V2G cluster classification optimization scheduling method based on fuzzy control

Abstract

The invention relates to a V2G cluster classification optimization scheduling method based on fuzzy control, and belongs to the field of electric vehicle cluster optimization scheduling. The method comprises the following steps: firstly, dividing electric vehicles into different V2G clusters according to state characteristics of the electric vehicles after network access; then, fuzzy distribution of input and output quantities is determined, a fuzzy rule is established, and a three-input-single-output charging and discharging power fuzzy controller is designed; and finally, making an optimal scheduling strategy according with the characteristics of the cluster according to the characteristics of the cluster, wherein the charging and discharging power of the cluster is controlled through fuzzy control in the strategy so as to achieve peak load shifting of the regional power grid load.

Description

Technical Field

[0001] The present invention belongs to the field of optimal scheduling of electric vehicle clusters, and particularly relates to a method for optimizing the classification and scheduling of V2G clusters based on fuzzy control. Background Art

[0002] The disordered charging method of electric vehicle clusters is flexible, and the charging pattern conforms to the travel pattern. Therefore, a large number of electric vehicles connected to the power grid during the peak commuting hours will generate a large charging load, increasing the peak-valley difference of the regional power grid load, thus bringing problems such as equipment overload risk, energy efficiency decline, and power grid stability. Considering formulating an optimal scheduling strategy for V2G cluster output to eliminate the impact of disordered charging is an effective means to cope with the load fluctuations brought by the large-scale access of electric vehicles to the grid.

[0003] Currently, the research on V2G cluster optimal scheduling mainly includes the optimal scheduling strategy for electric vehicle charging and discharging considering multiple objective optimizations and the scheduling strategy for electric vehicle charging and discharging considering two-layer optimizations.

[0004] Multi-objective optimization is a mathematical optimization method aiming to optimize multiple interrelated and possibly conflicting objectives simultaneously. In the research on the optimal scheduling strategy for electric vehicle charging and discharging, multi-objective optimization is used to comprehensively consider the interests and constraints of multiple aspects such as the grid side, the user side, and the environment, and seek a set of optimal solutions that can satisfy all objectives as much as possible. However, in the process of optimizing multiple objectives, it is difficult to obtain the best optimization weights for each objective. If excessive emphasis is placed on reducing the user's charging cost, it may prompt the scheduling strategy to frequently guide electric vehicles to charge during low electricity price periods. Although this helps users save expenses, it may exacerbate the load pressure on the grid during specific periods, is not conducive to the smoothing of the grid load curve, and even affects the safe and stable operation of the grid. On the contrary, if too much emphasis is placed on peak shaving and valley filling and improving the load curve, electric vehicles may need to be forced to discharge during certain periods. Although this is beneficial to the grid, it may increase the charging inconvenience for users, violate the original intention of its economy and convenience, and lead to a decrease in user acceptance.

[0005] The two - layer optimization is a composite optimization structure, which is often used to deal with complex decision - making problems with inherent hierarchical relationships and interactive influences. When studying the optimal charging and discharging scheduling strategy of electric vehicles, the two - layer optimization framework can effectively handle multi - objectives, multi - agents, and the dependency relationships between decision - making levels, especially suitable for scenarios that consider the interests of both the grid side and the user side simultaneously. However, in the process of model construction and solution, such strategies generally rely on solvers, planning algorithms, or intelligent optimization techniques. When facing large - scale application scenarios containing a large amount of electric vehicle data, the dimension and complexity of the optimization problem increase sharply. When traditional solvers or algorithms handle such high - dimensional, non - linear, and multi - constraint problems, their solution accuracy is often significantly affected. Secondly, in the environment of large - scale electric vehicle data, the computational amount of the optimization model increases significantly, resulting in a slow running speed of the model and making it difficult to meet the requirements of fast response for real - time scheduling. Summary of the Invention

[0006] The purpose of the present invention is to provide a fuzzy - control - based classification and optimal scheduling method for V2G clusters. First, it is considered to classify electric vehicles into different V2G clusters according to the state characteristics after the electric vehicles are connected to the grid. Then, the fuzzy distributions of input and output variables are determined, fuzzy rules are established, and a three - input - single - output fuzzy controller for charging and discharging power is designed. Finally, an optimal scheduling strategy that conforms to the characteristics of the cluster is formulated according to the cluster characteristics. In the strategy, the charging and discharging power of the cluster is controlled by fuzzy control to achieve "peak shaving and valley filling" of the regional power grid load.

[0007] To achieve the above - mentioned purpose, the technical solution of the present invention is: a fuzzy - control - based classification and optimal scheduling method for V2G clusters, including:

[0008] Considering classifying electric vehicles into different V2G clusters according to the state characteristics after the electric vehicles are connected to the grid;

[0009] Determining the fuzzy distributions of input and output variables, establishing fuzzy rules, and designing a three - input - single - output fuzzy controller for charging and discharging power;

[0010] Formulating an optimal scheduling strategy that conforms to the characteristics of the cluster according to the cluster characteristics. In the strategy, the charging and discharging power of the cluster is controlled by fuzzy control to achieve "peak shaving and valley filling" of the regional power grid load.

[0011] In an embodiment of the present invention, the method includes the following steps:

[0012] Step 1: V2G cluster classification: Starting from the state information characteristics of electric vehicles, electric vehicles with similar state characteristics are grouped into the same V2G cluster;

[0013] Step 2: Modeling the SOC and power boundaries during the charging and discharging process of the V2G cluster according to different clusters;

[0014] Step 3: Introduce the schedulable capacity \(k\) to characterize the influence of the access duration on the charging and discharging power control of the V2G cluster;

[0015] Step 4: According to the characteristics of cluster classification, adopt the method of fuzzy control to control the charging and discharging power, design a three-input single-output fuzzy controller for charging and discharging power, and establish fuzzy control rules;

[0016] Step 5: Develop an optimized scheduling strategy for the V2G cluster: According to the classification criteria of V2G cluster classification in Step 1, classify the clusters accessing the network into 3 types of clusters, and formulate a charging and discharging output strategy that conforms to the characteristics of different V2G clusters;

[0017] Step 6: Through simulation experiments, obtain the total load curve including the electric vehicle load under the optimized scheduling strategy of V2G output, obtain the load peak and valley values, and calculate the load peak-valley ratio.

[0018] In an embodiment of the present invention, Step 1 is specifically implemented as follows:

[0019] Step 1.1: Define 3 state characteristics of the electric vehicle, the initial state of charge SOC of the electric vehicle in , the shortest charging time is \(T\) e , and the access duration of the electric vehicle is \(\Delta T\);

[0020] Step 1.2: According to the state characteristics of the electric vehicle, divide the V2G clusters in the area into the following three types of clusters according to a predetermined criterion: non-schedulable clusters, i.e., \(\Delta T\leq T\) e , rechargeable schedulable clusters, i.e., \(\Delta T>T\) e & SOC in < SOC dis , charge-discharge schedulable clusters, i.e., \(\Delta T>T\) e & SOC in \(\geq\) SOC dis ; where SOC dis is the SOC threshold at which the V2G cluster can participate in discharging.

[0021] In an embodiment of the present invention, in Step 1.1, the calculation formula of \(T\) e is as follows:

[0022]

[0023] In the formula, SOC out is the SOC that the vehicle owner expects to reach for charging, SOC in is the initial SOC of the electric vehicle when it accesses the charging pile, \(E\) e is the rated capacity of the electric vehicle battery pack, \(\eta\) c is the charging efficiency of the electric vehicle, \(P\) ce is the rated charging power of the electric vehicle;

[0024] The calculation formula of ΔT is as follows:

[0025] ΔT = t out - t in

[0026] In the formula, t out is the off-grid time of the electric vehicle, and t in is the on-grid time of the electric vehicle.

[0027] In an embodiment of the present invention, in step 3, the definition of k is as follows:

[0028]

[0029] In the formula, ΔT is the on-grid duration, and T e is the shortest charging time. The value range of k is (0, 1), and the larger the k value, the stronger the dispatchable ability.

[0030] In an embodiment of the present invention, step 4 is specifically implemented as follows:

[0031] Step 4.1: Set the fuzzy distribution of the input and output quantities. The input quantities include the base load P l , the dispatchable ability k, the charging demand Q c , the dischargeable quantity Q d , and the output quantities include the charging power P c , the discharge power P d ;

[0032] Step 4.2: Considering the characteristics of the base load of the regional power grid, 75 fuzzy rules are established for the charging fuzzy controller and the discharge fuzzy controller in the V2G cluster output optimization dispatch strategy respectively. The rules are in the "If-Then" format and consist of a premise and a conclusion. The premise describes the conditions of the input variables, and the conclusion describes the fuzzy sets of the output variables.

[0033] In an embodiment of the present invention, step 4.1 is specifically implemented as follows:

[0034] Step 4.1.1: Set the fuzzy subsets of the base load P l as: very small base load VSP l , small base load SP l , medium base load MP l , large base load LP l , very large base load VLP l . Normalize the base load P l and define it as the input quantity x. According to the definitions of the fuzzy set and the triangular membership function: covering the base load P l The domain of the input quantity x is [0, 1], and the membership functions of each fuzzy subset are as follows:

[0035] VSP l (x) = (0.25 - x) / 0.25 0 ≤ x ≤ 0.25

[0036]

[0037]

[0038] VLP l (x) = (x - 0.75) / 0.25 0.75 ≤ x ≤ 1

[0039] Step 4.1.2, set the dispatchable capacity k, charging demand Q c , dischargeable capacity Q d , charging power P c , discharging power P d , and the membership functions of the fuzzy subsets of each of them;

[0040] The fuzzy subsets of the dispatchable capacity k are set as: small dispatchable capacity Sk, medium dispatchable capacity Mk, large dispatchable capacity Lk. Define the dispatchable capacity as the input quantity y, and the universe of discourse covering the input quantity y is (0, 1);

[0041] Charging demand Q c The fuzzy subsets of are set as: extremely small charging demand VSQ c , small charging demand SQ c , medium charging demand MQ c , large charging demand LQ c , extremely large charging demand VLQ c , and define the charging demand as the input quantity z of the charging fuzzy controller 1 , and the universe of discourse covering the input quantity z 1 is (0, 90);

[0042] Dischargeable capacity Q d The fuzzy subsets of are set as: extremely small dischargeable capacity VSQ d , small dischargeable capacity SQ d , medium dischargeable capacity MQ d , large dischargeable capacity LQ d , extremely large dischargeable capacity VLQ d , and define the dischargeable capacity as the input quantity z of the discharging fuzzy controller 2 , and the universe of discourse covering the input quantity z 2 is (0, 72);

[0043] Charging power P c The fuzzy subsets of are set as: extremely low charging power VLP c , low charging power LPc 、Low to medium charging power MLP c 、Low to medium-high charging power LMP c 、Medium charging power MP c 、High to medium charging power HMP c 、Medium to high charging power MHP c 、High charging power HP c 、Very high charging power VHP c Define the charging power P c as the output quantity o of the charging fuzzy controller 1 for covering the output quantity o 1 whose universe of discourse is [0, 22];

[0044] Discharge power P d The fuzzy subset of is set as: Very low discharge power VLP d 、Low discharge power LP d 、Medium-low discharge power MLP d 、Low to medium discharge power LMP d 、Medium discharge power MP d 、High to medium discharge power HMP d 、Medium to high discharge power MHP d 、High discharge power HP d 、Very high discharge power VHP d Define the discharge power P d as the output quantity o of the discharge fuzzy controller 2 for covering the output quantity o 2 whose universe of discourse is [-22, 0]

[0045] Based on the above setting methods of each fuzzy subset, define the membership functions of each fuzzy subset.

[0046] In an embodiment of the present invention, step 5 is specifically implemented as follows:

[0047] Randomly select M vehicles from the sample data, i = 1, 2,..., M, the network access time T(i), the shortest charging time Tc_min(i), the charging demand E c the dischargeable amount E d the dispatchable capacity K, the base load value P l the charging power P c the discharge power P d the initial SOC is SOC in (i), the lowest SOC restricted by discharge is SOC_ min the network access moment t_in, let i = 0;

[0048] Step 5.1, let i = i + 1, if i = M + 1, then go to step 6;

[0049] Step 5.2: Determine whether Tc_min(i)≥T(i) is satisfied. If yes, go to Step 5.3; if not, go to Step 5.6;

[0050] Step 5.3: Cluster 1. Let t = t_in(i), and vehicle i starts charging at the rated power at time t;

[0051] Step 5.4: Calculate the SOC of the i-th vehicle at time t + 1;

[0052] Step 5.5: Determine whether the off-grid time is reached. If not, let t = t + 1 and go to Step 5.4; if yes, go to Step 5.1;

[0053] Step 5.6: Determine whether SOC in (i)≥0.5 is satisfied. If not, go to Step 5.7; if yes, go to Step 5.12;

[0054] Step 5.7: Cluster 2. Let t = t_in(i), calculate E c and K of vehicle i at time t, and load P l , and calculate P at time t through the charging fuzzy controller c ;

[0055] Step 5.8: Calculate the SOC of the i-th vehicle at time t;

[0056] Step 5.9: Determine whether SOC < 1 is satisfied. If yes, go to Step 5.10; if not, go to Step 5.1;

[0057] Step 5.10: Determine whether the shortest charging time is reached. If yes, charge at the rated power at the next moment; if not, calculate E c and K of vehicle i at time t + 1, and load P l , and calculate P at time t + 1 through the charging fuzzy controller c ;

[0058] Step 5.11: Determine whether the off-grid time is reached. If not, let t = t + 1 and go to Step 5.8; if yes, go to Step 5.1;

[0059] Step 5.12: Cluster 3. Let t = t_in(i), calculate E d and K of vehicle i at time t, and load P l , and calculate P at time t through the discharging fuzzy controller d ;

[0060] Step 5.13: Calculate the SOC of the i-th vehicle at time t + 1;

[0061] Step 5.14. Determine whether SOC ≤ SOC_ min , if not, go to Step 5.15; if so, go to Step 5.16;

[0062] Step 5.15. Calculate E d , K at the (t + 1)-th moment of vehicle i, and load P l , calculate P d at the (t + 1)-th moment through the discharge fuzzy controller, let t = t + 1 and go to Step 5.13;

[0063] Step 5.16. Determine whether the shortest charging moment is reached. If so, charge at the rated power at the next moment; if not, calculate E c , K at the (t + 1)-th moment of vehicle i, and load P l at the (t + 1)-th moment, calculate P c at the (t + 1)-th moment through the charge fuzzy controller;

[0064] Step 5.17. Determine whether the off-grid moment is reached. If not, let t = t + 1 and go to Step 5.13; if so, go to Step 5.1.

[0065] The present invention also provides an electronic device, including a memory, a processor, and computer program instructions stored on the memory and capable of being run by the processor. When the processor runs the computer program instructions, the method steps as described above can be implemented.

[0066] The present invention also provides a computer-readable storage medium, on which computer program instructions capable of being run by the processor are stored. When the processor runs the computer program instructions, the method steps as described above can be implemented.

[0067] Compared with the prior art, the present invention has the following beneficial effects:

[0068] ① Starting from the state information characteristics of electric vehicles, the present invention classifies electric vehicles with similar state characteristics into the same V2G cluster, and then formulates corresponding optimized scheduling strategies according to the state characteristics of different V2G clusters, so as to perform a charging and discharging process more in line with the actual state for V2G clusters with different state characteristics, in order to achieve the optimized scheduling of the electric vehicle cluster in the entire region. Using the cluster classification idea, subdividing the V2G cluster can not only meet the charging needs of users but also improve the power grid scheduling ability, and solve the problem of difficult to determine the weight of multi-objective optimization.

[0069] ② The present invention uses the method of fuzzy control to control the charging and discharging power. By determining the fuzzy distribution of input and output quantities, establishing fuzzy rules, and designing a three-input - single-output charging and discharging power fuzzy controller, the optimal charging and discharging power is calculated for V2G scheduling. Description of the Drawings

[0070] Figure 1 It is the overall framework for optimizing the scheduling of the V2G cluster.

[0071] Figure 2 It is the flow chart of the optimization scheduling strategy for the V2G cluster output.

[0072] Figure 3 It is to model the SOC and power boundaries during the charging and discharging process of the V2G cluster.

[0073] Figure 4 For the base load P l Fuzzy subset distribution. Specific implementation manner

[0074] The following combines the attached drawings to specifically illustrate the technical solutions of the present invention.

[0075] It should be noted that the following detailed description is exemplary and is intended to provide further illustration of the present application. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by those of ordinary skill in the technical field to which the present application belongs.

[0076] It should be noted that the terms used herein are only for describing specific implementation manners and are not intended to limit the exemplary embodiments according to the present application. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. In addition, it should be understood that when the terms "comprising" and / or "including" are used in this specification, they indicate the presence of features, steps, operations, devices, components, and / or combinations thereof.

[0077] The present invention provides a method for classifying and optimizing the scheduling of a V2G cluster based on fuzzy control, including:

[0078] Considering classifying electric vehicles into different V2G clusters according to the state characteristics after the electric vehicles are connected to the grid;

[0079] Determining the fuzzy distribution of input and output quantities, establishing fuzzy rules, and designing a three-input - single-output fuzzy controller for charging and discharging power;

[0080] Formulating an optimization scheduling strategy that conforms to its characteristics according to the cluster characteristics, and controlling the charging and discharging power of the cluster through fuzzy control in the strategy to achieve "peak shaving and valley filling" of the regional power grid load.

[0081] The following is the specific implementation process of the present invention.

[0082] Such as Figure 1 、 2As shown in the figure, the optimized scheduling scenario of this embodiment is the scenario of electric private cars charging in public areas. It is assumed that the number of V2G clusters is M = 150, the rated charging power Pce = 22kW, the rated discharging power Pde = -22kW, and the SOC threshold for discharging of the V2G clusters that can discharge is SOC_min = 0.2. A method for classifying and optimizing the scheduling of V2G clusters based on fuzzy control in this embodiment includes the following steps:

[0083] Step 1: Classify V2G clusters. Starting from the state information characteristics of electric vehicles, electric vehicles with similar state characteristics are grouped into the same V2G cluster.

[0084] Step 1.1: Define 3 state characteristics, the initial state of charge SOC of the electric vehicle in , the shortest charging time is T e , and the duration of the electric vehicle's network access is ΔT. The calculation formulas for T e and ΔT are as follows:

[0085]

[0086] In the formula, SOC out is the SOC that the vehicle owner expects to reach after charging (in this paper, it is assumed that SOC out = 1), SOC in is the initial SOC of the electric vehicle when it is connected to the charging pile, E e is the rated capacity of the electric vehicle's battery pack, η c is the charging efficiency of the electric vehicle, taking η c = 0.9, P ce is the rated charging power of the electric vehicle, taking P ce = 22kW.

[0087] ΔT = t out - t in

[0088] In the formula, t out is the off-grid time of the electric vehicle, and t in is the on-grid time of the electric vehicle.

[0089] Step 1.2: According to the above-described state characteristics of electric vehicles, the V2G clusters in the area are divided into the following three types of clusters according to certain criteria: non-schedulable clusters (ΔT ≤ T e ), rechargeable schedulable clusters (ΔT > T e & SOC in < SOC dis ), and charge-discharge schedulable clusters (ΔT > T e & SOC in ≥ SOC dis ). Among them, SOC disIt is the SOC threshold at which the V2G cluster can participate in discharging.

[0090] Step 2: According to different clusters, model the SOC and power boundaries during the charging and discharging process of the V2G cluster, see Figure 3 .

[0091] Step 3: Introduce the schedulable capacity k to characterize the influence of the grid connection duration on the charging and discharging power control of the V2G cluster. The definition of k is as follows:

[0092]

[0093] In the formula, ΔT is the grid connection duration, and T e is the shortest charging time. The value range of k is (0, 1), and the larger the k value, the stronger the schedulable capacity.

[0094] Step 4: For the characteristics of cluster classification, adopt the method of fuzzy control to control the charging and discharging power, design a three-input single-output charging and discharging power fuzzy controller, and establish fuzzy control rules.

[0095] Step 4.1: Set the fuzzy distribution of the input and output quantities. The input quantities include the base load P l , the schedulable capacity k, the charging demand Q c , the dischargeable quantity Q d , and the output quantities include the charging power P c , the discharge power P d .

[0096] Step 4.1.1: As Figure 4 shown, set the fuzzy subsets of the base load P l to be: very small base load (VSP l ), small base load (SP l ), medium base load (MP l ), large base load (LP l ), very large base load (VLP l ). Normalize the base load P l and define it as the input quantity x. According to the definitions of the fuzzy set and the triangular membership function: covering the base load P l The domain of the input quantity x is [0, 1], and the membership functions of each fuzzy subset are as follows:

[0097] VSP l (x) = (0.25 - x) / 0.25 0 ≤ x ≤ 0.25

[0098]

[0099] VLP l$(x)=(x - 0.75) / 0.25\quad0.75\leq x\leq1$

[0100] Step 4.1.2: Similar to Step 4.1.1, set the fuzzy subsets of other input and output quantities.

[0101] The fuzzy subsets of the dispatchable capacity $k$ are set as: small dispatchable capacity ($Sk$), medium dispatchable capacity ($Mk$), large dispatchable capacity ($Lk$). Define the dispatchable capacity as the input quantity $y$, and the universe of discourse covering the input quantity $y$ is $(0,1)$;

[0102] Charging demand $Q$ c The fuzzy subsets are set as: extremely low charging demand ($VSQ$ c ), low charging demand ($SQ$ c ), medium charging demand ($MQ$ c ), high charging demand ($LQ$ c ), extremely high charging demand ($VLQ$ c ). Define the charging demand as the input quantity $z$ of the charging fuzzy controller 1 , and the universe of discourse covering the input quantity $z$ 1 is $(0,90)$;

[0103] Dischargeable quantity $Q$ d The fuzzy subsets are set as: extremely low dischargeable quantity ($VSQ$ d ), low dischargeable quantity ($SQ$ d ), medium dischargeable quantity ($MQ$ d ), high dischargeable quantity ($LQ$ d ), extremely high dischargeable quantity ($VLQ$ d ). Define the dischargeable quantity as the input quantity $z$ of the discharge fuzzy controller 2 , and the universe of discourse covering the input quantity $z$ 2 is $(0,72)$;

[0104] Charging power $P$ c The fuzzy subsets are set as: extremely low charging power ($VLP$ c ), low charging power ($LP$ c ), medium - low charging power ($MLP$ c ), low - medium charging power ($LMP$ c ), medium charging power ($MP$ c ), high - medium charging power ($HMP$ c ), medium - high charging power ($MHP$ c ), high charging power ($HP$ c ), extremely high charging power ($VHP$ c ). Define the charging power $P$ c as the output quantity $o$ of the charging fuzzy controller 1, for covering the output quantity o 1 The universe of discourse is [0, 22];

[0105] Discharge power P d The fuzzy subsets are set as: very low discharge power (VLP d ), low discharge power (LP d ), medium - low discharge power (MLP d ), low - medium discharge power (LMP d ), medium discharge power (MP d ), high - medium discharge power (HMP d ), medium - high discharge power (MHP d ), high discharge power (HP d ), very high discharge power (VHP d ). Define the discharge power P d as the output quantity o of the discharge fuzzy controller 2 , for covering the output quantity o 2 The universe of discourse is [-22, 0]

[0106] The membership functions and distribution diagrams of each fuzzy subset are not elaborated here.

[0107] Step 4.2: Considering the characteristics of the basic load of the regional power grid, 75 fuzzy rules are established for the charging fuzzy controller and the discharge fuzzy controller in the V2G cluster output optimization scheduling strategy respectively. In the format of "If - Then", it consists of two parts: the premise and the conclusion. The premise describes the conditions of the input variables, and the conclusion describes the fuzzy set of the output variables. Examples are shown in Table 1 and Table 2:

[0108] Table 1 Fuzzy control rules of the charging fuzzy controller

[0109]

[0110] Table 2 Fuzzy control rules of the discharge fuzzy controller

[0111]

[0112] Step 5: Develop the V2G cluster optimization scheduling strategy. According to the classification criteria in Step 1.2, the incoming clusters are classified into 3 types of clusters, and charge - discharge output strategies that conform to the characteristics of different V2G clusters are developed. Randomly select M vehicles from the sample data, i = 1, 2, …, M, the access time T(i), the shortest charging time Tc_min(i), the charging demand E c , the dischargeable energy E d , the schedulable capacity K, the basic load value P l , the charging power P c , the discharge power P d, the initial SOC is SOC in (i), the lowest SOC limited by discharge is SOC_ min , at the grid connection time t_in, let i = 0.

[0113] Step 5.1: Let i = i + 1. If i = M + 1, then go to Step 6

[0114] Step 5.2: Judge whether Tc_min(i) ≥ T(i) is satisfied. If so, go to Step 5.3; if not, go to Step 5.6

[0115] Step 5.3: For Cluster 1, let t = t_in(i), and vehicle i starts charging at the rated power at time t

[0116] Step 5.4: Calculate the SOC of the i-th vehicle at time t + 1

[0117] Step 5.5: Judge whether the off-grid time is reached. If not, let t = t + 1 and go to Step 5.4; if so, go to Step 5.1.

[0118] Step 5.6: Judge whether SOC in (i) ≥ 0.5 is satisfied. If not, go to Step 5.7; if so, go to Step 5.12

[0119] Step 5.7: For Cluster 2, let t = t_in(i), and calculate the E of vehicle i at time t c 、K, and load P l , calculate the P at time t through the charging fuzzy controller c

[0120] Step 5.8: Calculate the SOC of the i-th vehicle at time t

[0121] Step 5.9: Judge whether SOC < 1 is satisfied. If so, go to Step 5.10; if not, go to Step 5.1

[0122] Step 5.10: Judge whether the shortest charging time is reached. If so, charge at the rated power at the next moment; if not, calculate the E of vehicle i at time t + 1 c 、K, and load P l , calculate the P at time t + 1 through the charging fuzzy controller c

[0123] Step 5.11: Judge whether the off-grid time is reached. If not, let t = t + 1 and go to Step 5.8; if so, go to Step 5.1

[0124] Step 5.12: For Cluster 3, let t = t_in(i), and calculate the E of vehicle i at time t d 、K, and load P l, calculate the P at time t through the discharge fuzzy controller d

[0125] Step 5.13: Calculate the SOC of the i-th vehicle at time t+1

[0126] Step 5.14: Determine whether SOC≤SOC_ is satisfied. If not, go to Step 5.15; if so, go to Step 5.16 min , if not, go to Step 5.15; if so, go to Step 5.16

[0127] Step 5.15: Calculate the E of vehicle i at time t+1 d 、K, and load P l , calculate the P at time t+1 through the discharge fuzzy controller d , let t=t+1 and go to Step 5.13

[0128] Step 5.16: Determine whether the shortest charging time is reached. If so, charge at the rated power at the next moment; if not, calculate the E of vehicle i at time t+1 c 、K, and load the P at time t+1 l , calculate the P at time t+1 through the charging fuzzy controller c

[0129] Step 5.17: Determine whether the off-grid time is reached. If not, let t=t+1 and go to Step 5.13; if so, go to Step 5.1

[0130] Step 6: Obtain the total load curve including the electric vehicle load under the V2G output optimization scheduling strategy through simulation experiments, obtain the load peak and valley values, and calculate the load peak-valley ratio.

[0131] The present invention also provides an electronic device, including a memory, a processor, and computer program instructions stored on the memory and capable of being run by the processor. When the processor runs the computer program instructions, the method steps as described above can be implemented.

[0132] The present invention also provides a computer-readable storage medium, on which computer program instructions capable of being run by the processor are stored. When the processor runs the computer program instructions, the method steps as described above can be implemented.

[0133] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, a system, or a computer program product. Therefore, the present application can adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can adopt 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.

[0134] This application is described with reference to the flowcharts and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the present application. It should be understood that each flow and / or block in the flowchart and / or block diagram, and the combination of flows and / or blocks in the flowchart and / or block diagram, 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 device to generate a machine, such that the instructions executed by the processor of the computer or other programmable data processing device generate means for implementing the functions specified in one flow Figure 1 one flow or multiple flows and / or blocks Figure 1 or multiple blocks.

[0135] 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, such that the instructions stored in the computer-readable memory generate a manufactured article including instruction means that implement the functions specified in one flow Figure 1 one flow or multiple flows and / or blocks Figure 1 or multiple blocks.

[0136] These computer program instructions can also be loaded onto a computer or other programmable data processing device, such that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process, so that the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in one flow Figure 1 one flow or multiple flows and / or blocks Figure 1 or multiple blocks.

[0137] As mentioned above, it is only the preferred embodiment of the present invention, and it is not a limitation to the present invention in other forms. Any person skilled in the art may use the disclosed technical content to make changes or modifications into equivalent embodiments with equivalent changes. However, any simple modification, equivalent change, and modification made to the above embodiments based on the technical essence of the present invention without departing from the technical solution content of the present invention still fall within the protection scope of the technical solution of the present invention.

Claims

1. A V2G cluster classification optimization scheduling method based on fuzzy control, characterized in that: include: Consider dividing electric vehicles into different V2G clusters according to their status characteristics after they are connected to the grid; Determine the fuzzy distribution of input and output quantities, establish fuzzy rules, and design a three-input-single-output charging and discharging power fuzzy controller; An optimal dispatching strategy is formulated according to the characteristics of the cluster. In the strategy, the charging and discharging power of the cluster is controlled by fuzzy control to achieve "peak shaving and valley filling" of the regional power grid load.

2. The V2G cluster classification optimization scheduling method based on fuzzy control according to claim 1 is characterized in that: The method comprises the following steps: Step 1: V2G cluster classification: Based on the status information characteristics of electric vehicles, electric vehicles with similar status characteristics are classified into the same V2G cluster; Step 2: Model the SOC and power boundary of the V2G cluster during charging and discharging according to different clusters; Step 3: Introduce the dispatchable capacity k to characterize the impact of network access time on the charging and discharging power control of the V2G cluster; Step 4: According to the characteristics of cluster classification, the fuzzy control method is used to control the charging and discharging power, a three-input-single-output charging and discharging power fuzzy controller is designed, and the fuzzy control rules are established; Step 5: Formulate a V2G cluster optimization scheduling strategy: According to the classification criteria for V2G cluster classification in step 1, classify the clusters that have joined the network into three types of clusters, and formulate charging and discharging output strategies that meet the characteristics of different V2G clusters; Step 6: The simulation experiment obtains the total load curve including the electric vehicle load under the V2G output optimization scheduling strategy, obtains the load peak and valley values, and calculates the load peak and valley rate.

3. The V2G cluster classification optimization scheduling method based on fuzzy control according to claim 2 is characterized in that: Step 1 is implemented as follows: Step 1.1: Define the three state characteristics of electric vehicles, the initial state of charge SOC of electric vehicles in , the shortest charging time is T e , and the duration of electric vehicle access to the grid is ΔT; Step 1.2: According to the state characteristics of electric vehicles, the V2G clusters in the region are divided into the following three types of clusters according to the predetermined criteria: Unschedulable clusters, i.e., ΔT≤T e , rechargeable dispatch cluster, i.e. ΔT>T e &SOC in <SOC dis , charge-discharge scheduling cluster, i.e. ΔT>T e &SOC in ≥SOC dis ; Among them, SOC dis is the SOC threshold at which the V2G cluster can participate in discharge.

4. The V2G cluster classification optimization scheduling method based on fuzzy control according to claim 3 is characterized in that: In step 1.1, T e The calculation formula is as follows: In the formula, SOC out The SOC that the owner expects to achieve after charging, SOC in The initial SOC of the electric vehicle connected to the charging pile, E e is the rated capacity of the electric vehicle battery pack, η c is the charging efficiency of electric vehicles, P ce Rated charging power for electric vehicles; The ΔT calculation formula is as follows: ΔT=t out -t in Where, t out is the off-grid time of the electric vehicle, t in is the time when electric vehicles enter the grid.

5. The V2G cluster classification optimization scheduling method based on fuzzy control according to claim 2 is characterized in that: In step 3, k is defined as follows: In the formula, ΔT is the network access time, T e is the shortest charging time, the value range of k is (0,1), and the larger the k value is, the stronger the dispatchability is.

6. The V2G cluster classification optimization scheduling method based on fuzzy control according to claim 2 is characterized in that: Step 4 is implemented as follows: Step 4.1: Set the fuzzy distribution of input and output quantities. The input quantity includes the basic load P l , dispatchable capacity k, charging demand Q c , Dischargeable capacity Q d , the output includes the charging power P c , discharge power P d ; Step 4.2, considering the basic load characteristics of the regional power grid, 75 fuzzy rules are established for the charging fuzzy controller and the discharging fuzzy controller in the V2G cluster output optimization scheduling strategy, respectively. The "If-Then" format is adopted, which consists of two parts: premise and conclusion. The premise describes the conditions of the input variables, and the conclusion describes the fuzzy set of the output variables.

7. The V2G cluster classification optimization scheduling method based on fuzzy control according to claim 6 is characterized in that: Step 4.1 is implemented as follows: Step 4.1.1 Set the base load P l The fuzzy subset is: VSP with minimal base load l , Basic load small SP l 、MP in base load l 、Base load large LP l , VLP with extremely high base load l , the basic load P l Normalization, defined as the input quantity x, according to the definition of fuzzy sets and triangular membership functions: covers the base load P l The domain of the input quantity x is [0,1], and the membership functions of each fuzzy subset are as follows: VSP l (x)=(0.25-x) / 0.25 0≤x≤0.25 VLP l (x)=(x-0.75) / 0.25 0.75≤x≤1 Step 4.1.2: Set the dispatchable capacity k and charging demand Q c , Dischargeable capacity Q d 、Charging power P c , discharge power P d The fuzzy subsets and the membership functions of each fuzzy subset; The fuzzy subsets of the dispatchable capacity k are set as: dispatchable capacity small Sk, dispatchable capacity medium Mk, dispatchable capacity large Lk, and the dispatchable capacity is defined as the input quantity y. The domain used to cover the input quantity y is (0,1); Charging demand Q c The fuzzy subset is set as: charging demand is minimal VSQ c 、Small charging demand SQ c 、Charging demand MQ c 、Large demand for charging c 、VLQ has a huge demand for charging c , the charging demand is defined as the input z1 of the charging fuzzy controller, and the domain used to cover the input z1 is (0,90); Dischargeable capacity Q d The fuzzy subset is set as: the minimum discharge quantity VSQ d , small discharge capacity SQ d 、Dischargeable capacity MQ d , large discharge capacity LQ d 、VLQ can discharge a large amount of d , the dischargeable amount is defined as the input quantity z2 of the discharge fuzzy controller, and the domain used to cover the input quantity z2 is (0,72); Charging power P c The fuzzy subset is set as: Very Low Charging Power VLP c , low charging power LP c , charging power low and medium MLP c 、Low medium charging power LMP c , charging power medium MP c , charging power high medium HMP c , charging power medium to high MHP c , High charging power HP c , Very high charging power VHP c , the charging power P c It is defined as the output o1 of the charging fuzzy controller, and the domain used to cover the output o1 is [0,22]; Discharge power P d The fuzzy subset is set as: Very low discharge power VLP d , low discharge power LP d , medium low discharge power MLP d , discharge power low medium LMP d , medium discharge power MP d , high discharge power HMP d , medium and high discharge power MHP d , high discharge power HP d , extremely high discharge power VHP d , the discharge power P d Defined as the output o2 of the discharge fuzzy controller, the domain used to cover the output o2 is [-22,0] Based on the above setting methods of each fuzzy subset, the membership function of each fuzzy subset is defined.

8. The V2G cluster classification optimization scheduling method based on fuzzy control according to claim 2 is characterized in that: Step 5 is implemented as follows: Randomly select M vehicles from the sample data, i = 1, 2, ..., M, network access time T(i), shortest charging time Tc_min(i), charging demand E c , discharge capacity E d , dispatchable capacity K, basic load value P l , charging power P c , discharge power P d , the initial SOC is SOC in (i) The minimum SOC limited by discharge is SOC_ min , network access time t_in, let i = 0; Step 5.1, let i = i + 1, if i = M + 1, go to step 6; Step 5.2: Determine whether Tc_min(i)≥T(i). If yes, go to step 5.3; if no, go to step 5.

6. Step 5.3, cluster 1, let t = t_in(i), vehicle i starts charging at rated power at time t; Step 5.4, calculate the SOC of the i-th vehicle at time t+1; Step 5.5, determine whether the off-grid time has been reached, if not, set t=t+1 and go to step 5.4; if yes, go to step 5.1; Step 5.6: Determine whether SOC is met in (i) ≥ 0.5, if not, go to step 5.7; if yes, go to step 5.12; Step 5.7, Cluster 2, let t = t_in(i), calculate E of vehicle i at time t c , K, and load P l , the charging fuzzy controller calculates P at time t c ; Step 5.8, calculate the SOC of the i-th vehicle at time t; Step 5.9: Determine whether SOC<1 is satisfied. If so, go to step 5.10; if not, go to step 5.1; Step 5.10: Determine whether the shortest charging time has been reached. If so, charge at rated power at the next moment. If not, calculate the E of vehicle i at time t+1. c , K, and load P l , the charging fuzzy controller calculates P at time t+1 c ; Step 5.11, determine whether the off-grid time has been reached, if not, set t=t+1 and go to step 5.8; if yes, go to step 5.1; Step 5.12, Cluster 3, let t = t_in(i), calculate E of vehicle i at time t d , K, and load P l , the discharge fuzzy controller calculates P at time t d ; Step 5.13, calculate the SOC of the i-th vehicle at time t+1; Step 5.14: Determine whether SOC≤SOC_ min If not, go to step 5.15; if yes, go to step 5.16; Step 5.15: Calculate E of vehicle i at time t+1 d , K, and load P l , the discharge fuzzy controller calculates P at time t+1 d , let t = t + 1 and go to step 5.13; Step 5.16: Determine whether the shortest charging time has been reached. If so, charge at rated power at the next moment. If not, calculate the E of vehicle i at time t+1. c , K, and load P at time t+1 l , the charging fuzzy controller calculates P at time t+1 c ; Step 5.17: Determine whether the off-grid time has been reached. If not, set t=t+1 and go to step 5.13; if so, go to step 5.

1.

9. An electronic device, characterized in that: The method comprises a memory, a processor and computer program instructions stored in the memory and capable of being executed by the processor. When the processor executes the computer program instructions, the method steps as claimed in any one of claims 1 to 8 can be implemented.

10. A computer-readable storage medium having stored thereon computer program instructions that can be executed by a processor, and when the processor executes the computer program instructions, the method steps according to any one of claims 1 to 8 can be implemented.

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