Optimization Method, Device and Electronic Equipment for Active Distribution Network

Abstract

The present invention is applicable to the field of power technology, and provides an optimization method, device and electronic device for an active distribution network. The method includes: based on the operation parameter information of the active distribution network, taking the minimum weighted sum of a first function value and a second function value as the objective, and taking the on-off states of each connection switch, the access capacity of the static var compensator and the number of access groups of the shunt capacitor bank in the active distribution network as decision variables, to establish an objective function; wherein, the first function value is the ratio of the active power loss after the optimization of the active distribution network to that before the optimization, and the second function value is the ratio of the node voltage deviation after the optimization of the active distribution network to that before the optimization; to establish the constraint conditions of the objective function, and solve the objective function according to the constraint conditions, and optimize the active distribution network based on the solution result. The present invention can improve the power quality of the active distribution network and reduce the energy loss.

Description

Technical Field

[0001] The present invention belongs to the technical field of electric power, and particularly relates to an optimization method, device and electronic equipment for an active distribution network. Background Art

[0002] An active distribution network is a distribution network whose network topology can be flexibly adjusted and has distributed power sources such as wind energy and solar energy (Distributed Generation, DG) inside. The active distribution network is closely related to DG. However, the randomness of DG output will cause great changes in the node voltage, network loss, branch power, etc. of the active distribution network, seriously affecting the safe and stable operation of the power system.

[0003] Means such as network reconfiguration and reactive power optimization can effectively optimize the operation of the active distribution network. Network reconfiguration changes the power flow distribution and improves the system voltage distribution by controlling the on / off state of switches, and can effectively reduce network loss and improve energy utilization rate. Reactive power optimization uses shunt capacitors (Shunt Capacity, SC), static var compensators (Static Var Compensator, SVC), etc. for reactive power compensation and optimizes the power flow, which can effectively reduce the generation cost, reduce energy loss, enhance the system voltage stability and improve the power quality.

[0004] At present, the optimization of the active distribution network mostly studies a single optimization means, such as network reconfiguration or reactive power optimization, without considering the collaborative optimization of multiple optimization means, and the optimization result needs to be further improved. Summary of the Invention

[0005] In view of this, embodiments of the present invention provide an optimization method, device and electronic equipment for an active distribution network to optimize the active distribution network, improve the power quality and reduce the energy loss.

[0006] The first aspect of the embodiments of the present invention provides an optimization method for an active distribution network, including:

[0007] Obtain the operation parameter information of the active distribution network;

[0008] According to the operation parameter information, with the goal of minimizing the weighted sum of the first function value and the second function value, and taking the on / off state of each connection switch, the access capacity of the static var compensator and the access number of the shunt capacitor bank in the active distribution network as decision variables, establish an objective function; wherein, the first function value is the ratio of the active power loss after optimization to that before optimization of the active distribution network, and the second function value is the ratio of the node voltage deviation after optimization to that before optimization of the active distribution network;

[0009] Establish the constraint conditions of the objective function, solve the objective function according to the constraint conditions, and optimize the active distribution network based on the solution result.

[0010] Optionally, the objective function is:

[0011]

[0012]

[0013]

[0014] In the formula, F = min(f 1 , f 2 ) is the objective function; f 1 is the first function value; f 2 is the second function value; N b is the number of branches of the active distribution network; R k is the resistance of the K-th branch; I k is the current of the K-th branch; N is the number of nodes of the active distribution network; V i is the voltage amplitude of the i-th node; V ir is the rated voltage of the i-th node; h 1 is the weight coefficient of the active power loss; h 2 is the weight coefficient of the node voltage deviation; Z 1 is the active power loss before optimization; Z 2 is the node voltage deviation before optimization.

[0015] Optionally, the constraint conditions include power flow balance constraints and multiple inequality constraints;

[0016] The power flow balance constraint is:

[0017]

[0018] In the formula, P SS and Q SS are the active power and reactive power supplied by the line respectively; P DG,i and Q DG,i are the active power output and reactive power output of the i-th DG respectively; P D,j and Q D,j are the active power demand and reactive power demand of the load at the j-th node respectively; P L,K and Q L,K are the active power loss and reactive power loss of the K-th line respectively; N D,G is the number of DGs; N B is the number of nodes; N L is the number of lines;

[0019] The multiple inequality constraints include:

[0020]

[0021] In the formula, U i is the voltage of node i; U i,max , U i,min are the upper and lower limits of the voltage of node i respectively; I k is the current flowing through branch k; is the maximum current that line k is allowed to carry; Q SVC is the input capacity of the SVC; Q SVC,min , Q SVC,max are the upper and lower limits of the input capacity of the SVC; is the number of SC groups put into operation; are the upper and lower limits of the number of SC groups put into operation; h is the reconstructed network topology; H is the set of all feasible network topologies.

[0022] Optionally, the objective function to be solved includes solving the objective function using an improved coot optimization algorithm;

[0023] The process of solving the objective function using the improved coot optimization algorithm is as follows:

[0024] Step 1: Set the parameters of the optimization algorithm;

[0025] Step 2: Initialize the coot individual population through a third-order chaotic mapping strategy, and delete the coot individuals in the population that do not satisfy the constraint conditions; where the coot individual is the solution of the objective function;

[0026] Step 3: Update the positions of the leader coot individuals and ordinary coot individuals in the population;

[0027] Step 4: Perform Gaussian mutation on the positions of the leader coot individuals and ordinary coot individuals;

[0028] Step 5: Calculate the fitness of each coot individual before and after mutation according to the objective function, and select the optimal coot individuals according to the fitness of each coot individual before and after mutation;

[0029] Step 6: Repeat Steps 3 to 5 until the iteration termination condition is reached, output the solution set of the objective function, and determine the optimal solution from the solution set.

[0030] Optionally, the formula for updating the position of an ordinary coot individual is:

[0031]

[0032]

[0033] The formula for updating the position of a leader coot individual is:

[0034]

[0035]

[0036] Wherein, and respectively represent the position of the i-th common coot individual at the (t + 1)-th iteration, the position of the i-th common coot individual at the t-th iteration, the position of the (i - 1)-th common coot individual at the t-th iteration, and the position of the k-th leader coot individual at the t-th iteration; Levy is the Levy step factor; t and T respectively represent the current iteration number and the maximum iteration number; d and N L respectively represent the dimension of the decision variable and the number of leader coot individuals; H 1×d is a 1×d-dimensional matrix, and its elements all belong to the range between 0 and 1; u b and l b respectively represent the upper and lower bounds of the b-th dimensional variable in the search space; R 1 、R 2 、R 3 、R, k, r 1 、r 2 、r 3 、k 1 and k 2 are all random variables; β is a fixed value taken as 1.5; and respectively represent the positions of the i-th leader coot individual at the (t + 1)-th and t-th iterations; X best is the position of the global fitness optimal coot individual.

[0037] Optionally, the formula of the third-order chaotic mapping strategy is:

[0038]

[0039] Wherein, u b is the upper bound of the b-th dimensional variable in the search space; l b is the lower bound of the b-th dimensional variable in the search space; C i.b is the b-th dimensional coordinate of the i-th individual in the search space of the population; D i.b is the b-th dimensional coordinate of the i-th individual in the chaotic space; N pop is the population size.

[0040] Optionally, determining the optimal solution from the solution set includes:

[0041] Determining the positive ideal solution and the negative ideal solution according to the solution set;

[0042] Calculating the Euclidean distances between each solution in the solution set and the positive ideal solution and the negative ideal solution respectively, and calculating the evaluation value of each solution according to ; wherein, C i represents the evaluation value of the i-th solution, respectively represent the Euclidean distances between the i-th solution and the positive ideal solution and the negative ideal solution;

[0043] Determine the solution with the highest evaluation value in the solution set as the optimal solution.

[0044] The second aspect of the embodiments of the present invention provides an active distribution network optimization device, including:

[0045] An acquisition module, configured to acquire the operation parameter information of the active distribution network;

[0046] A first establishment module, configured to establish an objective function based on the operation parameter information, with the goal of minimizing the weighted sum of the first function value and the second function value, and using the on-off states of each connection switch, the access capacity of the static var compensator, and the access number of shunt capacitor banks in the active distribution network as decision variables; wherein, the first function value is the ratio of the active power loss after optimization to that before optimization of the active distribution network, and the second function value is the ratio of the node voltage deviation after optimization to that before optimization of the active distribution network;

[0047] A second establishment module, configured to establish the constraint conditions of the objective function;

[0048] A solution and optimization module, configured to solve the objective function according to the constraint conditions and optimize the active distribution network based on the solution result.

[0049] The third aspect of the embodiments of the present invention provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the active distribution network optimization method in the first aspect as described above are implemented.

[0050] The fourth aspect of the embodiments of the present invention provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the steps of the active distribution network optimization method in the first aspect as described above are implemented.

[0051] The beneficial effects of the embodiments of the present invention compared with the prior art are:

[0052] The objective function established in the embodiments of the present invention aims to minimize the weighted sum of the first function value and the second function value. The first function value is the ratio of the active power loss after the optimization of the active distribution network to that before the optimization, and the second function value is the ratio of the node voltage deviation after the optimization of the active distribution network to that before the optimization. The on-off states of each connection switch, the access capacity of the static var compensator, and the number of access groups of the shunt capacitor bank in the active distribution network are used as decision variables. That is, both network reconfiguration and reactive power optimization are adopted, and an active distribution network network reconfiguration and reactive power collaborative optimization model is established for solution and optimization. Compared with the prior art in which only a single optimization method is used to optimize the active distribution network, the embodiments of the present invention have better optimization effects, can effectively improve the power quality of the active distribution network, and reduce energy losses. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required for use in the embodiments or the description of the prior art. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0054] Figure 1 It is a flowchart of the optimization method for the active distribution network provided by the embodiments of the present invention;

[0055] Figure 2 It is a flowchart of the ICOA optimization algorithm provided by the embodiments of the present invention;

[0056] Figure 3 It is a schematic diagram of the unimodal test function provided by the embodiments of the present invention;

[0057] Figure 4 It is a schematic diagram of the multimodal test function provided by the embodiments of the present invention;

[0058] Figure 5 It is a schematic diagram of the topological structure of an active distribution network provided by the embodiments of the present invention;

[0059] Figure 6 It is a schematic diagram of the iteration curves of each algorithm provided by the embodiments of the present invention;

[0060] Figure 7 It is a distribution diagram of the node voltage amplitudes under each algorithm provided by the embodiments of the present invention;

[0061] Figure 8 It is a schematic diagram of the structure of the optimization device for the active distribution network provided by the embodiments of the present invention;

[0062] Figure 9 It is a schematic diagram of the structure of the electronic device provided by the embodiments of the present invention. Detailed implementation manners

[0063] In the following description, specific details such as specific system structures and technologies are presented for the purpose of illustration rather than limitation, so as to thoroughly understand the embodiments of the present invention. However, those skilled in the art should clearly understand that the present invention can also be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to avoid unnecessary details from interfering with the description of the present invention.

[0064] In order to illustrate the technical solutions described in the present invention, the following will be described through specific embodiments.

[0065] The topology of the active distribution network is complex and contains various DGs. Therefore, the optimal operation of the active distribution network is a complex non-linear optimization problem with multiple constraints, high dimensions, and multiple objectives. The optimization problem of the active distribution network is a current hot research topic, and its optimization methods include Particle Swarm Optimization (PSO), Whale Optimization Algorithm (WOA), Multi-Verse Optimization (MVO), Moth Flame Optimization (MFO), etc.

[0066] The existing literature one uses the Whale Optimization Algorithm (WOA) to solve the optimal reactive power dispatch problem, searching for the best vector of control variables to achieve a reduction in power loss. However, it only considers power loss and still cannot meet the actual requirements. The existing literature two uses the Simulated Fishermen Fishing Optimization Algorithm to solve the multi-objective reactive power optimization problem, proposing a method of transforming the objective function into a constraint based on the user reference area to improve the search speed. However, the above meta-heuristic algorithms have a slow convergence speed and are prone to falling into local optima. The existing literature three establishes static and dynamic mathematical models of the system and uses the Fuzzy Particle Swarm Optimization (FPSO) to achieve reactive power optimization, but does not consider the impact of distributed generation on the system. The existing literature four considers the access of distributed generation and conducts reactive power optimization on the distribution network model including wind power and photovoltaic power to reduce network loss and improve power quality, but does not consider the operating cost. The existing literature five proposes a hybrid improved particle swarm and grey wolf optimization algorithm (Improved Particle Swarm Optimization - Grey Wolf Optimization, IPSO - GWO), considering the time-variation of real-time electricity prices and loads, and establishing a dynamic reconfiguration model with network loss, operating cost, and energy loss as objectives to improve the optimization speed. However, the above studies only perform network reconfiguration or reactive power optimization separately and do not consider the collaborative optimization of multiple methods, which limits the penetration of renewable energy in modern power systems and there is still much room for improvement in power quality. The existing literature six proposes a new Thief and Police Algorithm (TPA) to optimize the configuration of DG and capacitors and conduct network reconfiguration, but it only conducts static analysis. To perform dynamic analysis on the system, the existing literature seven proposes a novel improved elite-Jaya algorithm to solve the problem of simultaneous network dynamic reconfiguration and DG allocation, but ignores the contradictory relationship between multiple objective functions and may not obtain the actual optimal solution. The existing literature eight proposes an adaptive particle swarm optimization algorithm to optimize the active distribution network, first performing reactive power compensation and then network reconfiguration. However, it ignores the impact of the access of DG on the system and cannot meet the actual requirements. The existing literature nine establishes an optimal power flow model for the dynamic active distribution network, comprehensively considering optimization means such as network reconfiguration and reactive power compensation to reduce network loss and increase the consumption of renewable energy. However, the above studies only take reducing network loss or optimal power flow distribution as the objective and do not meet the actual engineering requirements.

[0067] In summary, the existing optimization methods for active distribution networks have their respective disadvantages. Therefore, the embodiments of the present invention propose to use an improved coot optimization algorithm to solve the multi-constraint and multi-objective non-linear active distribution network reconstruction and reactive power collaborative optimization problem, aiming to optimize the network topology of the active distribution network with distributed power sources, improve the power quality of the system, reduce energy losses, and verify the effectiveness of the proposed method.

[0068] See Figure 1 As shown, the optimization method for the active distribution network provided by the embodiments of the present invention specifically includes the following steps:

[0069] Step S101, obtain the operation parameter information of the active distribution network.

[0070] In the embodiments of the present invention, the operation parameter information of the active distribution network includes the actual active power loss of the active distribution network, the actual node voltage deviation, the total number of branches included, the resistance of each branch, the current of each branch, the total number of nodes included, the node voltage amplitude, etc.

[0071] Step S102, according to the operation parameter information, with the goal of minimizing the weighted sum of the first function value and the second function value, and taking the on-off states of each connection switch in the active distribution network, the access capacity of the static var compensator, and the number of access groups of the shunt capacitor bank as decision variables, establish an objective function; where the first function value is the ratio of the active power loss after optimization to the active power loss before optimization of the active distribution network, and the second function value is the ratio of the node voltage deviation after optimization to the node voltage deviation before optimization of the active distribution network.

[0072] In the current research on distribution network reconstruction, network reconstruction can be divided into static reconstruction and dynamic reconstruction according to time sections. Among them, static reconstruction mainly considers finding the optimal topological structure in the current situation when the power output of the power source and the power consumed by the load are all known at a certain time section. Dynamic reconstruction considers the operation situation in a continuous time period, that is, considering the time sequence characteristics of the power source and the load, and making the operation optimization goal globally optimal for the distribution network reconstruction when the power of the power source and the load changes continuously over time. The embodiments of the present invention adopt static reconstruction.

[0073] In the embodiments of the present invention, the active power loss before optimization and the node voltage deviation before optimization are determined. Therefore, the smaller the active power loss after optimization of the active distribution network, the smaller the ratio of the active power loss after optimization to the active power loss before optimization, and the better the optimization effect. Similarly, the smaller the ratio of the active power loss after optimization to the active power loss before optimization of the active distribution network, the better the optimization effect. Then, taking the minimum of the weighted sum of the two function values as the optimization goal and taking the on-off states of each connection switch in the active distribution network, the access capacity of the static var compensator, and the number of access groups of the shunt capacitor bank as decision variables, an active distribution network network reconstruction and reactive power collaborative optimization model with distributed power sources is established.

[0074] Step S103: Establish the constraint conditions of the objective function.

[0075] Step S104: Solve the objective function according to the constraint conditions, and optimize the active distribution network based on the solution results.

[0076] It can be seen that the objective function established in the embodiment of the present invention aims to minimize the weighted sum of the first function value and the second function value. The first function value is the ratio of the active power loss after optimization to that before optimization of the active distribution network, and the second function value is the ratio of the node voltage deviation after optimization to that before optimization of the active distribution network. Taking the on-off states of each connection switch, the access capacity of the static var compensator, and the number of access groups of the shunt capacitor bank in the active distribution network as decision variables, that is, simultaneously adopting two means of network reconfiguration and reactive power optimization, an active distribution network network reconfiguration and reactive power collaborative optimization model is established for solution and optimization. Compared with the prior art that only uses a single optimization means to optimize the active distribution network, the embodiment of the present invention has a better optimization effect, can effectively improve the power quality of the active distribution network, and reduce energy losses.

[0077] In a possible implementation manner, the objective function established in step S102 is:

[0078]

[0079]

[0080]

[0081] In the formula, F = min(f 1 , 2) is the objective function; f 1 is the first function value; f 2 is the second function value; N b is the number of branches of the active distribution network; R k is the resistance of the Kth branch; I k is the current of the Kth branch; N is the number of nodes of the active distribution network; V i is the voltage amplitude of the ith node; V ir is the rated voltage of the ith node; h 1 is the weight coefficient of the active power loss; h 2 is the weight coefficient of the node voltage deviation; Z 1 is the active power loss before optimization; Z 2 is the node voltage deviation before optimization.

[0082] In a possible implementation manner, the constraint conditions established in step S103 include: power flow balance constraint and multiple inequality constraints.

[0083] The power flow balance constraint is:

[0084]

[0085] Wherein, P SS and Q SS are the active power and reactive power supplied by the line respectively; P DG,i and Q DG,i are the active power output and reactive power output of the i-th DG respectively; P D,j and Q D,j are the active power demand and reactive power demand of the load at the j-th node respectively; P L,K and Q L,K are the active power loss and reactive power loss of the K-th line respectively; N D,G is the number of DGs; N B is the number of nodes; N L is the number of lines.

[0086] The multiple inequality constraints include:

[0087]

[0088] Wherein, U i is the voltage of node i; U i,max , U i,min are the upper and lower limits of the voltage of node i respectively; I k is the current flowing through branch k; is the maximum current that line k is allowed to carry; Q SVC is the input capacity of the SVC; Q SVC,min , Q SVC,max are the upper and lower limits of the input capacity of the SVC; is the number of SC input groups; are the upper and lower limits of the number of SC input groups; h is the reconstructed network topology; H is the set of all feasible network topologies.

[0089] In a possible implementation manner, before establishing the objective function in step S102, it further includes simplifying the topological structure of the active distribution network to generate decision variables that satisfy the radial structure of the active distribution network, reducing infeasible solutions, and improving the subsequent solution optimization speed.

[0090] The specific steps for simplifying the topological structure of the active distribution network are:

[0091] (1) Remove the branches containing tie switches to generate the incidence matrix A b*n , where b is the number of branches and n is the number of nodes;

[0092] (2) Add the branch containing the tie switch to the last row of the association matrix A, calculate the sum of the elements in each column of the matrix A, and remove the branches connected to the nodes whose sum is 1;

[0093] (3) Repeat step 2 until the sum of the elements in each column of matrix A is not 1, and the remaining branch is the branch in the loop corresponding to the first tie switch;

[0094] (4) Add a branch with a tie switch to the last row of the association matrix A, repeat the above steps, find the loop corresponding to each tie switch, and finally perform a radial structural constraint check.

[0095] In a possible implementation, solving the objective function includes using an improved coot optimization algorithm to solve the objective function. The coot optimization algorithm is a novel intelligent optimization algorithm. In the algorithm, the coot group is divided into two categories: ordinary coot individuals and leader coot individuals. In order to increase the diversity of the algorithm population, improve the algorithm's search ability and convergence speed, the embodiment of the present invention introduces a third-order chaotic mapping strategy, a Levy flight strategy and a Gaussian mutation strategy on the basis of the coot optimization algorithm, and proposes an improved coot optimization algorithm (Improve Coot Optimization Algorithm, ICOA).

[0096] See also Figure 2 As shown in the figure, the process of solving the objective function using the improved webbed chicken optimization algorithm is:

[0097] Step 1: Set the parameters of the optimization algorithm.

[0098] Step 2: Initialize the webbed chicken individual population through the third-order chaotic mapping strategy, and delete the webbed chicken individuals in the population that do not meet the constraints; among them, the webbed chicken individuals are the solutions of the objective function.

[0099] Step 3: Update the positions of the leader webbed chicken individuals and the common webbed chicken individuals in the population.

[0100] Step 4: Perform Gaussian mutation on the positions of the leader webbed chicken individuals and the ordinary webbed chicken individuals.

[0101] Step 5: Calculate the fitness of each webbed chicken individual before and after the mutation according to the objective function, and select the best webbed chicken individual through the survival of the fittest according to the fitness of each webbed chicken individual before and after the mutation.

[0102] Step 6: Repeat steps 3 to 5 until the iteration termination condition is reached, output the solution set of the objective function, and determine the optimal solution from the solution set.

[0103] Optionally, in the above step 2, ICOA introduces a third-order chaotic mapping strategy to initialize the population to increase population diversity:

[0104]

[0105] where u b is the upper bound of the b-th dimensional variable in the search space; l b is the lower bound of the b-th dimensional variable in the search space; C i.b is the b-th dimensional coordinate of the i-th individual in the search space of the population; D i.b is the b-th dimensional coordinate of the i-th individual in the chaotic space; N pop is the number of populations.

[0106] Optionally, in the above step three, in order to enhance the search ability of the algorithm, a Levy step factor is introduced in the population chain movement. As follows, the formula for ICOA to update the position of the ordinary coot individual is:

[0107]

[0108]

[0109] Optionally, in the above step three, the formula for ICOA to update the position of the leader coot individual is:

[0110]

[0111]

[0112] where and represent the position of the i-th ordinary coot individual at the (t + 1)-th iteration, the position of the i-th ordinary coot individual at the t-th iteration, the position of the (i - 1)-th ordinary coot individual at the t-th iteration, and the position of the k-th leader coot individual at the t-th iteration respectively; Levy is the Levy step factor; t and T represent the current iteration number and the maximum iteration number respectively; d and N L represent the dimension of the decision variable and the number of leader coot individuals respectively; H 1×d is a 1×d dimensional matrix, and its elements all belong to the range between 0 and 1; u b and l b are the upper and lower bounds of the b-th dimensional variable in the search space respectively; R 1 、R 2 、R 3 、R, k, r 1 、r 2 、r 3 、k 1 and k 2 are all random variables; β is a fixed value taken as 1.5; and are the positions of the i-th leader coot individual at the (t + 1)-th and t-th iterations respectively; Xbest is the position of the coot individual with the optimal global fitness.

[0113] Optionally, in the above step four, the Gaussian mutation strategy is as follows:

[0114]

[0115] In the formula, x i.k and X i.k are respectively the k-th decision variables of the i-th individual before and after Gaussian mutation; e is a random variable and follows a normal distribution; N pop and N b represent the population size and the dimension of decision variables respectively.

[0116] Optionally, in the above step five, the value of the objective function is used as the fitness. The smaller the fitness, the better the corresponding solution. Additionally, an external archive mechanism can also be introduced to find the optimal non-dominated solution.

[0117] Optionally, in the above step six, the iteration condition can be reaching a preset maximum number of iterations.

[0118] Optionally, in the above step six, determining the optimal solution from the solution set includes:

[0119] Determining the positive ideal solution and the negative ideal solution according to the solution set;

[0120] Calculating the Euclidean distances between each solution in the solution set and the positive ideal solution and the negative ideal solution respectively, and according to calculating the evaluation value of each solution; in the formula, C i represents the evaluation value of the i-th solution, respectively represent the Euclidean distances between the i-th solution and the positive ideal solution and the negative ideal solution;

[0121] Determining the solution with the highest evaluation value in the solution set as the optimal solution.

[0122] To verify the performance of ICOA, the embodiments of the present invention select 5 benchmark test functions to test and verify it. CSO, PSO, MVO, and COA are selected as comparison algorithms. The main parameters of the algorithms are set as follows: the total number of iterations T = 100, and the population size N pop = 30. The test functions are shown in Table 1. The unimodal test functions are as Figure 3 shown, and are used to test the convergence speed and accuracy of the algorithms. The multimodal test functions are as Figure 3 shown, and are used to test the ability of the algorithms to jump out of local optima and the global search ability.

[0123] Table 1 Test function information

[0124]

[0125] To ensure fairness, each algorithm runs independently 30 times. Table 2 shows the results of CSO, PSO, MVO, COA, and ICOA on unimodal test functions. Table 3 shows the test results of CSO, PSO, MVO, COA, and ICOA on multimodal test functions.

[0126] Table 2 Test Results of Unimodal Test Functions

[0127]

[0128]

[0129] Table 3 Test Results of Multimodal Test Functions

[0130]

[0131] It can be seen from the data in the table that ICOA has the best indicators on all test functions. ICOA has the fastest convergence speed and the highest convergence accuracy. The average value, standard deviation, and optimal value of ICOA in 30 runs under all multimodal test functions are the smallest, and the optimal values on test function F5 all converge to 0. ICOA has good performance in solving multimodal functions, with the fastest convergence speed, the highest convergence accuracy, and converges to the optimal value.

[0132] The following is a simulation verification. The IEEE 33-node system is selected as the test system. The system base capacity, base voltage, and total load are 10 MVA, 12.66 kV, and 3715 + j2350 kVA respectively. PVs are connected to nodes 5 and 18, and the PV installed capacity of each is 500 kW. A WT is connected to node 31, and the WT installed capacity is 500 kW. The power factors of the PV and WT are set to 0.85 (lagging) and 0.9 (lagging) respectively. An SVC is connected to node 7, and SCs are connected to nodes 21 and 30. The network topology is as Figure 5 shown.

[0133] To verify the superiority of ICOA in solving the active distribution network network reconfiguration and reactive power coordinated optimization problem, a comparative experiment is carried out with PSO, MVO, CSO, and COA in the IEEE 33 system. The iteration curves of each algorithm are as Figure 6As shown. According to the iteration curves of each algorithm, compared with COA, PSO, CSO, and MVO, the objective value of ICOA at the first iteration is the lowest. The chaotic initialization makes the initial population more evenly distributed in the search space, and the fitness of the initial solution is better. Compared with other comparison algorithms, the convergence value of ICOA is the lowest, the convergence accuracy of ICOA is the highest, and the search ability is the strongest. ICOA converges to the optimal value at the 182nd iteration, with the fastest convergence speed and the strongest ability to jump out of local optima. Compared with COA, PSO, CSO, and MVO, ICOA has better performance in solving the network reconfiguration and reactive power coordinated optimization problem of active distribution networks, with the fastest convergence speed, the highest convergence accuracy, and the strongest ability to jump out of local optima.

[0134] The voltage amplitude distribution diagrams of each node under the initial state, after connecting DGs, after COA optimization, and after ICOA optimization are as Figure 7 shown. The lowest voltage value in the initial state of the distribution network is 0.91, the voltage fluctuation is too large, and the power quality is low; after connecting DGs, the voltage fluctuation is weakened to some extent, but the lowest voltage value is 0.95, and there is still much room for improvement; after using COA and ICOA for network reconfiguration and reactive power coordinated optimization of the active distribution network, the voltage fluctuations are significantly weakened, and the lowest voltage values are both 0.98. At nodes 11, 12, 13, and 14, the node voltages after ICOA optimization are higher than those after COA optimization, with smaller fluctuations, effectively suppressing the voltage fluctuations of the active distribution network and significantly improving the power quality of the active distribution network.

[0135] The simulation results of different scenarios in the IEEE33 system are shown in Table 4.

[0136] Table 4 Simulation Results of Different Scenarios in the IEEE33 System

[0137]

[0138]

[0139] According to the data shown in Table 4, the initial state is that the distribution network operates normally without optimization, no DGs are connected, the node voltage is lower than the lower limit, and both the active power loss and the node voltage deviation are too high. After connecting DGs, the node voltage of the distribution network has improved, the active power loss has decreased by 63.66%, the node voltage deviation has decreased by 48.79%, and the power quality has been improved to some extent, but there is still much room for improvement. After optimizing the active distribution network using COA, the active power loss has decreased by 83.37%, and the node voltage deviation has decreased by 53.68%, effectively suppressing voltage fluctuations and reducing power losses. After optimizing the active distribution network using ICOA, the active power loss and the node voltage deviation have decreased by 82.87% and 86.92% respectively, suppressing the node voltage fluctuations to the greatest extent, significantly reducing the network active power loss, with the lowest comprehensive target value, and the obtained results considering the most balanced of each objective function and the best comprehensive performance. It can be seen that the embodiment of the present invention simultaneously adopts two means of network reconfiguration and reactive power optimization, establishes a network reconfiguration and reactive power collaborative optimization model for the active distribution network, and introduces a chaotic initialization strategy, a Gaussian mutation strategy and a Levy flight strategy on the basis of the coot optimization algorithm (COA), and proposes an improved coot optimization algorithm (Improve Coot Optimization Algorithm, ICOA) for optimizing the model, improving the optimization speed, reducing the network loss of the active distribution network, suppressing the node voltage fluctuations, improving the power quality, reducing energy waste, and having important significance for promoting economic production, increasing the renewable energy penetration rate and improving the economic and stable operation of the active distribution network.

[0140] It should be understood that the magnitudes of the sequence numbers of the steps in the above embodiments do not mean the order of execution, and the execution order of each process should be determined by its function and internal logic, and should not constitute any limitation to the implementation process of the embodiments of the present invention.

[0141] The embodiment of the present invention also provides an optimization device for an active distribution network. Refer to Figure 8 as shown, the device 80 includes:

[0142] An acquisition module 81, configured to acquire operation parameter information of the active distribution network.

[0143] A first establishment module 82, configured to establish an objective function with the weighted sum of a first function value and a second function value being the minimum, and using the on-off states of each connection switch in the active distribution network, the access capacity of the static var compensator, and the number of access groups of the shunt capacitor bank as decision variables according to the operation parameter information; wherein, the first function value is the ratio of the active power loss after optimization to that before optimization of the active distribution network, and the second function value is the ratio of the node voltage deviation after optimization to that before optimization of the active distribution network.

[0144] The second establishment module 83 is used to establish the constraint conditions of the objective function.

[0145] The solution optimization module 84 is used to solve the objective function according to the constraint conditions and optimize the active distribution network based on the solution results.

[0146] In a possible implementation manner, the objective function established by the first establishment module 82 is:

[0147]

[0148]

[0149]

[0150] In the formula, F = min(f 1 , 2) is the objective function; f 1 is the first function value; f 2 is the second function value; N b is the number of branches of the active distribution network; R k is the resistance of the Kth branch; I k is the current of the Kth branch; N is the number of nodes of the active distribution network; V i is the voltage amplitude of the ith node; V ir is the rated voltage of the ith node; h 1 is the weight coefficient of the active power loss; h 2 is the weight coefficient of the node voltage deviation; Z 1 is the active power loss before optimization; Z 2 is the node voltage deviation before optimization.

[0151] In a possible implementation manner, the constraint conditions established by the second establishment module 83 include power flow balance constraints and multiple inequality constraints.

[0152] The power flow balance constraint is:

[0153]

[0154] In the formula, P SS and Q SS are the active power and reactive power supplied by the line respectively; P DG,i and Q DG,i are the active power output and reactive power output of the ith DG respectively; P D,j and Q D,j are the active power demand and reactive power demand of the jth node respectively; P L,K and Q L,K are the active power loss and reactive power loss of the Kth line respectively; N D,Gis the number of DGs; N B is the number of nodes; N L is the number of lines;

[0155] The multiple inequality constraints include:

[0156]

[0157] In the formula, U i is the voltage of node i; U i,max , U i,min are respectively the upper and lower limits of the voltage of node i; I k is the current flowing through branch k; is the maximum current allowed to flow through line k; Q SVC is the input capacity of the SVC; Q SVC,min , Q SVC,max are the upper and lower limits of the input capacity of the SVC; is the number of SC input groups; are the upper and lower limits of the number of SC input groups; h is the reconstructed network topology; H is the set of all feasible network topologies.

[0158] In a possible implementation, the optimization module 84 uses an improved coot optimization algorithm to solve the objective function.

[0159] The process of using the improved coot optimization algorithm to solve the objective function is as follows:

[0160] Step 1: Set the parameters of the optimization algorithm;

[0161] Step 2: Initialize the coot individual population through a third-order chaotic mapping strategy, and delete the coot individuals in the population that do not meet the constraint conditions; where the coot individual is the solution of the objective function;

[0162] Step 3: Update the positions of the leader coot individuals and ordinary coot individuals in the population;

[0163] Step 4: Perform Gaussian mutation on the positions of the leader coot individuals and ordinary coot individuals;

[0164] Step 5: Calculate the fitness of each coot individual before and after mutation according to the objective function, and select the optimal coot individual according to the fitness of each coot individual before and after mutation;

[0165] Step 6: Repeat Steps 3 to 5 until the iteration termination condition is reached, output the solution set of the objective function, and determine the optimal solution from the solution set.

[0166] In a possible implementation, the formula for updating the position of an ordinary coot is:

[0167]

[0168]

[0169] In a possible implementation, the formula for updating the position of the leader coot is:

[0170]

[0171]

[0172] Wherein, and respectively represent the position of the i-th ordinary coot individual at the (t + 1)-th iteration, the position of the i-th ordinary coot individual at the t-th iteration, the position of the (i - 1)-th ordinary coot individual at the t-th iteration, and the position of the k-th leader coot individual at the t-th iteration; Levy is the Levy step factor; t and T respectively represent the current iteration number and the maximum iteration number; d and N L respectively represent the dimension of the decision variable and the number of leader coot individuals; H 1×d is a 1×d-dimensional matrix, and its elements all belong to the range between 0 and 1; u b and l b are respectively the upper and lower bounds of the b-th dimension variable in the search space; R 1 、R 2 、R 3 、R, k, r 1 、r 2 、r 3 、k 1 and k 2 are all random variables; β is a fixed value taken as 1.5; and are respectively the positions of the i-th leader coot individual at the (t + 1)-th and t-th iterations; X best is the position of the global fitness optimal coot individual.

[0173] In a possible implementation, the formula for the third-order chaotic mapping strategy is:

[0174]

[0175] Wherein, u b is the upper bound of the b-th dimension variable in the search space; l b is the lower bound of the b-th dimension variable in the search space; C i.b is the b-th dimension coordinate of the i-th individual in the search space of the population; D i.b is the b-th dimension coordinate of the i-th individual in the chaotic space; N pop is the population size.

[0176] In a possible implementation, the solution optimization module 84 is specifically configured to:

[0177] Determine the positive ideal solution and the negative ideal solution according to the solution set;

[0178] Calculate the Euclidean distances between each solution in the solution set and the positive ideal solution and the negative ideal solution respectively, and according to Calculate the evaluation value of each solution; where C i represents the evaluation value of the i-th solution, respectively represent the Euclidean distances between the i-th solution and the positive ideal solution and the negative ideal solution;

[0179] Determine the solution with the highest evaluation value in the solution set as the optimal solution.

[0180] Figure 9 is a schematic diagram of the electronic device 90 provided by an embodiment of the present invention. As Figure 9 shown, the electronic device 90 of this embodiment includes: a processor 91, a memory 92, and a computer program 93 stored in the memory 92 and executable on the processor 91, such as an optimization program for an active distribution network. When the processor 91 executes the computer program 93, the steps in the above-mentioned embodiments of the optimization method for each active distribution network are implemented, such as Figure 1 the steps S101 to S104 shown. Alternatively, when the processor 91 executes the computer program 93, the functions of each module in the above-mentioned device embodiments are implemented, such as Figure 8 the functions of the modules 81 to 84 shown.

[0181] Exemplarily, the computer program 93 can be divided into one or more modules / units. One or more modules / units are stored in the memory 92 and executed by the processor 91 to complete the present invention. One or more modules / units can be a series of computer program instruction segments capable of performing specific functions, and these instruction segments are used to describe the execution process of the computer program 93 in the electronic device 90. For example, the computer program 93 can be divided into an acquisition module 81, a first establishment module 82, a second establishment module 83, and a solution optimization module 84 (modules in a virtual device), and the specific functions of each module are as follows:

[0182] The acquisition module 81 is used to acquire the operation parameter information of the active distribution network.

[0183] The first establishment module 82 is used to establish an objective function with the weighted sum of the first function value and the second function value being the minimum, and with the on-off states of each connection switch, the access capacity of the static var compensator, and the number of access groups of the shunt capacitor bank in the active distribution network as decision variables, according to the operation parameter information; where the first function value is the ratio of the active power loss after optimization to the active power loss before optimization of the active distribution network, and the second function value is the ratio of the node voltage deviation after optimization to the node voltage deviation before optimization of the active distribution network.

[0184] A second establishment module 83, configured to establish the constraint conditions of the objective function.

[0185] An optimization solving module 84, configured to solve the objective function according to the constraint conditions and optimize the active distribution network based on the solution result.

[0186] The electronic device 90 may be a computing device such as a desktop computer, a notebook, a palm computer, and a cloud server. The electronic device 90 may include, but is not limited to, a processor 91 and a memory 92. Those skilled in the art can understand that Figure 9 merely examples of the electronic device 90, which do not constitute a limitation to the electronic device 90, may include more or fewer components than those shown in the figure, or combine some components, or different components. For example, the electronic device 90 may further include input / output devices, network access devices, a bus, etc.

[0187] The so-called processor 91 may be a central processing unit (CPU), or may also be other general-purpose processors, digital signal processors (DSPs), application specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0188] The memory 92 may be an internal storage unit of the electronic device 90, such as the hard disk or memory of the electronic device 90. The memory 92 may also be an external storage device of the electronic device 90, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. equipped on the electronic device 90. Further, the memory 92 may also include both the internal storage unit and the external storage device of the electronic device 90. The memory 92 is used to store computer programs and other programs and data required by the electronic device 90. The memory 92 may also be used to temporarily store the data that has been output or will be output.

[0189] Those skilled in the art can clearly understand that, for the convenience and brevity of description, only the above division of each functional unit and module is used as an example. In actual applications, the above functions can be allocated to different functional units and modules according to needs, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. Each functional unit and module in the embodiment can be integrated into a processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit. The above integrated unit can be implemented in the form of hardware or in the form of a software functional unit. In addition, the specific names of each functional unit and module are only for the convenience of mutual distinction and do not limit the protection scope of this application. The specific working processes of the units and modules in the above system can refer to the corresponding processes in the foregoing method embodiments and will not be elaborated herein.

[0190] In the above embodiments, the descriptions of the respective embodiments have their own emphases. For parts not detailed or recorded in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0191] Those of ordinary skill in the art can realize that the units and algorithm steps of each example described in combination with the embodiments disclosed herein can be implemented by electronic hardware, or by a combination of computer software and electronic hardware. Whether these functions are executed in the form of hardware or software depends on the specific application and design constraints of the technical solution. Professionals can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present invention.

[0192] In the embodiments provided by the present invention, it should be understood that the disclosed device / electronic device and method can be implemented in other ways. For example, the device / electronic device embodiments described above are only illustrative. For example, the division of modules or units is only a logical function division. In actual implementation, there may be other division methods. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed coupling or direct coupling or communication connection to each other can be through some interfaces. The indirect coupling or communication connection of the device or unit can be in an electrical, mechanical or other form.

[0193] The units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they can be located in one place or distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0194] In addition, in each embodiment of the present invention, each functional unit can be integrated into a processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit. The above integrated unit can be implemented in the form of hardware or in the form of a software functional unit.

[0195] If the integrated module / unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on such an understanding, to implement all or part of the processes in the above-described embodiment methods of the present invention, it can also be completed by instructing relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by a processor, the steps of the above-described various method embodiments can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in the form of source code, object code, executable file, or some intermediate form, etc. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disc, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal, and software distribution medium, etc. It should be noted that the content included in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, the computer-readable medium does not include electrical carrier signals and telecommunication signals.

[0196] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements on some of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the various embodiments of the present invention, and should all be included in the protection scope of the present invention.

Claims

1. An optimization method for an active distribution network, characterized in that, it includes: Obtain the operation parameter information of the active distribution network; According to the operation parameter information, with the goal of minimizing the weighted sum of the first function value and the second function value, and taking the on-off states of each connection switch, the access capacity of the static var compensator, and the number of access groups of the shunt capacitor bank in the active distribution network as decision variables, establish an objective function; wherein, the first function value is the ratio of the active power loss after optimization to that before optimization of the active distribution network, and the second function value is the ratio of the node voltage deviation after optimization to that before optimization of the active distribution network; Establish the constraint conditions of the objective function, solve the objective function according to the constraint conditions, and optimize the active distribution network based on the solution results; The objective function is: where \(F = \min(f 1 , f 2 )\) is the objective function; \(f 1 \) is the first function value; \(f 2 \) is the second function value; \(N b \) is the number of branches of the active distribution network; \(R k \) is the resistance of the \(K\)-th branch; \(I k \) is the current of the \(K\)-th branch; \(N\) is the number of nodes of the active distribution network; \(V i \) is the voltage amplitude of the \(i\)-th node; \(V ir \) is the rated voltage of the \(i\)-th node; \(h 1 \) is the weight coefficient of the active power loss; \(h 2 \) is the weight coefficient of the node voltage deviation; \(Z 1 \) is the active power loss before optimization; \(Z 2 \) is the node voltage deviation before optimization; The constraint conditions include power flow balance constraints and multiple inequality constraints; The power flow balance constraint is: Wherein, P SS and Q SS are respectively the active power and reactive power supplied by the line; P DG,i and Q DG,i are respectively the active power output and reactive power output of the i-th DG; P D,j and Q D,j are respectively the active power demand and reactive power demand of the load at the j-th node; P L,K and Q L,K are respectively the active power loss and reactive power loss of the K-th line; N D,G is the number of DGs; N B is the number of nodes; N L is the number of lines; The multiple inequality constraints include: where U i is the voltage of node i; U i,max , U i,min are the upper and lower limits of the voltage of node i respectively; I k is the current flowing through branch k; is the maximum current allowed to flow through line k; Q SVC is the SVC input capacity; Q SVC,min , Q SVC,max are the upper and lower limits of the SVC input capacity; is the number of SC groups put into operation; are the upper and lower limits of the number of SC groups put into operation; h is the reconstructed network topology; H is the set of all feasible network topologies.

2. The optimization method for an active distribution network according to claim 1, characterized in that, Solving the objective function includes using an improved coot optimization algorithm to solve the objective function; The process of using an improved coot optimization algorithm to solve the objective function is: Step 1: Set the parameters of the optimization algorithm; Step 2: Initialize the coot individual population through a third-order chaotic mapping strategy, and delete the coot individuals in the population that do not meet the constraint conditions; wherein, the coot individual is the solution of the objective function; Step 3: Update the positions of the leader coot individuals and ordinary coot individuals in the population; Step 4: Perform Gaussian mutation on the positions of the leader coot individuals and ordinary coot individuals; Step 5: Calculate the fitness of each coot individual before and after mutation according to the objective function, and screen out the optimal coot individuals according to the fitness of each coot individual before and after mutation; Step 6: Repeat steps 3 to 5 until the iteration termination condition is reached, output the solution set of the objective function, and determine the optimal solution from the solution set.

3. The optimization method for an active distribution network according to claim 2, characterized in that, The formula for updating the position of an ordinary coot individual is: The formula for updating the position of a leader coot individual is: Wherein, and represent the position of the i-th common coot individual at the (t + 1)-th iteration, the position of the i-th common coot individual at the t-th iteration, the position of the (i - 1)-th common coot individual at the t-th iteration, and the position of the k-th leader coot individual at the t-th iteration, respectively; Levy is the Levy step factor; t and T represent the current iteration number and the maximum iteration number, respectively; d and N L represent the dimension of the decision variable and the number of leader coot individuals, respectively; H 1×d is a 1×d-dimensional matrix, and its elements all belong to the range between 0 and 1; u b and l b represent the upper and lower bounds of the b-th dimension variable in the search space, respectively; R 1 、R 2 、R 3 、R, k, r 1 、r 2 、r 3 、k 1 and k 2 are all random variables; β is a fixed value taken as 1.5; and represent the positions of the i-th leader coot individual at the (t + 1)-th and t-th iterations, respectively; X best is the position of the coot individual with the optimal global fitness.

4. The optimization method for an active distribution network according to claim 2, characterized in that, The formula of the third-order chaotic mapping strategy is: where u b is the upper bound of the b-th dimensional variable in the search space; l b is the lower bound of the b-th dimensional variable in the search space; C i.b is the b-th dimensional coordinate of the i-th individual in the search space of the population; D i.b is the b-th dimensional coordinate of the i-th individual in the chaotic space; N pop is the number of populations.

5. The optimization method for an active distribution network according to claim 2, characterized in that, Determining the optimal solution from the solution set includes: Determine the positive ideal solution and the negative ideal solution according to the solution set; Calculate the Euclidean distances between each solution in the solution set and the positive ideal solution and the negative ideal solution respectively, and according to calculate the evaluation value of each solution; in the formula, C i represents the evaluation value of the i-th solution, respectively represent the Euclidean distances between the i-th solution and the positive ideal solution and the negative ideal solution; Determine the solution with the highest evaluation value in the solution set as the optimal solution.

6. An optimization device for an active distribution network, characterized in that, it includes: An acquisition module for acquiring the operation parameter information of the active distribution network; A first establishment module, configured to establish an objective function with the weighted sum of a first function value and a second function value being minimized, and with the on-off states of each connection switch in the active distribution network, the access capacity of the static var compensator, and the number of access groups of the shunt capacitor bank as decision variables according to the operation parameter information; wherein, the first function value is the ratio of the active power loss of the active distribution network after optimization to that before optimization, and the second function value is the ratio of the node voltage deviation of the active distribution network after optimization to that before optimization; A second establishment module, configured to establish the constraint conditions of the objective function; A solution and optimization module, configured to solve the objective function according to the constraint conditions and optimize the active distribution network based on the solution result; Obtain the operation parameter information of the active distribution network; According to the operation parameter information, establish an objective function with the weighted sum of a first function value and a second function value being minimized, and with the on-off states of each connection switch in the active distribution network, the access capacity of the static var compensator, and the number of access groups of the shunt capacitor bank as decision variables; wherein, the first function value is the ratio of the active power loss of the active distribution network after optimization to that before optimization, and the second function value is the ratio of the node voltage deviation of the active distribution network after optimization to that before optimization; Establish the constraint conditions of the objective function, solve the objective function according to the constraint conditions, and optimize the active distribution network based on the solution result; The objective function is: where F = min(f 1 , f 2 ) is the objective function; f 1 is the first function value; f 2 is the second function value; N b is the number of branches of the active distribution network; R k is the resistance of the Kth branch; I k is the current of the Kth branch; N is the number of nodes of the active distribution network; V i is the voltage amplitude of the ith node; V ir is the rated voltage of the ith node; h 1 is the weight coefficient of the active power loss; h 2 is the weight coefficient of the node voltage deviation; Z 1 is the active power loss before optimization; Z 2 is the node voltage deviation before optimization; The constraint conditions include power flow balance constraints and multiple inequality constraints; The power flow balance constraint is: Where P SS and Q SS are the active power and reactive power supplied by the line respectively; P DG,i and Q DG,i are the active power output and reactive power output of the i-th DG respectively; P D,j and Q D,j are the active power demand and reactive power demand of the load at the j-th node respectively; P L,K and Q L,K are the active power loss and reactive power loss of the k-th line respectively; N D,G is the number of DGs; N B is the number of nodes; N L is the number of lines; The multiple inequality constraints include: Where, U i is the voltage of node i; U i,max , U i,min are the upper and lower limits of the voltage of node i respectively; I k is the current flowing through branch k; is the maximum current that line k is allowed to carry; Q SVC is the SVC input capacity; Q SVC,min , Q SVC,max are the upper and lower limits of the SVC input capacity; is the number of SC groups put into operation; are the upper and lower limits of the number of SC groups put into operation; h is the reconfigured network topology; H is the set of all feasible network topologies.

7. An electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein, when the processor executes the computer program, the steps of the method according to any one of claims 1 to 5 are implemented.

8. A computer-readable storage medium storing a computer program, wherein, when the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 5 are implemented.

Images