An optimization method for electric vehicle distribution networks based on fuzzy Pareto optimization.

Abstract

This application relates to an optimization method for electric vehicle distribution networks based on fuzzy Pareto optimization, comprising the following steps: S1: Constructing a network load model of distributed generation (DG), parallel capacitors (SC), and electric vehicles (EVs); S2: Proposing a multi-objective function based on fuzzy Pareto optimization; S3: Verifying the radiality of the parts of the distribution network requiring network optimization based on graph theory; S4: Optimizing the distribution network topology based on fuzzy Pareto optimization. This application can improve the system performance of the distribution network, such as reducing power loss and improving voltage distribution.

Description

Technical Field

[0001] This application relates to the field of electric vehicle distribution network optimization, specifically to an optimization method for electric vehicle distribution networks based on fuzzy Pareto optimization. Background Technology

[0002] With the increasing prevalence of electric vehicles (EVs), the charging load in distribution networks is gradually increasing. This necessitates efficient and orderly management of charging loads to ensure users enjoy high-quality electricity. Furthermore, the large-scale integration of EV loads also presents challenges to grid system performance, such as power loss and voltage drop. Currently, distribution networks can improve power flow distribution, increase node voltages in the power system, and reduce active power losses during power transmission through distributed generation and parallel capacitors. Optimizing the distribution network topology can further mitigate the negative impacts of EV load integration. Summary of the Invention

[0003] The purpose of this application is to provide an optimization method for electric vehicle distribution networks based on fuzzy Pareto optimization, which reduces active power loss in the distribution network, improves voltage distribution, and enhances the performance of the power grid system and provides users with high-quality electricity.

[0004] To achieve the above objectives, this application provides the following technical solution:

[0005] This application provides an optimization method for electric vehicle distribution networks based on fuzzy Pareto optimization, comprising the following steps:

[0006] S1: Construct a network load model for distributed generation (DG), parallel capacitors (SC), and electric vehicles (EV);

[0007] S2: Propose a multi-objective function based on fuzzy Pareto optimization;

[0008] S3: Verify the radiality of the parts of the distribution network that require network optimization based on graph theory;

[0009] S4: Optimize the distribution network topology based on fuzzy Pareto optimization.

[0010] The active and reactive load formulas for the constructed load model are as follows:

[0011] P(i)=PL(i)-P DG (i)+P EV (i) (1)

[0012] Q(i)=QL(i)-Q SC (i)-Q DG (i)+Q EV (i) (2)

[0013] Where P(i) is the net active load, PL(i) is the active load of the i-th node in the distribution network system, and P DG (i), P EV (i) represent the DG capacity of the i-th node of the distribution system and the active power load of the electric vehicle charging station, respectively. Correspondingly, Q(i) is the net reactive load, and QL(i) is the reactive load of the i-th node of the distribution system. SC (i), Q DG (i), Q EV (i) represents the reactive power provided by the power factor-lagging DG, SC reactive power injection, and EV charging load to the i-th node of the power distribution system, respectively.

[0014] The equations for active and reactive power injected by DG are as follows:

[0015]

[0016] in For the power angle of the DG unit,

[0017] The total load of an electric vehicle charging station at any given time depends on the number of vehicles being charged and the capacity of the charger to supply power to the electric vehicle batteries. The active power load and reactive power load at the charging station are expressed as follows:

[0018] P EV (i)=NEV(i)P BL (4)

[0019]

[0020] Where NEV(i) is the number of electric vehicles at the i-th node, P BL It is the maximum charging load of a single charger. The power angle for an electric vehicle charger.

[0021] To achieve efficient power transmission, it is necessary to minimize active power loss. In fuzzy algorithms, an objective function for minimizing active power loss is proposed:

[0022]

[0023] Where Ploss Base Ploss represents the basic active power loss at this iteration number. BERC To account for the active power loss after successive iterations of algorithm improvement,

[0024] J PLI It is the ratio of actual active power loss to active power loss under the basic condition, for the objective function J. PLI The conditions for improvement are:

[0025]

[0026] Where K is the number of iterations in the algorithm;

[0027] The objective of improving the minimum node voltage is formulated as a fuzzy objective function, as follows:

[0028]

[0029] The fuzzy objective function represents μ V For less than or equal to V MINB The value is set to zero when the value is less than V. L1 And greater than V MINB At that time, the value is assigned between 0 and 1, μ V Value in V L1 and V L2 When the value is between 1 and V, it is greater than V. L2 When it is 0, V L1 and V L2 The values ​​of are taken as 1.0 and 1.05, and the objective function for improving the minimum voltage is proposed:

[0030] J VMINB =μ V (9)

[0031] The second objective function J VMINB The goal is to increase the minimum node voltage to within a certain standard limit. The minimum node voltage is modeled as a fuzzy Pareto objective function. If the minimum node voltage of the system is within a certain range, then J... VMINB If it is 1, then J VMINB If the value is less than 1, the switch position configuration is only updated when the objective function is improved. Therefore, the improvement condition for the objective function is:

[0032]

[0033] Adding electric vehicle loads to the distribution network will increase the line burden. The branch current should be less than the maximum branch current limit. A maximum branch current constraint objective function is proposed:

[0034]

[0035] Where I i with I Ci These are the current of the i-th branch and the rated current of the i-th branch, respectively. When the maximum branch current ratio is less than 1, J MBCR The value of J is 1; when it exceeds 1, J... MBCR The value of the third objective function J is 0. MBCR The branch current must always be less than the maximum conductor current-carrying current, only when J MBCRWhen the value is 1, network configuration optimization is considered. The improvement condition for this objective function is:

[0036]

[0037] Simultaneously minimize three objectives, J PLI J VMINB and J MBCR The fuzzy Pareto multi-objective vector J is represented as follows:

[0038]

[0039] Where J is the Pareto multi-objective vector function. It is a vector containing the position of the open switch.

[0040] The specific steps for verifying the radiality of the portion of the distribution network requiring network optimization based on graph theory are as follows:

[0041] Represent the power distribution network as an undirected graph. Use the following formula to verify the radiality of the network:

[0042] TF = graphisspantree(G) (15)

[0043] Where G is an N×N sparse matrix, and its lower triangle represents an undirected graph of the power distribution system. Non-zero entries in matrix G indicate the existence of edges. If the network is radial, TF is 1; otherwise, TF is 0.

[0044] The optimization of the distribution network topology based on fuzzy Pareto optimization specifically involves the following steps:

[0045] Step (1): Read the power distribution system line and load data;

[0046] Step (2): Read the location and load data of DG, SC and EV charging stations;

[0047] Step (3): Identify the first loop sequence;

[0048] Step (4): Set the initial iteration count K = 0, the loop sequence count LC = 0, and the branch count BC = 0;

[0049] Step (5): Increment the iteration count K = K + 1;

[0050] Step (6): Calculate the objective function J through power flow analysis. PLI J VMINB J MBCR ;

[0051] Step (7): Incrementing loop sequence count LC = LC + 1;

[0052] Step (8): Close all branches of the loop sequence, except for branches shared with other loop sequences and branches whose respective loop sequence states are in an open state;

[0053] Step (9): Increment the branch count BC = BC + 1;

[0054] Step (10): Open branch "BC" in the loop, keeping the other branches in their original positions;

[0055] Step (11): If the radial property is satisfied, calculate the target J through power flow analysis. PLI J VMINB J MBCR ;

[0056] Step (12): If the Pareto optimality condition is met, then update J. K PLI =J (BC,LC) PLI J K VMINB =J (BC ,LC) VMINB J K MBCR =J (BC,LC) MBCR =1, and update the network configuration; otherwise, retain the previous network configuration, and update VMINB and Ploss. base The value;

[0057] Step (13): If BC < BC max lc Return to step (9);

[0058] Step (14): If LC < LC max Return to step (7);

[0059] Step (15): Calculation and

[0060] Step (16): If and Proceed to the next step; otherwise, return to step (5).

[0061] Step (17): Stop iteration and store the optimal network optimization configuration switch position.

[0062] Compared with existing technologies, the beneficial effects of this invention are as follows: By constructing a network load model of distributed generator sets, parallel capacitors, and electric vehicle loads, and then proposing a multi-objective function based on fuzzy Pareto optimization from three aspects: reducing active power loss, improving voltage distribution, and constraining branch current, the network configuration is updated only when one of the objective functions is improved. Next, radial verification is performed on the parts that need to be optimized to ensure the feasibility of the algorithm. Finally, an algorithm for optimizing the distribution network configuration based on fuzzy Pareto optimization is proposed, thereby achieving the goals of minimizing active power loss, improving voltage distribution, and constraining branch current, improving system performance, and enhancing the stability and reliability of the distribution network. Attached Figure Description

[0063] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments of this application will be briefly introduced below. It should be understood that the following drawings only show some embodiments of this application and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0064] Figure 1 This is a flowchart of the method of the present invention.

[0065] Figure 2 This is a flowchart illustrating the optimization process of distribution network topology based on fuzzy Pareto optimization according to the present invention. Detailed Implementation

[0066] The technical solutions of the embodiments of this application will now be described with reference to the accompanying drawings. It should be noted that similar reference numerals and letters in the following drawings indicate similar items; therefore, once an item is defined in one drawing, it does not need to be further defined and explained in subsequent drawings.

[0067] The terms “comprising,” “including,” or any other variations thereof are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase “comprising one…” does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0068] The terms “first,” “second,” etc., are used only to distinguish one entity or operation from another, and should not be construed as indicating or implying relative importance, nor as requiring or implying any such actual relationship or order between these entities or operations.

[0069] The following is combined Figures 1 to 2 The embodiments of the present invention are described below. Figure 1 The flowchart of a distribution network optimization process based on the fuzzy Pareto optimization algorithm of this invention is as follows:

[0070] S01: Construct a network load model for distributed generation (DG), parallel capacitors (SC), and electric vehicles (EV).

[0071] S02: A multi-objective function based on fuzzy Pareto optimization is proposed.

[0072] S03: Verify the radiality of the parts of the distribution network that require network optimization based on graph theory.

[0073] S04: An algorithm flow for distribution network configuration optimization based on fuzzy Pareto optimality is proposed.

[0074] In step S01, a network load model is constructed for distributed generation (DG), parallel capacitors (SC), and electric vehicles (EV).

[0075] First, a PQ load model is constructed for distributed generation, parallel capacitors, and electric vehicle charging stations to be used for power flow analysis and to determine the initial performance of the system to achieve the algorithm's target system. In the power flow analysis, distributed generators are considered as negative active and reactive loads, and DG units are considered to operate with a lagging power factor of 0.95. Parallel capacitors (SC) are considered as negative reactive loads. The active and reactive load formulas for this load model are:

[0076] P(i)=PL(i)-P DG (i)+P EV (i) (1)

[0077] Q(i)=QL(i)-Q SC (i)-Q DG (i)+Q EV (i) (2)

[0078] Where P(i) is the net active load, and PL(i) is the active load of the i-th node in the distribution network system. DG (i), P EV (i) represent the DG capacity of the i-th node of the distribution system and the active power load of the electric vehicle charging station, respectively. Correspondingly, Q(i) is the net reactive load, QL(i) is the reactive load of the i-th node of the distribution system, and Q... SC (i), Q DG (i), Q EV(i) represents the reactive power provided by the power factor-lagging DG, SC reactive power injection and EV charging load to the i-th node of the power distribution system.

[0079] Since the distributed generation (DG) is considered to operate with a lagging power factor of 0.95, the equation for the active power injected by the DG versus the reactive power is as follows:

[0080]

[0081] in This refers to the power angle of the DG unit.

[0082] The total load of an electric vehicle charging station at any given time depends on the number of vehicles charging and the capacity of the charger to supply power to the electric vehicle batteries. The active power load and reactive power load at the charging station are expressed as follows:

[0083] P EV (i)=NEV(i)P BL (4)

[0084]

[0085] Where NEV(i) is the number of electric vehicles at the i-th node, P BL It is the maximum charging load of a single charger. This is the power angle for the electric vehicle charger. The charging load of the electric vehicle is considered only at the optimal charging station; at other locations, it is set to zero.

[0086] In step S02, a multi-objective function based on fuzzy Pareto optimization is proposed.

[0087] In the process of optimizing the distribution network configuration, reducing active power loss, improving voltage distribution, and branch current constraints are taken as optimization objectives.

[0088] To achieve efficient power transmission, it is necessary to minimize active power loss. In fuzzy algorithms, an objective function for minimizing active power loss is proposed:

[0089]

[0090] Where Ploss Base This represents the basic active power loss at this iteration number. (Ploss) BERC This refers to the active power loss after successive iterations of algorithm improvement.

[0091] J PLI This is considered to be the ratio of actual active power loss to active power loss under the base case. In this algorithm, network configuration updates are only considered when at least one of the objective functions is improved. For this objective function J... PLI The conditions for improvement are:

[0092]

[0093] Where K is the number of iterations in the algorithm.

[0094] The voltage level at each node is a measure of the quality of reliable power supply to users, and the lowest node voltage should always be within the standard limit. Fuzzy algorithms are better suited to defining the objective function as a set of distinct groups. Therefore, the minimum node voltage improvement objective is formed as a fuzzy objective function, as follows:

[0095]

[0096] The fuzzy objective function represents μ V For less than or equal to V MINB The value is set to zero. When the value is less than V L1 And greater than V MINB At that time, the value is assigned between 0 and 1. μ V Value in V L1 and V L2 When the value is between 1 and V, it is greater than V. L2 The time is 0. V L1 and V L2 The values ​​are taken as 1.0 and 1.05. An objective function for improving the minimum voltage is proposed:

[0097] J VMINB =μ V (9)

[0098] The second objective function J VMINB The goal is to increase the minimum node voltage to within a certain standard limit. The minimum node voltage is modeled as a fuzzy Pareto objective function. If the system's minimum node voltage is within a certain range, then J... VMINB If it is 1, then J VMINB Less than 1. The switch position configuration is only updated when the objective function is improved. Therefore, the improvement condition for the objective function is:

[0099]

[0100] Adding electric vehicle loads to the distribution network increases the line burden. Therefore, it is important to ensure that the branch current is less than the maximum branch current limit. The objective function for the maximum branch current constraint is proposed as follows:

[0101]

[0102] Where I i with I Ci These are the current of the i-th branch and the rated current of the i-th branch, respectively. When the maximum branch current ratio is less than 1, J MBCR The value of J is 1; when it exceeds 1, J...MBCR The value of is 0. The third objective function J MBCR The branch current must always be less than the maximum conductor current-carrying capacity. Only when J MBCR Optimizing the network configuration is only considered when the value is 1. Therefore, the improvement condition for this objective function is:

[0103]

[0104] vector The performance of any objective function is no better than that of a vector. Poor, and It outperforms vectors in a certain objective function. Then it is called Pareto is superior Using the concept of Pareto dominance to explain Pareto optimization: Suppose there are N objective functions. For solution A, if there is no other solution in the variable space that is Pareto dominant over A, then solution A is the Pareto optimal solution.

[0105] Simultaneously minimize three objectives, J PLI J VMINB and J MBCR The fuzzy Pareto multi-objective vector J is represented as follows:

[0106]

[0107] Where J is the Pareto multi-objective vector function. This is a vector containing the positions of disconnected switches. In a power distribution system, each tie line forms a loop, and during normal operation, all tie lines will be in the open position. At any given time, the total number of switches in the open position equals the total number of loops during the reconfiguration algorithm, provided the radial condition is met. The objective function is calculated only if the radial condition is satisfied.

[0108] Step S03 verifies the radiality of the parts of the distribution network that require network optimization based on graph theory.

[0109] For optimizing network configuration, the radiality of the network must be verified before updating the solution. In network optimization, the algorithm computes a multi-objective function by sequentially closing and opening switches. The computation of this multi-objective function is related to the vector of switch positions, necessitating radial verification of the network under different states. A network is radial if each node is connected to the substation via a unique path, without any loops to avoid unnecessary circulating current. According to graph theory, a spanning tree is an undirected graph that contains all nodes and must not form closed loops. If the undirected graph generated from the distribution network graph during reconstruction satisfies the spanning tree condition, the distribution network is said to be radially radial. Breadth-first search and depth-first search are graph-based spanning tree verification algorithms.

[0110] Radiality can be determined using built-in functions in the MATLAB toolbox. The radiality of a distribution network is verified using the MATLAB function "graphisspantree". This function determines whether the network is a spanning tree. The existence of a spanning tree verifies the radial property of the network. First, the distribution network is represented as an undirected graph. The radiality of the network is verified using the following formula:

[0111] TF = graphisspantree(G) (15)

[0112] Where G is an N×N sparse matrix, and its lower triangle represents an undirected graph of the power distribution system. Non-zero entries in matrix G indicate the existence of edges. If the network is radial, TF is 1; otherwise, TF is 0.

[0113] S04: An algorithm flow for distribution network topology optimization based on fuzzy Pareto optimality is proposed.

[0114] This paper presents a Pareto-optimal network reconfiguration algorithm and discusses it in detail. First, the loop sequence is determined, and the optimization algorithm is executed based on the loop sequence. When a loop sequence requiring reconfiguration is identified, the first iteration begins by closing the connection line of the first loop sequence. Subsequently, each branch of the loop sequence is opened, and the Pareto objective is calculated. The optimal combination of switching actions leading to reduced active power loss and improved voltage is updated accordingly. Only the three objective functions J proposed above are considered. PLI J VMINB J MBCR The network configuration is improved and the Pareto solution is updated only when at least one loop sequence is improved. Similarly, this process is repeated for all other loop sequences. Finally, the iteration terminates and the optimal network configuration is stored if the difference between the current iteration objective function and the previous iteration objective function reaches a pre-specified minimum. The detailed algorithm steps based on fuzzy Pareto optimization are described below.

[0115] Step (1): Read the power distribution system line and load data.

[0116] Step (2): Read the location and load data of DG, SC and EV charging stations.

[0117] Step (3): Identify the first loop sequence.

[0118] Step (4): Set the initial iteration count K = 0, the loop sequence count LC = 0, and the branch count BC = 0.

[0119] Step (5): Increment the iteration count K = K + 1.

[0120] Step (6): Calculate the objective function J through power flow analysis. PLI J VMINB J MBCR .

[0121] Step (7): Incrementing loop sequence count LC = LC + 1.

[0122] Step (8): Close all branches of the loop sequence except for branches shared with other loop sequences and branches whose respective loop sequence states are in an open state.

[0123] Step (9): Increment the branch count BC = BC + 1.

[0124] Step (10): Open branch "BC" in the loop, keeping the other branches in their original positions.

[0125] Step (11): If the radial property is satisfied, calculate the target J through power flow analysis. PLI J VMINB J MBCR .

[0126] Step (12): If the Pareto optimality condition is met, then update J. K PLI =J (BC,LC) PLI J K VMINB =J (BC ,LC) VMINB J K MBCR =J (BC,LC) MBCR =1, and update the network configuration. Otherwise, retain the previous network configuration. Update VMINB and Ploss. base The value of .

[0127] Step (13): If BC < BC maxlc Return to step (9).

[0128] Step (14): If LC < LC max Return to step (7).

[0129] Step (15): Calculation and

[0130] Step (16): If and Proceed to the next step; otherwise, return to step (5).

[0131] Step (17): Stop iteration and store the optimal network optimization configuration switch position.

[0132] The network load model proposed in this invention, which includes distributed generation, parallel capacitors, and electric vehicle loads, is based on the load characteristics of distributed generator sets, parallel capacitors, and electric vehicle loads, and is used for power flow analysis and calculation of the multi-objective function of this algorithm.

[0133] The present invention proposes a multi-objective function based on fuzzy Pareto optimization, with the objectives of reducing active power loss, improving voltage distribution, and constraining branch current. The invention proposes a multi-objective function and applies the concept of Pareto optimization to specify the improvement conditions for the three objective functions.

[0134] This invention proposes a graph theory-based method to verify the radial property of the parts of a distribution network that require network optimization. The objective function is calculated only when the network satisfies the radial property. The radial property of a network can be verified using graph theory. According to graph theory, a spanning tree is an undirected graph that contains all nodes and does not form closed loops. If the undirected graph generated from the distribution network satisfies the conditions of a spanning tree, then the distribution network is said to have radial properties. This can also be verified using built-in functions in MATLAB.

[0135] The proposed algorithm for optimizing distribution network configuration based on fuzzy Pareto optimization focuses on improving the multi-objective function. It performs radial verification and calculates the objective function step by step for different branches of different loop sequences. When the difference between the objective function of the current iteration and the objective function of the previous iteration reaches the set minimum value, the current network configuration is saved.

[0136] The above description is merely an embodiment of this application and is not intended to limit the scope of protection of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A method for optimizing an electric vehicle distribution network based on fuzzy Pareto optimization, characterized in that, The method comprises the following steps: S1: constructing a network load model of distributed generation (DG), shunt capacitor (SC) and electric vehicle (EV); S2: proposing a multi-objective function based on fuzzy Pareto optimization; S3: verifying radiality of a part of the distribution network that needs to be optimized based on graph theory; S4: optimizing the network topology of the distribution network based on fuzzy Pareto optimization; The multi-objective function based on fuzzy Pareto optimization is specifically: in order to realize efficient power transmission, the active power loss needs to be minimized, and in the fuzzy algorithm, the active power loss minimization objective function is proposed as follows: （6） wherein is the basic active power loss for this iteration number, is the active power loss after the successive iteration improvement of the algorithm, is the ratio of the actual active power loss to the active power loss under the base case, for the objective function , the improvement condition is: （7） Wherein K is the iteration number in the algorithm; The minimum node voltage improvement objective function is formed as a fuzzy objective function as follows: （8） The fuzzy objective function is represented as For values less than or equal to zero is taken, when the value is less than and greater than the value is assigned between 0 and 1, 1 when the value is between and 0 when the value is greater than , and values of 1.0 and 1.05, an improved minimum voltage objective function is proposed: （9） The second objective function is to raise the minimum node voltage to between the standard limits, the minimum node voltage is modeled as a fuzzy Pareto objective function, if the minimum node voltage of the system is within a certain range, is 1, otherwise is less than 1, the configuration of the switch positions is only updated if the objective function is improved, therefore, the objective function improvement condition is: （10） The increase of the electric vehicle load in the distribution network increases the line burden, and the branch current should be less than the maximum branch current limit, and the maximum branch current constraint objective function is proposed as follows: （11） in and These are the current of the i-th branch and the rated current of the i-th branch, respectively. When the maximum branch current ratio is less than 1, The value is 1; when it exceeds 1, The value is 0, the third objective function The branch current must always be less than the maximum conductor current-carrying current, only when Optimizing the network configuration is only considered when the value is 1. The improvement condition for this objective function is: （12） minimize simultaneously three objectives, , and The fuzzy-based Pareto multi-objective vector J representing the three objectives,,, and is given by: (13) (14) where J is a Pareto multi-objective vector function, is a vector containing the open switch positions.

2. The method for optimizing the power distribution network of an electric vehicle based on fuzzy Pareto optimization according to claim 1, characterized in that, The active power and reactive power injection equation of the DG is as follows: （1） （2） where Pnet is the net active load, Pi is the active load of the i-th node in the distribution system, Pdi is the DG capacity of the i-th node in the distribution system, , Pci is the active power load of the i-th node in the distribution system, and Pdi and Pci are the corresponding DG capacity and active power load of the electric vehicle charging station, respectively, Qnet is the net reactive load, Qi is the reactive load of the i-th node in the distribution system, , , Qi,dis, Qi,sc, and Qi,ev are the reactive power provided by the power factor lagging DG, SC reactive power injection, and EV charging load of the i-th node in the distribution system, respectively, The total load of the electric vehicle charging station at any time depends on the number of charging vehicles and the capacity of the charger to supply power to the electric vehicle battery, and the active power load and the reactive power load at the charging station are expressed as: （3） wherein is the power angle of the DG unit, The radiality of the part of the distribution network that needs to be optimized based on graph theory is specifically, （4） （5） wherein is the number of electric vehicles at the i-th node, is the maximum charging load of a single charger, is the power angle of the electric vehicle charger.

3. The method for optimizing the power distribution network of an electric vehicle based on fuzzy Pareto optimization according to claim 1, characterized in that, The distribution network is represented as an undirected graph, and the radiality of the network is verified using the following formula: Wherein G is an N × N sparse matrix, the lower triangle of which represents the undirected graph of the distribution system, and the non-zero items in the matrix G represent the existence of edges, if the network is radial, TF is 1; otherwise, TF is 0. (15) The optimization of the network topology of the distribution network based on fuzzy Pareto optimization is specifically, 4. The method for optimizing the power distribution network of an electric vehicle based on fuzzy Pareto optimization according to claim 1, characterized in that, Step (1): reading the line and load data of the distribution system; Step (2): reading the DG, SC and EV charging station position and load data; Step (3): identifying the first loop sequence; Step (4): setting the initial iteration count K=0, the loop sequence count LC=0, and the branch count BC=0; Step (5): incrementing the iteration count K=K+1; Step (7): incrementing the loop sequence count LC=LC+1; Step (6): Calculate the objective function by tide analysis , , ; Step (8): closing all branches of the loop sequence, except for the branches shared with other loop sequences and the branches in the open state due to the respective loop sequence state; Step (9): incrementing the branch count BC=BC+1; Step (10): opening the branch "BC" in the loop and keeping the remaining branches in their original positions; Step (17): stopping iteration and storing the optimal network optimization configuration switch position. Step (11): If the radial property is satisfied, calculate the target by power flow analysis , , ; Step (12): If the Pareto optimization condition is satisfied, then update , , and update the network configuration; otherwise, keep the previous network configuration, update the values of VMINBand . Step (13): If , return to step (9); Step (14): If , return to step (7); Step (15): calculating with ; Step (16): If and , go to next step, otherwise, return to step (5); ​

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