V2g cluster classification and optimal scheduling method based on fuzzy control

Abstract

The present application relates to a kind of V2G cluster classification optimization scheduling method based on fuzzy control, belong to electric vehicle cluster optimization scheduling field.The method, first, consider according to the state feature of electric vehicle after entering network, electric vehicle is divided into different V2G cluster.Then, the fuzzy distribution of input, output is determined, fuzzy rule is established, and a three-input-single-output charge-discharge power fuzzy controller is designed.Finally, according to the characteristics of cluster, the optimization scheduling strategy that meets its characteristics is formulated, and the charge-discharge power of cluster is controlled in the strategy by fuzzy control to achieve the "peak load shifting" of regional power grid load.

Description

Technical Field

[0001] This invention belongs to the field of electric vehicle cluster optimization scheduling, specifically involving a V2G cluster classification optimization scheduling method based on fuzzy control. Background Technology

[0002] Electric vehicle (EV) clusters exhibit flexible, unregulated charging patterns that align with travel patterns. Therefore, the large number of EVs connected to the grid during peak commuting hours generates significant charging loads, increasing the peak-to-valley load difference in the regional power grid. This leads to issues such as equipment overload risks, decreased energy efficiency, and grid instability. Developing a V2G cluster output optimization scheduling strategy to eliminate the impact of unregulated charging is an effective means of addressing the load fluctuations caused by the large-scale integration of EVs into the grid.

[0003] Currently, research on V2G cluster optimization scheduling mainly includes electric vehicle charging and discharging optimization scheduling strategies that consider multiple objectives and electric vehicle charging and discharging scheduling strategies that consider two-layer optimization.

[0004] Multi-objective optimization is a mathematical optimization method aimed at simultaneously optimizing multiple interrelated and potentially conflicting objectives. In research on electric vehicle charging and discharging scheduling strategies, multi-objective optimization is used to comprehensively consider the interests and constraints of multiple aspects, including the grid side, the user side, and the environment, to seek a set of optimal solutions that satisfy as many objectives as possible. However, in the process of optimizing multiple objectives, it is difficult to achieve the optimal weight for each objective. If the emphasis is too much on reducing user charging costs, the scheduling strategy may frequently guide electric vehicles to charge during periods of low electricity prices. Although this helps users save money, it may exacerbate the load pressure on the grid during certain periods, which is not conducive to the smoothing of the grid load curve and may even affect the safe and stable operation of the grid. Conversely, if too much emphasis is placed on peak shaving and valley filling and improving the load curve, it may require electric vehicles to discharge during certain periods. While this is beneficial to the grid, it may increase the inconvenience of charging for users, violating the original intention of economic efficiency and convenience, and leading to a decrease in user acceptance.

[0005] Bi-level optimization is a composite optimization structure often used to handle complex decision-making problems with inherent hierarchical relationships and interactive effects. In researching electric vehicle charging and discharging optimization scheduling strategies, the bi-level optimization framework can effectively address multi-objective, multi-agent, and inter-decision-level dependencies, especially suitable for scenarios that simultaneously consider the interests of the grid and users. However, such strategies generally rely on solvers, planning algorithms, or intelligent optimization techniques during model construction and solution. When facing large-scale application scenarios containing massive amounts of electric vehicle data, the dimensionality and complexity of the optimization problem increase dramatically. Traditional solvers or algorithms often suffer significant impacts on their solution accuracy when handling such high-dimensional, nonlinear, and multi-constraint problems. Secondly, in large-scale electric vehicle data environments, the computational load of the optimization model increases significantly, leading to slower model execution and making it difficult to meet the rapid response requirements of real-time scheduling. Summary of the Invention

[0006] The purpose of this invention is to provide a V2G cluster classification and optimization scheduling method based on fuzzy control. First, electric vehicles are classified into different V2G clusters based on their state characteristics after joining the grid. Then, the fuzzy distribution of input and output quantities is determined, fuzzy rules are established, and a three-input, single-output fuzzy controller for charging and discharging power is designed. Finally, an optimized scheduling strategy is formulated based on the cluster characteristics, where fuzzy control is used to control the charging and discharging power of the cluster to achieve peak shaving and valley filling of the regional power grid load.

[0007] To achieve the above objectives, the technical solution of the present invention is: a V2G cluster classification optimization scheduling method based on fuzzy control, comprising:

[0008] Consider classifying electric vehicles into different V2G clusters based on their status characteristics after they are connected to the network.

[0009] Determine the fuzzy distribution of input and output quantities, establish fuzzy rules, and design a three-input-single-output fuzzy controller for charging and discharging power.

[0010] Based on the characteristics of the cluster, an optimized scheduling strategy is formulated that conforms to its features. The strategy uses fuzzy control to control the charging and discharging power of the cluster in order to achieve "peak shaving and valley filling" of the regional power grid load.

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

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

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

[0014] Step 3: Introduce schedulable capability k to characterize the impact of network access duration on V2G cluster charging and discharging power control;

[0015] Step 4: Based on the characteristics of cluster classification, fuzzy control is used to control the charging and discharging power. A three-input single-output fuzzy controller for charging and discharging power is designed, and fuzzy control rules are established.

[0016] Step 5: Formulate V2G cluster optimization scheduling strategy: Based on the classification criteria of V2G cluster classification in Step 1, classify the clusters entering the network into 3 types of clusters, and formulate charging and discharging power output strategies that conform to the characteristics of different V2G clusters.

[0017] Step 6: Simulation experiment to obtain the total load curve including electric vehicle load under V2G output optimization scheduling strategy, obtain load peak and valley values, and calculate load peak and valley ratio.

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

[0019] Step 1.1: Define three state characteristics of an electric vehicle, including its initial state of charge. The shortest charging time is The network access time for electric vehicles is ;

[0020] Step 1.2: Based on the state characteristics of electric vehicles, the V2G clusters in the region are divided into the following three categories according to predetermined criteria: unschedulable clusters, i.e. Rechargeable scheduling cluster & Rechargeable and dischargeable scheduling clusters & ;in The SOC threshold at which a V2G cluster can participate in discharge.

[0021] In one embodiment of the present invention, in step 1.1, The calculation formula is as follows:

[0022]

[0023] In the formula, The SOC (State of Charge) that car owners expect to achieve when charging. The initial state of charge (SOC) for connecting electric vehicles to charging stations. The rated capacity of the electric vehicle battery pack. Improve the charging efficiency of electric vehicles. Rated charging power for electric vehicles;

[0024] The calculation formula is as follows:

[0025]

[0026] In the formula, This refers to the off-grid time of electric vehicles. This refers to the registration time of electric vehicles on the grid.

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

[0028]

[0029] In the formula, For network access duration, To determine the shortest charging time, k takes values ​​in the range of (0, 1). The larger the value of k, the stronger the scheduling capability.

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

[0031] Step 4.1: Set the fuzzy distribution of input and output quantities. Input quantities include the base load. Dispatchability k, charging demand Dischargeable quantity Output includes charging power P c Discharge power P d ;

[0032] Step 4.2: Considering the basic load characteristics of the regional power grid, 75 fuzzy rules are established for the charging fuzzy controller and discharging fuzzy controller in the V2G cluster output optimization scheduling strategy. The rules adopt the "If-Then" format and consist 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.

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

[0034] Step 4.1.1 Set the base load The fuzzy subset is: Minimal base load VSP l Small base load SP l MP in the base load l Large base load LP l VLP with extremely high base load l , base load Normalization, defined as input x, according to the definitions of fuzzy sets and triangular membership functions, covers the basic load. The universe of discourse for the input x is [0, 1], and the membership functions of each fuzzy subset are as follows:

[0035]

[0036]

[0037]

[0038]

[0039]

[0040] Step 4.1.2: Set the dispatchable capacity k and charging demand. Dischargeable quantity Charging power P c Discharge power P d The fuzzy subsets and their membership functions;

[0041] The fuzzy subset of schedulable capability k is set as: schedulable capability small Sk, schedulable capability medium Mk, and schedulable capability large Lk. Schedulable capability is defined as input quantity y, and the universe of discourse covering input quantity y is (0, 1).

[0042] Charging demand The fuzzy subset is set as: VSQ with minimal charging demand. c Low charging demand SQ c MQ charging demand c High charging demand LQ c The charging demand is extremely high for VLQ c The charging demand is defined as the input of the charging fuzzy controller. , used to cover input volume The domain of discourse is (0, 90);

[0043] Dischargeable amount The fuzzy subset is set as follows: VSQ with extremely small discharge capacity. d Small discharge capacity SQ d MQ in the discharge capacity d Large discharge capacity LQ d Extremely high discharge capacity VLQ d The dischargeable quantity is defined as the input quantity of the discharge fuzzy controller. , used to cover input volume The domain of discourse is (0, 72);

[0044] Charging power P c The fuzzy subset is set as: extremely low charging power VLP c Low charging power LP c Low to medium charging power MLP c Low to medium charging power LMP c Medium charging power MP cHigh-power HMP c Medium to high charging power (MHP) c High charging power HP c Extremely high charging power (VHP) c The charging power P c Defined as the output of the charging fuzzy controller Used to cover output quantity The domain of discourse is [0, 22].

[0045] Discharge power P d The fuzzy subset is set as: VLP with extremely low discharge power. d Low discharge power LP d Medium to low discharge power MLP d Low to medium discharge power LMP d Medium discharge power MP d High-power discharge HMP d 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 of the discharge fuzzy controller Used to cover output quantity The domain of discourse is [-22, 0].

[0046] Based on the above settings for each fuzzy subset, the membership function of each fuzzy subset is defined.

[0047] In one embodiment of the present invention, step 5 is specifically implemented as follows:

[0048] M vehicles are randomly selected from the sample data, i=1,2,…,M, with a network access duration of… Shortest charging time Charging demand Discharge capacity Dispatchable capacity K, base load 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 Let i = 0;

[0049] Step 5.1: Let i = i + 1. If i = M + 1, then go to step 6.

[0050] Step 5.2: Determine if the condition is met. ≥ If yes, proceed to step 5.3; otherwise, proceed to step 5.6.

[0051] Step 5.3, Cluster 1, let t = (i), the vehicle starts charging at rated power at time t;

[0052] Step 5.4: Calculate the SOC of the i-th vehicle at time t+1;

[0053] Step 5.5: Determine if the disconnection time has been reached. If not, set t = t + 1 and go to step 5.4; if yes, go to step 5.1.

[0054] Step 5.6: Determine if SOC is satisfied. in (i)≥0.5, if not, go to step 5.7; if yes, go to step 5.12;

[0055] Step 5.7, Cluster 2, let t = (i), calculate vehicle at time t (i). K, and load P l P at time t is calculated using a charging fuzzy controller. c ;

[0056] Step 5.8: Calculate the SOC of the i-th vehicle at time t;

[0057] Step 5.9: Determine if SOC < 1. If yes, proceed to step 5.10; otherwise, proceed to step 5.1.

[0058] Step 5.10: Determine if the shortest charging time has been reached. If yes, charge at rated power at the next time step; otherwise, calculate the charging time of vehicle i at time t+1. K, and load P l P at time t+1 is calculated using a charging fuzzy controller. c ;

[0059] Step 5.11: Determine if the disconnection time has been reached. If not, set t = t + 1 and go to step 5.8; if yes, go to step 5.1.

[0060] Step 5.12, Cluster 3, let t = (i), calculate vehicle at time t i. K, and load P l P at time t is calculated using a discharge fuzzy controller. d ;

[0061] Step 5.13: Calculate the SOC of the i-th vehicle at time t+1;

[0062] Step 5.14: Determine if SOC ≤ SOC_ minIf not, proceed to step 5.15; if yes, proceed to step 5.16.

[0063] Step 5.15: Calculate the time t+1 for vehicle i. K, and load P l P at time t+1 is calculated using a discharge fuzzy controller. d Let t = t + 1 and proceed to step 5.13;

[0064] Step 5.16: Determine if the shortest charging time has been reached. If yes, charge at rated power at the next time step; otherwise, calculate the charging time of vehicle i at time t+1. K, and load P at time t+1. l P at time t+1 is calculated using a charging fuzzy controller. c ;

[0065] Step 5.17: Determine whether the disconnection time has been reached. If not, set t=t+1 and go to step 5.13; if yes, go to step 5.1.

[0066] The present invention also provides an electronic device, including a memory, a processor, and computer program instructions stored in the memory and executable by the processor, wherein when the processor executes the computer program instructions, it can implement the steps of the method described above.

[0067] The present invention also provides a computer-readable storage medium having stored thereon computer program instructions that can be executed by a processor, wherein when the processor executes the computer program instructions, it can implement the steps of the method described above.

[0068] Compared with the prior art, the present invention has the following beneficial effects:

[0069] ① This invention starts with the state information characteristics of electric vehicles, grouping electric vehicles with similar state characteristics into the same type of V2G cluster. Then, based on the state characteristics of different V2G clusters, corresponding optimized scheduling strategies are formulated. This allows for charging and discharging processes that better reflect the actual state of V2G clusters with different state characteristics, thereby achieving optimized scheduling of electric vehicle clusters throughout the region. Utilizing the cluster classification concept, subdividing V2G clusters satisfies user charging needs while improving grid dispatching capabilities, solving the problem of determining weights in multi-objective optimization.

[0070] ② This invention uses fuzzy control to control the charging and discharging power. By determining the fuzzy distribution of input and output quantities, fuzzy rules are established, and a three-input-single-output fuzzy controller for charging and discharging power is designed to calculate the optimal charging and discharging power for V2G scheduling. Attached Figure Description

[0071] Figure 1Optimize the overall scheduling framework for V2G clusters.

[0072] Figure 2 Flowchart for optimizing scheduling strategies for V2G clusters.

[0073] Figure 3 Modeling the SOC and power boundary during the charging and discharging process of V2G clusters.

[0074] Figure 4 Based on the load P l Fuzzy subset distribution. Detailed Implementation

[0075] The technical solution of the present invention will now be described in detail with reference to the accompanying drawings.

[0076] It should be noted that the following detailed descriptions are exemplary and intended to provide further explanation of this application. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains.

[0077] It should be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the exemplary embodiments according to this application. As used herein, the singular form is intended to include the plural form as well, unless the context clearly indicates otherwise. Furthermore, 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.

[0078] This invention provides a V2G cluster classification optimization scheduling method based on fuzzy control, comprising:

[0079] Consider classifying electric vehicles into different V2G clusters based on their status characteristics after they are connected to the network.

[0080] Determine the fuzzy distribution of input and output quantities, establish fuzzy rules, and design a three-input-single-output fuzzy controller for charging and discharging power.

[0081] Based on the characteristics of the cluster, an optimized scheduling strategy is formulated that conforms to its features. The strategy uses fuzzy control to control the charging and discharging power of the cluster in order to achieve "peak shaving and valley filling" of the regional power grid load.

[0082] The following is a detailed implementation process of the present invention.

[0083] like Figure 1 , 2As shown, the optimized scheduling scenario in this embodiment is the charging of electric private cars 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 the discharging of a dischargeable V2G cluster is SOC_min = 0.2. This embodiment presents a V2G cluster classification optimization scheduling method based on fuzzy control, including the following steps:

[0084] Step 1: V2G cluster classification. Based on the state information characteristics of electric vehicles, electric vehicles with similar state characteristics are grouped into the same type of V2G cluster.

[0085] Step 1.1: Define three state characteristics for the initial state of charge of an electric vehicle. The shortest charging time is The network access time for electric vehicles is , and The calculation formula is as follows:

[0086]

[0087] In the formula, The SOC (State of Charge) expected by the car owner (as assumed in this article) =1), The initial state of charge (SOC) for connecting electric vehicles to charging stations. The rated capacity of the electric vehicle battery pack. For electric vehicle charging efficiency, take =0.9, For the rated charging power of electric vehicles, take =22kW.

[0088]

[0089] In the formula, This refers to the off-grid time of electric vehicles. This refers to the registration time of electric vehicles on the grid.

[0090] Step 1.2: Based on the electric vehicle state characteristics described above, the V2G clusters in the region are divided into the following three categories according to certain criteria: unschedulable clusters ( ), rechargeable scheduling cluster ( & ) and rechargeable scheduling clusters ( & ).in The SOC threshold at which a V2G cluster can participate in discharge.

[0091] Step 2: Model the SOC and power boundary during the charging and discharging process of the V2G cluster according to different clusters. See [link / reference] Figure 3 .

[0092] Step 3: Introduce the schedulable capability k to characterize the impact of network access duration on the charging and discharging power control of V2G clusters. The definition of k is as follows:

[0093]

[0094] In the formula, For network access duration, This represents the shortest charging time. The value of k ranges from (0, 1), and the larger the value of k, the stronger the scheduling capability.

[0095] Step 4: Based on the characteristics of cluster classification, fuzzy control is adopted to control the charging and discharging power. A three-input single-output fuzzy controller for charging and discharging power is designed, and fuzzy control rules are established.

[0096] Step 4.1: Set the fuzzy distribution of input and output quantities. Input quantities include the base load. Dispatchability k, charging demand Dischargeable quantity Output includes charging power P c Discharge power P d .

[0097] Step 4.1.1, as follows Figure 4 As shown, set the base load. The fuzzy subset is: Minimal Base Load (VSP) l ), low base load (SP) l ), in the base load (MP) l ), large base load (LP) l ), Maximum Base Load (VLP) l ), will base load Normalization, defined as input x, according to the definitions of fuzzy sets and triangular membership functions, covers the basic load. The universe of discourse for the input x is [0, 1], and the membership functions of each fuzzy subset are as follows:

[0098]

[0099]

[0100]

[0101]

[0102]

[0103] Step 4.1.2 is similar to step 4.1.1; set fuzzy subsets of other input and output quantities.

[0104] The fuzzy subset of schedulable capability k is set as: small schedulable capability (Sk), medium schedulable capability (Mk), and large schedulable capability (Lk). Schedulable capability is defined as input quantity y, and the universe of discourse covering input quantity y is (0, 1).

[0105] Charging demand The fuzzy subset is set to: very low charging demand (VSQ) c ), low charging demand (SQ) c ), charging demand (MQ) c High charging demand (LQ) c The charging demand is extremely high (VLQ) c The charging demand is defined as the input of the charging fuzzy controller. , used to cover input volume The domain of discourse is (0, 90);

[0106] Dischargeable amount The fuzzy subset is set to: extremely small discharge capacity (VSQ) d Small discharge capacity (SQ) d ), dischargeable quantity (MQ) d Large discharge capacity (LQ) d ), with extremely high discharge capacity (VLQ) d The dischargeable quantity is defined as the input quantity of the discharge fuzzy controller. , used to cover input volume The domain of discourse is (0, 72);

[0107] Charging power P c The fuzzy subset is set to: Very Low Charging Power (VLP) c Low charging power (LP) c Medium and low charging power (MLP) c Low to medium charging power (LMP) c ), medium charging power (MP) c High-performance charging (HMP) c Medium to high charging power (MHP) c High charging power (HP) c Extremely high charging power (VHP) c ), will charge power P c Defined as the output of the charging fuzzy controller Used to cover output quantity The domain of discourse is [0, 22].

[0108] Discharge power P d The fuzzy subset is set to: extremely low discharge power (VLP) d Low discharge power (LP) d ), medium to low discharge power (MLP) d ), low to medium discharge power (LMP) d ), medium discharge power (MP) d High-middle discharge power (HMP) d ), medium to high discharge power (MHP) d High discharge power (HP) d Extremely high discharge power (VHP) d ), will discharge power P d Defined as the output of the discharge fuzzy controller Used to cover output quantity The domain of discourse is [-22, 0].

[0109] The membership functions and distribution diagrams of each fuzzy subset are not described here.

[0110] Step 4.2: Considering the characteristics of the regional power grid's basic load, 75 fuzzy rules were established for the charging and discharging fuzzy controllers in the V2G cluster output optimization scheduling strategy. These rules, using an "If-Then" format, consist of two parts: a premise describing the conditions of the input variables and a conclusion describing the fuzzy set of the output variables. Examples are shown in Tables 1 and 2.

[0111] Table 1 Fuzzy Control Rules for Charging Fuzzy Controller

[0112]

[0113] Table 2 Fuzzy Control Rules for Discharge Fuzzy Controller

[0114]

[0115] Step 5: Develop an optimized scheduling strategy for V2G clusters. Based on the classification criteria in Step 1.2, classify the connected clusters into three types and develop charging and discharging power output strategies tailored to the characteristics of each cluster. Randomly select M vehicles (i=1,2,…,M) from the sample data, with network access durations of… Shortest charging time Charging demand Discharge capacity Dispatchable capacity K, base load 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 Let i = 0.

[0116] Step 5.1: Let i = i + 1. If i = M + 1, then go to step 6.

[0117] Step 5.2: Determine if the condition is met. ≥ If yes, proceed to step 5.3; otherwise, proceed to step 5.6.

[0118] Step 5.3, Cluster 1, let t = (i), the vehicle starts charging at rated power at time t;

[0119] Step 5.4: Calculate the SOC of the i-th vehicle at time t+1;

[0120] Step 5.5: Determine if the disconnection time has been reached. If not, set t = t + 1 and go to step 5.4; if yes, go to step 5.1.

[0121] Step 5.6: Determine if SOC is satisfied. in (i)≥0.5, if not, go to step 5.7; if yes, go to step 5.12;

[0122] Step 5.7, Cluster 2, let t = (i), calculate vehicle at time t i. K, and load P l P at time t is calculated using a charging fuzzy controller. c ;

[0123] Step 5.8: Calculate the SOC of the i-th vehicle at time t;

[0124] Step 5.9: Determine if SOC < 1. If yes, proceed to step 5.10; otherwise, proceed to step 5.1.

[0125] Step 5.10: Determine if the shortest charging time has been reached. If yes, charge at rated power at the next time step; otherwise, calculate the charging time of vehicle i at time t+1. K, and load P l P at time t+1 is calculated using a charging fuzzy controller. c ;

[0126] Step 5.11: Determine if the disconnection time has been reached. If not, set t = t + 1 and go to step 5.8; if yes, go to step 5.1.

[0127] Step 5.12, Cluster 3, let t = (i), calculate vehicle at time t i. K, and load P l P at time t is calculated using a discharge fuzzy controller. d ;

[0128] Step 5.13: Calculate the SOC of the i-th vehicle at time t+1;

[0129] Step 5.14: Determine if SOC ≤ SOC_ min If not, proceed to step 5.15; if yes, proceed to step 5.16.

[0130] Step 5.15: Calculate the time t+1 for vehicle i. K, and load P l P at time t+1 is calculated using a discharge fuzzy controller. d Let t = t + 1 and proceed to step 5.13;

[0131] Step 5.16: Determine if the shortest charging time has been reached. If yes, charge at rated power at the next time step; otherwise, calculate the charging time of vehicle i at time t+1. K, and load P at time t+1. l P at time t+1 is calculated using a charging fuzzy controller. c ;

[0132] Step 5.17: Determine if the disconnection time has been reached. If not, set t = t + 1 and go to step 5.13; if yes, go to step 5.1.

[0133] Step 6: Simulation experiment to obtain the total load curve including electric vehicle load under V2G output optimization scheduling strategy, obtain load peak and valley values, and calculate load peak and valley ratio.

[0134] The present invention also provides an electronic device, including a memory, a processor, and computer program instructions stored in the memory and executable by the processor, wherein when the processor executes the computer program instructions, it can implement the steps of the method described above.

[0135] The present invention also provides a computer-readable storage medium having stored thereon computer program instructions that can be executed by a processor, wherein when the processor executes the computer program instructions, it can implement the steps of the method described above.

[0136] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied 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.

[0137] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0138] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0139] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0140] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any other way. Any person skilled in the art may make changes or modifications to the above-disclosed technical content to create equivalent embodiments. However, any simple modifications, equivalent changes, and modifications made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the protection scope of the present invention.

Claims

1. A V2G cluster classification and optimization scheduling method based on fuzzy control, characterized in that, include: Consider classifying electric vehicles into different V2G clusters based on their status characteristics after they are connected to the network. Determine the fuzzy distribution of input and output quantities, establish fuzzy rules, and design a three-input-single-output fuzzy controller for charging and discharging power. Based on the characteristics of the cluster, an optimized scheduling strategy is formulated that conforms to its features. The strategy uses fuzzy control to control the charging and discharging power of the cluster in order to achieve "peak shaving and valley filling" of the regional power grid load. The method includes the following steps: Step 1, V2G cluster classification: Based on the state information characteristics of electric vehicles, electric vehicles with similar state characteristics are grouped into the same type of V2G cluster; Step 2: Model the SOC and power boundary of the V2G cluster during the charging and discharging process according to different clusters; Step 3: Introduce schedulable capability k to characterize the impact of network access duration on V2G cluster charging and discharging power control; Step 4: Based on the characteristics of cluster classification, fuzzy control is used to control the charging and discharging power. A three-input single-output fuzzy controller for charging and discharging power is designed, and fuzzy control rules are established. Step 5: Formulate V2G cluster optimization scheduling strategy: Based on the classification criteria of V2G cluster classification in Step 1, classify the clusters entering the network into 3 types of clusters, and formulate charging and discharging power output strategies that conform to the characteristics of different V2G clusters. Step 6: Simulation experiment to obtain the total load curve including electric vehicle load under V2G output optimization scheduling strategy, obtain load peak and valley values, and calculate load peak and valley ratio; Step 1 is implemented as follows: Step 1.1: Define three state characteristics of an electric vehicle, including its initial state of charge. The shortest charging time is The network access time for electric vehicles is ; Step 1.2: Based on the state characteristics of electric vehicles, the V2G clusters in the region are divided into the following three categories according to predetermined criteria: unschedulable clusters, i.e. Rechargeable scheduling cluster & Rechargeable and dischargeable scheduling clusters & ;in The SOC threshold at which a V2G cluster can participate in discharge; In step 3, k is defined as follows: In the formula, For network access duration, To determine the shortest charging time, k takes values ​​in the range (0, 1). The larger the value of k, the stronger the scheduling capability. Step 4 is implemented as follows: Step 4.1: Set the fuzzy distribution of input and output quantities. Input quantities include the base load. Dispatchability k, charging demand Dischargeable quantity Output includes 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 discharging fuzzy controller in the V2G cluster output optimization scheduling strategy. The rules adopt the "If-Then" format and consist of two parts: premise and conclusion. The premise describes the conditions of the input variables, and the conclusion describes the fuzzy set of output variables.

2. The V2G cluster classification and optimization scheduling method based on fuzzy control according to claim 1, characterized in that, In step 1.1, The calculation formula is as follows: In the formula, The SOC (State of Charge) that car owners expect to achieve when charging. The initial state of charge (SOC) for connecting electric vehicles to charging stations. The rated capacity of the electric vehicle battery pack. Improve the charging efficiency of electric vehicles. Rated charging power for electric vehicles; The calculation formula is as follows: In the formula, This refers to the off-grid time of electric vehicles. This refers to the registration time of electric vehicles on the grid.

3. The V2G cluster classification and optimization scheduling method based on fuzzy control according to claim 1, characterized in that, Step 4.1 is implemented as follows: Step 4.1.1 Set the base load The fuzzy subset is: Minimal base load VSP l Small base load SP l MP in the base load l Large base load LP l VLP with extremely high base load l , base load Normalization, defined as input x, according to the definitions of fuzzy sets and triangular membership functions, covers the basic load. The universe of discourse for the input x is [0, 1], and the membership functions of each fuzzy subset are as follows: Step 4.1.2: Set the dispatchable capacity k and charging demand. Dischargeable quantity Charging power P c Discharge power P d The fuzzy subsets and their membership functions; The fuzzy subset of schedulable capability k is set as: schedulable capability small Sk, schedulable capability medium Mk, and schedulable capability large Lk. Schedulable capability is defined as input quantity y, and the universe of discourse covering input quantity y is (0, 1). Charging demand The fuzzy subset is set as: VSQ with minimal charging demand. c Low charging demand SQ c MQ charging demand c High charging demand LQ c The charging demand is extremely high for VLQ c The charging demand is defined as the input of the charging fuzzy controller. Used to cover input volume The domain of discourse is (0, 90); Dischargeable amount The fuzzy subset is set as follows: VSQ with extremely small discharge capacity. d Small discharge capacity SQ d MQ in the discharge capacity d Large discharge capacity LQ d Extremely high discharge capacity VLQ d The dischargeable quantity is defined as the input quantity of the discharge fuzzy controller. Used to cover input volume The domain of discourse is (0, 72); Charging power P c The fuzzy subset is set as: extremely low charging power VLP c Low charging power LP c Low to medium charging power MLP c Low to medium charging power LMP c Medium charging power MP c High-power HMP c Medium to high charging power (MHP) c High charging power HP c Extremely high charging power (VHP) c The charging power P c Defined as the output of the charging fuzzy controller Used to cover output quantity The domain of discourse is [0, 22]. Discharge power P d The fuzzy subset is set as: VLP with extremely low discharge power. d Low discharge power LP d Medium to low discharge power MLP d Low to medium discharge power LMP d Medium discharge power MP d High-power discharge HMP d 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 of the discharge fuzzy controller Used to cover output quantity The domain of discourse is [-22, 0]. Based on the above settings for each fuzzy subset, the membership function of each fuzzy subset is defined.

4. The V2G cluster classification and optimization scheduling method based on fuzzy control according to claim 1, characterized in that, Step 5 is implemented as follows: M vehicles are randomly selected from the sample data, i=1,2,…,M, with a network access duration of… Shortest charging time Charging demand Discharge capacity Dispatchable capacity K, base load 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 Let i = 0; Step 5.1: Let i = i + 1. If i = M + 1, then go to step 6. Step 5.2: Determine if the condition is met. ≥ If yes, proceed to step 5.3; otherwise, proceed to step 5.

6. Step 5.3, Cluster 1, let t = (i), the vehicle 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 if the disconnection 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 if SOC is satisfied. 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 = (i), calculate vehicle at time t i. K, and load P l P at time t is calculated using a charging fuzzy controller. c ; Step 5.8: Calculate the SOC of the i-th vehicle at time t; Step 5.9: Determine if SOC < 1. If yes, proceed to step 5.10; otherwise, proceed to step 5.

1. Step 5.10: Determine if the shortest charging time has been reached. If yes, charge at rated power at the next time step; otherwise, calculate the charging time of vehicle i at time t+1. K, and load P l P at time t+1 is calculated using a charging fuzzy controller. c ; Step 5.11: Determine if the disconnection 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 = (i), calculate vehicle at time t i. K, and load P l P at time t is calculated using a discharge fuzzy controller. d ; Step 5.13: Calculate the SOC of the i-th vehicle at time t+1; Step 5.14: Determine if SOC ≤ SOC_ min If not, proceed to step 5.15; if yes, proceed to step 5.

16. Step 5.15: Calculate the time t+1 for vehicle i. K, and load P l P at time t+1 is calculated using a discharge fuzzy controller. d Let t = t + 1 and proceed to step 5.13; Step 5.16: Determine if the shortest charging time has been reached. If yes, charge at rated power at the next time step; otherwise, calculate the charging time of vehicle i at time t+1. K, and load P at time t+1. l P at time t+1 is calculated using a charging fuzzy controller. c ; Step 5.17: Determine whether the disconnection time has been reached. If not, set t=t+1 and go to step 5.13; if yes, go to step 5.

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5. An electronic device, characterized in that, It includes a memory, a processor, and computer program instructions stored in the memory and executable by the processor, which, when executed by the processor, enable the implementation of the steps of the method as described in any one of claims 1-4.

6. A computer-readable storage medium having stored thereon computer program instructions executable by a processor, wherein when the processor executes the computer program instructions, it is able to implement the steps of the method as described in any one of claims 1-4.

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