storage batteries for electric vehicles, including: The preliminary relationship between battery discharge depth and cycle life of energy storage batteries for electric vehicles includes: <h2 style=";text-align:left;direction:ltr">L=mDOD<h2 style=";text-align:left;direction:ltr"> n Among them, L is the number of cycles of the energy storage battery of the electric vehicle, and m and n are fitting coefficients; Based on V2G technology, the total available capacity of the energy storage battery of an electric vehicle when discharged through V2G includes: M c =E cap L N λ dc Among them, M c is the total available capacity of the battery affected by the number of cycles, L N is the number of cycles of the energy storage battery when DOD = 0.8, λ dc is the impact coefficient of electric vehicles after discharging through V2G, L V2G E is the number of cycles of the energy storage battery after the electric vehicle is discharged through V2G, cap is the battery capacity of the energy storage battery; Determine the influence coefficient of discharge depth and the influence coefficient of discharge depth range, including: Among them, λ dc,dep is the influence coefficient of discharge depth, λ dc,sec is the influence coefficient of the discharge depth range, DOD ref is the discharge depth reference value, DOD ini DOD is the depth of discharge at the beginning of the energy storage battery discharge. end is the discharge depth of the energy storage battery at the end of discharge, G, F, R, β D All are preset coefficients; The impact of electric vehicle discharge through V2G on the cycle number of energy storage batteries is obtained, including: Among them, L dc is the number of cycles lost by the energy storage battery after the electric vehicle is discharged through V2G, ψ ini is the influence coefficient of the discharge depth on the cycle number of the energy storage battery at the initial discharge of the energy storage battery; ψ end When the energy storage battery is discharged, the influence coefficient of the discharge depth on the number of cycles of the energy storage battery.

4. The electric vehicle charging and discharging method based on fuzzy logic reasoning and price incentive demand response as claimed in claim 3, characterized in that: Based on this relationship, the number of cycles of the energy storage battery after the electric vehicle is discharged through V2G is obtained, and the battery loss cost of the energy storage battery during the discharge of the electric vehicle through V2G is determined, including: The number of cycles that the energy storage battery can be used for after the electric vehicle is discharged through V2G, including: L V2G =L N -L dc When electric vehicles are discharged through V2G, the battery loss cost per unit of discharge includes: Among them, C loss is the loss cost of the energy storage battery caused by unit discharge, C buy The cost of energy storage batteries.

5. The electric vehicle charging and discharging method based on fuzzy logic reasoning and price incentive demand response as claimed in claim 1, characterized in that: Combining membership functions and fuzzy rules, and based on the Mamdani fuzzy logic algorithm, the user's intention to participate in charging and discharging scheduling is determined according to the user's charging time anxiety, charging cost reduction, and remaining SOC, including: When the electric vehicle is connected to the charging pile, the estimated charging amount and expected stay time of the electric vehicle are obtained; According to the expected charging amount and expected stay time, the charging time anxiety level, charging cost reduction level and remaining SOC of the user of the electric vehicle are determined and used as inputs of the Mamdani fuzzy logic algorithm; The charging time anxiety level, charging cost reduction level and remaining SOC are converted into fuzzy subsets respectively and characterized based on membership functions; Construct the fuzzy logic rule base corresponding to the fuzzy subsets; Based on Mamdani fuzzy logic algorithm, fuzzy reasoning is performed to obtain fuzzy results; Based on the centroid method, the fuzzy results are defuzzified to obtain the user's intention to participate in charging and discharging scheduling.

6. The electric vehicle charging and discharging method based on fuzzy logic reasoning and price incentive demand response as claimed in claim 1, characterized in that: Considering the user's intention to participate in charging and discharging scheduling, the price demand response mode and incentive demand response mode of electric vehicles are combined to optimize the operation status of the power grid, including: Considering the user's disagreement in participating in the charging and discharging scheduling, a price scheduling model for electric vehicles is constructed; Construct an incentive dispatch model based on the peak shaving target of the power grid; Combining the price dispatch model with the incentive dispatch model, and taking the minimum charging cost of electric vehicles and the minimum grid load fluctuation as the goal, a charging and discharging dispatch model for electric vehicles based on time-of-use electricity prices and incentive mechanisms is constructed; Under several preset constraints, the electric vehicle charging and discharging scheduling model is solved to optimize the operating state of the power grid.

7. The electric vehicle charging and discharging method based on fuzzy logic reasoning and price incentive demand response as claimed in claim 6, characterized in that: Electric vehicle charging and discharging scheduling model, including: Among them, min F is the electric vehicle charging and discharging scheduling model, ω1 is the weight coefficient of charging cost, ω2 is the weight coefficient of net load variance, F1 is the charging cost of electric vehicles, F2 is the grid load fluctuation, and F 1,max is the disorderly charging cost of electric vehicles, F 2,max is the net load variance of the power grid under disorderly charging of electric vehicles.

8. An electric vehicle charging and discharging device based on fuzzy logic reasoning and price incentive demand response, characterized in that: The device comprises: Model building module for modeling the losses of energy storage batteries in electric vehicles; The intention module is used to combine the membership function and fuzzy rules and determine the user's intention to participate in charging and discharging scheduling according to the user's charging time anxiety, charging cost reduction and remaining SOC based on the Mamdani fuzzy logic algorithm; The operation optimization module is used to optimize the operation state of the power grid used to charge the electric vehicle in combination with the price demand response mode and the incentive demand response mode of the electric vehicle under the intention of the user to participate in the charge and discharge scheduling.

9. An electronic device, characterized in that: include: processor; a memory for storing instructions executable by the processor; The processor is configured to execute to implement the electric vehicle charging and discharging method based on fuzzy logic reasoning and price incentive demand response as described in any one of claims 1 to 7.

10. A non-transitory computer-readable storage medium, characterized in that: When the instructions in the storage medium are executed by a processor of an electronic device, the electronic device is enabled to implement the electric vehicle charging and discharging method based on fuzzy logic reasoning and price incentive demand response as described in any one of claims 1 to 7.

Images