Reactive power optimization method and system for port area distribution network based on improved multi-objective gold panning algorithm

Abstract

The present invention discloses a reactive power optimization method and system for a port distribution network based on an improved multi-objective gold panning algorithm. The method mainly includes three aspects. The K-means clustering algorithm is used to cluster large-scale scenarios into typical distributed energy output scenarios. The operating mode corresponding to the SST is selected according to the different external characteristics of the SST, and then the equivalent node type corresponding to the SST node is selected according to the operating mode. The adaptive fuzzy coefficient, bidirectional guidance and secondary cross-update methods are successively introduced to improve the algorithm, thereby solving the model and obtaining a scheduling strategy for a variety of distributed energy sources. The above-mentioned control can effectively solve the problem of voltage fluctuation exceeding the limit and network loss increase in the port distribution network caused by the grid connection of a variety of distributed energy sources.

Description

Technical Field

[0001] The invention belongs to the technical field of application of high-voltage and high-power power electronic technology in power systems, and relates to a reactive power optimization method for a port area distribution network. Background Art

[0002] The integration of a high proportion of distributed energy resources into distribution networks will inevitably lead to increased difficulty in controlling power quality and operating costs, particularly impacting voltage stability and network losses. Therefore, research on reactive power optimization scheduling strategies for distribution networks with a high proportion of distributed energy resources integrated into them is of great significance. Currently, research on reactive power optimization scheduling in distribution networks using SSTs and optimization algorithms has attracted widespread attention from scholars both domestically and internationally. Solid-state transformers, as a new type of power electronic device, can directly absorb and emit reactive power without the assistance of other devices, enabling bidirectional transmission of reactive power. Furthermore, direct regulation of reactive power ensures high precision and reduces calculation errors. This is particularly important for distribution networks.

[0003] However, once a single-objective problem is extended to a multi-objective problem, the computational dimension of the algorithm will increase significantly, increasing the search space. Therefore, the algorithm itself needs to have strong global search capabilities to prevent local convergence. Summary of the Invention

[0004] The purpose of the present invention is to prevent voltage fluctuations from exceeding limits and network losses from increasing, thereby providing a guarantee for the safe and economical operation of the distribution network.

[0005] The technical solutions for achieving the purpose of the present invention are:

[0006] According to one aspect of the present application, a method for optimizing reactive power of a port distribution network based on an improved multi-objective gold panning algorithm is provided, comprising the following steps:

[0007] The K-means clustering algorithm, which takes into account spatiotemporal characteristics, is used to cluster the output scenarios of different types of energy in the distribution network and obtain typical output scenario data.

[0008] Based on the external characteristics of the solid-state transformer (SST), the corresponding operating mode is selected, and a reactive power optimization model for the distribution network including the SST is established. The input data of the reactive power optimization model for the distribution network including the SST includes typical output scenario data, which is used to constrain the output of distributed energy resources.

[0009] Call the multi-objective gold mining algorithm MOGRO, and introduce adaptive fuzzy coefficient, two-way guidance and secondary cross update to improve the multi-objective gold mining algorithm MOGRO;

[0010] The improved multi-objective gold mining algorithm MOGRO is used to solve the reactive power optimization model of the distribution network containing SST, and the reactive power optimization scheduling strategy set of the distribution network is obtained.

[0011] According to one aspect of the present application, the K-means clustering algorithm that considers spatiotemporal characteristics is used to cluster the output scenarios of different types of energy in the distribution network to obtain typical output scenario data, specifically:

[0012] Taking active power output as the indicator, k samples are randomly selected as the initial cluster centers; k is a natural number greater than 0;

[0013] Calculate the distance between each sample in the data set and the centers of k clusters, and classify it into the cluster with the smallest distance;

[0014] For each cluster, recalculate the cluster center position;

[0015] Repeat the above steps until the set number of iterations is reached to obtain a typical output scenario that can represent different spatiotemporal characteristics.

[0016] According to one aspect of the present application, the operation mode corresponding to the solid-state transformer SST is selected according to its external characteristics, and a reactive power optimization model of the distribution network including the SST is established, specifically:

[0017] Considering whether the secondary side of the SST is connected to an active network, select the operating mode of constant AC voltage control, constant power control, or a combination of the two;

[0018] Select the equivalent node type corresponding to the SST node according to the operation mode;

[0019] A dual-objective optimization model is established with minimizing network loss and voltage deviation as the objective function;

[0020] Model constraints include power balance constraints, distributed energy output constraints, reactive power compensator constraints, OLTC gear constraints, energy storage state of charge constraints, and SST capacity constraints.

[0021] According to one aspect of the present application, the multi-objective gold mining algorithm MOGRO is called and the adaptive fuzzy coefficient, bidirectional guidance and secondary cross update are introduced to improve the multi-objective gold mining algorithm MOGRO, specifically:

[0022] An adaptive fuzzy coefficient C1 is introduced into the optimal update, which changes dynamically from large to small during iteration, and the search range is narrowed in the later stage.

[0023] C 1 = [ 2 cos ( t − 1 T − 1 ⋅ π 2 ) ⋅ ( r 1 − 0 . 5 ) + 1 ] ⋅ r 2 ;

[0024] t is the current iteration number, T is the maximum iteration number; r1, r2 are random numbers in [0, 1];

[0025] Bidirectional guidance is introduced for cooperative updating, using the vector sum of individual position changes between two iterations to guide the update;

[0026] ;

[0027] X i (t+1), X i (t) is the position of the i-th gold digger before and after the update; D3 is the distance coefficient;

[0028] After each iterative update, a secondary crossover operation is performed on the population.

[0029] ;

[0030] Where: pc is the crossover probability, pc1 and pc2 are the crossover start and end probabilities, f is the maximum fitness of any objective function of any two particles, f average is the average value of the fitness of the objective function corresponding to f, f max , f min is the maximum and minimum value of the corresponding objective function fitness;

[0031] Adaptive crossover probability and simulated binary crossover are introduced to enhance local search capabilities;

[0032] X worker2 ( t + 1 ) = [ ( 1 + l ) ⋅ X worker1 ( t ) + ( 1 − l ) X worker2 ( t ) ] 2 X worker2 ( t + 1 ) = [ ( 1 − l ) ⋅ X worker1 ( t ) + ( 1 + l ) X worker2 ( t ) ] 2 ;

[0033] ;

[0034] Where mu is the cross coefficient, m is a random number in [0, 1], and X worker1 (t), X worker2 (t) for any two gold diggers;

[0035] Among them, the adjustment strategy of the adaptive fuzzy coefficient is:

[0036] In each iteration, calculate the information entropy H(t) of the current population: H(t)=-sum i p i (t)logp i (t);

[0037] Among them, p i (t) is the individual i The probability of selecting is proportional to the number of solutions it dominates;

[0038] Then, let the fuzzy coefficient C be negatively correlated with the entropy H(t): C(t)=C max -kH(t) / logN;

[0039] Where C maxis the maximum fuzzy coefficient, k is the proportional coefficient, and N is the population size.

[0040] According to one aspect of the present application, the improved multi-objective gold panning algorithm MOGRO is used to solve the distribution network reactive power optimization model containing SST to obtain a distribution network reactive power optimization scheduling strategy set, specifically:

[0041] Initialize the individual positions and Archive set of the population; the positions include the output of each distributed energy source and equipment parameters;

[0042] Perform non-dominated relationship screening on individuals in the current population, update the Archive set according to the congestion degree, and select the global optimal individual;

[0043] If the maximum number of iterations is not reached, the current determination factor, determination coefficient, fuzzy coefficient and distance coefficient are calculated respectively, and the fuzzy coefficient of the optimal update is improved by the adaptive fuzzy coefficient. Secondly, the update method of the cooperative update is improved by the two-way guidance.

[0044] Randomly select one of the three updating methods of the improved GRO to update the position, update the corresponding fitness, use binary simulated crossover to perform a secondary update, and screen the non-dominated relationship after the update;

[0045] The binary crossover principle of the genetic algorithm is used to update the population twice, and non-dominated relationships are screened, and the updated population is mixed with the archive to form a new population;

[0046] Select the reactive power optimization scheduling strategy that meets actual needs from the Pareto optimal solution of the final Archive set;

[0047] During the solution process, the current population is adaptively gridded every several generations, the target space is divided into several small areas, and then the number of individuals in each grid is counted.

[0048] Grids with too many individuals are clustered, and a representative individual is selected from each cluster to retain, and the rest are discarded. For grids with too few individuals, new individuals are generated by interpolating the individuals in adjacent grids to fill the grid. The diversity and uniformity of the population are maintained by dynamically adjusting the grid granularity.

[0049] According to one aspect of the present application, a reactive power optimization scheduling strategy that meets actual needs is selected from the Pareto optimal solution of the distribution network reactive power optimization scheduling strategy set, specifically:

[0050] Construct an evaluation index system, including balance index, robustness index and preference matching index,

[0051] BAL=1-sqrt(sum((F i -mean(F)) 2 ) / n); where F i To solve the i The function value on the target, mean(F) is the average value of all target function values, n is the number of targets, and the value range of BAL is [0, 1];

[0052] R=1 / (1+sum(abs(F'-F)) / n); where F and F' are the objective function value vectors of the original solution and the perturbation solution respectively, and the value range of R is (0, 1];

[0053] PREF=sum(w i ·(F i -F i min ) / (F i max -F i min )); where w i is the weight coefficient of the i-th target, reflecting its relative importance; F i min and F i max are the minimum and maximum values of the i-th objective function respectively;

[0054] Retrieve Pareto non-dominated solutions, extract the eigenvector of each solution, and perform a weighted sum of their qualities); select the top a% of solutions as positive samples and the remaining solutions as negative samples to train the machine model; the value of a ranges from 1 to 100;

[0055] The newly generated Pareto solutions of the optimization algorithm are evaluated and screened in real time, and the optimal scheduling strategy that meets actual needs is automatically and efficiently selected to obtain the optimization results. The optimization results are then fed back to the optimization algorithm to guide the evolution direction of the population.

[0056] Based on the optimization results, the active power output, reactive power, and energy storage charging and discharging power of various types of distributed energy are coordinated and controlled; the reactive power of various devices, including SST and distributed energy inverters, is coordinated, and transformer tap adjustment is coordinated to maintain voltage stability;

[0057] Continuously monitor changes in distribution network load and fluctuations in distributed energy output, and initiate rolling adjustment of reactive power optimization.

[0058] According to one aspect of the present application, clustering the output scenarios of different types of energy in the distribution network using the K-means clustering algorithm considering spatiotemporal characteristics to obtain typical output scenario data also includes:

[0059] Take the membership degrees of each load node to all cluster centers as its characteristic attributes. For the \(i\)-th load node, extract its multi-dimensional attribute vector \(P(i)=[P(i,1),P(i,2),\cdots,P(i,d)]\) including active power load, reactive power load, voltage amplitude and spatial coordinates. Use the fuzzy membership function \(\mu\) to calculate the membership degree of node \(i\) to the \(j\)-th cluster center \(C(j)\), \(\mu(i,j)=1 / (\sum_{k = 1}^{c}(\vert P(i)-C(j)\vert / \vert P(i)-C(k)\vert)^{m})\), where \(m\) is the fuzzy weighted exponent and \(c\) is the number of clusters; 2 / (m-1) )

[0060] Based on the Euclidean distance, introduce the attribute weight vector \(W = [w(1),w(2),\cdots,w(d)]\), and construct the weighted distance \(D(i,j)=\sum_{k = 1}^{d}(w(k)\cdot(P(i,k)-C(j,k))^{2})\). Use the gradient descent method to iteratively optimize the attribute weight vector \(W\). Each iteration updates the weight according to \(n(\sum_{i = 1}^{n}(\sum_{j = 1}^{c}(\mu(i,j)\cdot D(i,j))))\) until convergence, where \(n\) is the number of load nodes; k=1 d (w(k)\cdot(P(i,k)-C(j,k))^{2}) 2 ) i n(\sum_{i = 1}^{n}(\sum_{j = 1}^{c}(\mu(i,j)\cdot D(i,j)))) i=1 n \sum_{i = 1}^{n}(\sum_{j = 1}^{c}(\mu(i,j)\cdot D(i,j)))) j=1 c (\mu(i,j) m· D(i,j)))

[0061] When calculating the membership degree \(\mu(i,j)\) of node \(i\) to cluster center \(j\), not only consider the distance factor, but also introduce the mutual influence factor between nodes \(\alpha(i,k)=\exp(-\vert P(i)-P(k)\vert / \beta)\) to describe the influence intensity of node \(i\) by other node \(k\). Modify the membership degree calculation formula through \(\mu(i,j)=(1-\sum_{k = 1}^{n}(\alpha(i,k)\cdot\mu(k,j))) / (\sum_{l = 1}^{c}((D(i,j) / D(i,l))))\), and then perform row and column normalization processing on the membership degree matrix; k≠i \sum_{k = 1}^{n}(\alpha(i,k)\cdot\mu(k,j))) / (\sum_{l = 1}^{c}((D(i,j) / D(i,l)))) l=1 c ((D(i,j) / D(i,l)) (2 / (m-1) )))

[0062] Use the gradient descent method to update the cluster centers. Based on the current membership degree matrix, let \(C(j)=\sum_{i = 1}^{n}(\mu(i,j)\cdot P(i)) / \sum_{i = 1}^{n}(\mu(i,j))\) to update the cluster centers. To overcome the limitation of the fixed step size, each iteration updates \(C(j)\) through \(C(j)=C(j)+ \eta\cdot(\sum_{i = 1}^{n}(\mu(i,j)\cdot P(i)) / \sum_{i = 1}^{n}(\mu(i,j))-C(j))\), where \(\eta\) is the learning rate; i=1 n (\mu(i,j) m ·P(i)) / ∑ i=1 n (\mu(i,j) m ) new =C(j) old-η·∇J(C(j)) adjusts the clustering center, and the step size η is dynamically updated using a decay strategy η = η0·exp(-t / τ), where t is the number of iterations, and the hyperparameters η0 and τ control the initial step size and the decay rate, respectively;

[0063] Automatically determine the optimal number of clusters. Initially, set the number of clusters c equal to the number of load nodes n and perform iterative optimization. After each iteration, calculate the validity index XB(c) of the current clustering: XB(c) = ∑ i=1 n ∑ j=1 c (μ(i, j) m ·|P(i) - C(j)| 2 ) / (n·min(k≠l)(|C(k) - C(l)| 2 )). If XB(c) > XB(c - 1), then set c = c - 1, merge the two closest clustering centers, and restart the iteration; otherwise, set c best = c and terminate the clustering.

[0064] According to one aspect of the present application, the reactive power optimization model of the distribution network includes:

[0065] The distribution network power flow equations containing interval and fuzzy variables

[0066] The power balance equation of node i:

[0067] P Gi - P Li = U i * sum(U j * (G ij cos(theta ij ) + B ij s i n(theta ij ))), j = 1:N;

[0068] Q Gi - Q Li = U i ​​​​​​​​​​​​​​​​​​​​​​​​The tie-line switch state variables matrix {x} and continuous variables matrix {f} are introduced to couple the network topology optimization with the power flow solution.

[0071] x ij ∈{0, 1}, indicating the on-off state of the tie line between nodes i and j;

[0072] f ij ∈[0,f ij max], indicating the tie line flow;

[0073] Topological variables and power flow variables must satisfy the following constraints:

[0074] f ij -B ij *(theta i -theta j )=0;

[0075] |f ij |≤x ij *f ij max;

[0076] Among them B ij is the tie line susceptance, f ij max is the upper limit of the flow.

[0077] According to one aspect of the present application, a two-stage optimization strategy is adopted, in which the outer layer is optimized for the topological variables matr i x{x} is combined for optimization. The inner layer performs interval-fuzzy iterative solution to the power flow equations under a given topology. The outer layer uses the taboo search algorithm to minimize the network loss interval width and voltage deviation fuzziness as the optimization goal, and searches for the optimal topology combination matr i x{x}; The inner layer uses the interval Newton-Raphson method combined with the fuzzy expansion operator to iteratively solve the mixed uncertainty power flow equations to obtain the power flow interval and voltage fuzzy distribution;

[0078] The power flow calculation results under different topologies are evaluated from the two dimensions of interval width and fuzziness.

[0079] Interval width indicator: W=sum((P imax -P imin ) 2 +(Q imax -Q imin ) 2 , i=1:N) / N;

[0080] Fuzziness index: D=sum((U ic -U ia ) 2 +(Uic -U ib ) 2 , i=1:N) / (2*N);

[0081] The final optimal topology and corresponding power flow calculation results are selected on the Pareto front of the above two indicators.

[0082] According to another aspect of the present application, a reactive power optimization system for a port distribution network based on an improved MOGRO is provided, comprising:

[0083] at least one processor; and,

[0084] a memory communicatively connected to at least one of the processors; wherein,

[0085] The memory stores instructions that can be executed by the processor, and the instructions are used to be executed by the processor to implement the port area distribution network reactive power optimization method and system based on the improved multi-objective gold panning algorithm described in the above technical solution.

[0086] Beneficial effect: the present invention can obtain a more accurate dispatching strategy to prevent the problems of voltage fluctuation exceeding the limit and network loss increasing. BRIEF DESCRIPTION OF THE DRAWINGS

[0087] Figure 1 It is the IEEE33 node topology diagram.

[0088] Figure 2 This is the K-means clustering algorithm process

[0089] Figure 3 This is a flow chart of the reactive power optimization strategy for distribution network based on the improved multi-objective gold panning algorithm. DETAILED DESCRIPTION

[0090] The present invention will be further described below with reference to the accompanying drawings and specific embodiments.

[0091] According to one aspect of the present application, a method and system for optimizing reactive power of a port distribution network based on an improved multi-objective gold panning algorithm are provided, comprising the following steps:

[0092] The K-means clustering algorithm, which takes into account spatiotemporal characteristics, is used to cluster the output scenarios of different types of energy in the distribution network, obtaining typical output scenario data and providing a basis for output constraints of distributed energy resources operating at constant power factor.

[0093] The corresponding operating mode of the solid-state transformer (SST) is selected based on its external characteristics, and a reactive power optimization model of the distribution network including the SST is established. The input data of the reactive power optimization model of the distribution network including the SST includes typical output scenario data.

[0094] Call the multi-objective gold mining algorithm MOGRO, and introduce adaptive fuzzy coefficient, two-way guidance and secondary cross update to improve the multi-objective gold mining algorithm MOGRO;

[0095] The improved multi-objective gold mining algorithm MOGRO is used to solve the reactive power optimization model of the distribution network containing SST, and the reactive power optimization scheduling strategy set of the distribution network is obtained.

[0096] In this application, the K-means clustering algorithm that takes into account spatiotemporal characteristics is used to quickly and accurately divide massive amounts of distribution network operation data into several typical scenarios, each with similar load demand and new energy output characteristics. Through clustering simplification, not only the scale and frequency of power flow calculations are reduced, but the typical scenarios obtained are more representative and can cover the vast majority of actual operating conditions. The subsequent reactive power optimization model uses typical scenarios as input, which can reduce the computational complexity while increasing the applicability of the results, and has strong engineering practical value. It improves the accuracy and efficiency of distribution network operation scenario analysis.

[0097] Based on the external characteristics of the SST, its operating mode is optimized. The SST's multi-operating characteristics are then embedded in the distribution network reactive power optimization model. This fully utilizes the SST's reactive power regulation capabilities, dynamically tracking load and output fluctuations and improving the voltage quality of the distribution network. Compared to conventional transformers, SSTs enable continuous, accurate, and rapid reactive power dispatch, providing strong support for the safe and stable operation of the distribution network. This enables flexible and efficient operational control of solid-state transformers (SSTs).

[0098] To address the shortcomings of existing algorithms, three improvements were made: adaptive fuzzy coefficients dynamically balance global exploration and local exploitation; bidirectional guidance enhances swarm intelligence and accelerates convergence; and quadratic crossover amplifies the propagation of beneficial genes, improving solution quality. These improvements effectively overcome the "curse of dimensionality" challenge faced by multi-objective optimization. They can quickly locate the global optimal region within the vast solution space while achieving uniform distribution across the set of non-inferior solutions, thereby obtaining a highly accurate and widely distributed Pareto frontier. Extensive simulation experiments demonstrate that the improved MOGRO significantly outperforms existing mainstream multi-objective optimization algorithms in terms of convergence and diversity of solutions. This significantly improves the performance of reactive power optimization solutions for distribution networks.

[0099] After obtaining the reactive power optimization dispatch strategy set for the distribution network, the solution further screens out the optimal reactive power dispatch solution that meets the actual engineering requirements, including various reactive power control means such as DG output, capacitor switching, and tap regulation. This dispatch solution comprehensively considers multiple technical and economic indicators such as minimum network loss and qualified voltage, and can ensure the optimal operation of the distribution network under multiple scenarios and working conditions. In addition, the solution can also be integrated with the distribution automation system to achieve online correction and rolling execution of the optimization dispatch strategy, forming a closed-loop control based on real-time feedback, so that the distribution network can continuously maintain the best operating state in a complex and changeable environment. A closed-loop control strategy for reactive power optimization of the distribution network is formed.

[0100] According to one aspect of the present application, the use of the K-means clustering algorithm considering spatio-temporal characteristics to cluster the output scenarios of different types of energy in the distribution network, obtaining typical output scenario data, and providing a basis for the output constraints of distributed energy operating at a constant power factor, specifically:

[0101] Taking the active power output as an index, randomly select k samples as the initial cluster centers; k is a natural number greater than 0;

[0102] Calculate the distance from each sample in the dataset to the k cluster centers, and classify it into the cluster with the smallest distance;

[0103] For each cluster, recalculate the position of the cluster center;

[0104] Repeat the above steps until the set number of iterations is reached, and obtain typical output scenarios that can represent different spatio-temporal characteristics.

[0105] According to one aspect of the present application, the use of the K-means clustering algorithm considering spatio-temporal characteristics to cluster the output scenarios of different types of energy in the distribution network, obtaining typical output scenario data, and providing a basis for the output constraints of distributed energy operating at a constant power factor, further includes:

[0106] Taking the membership degree of each load node to all cluster centers as its characteristic attribute, for the i-th load node, extract its multi-dimensional attribute vector P(i)=[P(i,1), P(i,2),..., P(i,d)] including active load, reactive load, voltage amplitude, and spatial coordinates, and use the fuzzy membership function μ to calculate the i Membership degree of the node to the j-th cluster center C(j), μ(i,j)=1 / (∑(k = 1:c)(丨P(i)-C(j)丨 / 丨P(i)-C(k)丨) 2 / (m-1) ), where m is the fuzzy weighting exponent and c is the number of clusters;

[0107] Based on the Euclidean distance, introduce the attribute weight vector W = [w(1), w(2),..., w(d)], and construct the weighted distance D(i, j) = ∑ k=1 d (w(k) · (P(i, k) - C(j, k)) 2 ). Use the gradient descent method to iteratively optimize the attribute weight vector W. Each iteration updates the weight according to m i n(∑ i=1 n ∑ j=1 c (μ(i, j) m· D(i, j))), until convergence. n is the number of load nodes;

[0108] When calculating the membership degree μ(i, j) of node i to the cluster center j, not only consider the distance factor, but also introduce the mutual influence factor between nodes α(i, k) = exp(-丨P( i ) - P(k)丨 / β), which describes the intensity of node i affected by other nodes k. Through μ(i, j) = (1 - ∑ k≠i (α(i, k) · μ(k, j))) / (∑ l=1 c ((D(i, j) / D(i, l)) (2 / (m-1) ))), correct the membership degree calculation formula, and then normalize the membership degree matrix row by row and column by column;

[0109] Use the gradient descent method to update the cluster center. Based on the current membership degree matrix, let C(j) = ∑ i=1 n (μ(i, j) m · P(i)) / ∑ i=1 n (μ(i, j) m ) to update the cluster center. To overcome the limitation of the fixed step size, each iteration adjusts the cluster center through C(j) new = C(j) old - η · ∇J(C(j)). The step size η is dynamically updated using the decay strategy η = η0 · exp(-t / τ), where t is the number of iterations, and the hyperparameters η0 and τ control the initial step size and the decay rate respectively;

[0110] Automatically determine the optimal number of clusters. Initially, set the number of clusters c equal to the number of load nodes n, and perform iterative optimization. After each round of iteration, calculate the validity index XB(c) of the current clustering = ∑ i=1 n ∑ j=1 c (μ(i, j) m · 丨P(i) - C(j)丨2 ) / (n·m i n(k≠l)(|C(k) - C(l)| 2 )), if XB(c) > XB(c - 1), then let c = c - 1, merge the two closest cluster centers, and iterate again: otherwise let c best = c, and terminate the clustering.

[0111] In another embodiment of the present application, the clustering process further includes:

[0112] Introduce other attributes reflecting load and new energy output characteristics, including reactive power, power volatility, daily average power interval, and peak-valley ratio. Perform feature engineering on these original attributes, including data cleaning, normalization, dimensionality reduction, etc., to extract a set of comprehensive clustering feature indicators.

[0113] Use the Gap Statistics method to automatically determine the optimal number of clusters K. This method determines the optimal number of clusters by introducing a reference dataset, calculating the Gap statistic of the clustering under different K values, and selecting the K corresponding to the maximum Gap value.

[0114] The specific steps are as follows:

[0115] For a given number of clusters K, use the K-means algorithm to cluster the dataset D, and calculate the observed statistic of the clustering result: O K = sum(sum(dist(x i , c k ))), i = 1..n, k = 1..K;

[0116]

[0117] where x i is the i-th sample, c k is the k-th cluster center, and dist is the distance metric function. [[ID=3!]]

[0118] Through Monte Carlo simulation, generate B reference datasets D1, D2,..., D B with the same dimension and number of samples as D. Each reference dataset follows a uniform distribution.

[0118] For each reference dataset D b (b = 1..B), perform the clustering step and calculate its observed statistic O Kb .

[0119] Calculate the Gap statistic:

[0120] Gap(K) = (1 / B)sum(log(O _ Kb)) - log(O _ K), b = 1..B

[0121] Repeat the above steps, try different K values, and draw a curve of Gap(K) changing with K.

[0122] The K corresponding to the maximum Gap value is selected as the optimal cluster number K.

[0123] In another embodiment of the present application,

[0124] On the basis of K-means, fuzzy membership and weighted distance metrics are introduced to improve the robustness and adaptability of the algorithm. The improved fuzzy K-means clustering steps are as follows:

[0125] Randomly initialize the membership matrix U, where u ik Represents sample x i The membership of the k-th cluster satisfies the following conditions:

[0126] 0 <= u ik <= 1, sum(u ik ) = 1, i=1..n, k=1..K*;

[0127] b) Based on the current membership matrix U, calculate the cluster centers:

[0128] c k = sum(u ikm * x i ) / sum(u ik m ), i=1..n, k=1..K*;

[0129] Where m>1 is the fuzzy weighted index, which controls the degree of cluster overlap.

[0130] Update the membership matrix using weighted Euclidean distance:

[0131] u ik = 1 / sum((d i st(x i , c k ) / d i st(x i , c j )) 2 / (m-1) ), j=1..K*;

[0132] d i st(x i , c k ) = sqrt(sum(w d * (x id - c kd ) 2)), d=1..D;

[0133] where w d is the weight coefficient of the d-th dimension feature, reflecting its importance, and can be determined by entropy weight method, etc.

[0134] Until the membership matrix U converges or the maximum number of iterations is reached.

[0135] According to the membership matrix U, each sample x i Assigned to the cluster with the largest membership.

[0136] C i = argmax(u ik ), k=1..K*;

[0137] Using DB I The clustering effect is quantitatively evaluated and optimized by combining the method of the CNN and silhouette coefficient.

[0138] DB I The index measures the intra-cluster compactness and inter-cluster separation and is defined as:

[0139] DB I = (1 / K*) * sum(max(R _ i j)), i=1..K*, j!=i;

[0140] R ij = (avg dist (C i ) + avg dist (C j )) / dist(c i , c j );

[0141] Among them C i is the i-th cluster, c i As its center, avg dist The average distance from the sample in the cluster to the center. The smaller the DBI value, the better the clustering effect.

[0142] The silhouette coefficient measures the similarity between a sample and its cluster and other clusters, and is defined as:

[0143] S i = (b _ i - a _ i ) / max(a _ i , b _ i ),i =1..n

[0144] a i = (1 / |C _ k|) * sum(d i st(x i , x j )), x i , x j Belongs to C k , j!= i ;

[0145] b i = m i n((1 / |C l |) * sum(d i st(x i , x j ))), x j Belongs to C l , l!=k;

[0146] SC = (1 / n) * sum(S i );

[0147] where a i For sample x i The average distance to other samples in the same cluster, b i is x i The minimum average distance to other cluster samples. The silhouette coefficient SC takes a value between [-1, 1]. The closer it is to 1, the better the clustering effect. By iteratively adjusting the clustering parameters (such as m value, w _ d weight, etc.), continuously optimize DB I and SC indicators until the preset threshold is met or the maximum number of tuning times is reached, a high-quality scene clustering result can be obtained.

[0148] In this embodiment, clustering based on spatiotemporal characteristics has the following technical effects:

[0149] Traditional scenario analysis often only considers load and output data for a single time section, ignoring the continuity and correlation of scenarios across time and space. However, this clustering algorithm, by incorporating spatiotemporal characteristics, can capture the temporal and spatial evolution of different energy output types and explore the inherent connections between scenarios, resulting in more accurate and comprehensive classification of typical scenarios. This improves the accuracy of distribution network operation scenario analysis.

[0150] Large-scale distribution networks typically involve numerous load nodes and power supply nodes, and their operating scenarios exhibit high dimensionality, nonlinearity, and strong coupling. Directly performing power flow analysis on the entire scenario is not only computationally intensive but can also lead to non-convergence due to the "curse of dimensionality." This clustering algorithm automatically categorizes these massive scenarios into a few typical patterns. The characteristics of the scenarios within each pattern are similar, while the characteristics between patterns vary significantly. Subsequent power flow calculations and optimization decisions only need to be performed on these typical scenarios, significantly reducing computational complexity and time costs. This reduces the scale and frequency of power flow calculations for distribution networks.

[0151] Reactive power optimization in distribution networks requires accurate modeling and characterization of the active and reactive outputs of various types of distributed energy resources, particularly considering the strong coupling between active and reactive power under constant power factor operation. The typical output scenario data generated by this clustering algorithm can be directly used as the basis for constraining the output of distributed energy resources, avoiding the tedious process of modeling all scenarios one by one. Replacing the full scenario with typical scenarios significantly improves the applicability and computational efficiency of the reactive power optimization model while meeting optimization accuracy requirements. This also enhances the ability to constrain the output of distributed energy resources under constant power factor operation.

[0152] Distribution network loads have distinct regional distribution characteristics, and load trends in adjacent regions often show similarities. This clustering algorithm innovatively introduces inter-node interaction factors to quantitatively characterize the geographic topological proximity of load nodes and their impact on clustering. This helps identify typical load clusters within a region and optimize the power supply zoning and reactive power compensation points of the distribution network. It also explores the spatial topological relationships between load nodes.

[0153] In traditional K-means clustering, the number of clusters, K, must be manually set and remains constant during the iteration process, which relies heavily on experience and prior knowledge. This clustering algorithm uses the Xie-Beni metric to automatically determine the optimal number of clusters and adaptively adjusts the cluster size during the iteration process, overcoming the blindness of manual setting. Furthermore, the algorithm introduces attribute weight vectors and a learning rate decay strategy to adaptively adjust the importance of each attribute and the update step size based on the characteristics of the data distribution, effectively improving clustering performance. This achieves adaptive optimization of clustering parameters.

[0154] Distribution network scenario data typically exhibits non-uniform and non-convex distribution characteristics, with significant variations in data density across different regions. Traditional K-means clustering uses Euclidean distance to measure sample similarity, which can easily lead to overly fine clustering in areas with high data density and overly coarse clustering in areas with sparse data. This clustering algorithm combines fuzzy membership and weighted distance to dynamically adjust membership and distance weights based on the local density of the sample distribution, resulting in density-sensitive clustering results. This improves the adaptability of clustering results to non-uniform data distributions.

[0155] According to one aspect of the present application, the operation mode corresponding to the solid-state transformer SST is selected according to its external characteristics, and a reactive power optimization model of the distribution network including the SST is established, specifically:

[0156] Considering whether the secondary side of the SST is connected to an active network, select the operating mode of constant AC voltage control, constant power control, or a combination of the two;

[0157] Select the equivalent node type corresponding to the SST node according to the operation mode;

[0158] A dual-objective optimization model is established with minimizing network loss and voltage deviation as the objective function;

[0159] Model constraints include power balance constraints, distributed energy output constraints, reactive power compensator constraints, OLTC gear constraints, energy storage state of charge constraints, and SST capacity constraints.

[0160] According to one aspect of this application,

[0161] Optimization variable: Q Gi : Node i Reactive compensation capacity, continuous variable; b ij : Branch ij switching state, 0-1 integer variable;

[0162] tap k : transformer k-tap position, discrete variable; Q DG m : DG m Reactive power output, continuous variable. SST : Reactive output of SST, continuous variable.

[0163] Objective function: m i n F = w1sum(P lossi j ) + w2sum((V devi ) 2 );

[0164] P lossi j = G ij * (V i 2 + V j 2 - 2V i V j *cos(theta ij ));

[0165] V devi = (V i- V spec ) 2 ;

[0166] Where w1 and w2 are the weight coefficients of the two sub-goals, P lossi j is the branch active power loss, V devi is the node voltage deviation.

[0167] Constraints:

[0168] Node power flow balance constraints:

[0169] P Gi -P Li = V i * sum(V j * (G ij cos(theta ij ) + B ij s i n(theta ij )));

[0170] Q Gi -Q Li = V i * sum(V j * (G ij s i n(theta ij ) - B ij cos(theta ij )));

[0171] Voltage amplitude constraint: V min <= V i <= V max ;

[0172] Line power flow constraint: |P ij | <= b ij * P ij max ;|Q ij | <= b ij * Q ij max ;

[0173] Reactive power compensation constraint: Q Ci min <= Q Ci <= Q Ci max ;

[0174] Transformer tap constraints:

[0175] tapm i n <= tap k <= tap max ;

[0176] DG reactive output constraint: Q DG m min <= Q DG m <= Q DG m max ;

[0177] SST reactive power output constraint: Q SST min <= Q SST <= Q SST max ;

[0178] P SST 2 + Q SST 2 <= (S SST max ) 2 ;

[0179] According to whether the secondary side of the SST is connected to an active distribution network and the voltage / power control requirements, a rule-based hierarchical mode switching logic is designed.

[0180] The first layer: determine the properties of the distribution network to which the SST is connected;

[0181] If it is a passive network: execute constant voltage amplitude control mode (QV);

[0182] If it is an active network:

[0183] If voltage stability is the main concern: Execute constant voltage amplitude control mode (QV);

[0184] If power flow balance is the main consideration: Execute the fixed active and reactive power control mode (PQ);

[0185] The second layer: further refine the control parameters under the selected control mode;

[0186] If QV mode:

[0187] If the voltage at the feeder end is too high: Switch to the inductive operation area first to absorb reactive power;

[0188] If the voltage at the feeder end is too low: First, switch to the capacitive operating area and generate reactive power;

[0189] Dynamically adjust reactive power output Q _SST, so that the feeder terminal voltage V is maintained at [V min , V max ]Inside;

[0190] If PQ mode:

[0191] According to the real-time active load demand P of the distribution network load and reactive load demand Q load ;

[0192] Dynamically adjust the active power output P of SST _ SST and reactive output Q _ SST, which satisfies:

[0193] P SST + P DG = P load + P loss

[0194] Q SST + Q DG + Q C = Q load + Q loss ;

[0195] Among them, P DG and Q DG Help DG, Q C For other reactive compensation capacity;

[0196] The constructed mixed integer nonlinear programming (MINLP) model is solved using a decomposition-coordination optimization strategy.

[0197] Heuristic algorithms based on hill climbing and tabu search for integer decision variables, including:

[0198] Branch switching status b ij ; Transformer tap position tap k ;

[0199] Heuristic rules are constructed based on indicators such as network loss sensitivity and voltage deviation.

[0200] Thus, several feasible solutions of network topology and transformer tap locations are obtained.

[0201] With fixed integer variables, the original MINLP problem is transformed into a nonlinear programming (NLP) problem.

[0202] For each set of integer feasible solutions, PDIPM is used to solve the NLP subproblem and obtain the optimal solution of continuous variables, including:

[0203] Reactive compensation capacity Q Ci ; DG reactive output QDG m ; SST reactive power output Q SST ;

[0204] Compare the objective function values of all feasible solutions and select the global optimal solution as the final scheduling strategy for reactive power optimization.

[0205] According to one aspect of the present application, the multi-objective gold mining algorithm MOGRO is called and the adaptive fuzzy coefficient, bidirectional guidance and secondary cross update are introduced to improve the multi-objective gold mining algorithm MOGRO, specifically:

[0206] An adaptive fuzzy coefficient C1 is introduced into the optimal update, which changes dynamically from large to small during iteration, and the search range is narrowed in the later stage.

[0207] C 1 = [ 2 cos ( t − 1 T − 1 ⋅ π 2 ) ⋅ ( r 1 − 0 . 5 ) + 1 ] ⋅ r 2 ;

[0208] t is the current iteration number, T is the maximum iteration number; r1, r2 are random numbers in [0, 1];

[0209] Bidirectional guidance is introduced for cooperative updating, using the vector sum of individual position changes between two iterations to guide the update;

[0210] ;

[0211] X i (t+1), X i (t) is the position of the i-th gold digger before and after the update; D3 is the distance coefficient;

[0212] After each iterative update, a secondary crossover operation is performed on the population.

[0213] ;

[0214] Where: pc is the crossover probability, pc1 and pc2 are the crossover start and end probabilities, f is the maximum fitness of any objective function of any two particles, f average is the average value of the fitness of the objective function corresponding to f, f max , f min is the maximum and minimum value of the corresponding objective function fitness;

[0215] Adaptive crossover probability and simulated binary crossover are introduced to enhance local search capabilities;

[0216] X worker2 ( t + 1 ) = [ ( 1 + l ) ⋅ X worker1 ( t ) + ( 1 − l ) X worker2 ( t ) ] 2 X worker2 ( t + 1 ) = [ ( 1 − l ) ⋅ X worker1 ( t ) + ( 1 + l ) X worker2 ( t ) ] 2 ;

[0217] ;

[0218] Where mu is the cross coefficient, m is a random number in [0, 1], and X worker1 (t), X worker2 (t) for any two gold diggers;

[0219] Among them, the adjustment strategy of the adaptive fuzzy coefficient is:

[0220] In each iteration, calculate the information entropy H(t) of the current population: H(t)=-sum i p i (t)logp i (t);

[0221] Among them, p i (t) is the individual i The probability of selecting is proportional to the number of solutions it dominates;

[0222] Then, let the fuzzy coefficient C be negatively correlated with the entropy H(t): C(t)=C max -kH(t) / logN;

[0223] Where C max is the maximum fuzzy coefficient, k is the proportional coefficient, and N is the population size.

[0224] According to one aspect of the present application, the improved multi-objective gold panning algorithm MOGRO is used to solve the distribution network reactive power optimization model containing SST to obtain a distribution network reactive power optimization scheduling strategy set, specifically:

[0225] Initialize the individual positions and Archive set of the population; the positions include the output of each distributed energy source and equipment parameters;

[0226] Perform non-dominated relationship screening on individuals in the current population, update the Archive set according to the congestion degree, and select the global optimal individual;

[0227] If the maximum number of iterations is not reached, the current determination factor, determination coefficient, fuzzy coefficient and distance coefficient are calculated respectively, and the fuzzy coefficient of the optimal update is improved by the adaptive fuzzy coefficient. Secondly, the update method of the cooperative update is improved by the two-way guidance.

[0228] Randomly select one of the three updating methods of the improved GRO to update the position, update the corresponding fitness, use binary simulated crossover to perform a secondary update, and screen the non-dominated relationship after the update;

[0229] The binary crossover principle of the genetic algorithm is used to update the population twice, and non-dominated relationships are screened, and the updated population is mixed with the archive to form a new population;

[0230] Select the reactive power optimization scheduling strategy that meets actual needs from the Pareto optimal solution of the final Archive set;

[0231] During the solution process, the current population is adaptively gridded every several generations, the target space is divided into several small areas, and then the number of individuals in each grid is counted.

[0232] Grids with too many individuals are clustered, and a representative individual is selected from each cluster to retain, and the rest are discarded. For grids with too few individuals, new individuals are generated by interpolating the individuals in adjacent grids to fill the grid. The diversity and uniformity of the population are maintained by dynamically adjusting the grid granularity.

[0233] In another embodiment of the present application, a distribution estimation (EDA) mechanism is introduced during the solution process to enhance population diversity. The specific process is as follows:

[0234] In each iteration, the Gaussian mixture model (GMM) is used to estimate the distribution of individual fitness values:

[0235] p(x) = sum(w k * N(x|mu k , S i gma k )), k=1..K;

[0236] where w k is the mixing coefficient of the kth Gaussian component, mu k and S i gma k Its mean vector and covariance matrix.

[0237] New individuals are sampled from the estimated GMM distribution and merged with the original population, keeping the population size unchanged.

[0238] Adaptively adjust the EDA sampling ratio to dynamically balance exploration and utilization. In the early stages, the focus is on exploration, with a higher sampling ratio; in the later stages, the focus is on utilization, with a lower sampling ratio.

[0239] According to the shape characteristics of the Pareto front, the population selection and update strategies are improved.

[0240] In the non-dominated sorting, the individual's Pareto rank, crowding distance and super volume contribution are comprehensively considered.

[0241] NDS (non-dominated sorting), CD (crowding distance), and VC (excess volume contribution) are quantified as dimensionless indicators in the interval [0, 1].

[0242] Construct a comprehensive evaluation function to balance the three indicators: score = w1'*NDS + w2'*CD + w3'*VC;

[0243] Dynamically adjust the weight coefficients w1', w2', w3' to take into account the convergence and diversity of the solution.

[0244] Sort the scores of the individuals in the population and prioritize the top-ranked individuals as the parent population of the new generation.

[0245] Adaptively adjust the key parameters of the MOGRO algorithm. Based on the fuzzy coefficient C, an adaptive weight coefficient alpha is further introduced to enable C to adaptively switch between global search and local search.

[0246] C(t) = C min + (C max - C min ) * exp(-alpha(t) * t / T max );

[0247] alpha(t) = alpha0 + k * (SSD(t-1) - SSD(t)) / SSD(t);

[0248] Among them, alpha0 and k are control parameters, and SSD is the population spatial distribution metric, which measures the degree of dispersion of the population.

[0249] The p-best mechanism is introduced to record the individual's best historical position and intersect it with the current position with a certain probability to update the next generation of individual positions. This helps individuals escape local traps.

[0250] c) Based on the bidirectional guidance mechanism, adaptive step-size control is further introduced.

[0251] v ij (t) = w1 * v ij (t-1) + w2 * (gbest ij -x ij (t-1)) + w3 * (pbest ij -x ij (t-1));

[0252] w1= w max - (w max -w min ) * t / T max ;

[0253] w2= w min + (w max-w min ) * t / T max ;

[0254] w3 = c3 * rand();

[0255] where w max and w min They are the maximum and minimum values of the inertia weight, usually w max =0.9, w min =0.4. c3 is the pbest guidance coefficient, which should be a small positive number such as 0.1.

[0256] Here, a linearly decreasing inertia weight w1 is used, so that the algorithm focuses on global search in the early stage and local search in the later stage, balancing the convergence speed and the quality of the solution. _ Taking a random small value for 3 acts as a perturbation, helping to escape local traps. The weight w2 of the gbest term increases with the number of iterations, accelerating the population's convergence. Properly adjusting the values of w1, w2, and w3 allows the population to adaptively switch between global search and local exploitation, improving the algorithm's optimization capabilities.

[0257] In another embodiment of the present application, when calculating the dominance relationship between the newly generated individuals and the individuals in the Archive set, the concept of ε-non-domination is introduced.

[0258] If x i dominatex j , and for each goal:

[0259] f k (x i ) <= f k (x j ) - ε, k=1, 2,..., M;

[0260] Then x i ε-dominates x j ε is a preset small positive number that represents the significance level.

[0261] When updating the Archive set, individuals that are ε-non-dominated are deleted first. This can retain weak non-dominated solutions to a certain extent and improve the coverage of the solution set on the Pareto frontier.

[0262] When the Archive set overflows, individuals are deleted based on their crowding distance, ε-dominance relationship, and the time of last addition. Individuals with small crowding distance, ε-dominance, and recently added are deleted first. i ve sets of diversity and uniform distribution.

[0263] According to one aspect of the present application, a reactive power optimization scheduling strategy that meets actual needs is selected from the Pareto optimal solution of the distribution network reactive power optimization scheduling strategy set, specifically:

[0264] Construct an evaluation index system, including balance index, robustness index and preference matching index,

[0265] BAL=1-sqrt(sum((F i -mean(F)) 2 ) / n); where F i To solve the function value on the i-th target, mean(F) is the average value of all target function values, n is the number of targets, and the value range of BAL is [0, 1];

[0266] R=1 / (1+sum(abs(F'-F)) / n); where F and F' are the objective function value vectors of the original solution and the perturbation solution respectively, and the value range of R is (0, 1];

[0267] PREF=sum(w i ·(F i -F i min ) / (F i max -F i min )); where w i is the weight coefficient of the i-th target, reflecting its relative importance; F i min and F i max are the minimum and maximum values of the i-th objective function respectively;

[0268] Retrieve Pareto non-dominated solutions, extract the eigenvector of each solution, and perform a weighted sum of their qualities); select the top a% of solutions as positive samples and the remaining solutions as negative samples to train the machine model; the value of a ranges from 1 to 100;

[0269] The newly generated Pareto solutions of the optimization algorithm are evaluated and screened in real time, and the optimal scheduling strategy that meets actual needs is automatically and efficiently selected to obtain the optimization results. The optimization results are then fed back to the optimization algorithm to guide the evolution direction of the population.

[0270] Based on the optimization results, the active power output, reactive power, and energy storage charging and discharging power of various types of distributed energy are coordinated and controlled; the reactive power of various devices, including SST and distributed energy inverters, is coordinated, and transformer tap adjustment is coordinated to maintain voltage stability;

[0271] Continuously monitor changes in distribution network load and fluctuations in distributed energy output, and initiate rolling adjustment of reactive power optimization.

[0272] According to one aspect of the present application, a distribution network reactive power optimization model includes:

[0273] Distribution Network Flow Equations with Interval and Fuzzy Variables

[0274] The power balance equation of node i is:

[0275] P Gi -P Li =U i *sum(U j *(G ij cos(theta ij )+B ij s i n(theta ij ))), j=1:N;

[0276] Q Gi -Q Li =U i *sum(U j *(G ij s i n(theta ij )-B ij cos(theta ij ))), j=1:N;

[0277] Among them, P Gi , Q Gi Node i The active and reactive power generation range, U i is the triangular fuzzy number of the voltage amplitude of node i;

[0278] The tie-line switch state variables matrix {x} and continuous variables matrix {f} are introduced to couple the network topology optimization with the power flow solution.

[0279] x ij ∈{0, 1}, indicating a node i The on-off status of the tie line between and j;

[0280] f ij ∈[0,f ij max], indicating the tie line flow;

[0281] Topological variables and power flow variables must satisfy the following constraints:

[0282] f ij -B ij *(theta i-theta j )=0;

[0283] |f ij |≤x ij *f ij max;

[0284] Among them B ij is the tie line susceptance, f ij max is the upper limit of the flow.

[0285] According to one aspect of the present application, a two-stage optimization strategy is adopted, in which the outer layer is optimized for the topological variables matr i x{x} is combined for optimization. The inner layer performs interval-fuzzy iterative solution of the power flow equations under a given topology. The outer layer uses a tabu search algorithm to minimize the network loss interval width and voltage deviation fuzziness as the optimization goal, searching for the optimal topology combination matrix {x}. The inner layer uses the interval Newton-Raphson method combined with the fuzzy expansion operator to iteratively solve the mixed uncertainty power flow equations and obtain the power flow interval and voltage fuzzy distribution.

[0286] The power flow calculation results under different topologies are evaluated from the two dimensions of interval width and fuzziness.

[0287] Interval width indicator: W=sum((P imax -P imin ) 2 +(Q imax -Q imin ) 2 , i=1:N) / N;

[0288] Fuzziness index: D=sum((U ic -U ia ) 2 +(U ic -U ib ) 2 , i=1:N) / (2*N);

[0289] The final optimal topology and corresponding power flow calculation results are selected on the Pareto front of the above two indicators.

[0290] In another embodiment of the present application, all non-dominated solutions are extracted from the final Archive set and recorded as the Pareto optimal solution set PS.

[0291] For each solution S in PS i ( i =1, 2, ..., N), calculate its normalized fitness value in the objective function space:

[0292] floss (S i ) = (f loss max -f loss (S i )) / (f loss max -f loss m i n );

[0293] f dev (S i ) = (f dev max -f dev (S i )) / (f dev max -f dev m i n );

[0294] where f loss (S i ) and f dev (S i ) are the solutions to S i Corresponding network loss and voltage deviation value, f loss max 、f loss m i n 、f dev max 、f dev min are the maximum and minimum values of network loss and voltage deviation in PS respectively.

[0295] Perform weighted summation on multiple normalized objective function values to obtain the solution S i The comprehensive fitness value of:

[0296] f i t(S i ) = w loss * f loss (S i ) + w dev * f dev (S i );

[0297] where w loss and w dev is the weight coefficient of the two objectives, reflecting the decision preference.

[0298] Based on the comprehensive fitness value fit, calculate the selection probability of each solution:

[0299] p(S i ) = fit(S i ) / sum(fit(S j )), j=1,2,...,N;

[0300] Construct a roulette wheel based on the selection probability, corresponding to each solution S i The sector area and its selection probability p(S i ) is proportional to.

[0301] A uniform random number r in the interval [0, 1] is randomly generated and the sector into which it falls is determined. The corresponding solution is selected as the final reactive power optimization scheduling strategy.

[0302] In another embodiment of the present application, for each solution S in PS i The multiple objective function values of fuzzy processing are performed. Taking network loss as an example:

[0303] If f loss (S i ) <= f losslow : mu low (S i ) = 1

[0304] If f losslow < f loss (S i ) <= f loss mid :

[0305] mu low (S i ) = (f loss mid -f loss (S i )) / (f loss mid -f losslow )

[0306] mu mid (S i ) = (f loss (S i ) - f loss low ) / (f loss mid -f loss low )

[0307] If f lossmid < f _ loss (S _ i ) <= f _ loss _high :

[0308] mu mid (S i ) = (f loss high -f loss (S i )) / (f loss high -f loss mid )

[0309] mu high (S i ) = (f loss (S i ) - f loss mid ) / (f loss high -f loss mid )

[0310] If f loss (S i ) > f loss high : mu high (S i ) = 1

[0311] where f loss low 、f loss mid 、f loss high mu is the threshold value of low, medium and high levels of network loss. low (S i ), mu mid (S i ), mu high (S i ) are the solutions to S i The network loss belongs to low, medium and high levels. The membership of voltage deviation target can be defined similarly.

[0312] According to the target weight coefficient w loss and wdev, and the membership degrees are synthesized using the fuzzy weighted average operator.

[0313] mu low (Si ) = w loss mu loss low (S i ) + w dev mu dev low (S i )

[0314] mu mid (S i ) = w loss mu loss mid (S i ) + w dev mu dev mid (S i )

[0315] mu high (S i ) = w loss mu loss high (S i ) + w dev mu dev high (S i )

[0316] Design a fuzzy decision rule base and give the priority level of the solution according to its comprehensive membership. The rule base can be designed as follows:

[0317] If (mu low (S i )>=thr low 1) and (mu _mid (S i )<=thr mid 1) and (mu high (S i )<=thr high 1):dec(S i ) = Great;

[0318] If (mu low (S i )>=thr low 2) and (mu mid (S i )<=thr mid 2) and (mu high (S i )<=thr high 2):dec(S i ) = Good;

[0319] If (mu low (S i )<=thr low 3) and (mu mid (S i )>=thr mid 3) and (mu high (S i )<=thr high 3):dec(S i ) = Moderate;

[0320] If (mu low (S i )<=thr low 4) and (mu mid (S i )>=thr mid 4) and (mu high (S i )>=thr high 4):dec(S i ) = Poor;

[0321] Where thr is the threshold parameter, dec(S _ i ) is the solution S _ i The priority level, including Great, Good, Moderate, and Poor, reflects the comprehensive evaluation results of the scheduling strategy. The threshold parameters can be given by expert experience or automatically obtained from historical data through machine learning methods.

[0322] Further compare the comprehensive membership value mu of the solution that meets the highest priority level low (S i ), and the solution with the highest membership is selected as the final scheduling strategy. If the highest preference level contains multiple solutions with the same membership, additional screening conditions such as robustness can be combined, or the decision maker can select the scheduling strategy with the strongest subjective preference.

[0323] In another embodiment of the present application, an improved roulette wheel selection strategy based on fitness sorting and multi-angle rotation is provided, in which individuals in the population are sorted in descending order according to their fitness values to obtain a fitness sorting vector:

[0324] fit rank = sort(fit(S i )), i=1, 2, ..., N

[0325] According to the sorted vector fit_ rank, calculate the expected number of choices for each individual:

[0326] exp _ i = N * (N i +1) / (N*(N+1) / 2), i=1, 2,...,N

[0327] That is, the higher the ranking of the individual, the higher the expected number of selections, which shows a linear decreasing trend. N is the population size.

[0328] Based on the expected number of selections, a roulette wheel is constructed and divided into N sectors. i Sector corresponds to individual S _ i , its starting and ending angles are:

[0329] start i = sum(expj / N * 360), j=1, 2,..., i-1;

[0330] end i = start i +exp i / N * 360

[0331] Construct an adaptive rotation angle generation method. Define the fitness dispersion of the population:

[0332] D = sqrt(sum((fit (S i ) - fit _ avg) 2 )) / (N-1), i =1, 2, ..., N;

[0333] where fit avg is the average fitness value of all individuals.

[0334] Generate K rotation angles, the kth angle is:

[0335] theta k = k * 360 / (K * (1 + D)), k=1, 2,...,K;

[0336] When the population diversity is high (i.e., the D value is large), the rotation angle interval is small and the selection pressure is small; when the population tends to converge (i.e., the D value is small), the rotation angle interval is large and the selection pressure is large.

[0337] Let the pointer start from theta1 and rotate counterclockwise. Each time it rotates from theta2 to theta1, record the sector the pointer passes through. Rotate K times in total and get K candidate individuals.

[0338] Among the K candidate individuals, one is randomly selected as the final selection result.

[0339] Fitness ranking reduces the influence of superindividuals, making the roulette wheel insensitive to fitness differences and, to a certain extent, overcoming the problems of insufficient or excessive selection pressure. The expected number of selections is linearly correlated with the ranking, reducing the amplification effect of fitness estimation errors. Multi-angle rotation selects candidates from different regions of the population, helping to maintain population diversity. The rotation angle is adaptively adjusted based on the population's convergence state, dynamically aligning the selection pressure with the evolutionary process. Finally, random selection is performed from the candidate set, achieving a balance between exploration and exploitation. The algorithm is simple in structure, easy to implement, and computationally inexpensive.

[0340] According to another aspect of the present application, a reactive power optimization system for a port distribution network based on an improved MOGRO is provided, comprising:

[0341] at least one processor; and,

[0342] a memory communicatively connected to at least one of the processors; wherein,

[0343] The memory stores instructions that can be executed by the processor, and the instructions are used to be executed by the processor to implement the port area distribution network reactive power optimization method and system based on the improved multi-objective gold panning algorithm described in the above technical solution.

[0344] In summary, in this embodiment, the reactive power optimization method and system of the port distribution network based on the improved multi-objective gold panning algorithm include the following steps: a K-means clustering algorithm that takes into account the spatiotemporal characteristics, a distribution network reactive power optimization mathematical model containing SST, and an improved multi-objective gold panning algorithm; the K-means clustering algorithm is used to cluster large-scale distributed energy output scenarios with volatility and uncertainty, so as to increase the representativeness of the selected scenarios and reduce the flow calculation time; the distribution network reactive power optimization mathematical model containing SST includes an objective function, a constraint function and an SST operation mode; the improved MOGRO is used to solve the model.

[0345] According to one aspect of the present application, the principle of the K-means clustering algorithm is to achieve the minimum distance between the sample and the cluster center to which it belongs by iteration; the objective function is active network loss and voltage deviation; the constraint function includes power balance constraints, output constraints of various distributed energy sources, and parameter constraints of transformers; the ultimate goal of the improved MOGRO is the Pareto frontier, and the judgment rule is the judgment of the dominance relationship.

[0346] Embodiment 1: Provide a method and system for reactive power optimization of the port distribution network based on an improved multi-objective gold panning algorithm. The K-means clustering algorithm can cluster large-scale distributed energy output scenarios with different spatiotemporal characteristics into typical scenarios. The reactive power optimization model of the distribution network containing SST includes an objective function and a constraint function. The operation mode of the SST needs to be selected in combination with external characteristics. The improved multi-objective gold panning algorithm is used to solve the model. The principle of the K-means clustering algorithm is to achieve the minimum distance between the sample and the cluster center to which it belongs through iteration. The objective function is active network loss and voltage deviation. The constraint function includes power balance constraints, output constraints of various distributed energy sources, and parameter constraints of transformers. The flow calculation of the distribution network containing SST needs to select the operation mode according to whether the secondary side of the SST is connected to an active network. The ultimate goal of the multi-objective algorithm is the Pareto frontier, and the judgment rule is the judgment of the dominant relationship. The operation mode is divided into fixed AC voltage control, fixed power control, and a combination of the two. The judgment of the dominant relationship is: if a i is the characteristic element of set A, b i For the characteristic elements of set B, if all characteristic elements of A are superior to the corresponding characteristic elements of B, then A is said to dominate B. If not all characteristic elements of A are superior to the corresponding characteristic elements of B, then A and B are said to not dominate each other.

[0347] According to one aspect of the present application, the operation mode is divided into constant AC voltage control, constant power control and a combination of the two; the determination of the control relationship is: if a i is the characteristic element of set A, b i For the characteristic elements of set B, if all characteristic elements of A are superior to the corresponding characteristic elements of B, then A is said to dominate B. If not all characteristic elements of A are superior to the corresponding characteristic elements of B, then A and B are said to not dominate each other.

[0348] According to one aspect of the present application, it is divided into two parts: scene clustering and optimized scheduling.

[0349] Scene clustering includes the following steps:

[0350] Step (1): Taking active power output as the indicator, randomly select k samples as the initial cluster centers.

[0351] Step (2) calculates the distance between each sample in the data set and the k cluster centers, and assigns it to the class corresponding to the cluster center with the smallest distance.

[0352] Step (3): For each cluster, recalculate its cluster center position.

[0353] Step (4): Repeat steps (2) and (3) until the number of iterations is reached.

[0354] The optimized dispatching includes the following steps: To implement the distribution network dispatching strategy based on the improved multi-objective gold panning algorithm, the following steps are included:

[0355] Step (1), initialize the number of iterations, population size, storage archive set capacity, population location (output of each distributed energy source and parameters of each device).

[0356] In step (2), the non-dominated relationship of the population is screened, and the capacity of the archive set of the storage repository is used to determine whether to perform the elimination step on the filtered population. The elimination step is performed based on the degree of congestion to form an archive set, and the current global optimal individual gbest is selected according to the roulette wheel method.

[0357] Step (3) determines whether the number of iterations has been reached. If so, the process ends. If not, the process enters the improved GRO update.

[0358] In step (4), the current determination factor, determination coefficient, fuzzy coefficient and distance coefficient are calculated respectively, and the fuzzy coefficient of the optimal update is improved by the adaptive fuzzy coefficient, and then the update method of the cooperative update is improved by the two-way guidance.

[0359] In step (5), one of the three updating methods of the improved GRO is randomly selected to update the position, update the corresponding fitness, perform a secondary update using binary simulated crossover, and screen the non-dominated relationship after the update.

[0360] In step (6), the population is updated twice using the binary crossover principle of the genetic algorithm, and non-dominated relationships are screened. The updated population is mixed with the Archive to form a new population, and the process returns to step (2).

[0361] The present invention is carried out in two steps:

[0362] The first step is to construct a mathematical model for reactive power optimization of the distribution network, which includes two objective functions and multiple constraint functions.

[0363] The second step is to analyze and improve the gold mining algorithm and use it to solve the model.

[0364] In the first step, a dual-objective optimization problem is formulated, with the objective function of minimizing the useful network loss and voltage deviation of the distribution network. The general form of the model can be expressed as follows:

[0365] (1);

[0366] (2);

[0367] Where: f loss is the system network loss, f ΔU is the voltage offset, G ij is the real part of the node admittance matrix i Row j column, U i , U j are the voltage amplitudes of nodes i and j, θ ij is the voltage phase angle difference between node i and node j, Ω N is the set of all nodes, N is the number of nodes, U max , U min are the upper and lower limits of the node voltage amplitude, Φ f is the penalty function term.

[0368] The voltage limit penalty function expression is:

[0369] (3);

[0370] (4);

[0371] Where: is the penalty function coefficient.

[0372] When solving the first step of the optimization problem, some constraints need to be considered.

[0373] Constraint function equations include equality constraints and inequality constraint equations. Equality constraints are mainly the power balance constraint equations of the system, and sometimes also include constraint equations for special equipment; inequality constraints are variable constraint equations, mainly network node voltage and current, various equipment capacity constraints and reactive and active power constraints, charge state constraints, and transformer parameter constraints, etc.

[0374] a. Power balance constraint equation

[0375] When performing power flow calculations, the system's power balance equation will be reflected in the Newton method solution process. The specific equation is:

[0376] (5);

[0377] Where: PGrid , Q Grid are the active and reactive power generated by the power station respectively; PV，j , Q PV，j 、P WT，j , Q WT，j 、P load，j , Q load，j 、P i，j , Q i，j They are respectively the active power and reactive power generated by photovoltaic and wind turbines, the active power and reactive power of loads, and the active power and reactive power lost on the line; DS，j 、P EV，j are the active power of distributed energy storage and electric vehicle at node j respectively; Q CBs，j is the reactive power generated by the capacitor bank at node j; PV ,Ω WT ,Ω DS ,Ω EV ,Ω CBs They are respectively a collection of nodes equipped with photovoltaic, wind turbine, distributed energy storage, electric vehicles and capacitor group devices.

[0378] b. Variable constraint equations

[0379] Voltage and current constraints

[0380] (6);

[0381] Distributed Energy Constraints

[0382] (7);

[0383] Where: P DG，max 、P DG，min , Q DG，max , Q DG，min , which are the upper and lower limits of distributed energy active and reactive output respectively.

[0384] Reactive power compensator constraints

[0385] (8);

[0386] Where: Q CBs，max , Q CBs，min They are the upper and lower limits of the capacitor bank output respectively.

[0387] OLTC gear constraints

[0388] (9);

[0389] Where: T is the OLTC gear, T min , T maxThe upper and lower limits of the OLTC gear.

[0390] Distributed energy storage constraints

[0391] (10);

[0392] Where: is the minimum state of charge; The maximum state of charge.

[0393] Solid-state transformer constraints

[0394] (11);

[0395] Where: is the SST active power transfer efficiency, P s1 , Q s1 、P s2 , Q s2 are the active and reactive power of the primary and secondary sides of SST, respectively. SST is the solid-state transformer capacity.

[0396] The second step is to analyze and improve the gold mining algorithm.

[0397] There are three update methods for the gold mining algorithm: optimal update, communication update, and cooperative update. First, you need to determine the determining factor, which determines whether the current population is in a convergence state or a search state.

[0398] Determining factors:

[0399] (10);

[0400] Where: t is the current number of iterations, and T is the maximum number of iterations.

[0401] The specific forms of the three update methods are as follows.

[0402] Optimal update:

[0403] (11);

[0404] (12);

[0405] (13);

[0406] (14);

[0407] Where: X i (t+1), X i (t) is the position of the i-th gold digger before and after the update, Xgbest (t) is the current optimal solution position, A1 is the selection coefficient, l1 is the selection factor, D1 is the distance coefficient, C1 is the fuzzy coefficient, and the size is a random number in the range [0, 2]. r1 and r2 are random numbers in the range [0, 1]. l1 is a number that decreases linearly from 2 to 1 as the number of iterations increases.

[0408] Communication Updates

[0409] (15);

[0410] (16);

[0411] (17);

[0412] Where: X worker1 (t) is the position of any gold digger, A2 is the selection coefficient, l2 is the selection factor, and D2 is the distance coefficient.

[0413] Collaborative Updates

[0414] (18);

[0415] (19);

[0416] Where: X worker1 (t), X worker2 (t) are any two gold diggers, r1 is a random number in [0, 1], and D3 is the distance coefficient.

[0417] The improved method of this invention is as follows:

[0418] 1. Adaptive fuzzy coefficient

[0419] From the trending update, we know that its fuzzy coefficient range is [0, 2]. That is, the target update point is set to 0 to 2 times the position of the global optimal solution, so as to enhance the diversity of solutions. However, a constant fuzzy coefficient cannot meet the requirements of its iterative full-process search. As the iteration progresses, the differences between the Pareto frontier solutions of the distributed energy scheduling strategy decrease, and a large-scale search is no longer required. Based on this, its fuzzy coefficient is improved as follows:

[0420] C 1 = [ 2 cos ( t − 1 T − 1 ⋅ π 2 ) ⋅ ( r 1 − 0 . 5 ) + 1 ] ⋅ r 2 (20);

[0421] 2. Two-way guidance

[0422] In multi-objective problems, there is more than one marginal solution in the Pareto solution set. Unlike single-objective problems, the solution does not converge to one location, but converges to multiple locations. Therefore, it is impossible to judge the convergence of the current population by the distance between solutions. Therefore, cooperative updating is not suitable for the accuracy requirements of multi-objective problems. Based on this, this paper proposes to use the vector sum of the direction guidance of the previous iteration and the direction guidance of the current iteration, and assign each random weight coefficient to guide its update. The improved update is as follows:

[0423] (twenty one);

[0424] 3. Secondary cross update

[0425] The optimal update draws on the single wolf update method of the gray wolf algorithm. Compared with the three-in-one update method of the gray wolf algorithm, its convergence ability and local search ability are lacking. Since the genetic algorithm crossover has a strong local integrated search ability, an additional crossover update is added to the population after each iteration to ensure the comprehensiveness of the local search of the population after each iteration.

[0426] In order to conduct a comprehensive local search on the population with a lower degree of convergence without destroying the population with a higher degree of convergence, this paper introduces an improved adaptive crossover probability to meet the crossover probability requirements of the entire process.

[0427] a. Adaptive crossover probability

[0428] (twenty two);

[0429] Where: pc is the crossover probability, pc1 and pc2 are the crossover start and end probabilities, f is the maximum fitness of any objective function of any two particles, f average is the average value of the fitness of the objective function corresponding to f, f max , f min is the maximum and minimum value of the corresponding objective function fitness.

[0430] b. Simulate binary crossover

[0431] X worker2 ( t + 1 ) = [ ( 1 + l ) ⋅ X worker1 ( t ) + ( 1 − l ) X worker2 ( t ) ] 2 X worker2 ( t + 1 ) = [ ( 1 − l ) ⋅ X worker1 ( t ) + ( 1 + l ) X worker2 ( t ) ] 2 (twenty three);

[0432] (twenty four);

[0433] Where: mu is the cross coefficient, and m is a random number in [0, 1].

[0434] Figure 3This is a flow chart of reactive power optimization of distribution network based on improved multi-objective gold panning algorithm. It is divided into multi-objective problem and improved GRO update part. After initializing the number of iterations and population information, the population is screened for non-dominated relationship. The capacity of the archive set and the number of screened populations are used to determine whether to remove them. In this way, the archive set is formed, and it is determined whether the number of iterations is reached. If it is reached, the algorithm ends. If it is not reached, the improved GRO update is performed. The improved GRO update includes optimal, exchange and cooperative updates. The adaptive fuzzy coefficient and bidirectional guidance are improved for the optimal and cooperative updates respectively. In this way, the population is updated and the dominant relationship is screened. Then, the population is updated twice by using simulated binary crossover. The updated population is compared with the Arch i The ve sets are mixed and the dominance relationship is screened, and finally the elimination step is returned to. This cycle is repeated until the end, and the optimal solution is output to obtain the optimal reactive power scheduling strategy.

Claims

1. A reactive power optimization method for port distribution network based on improved multi-objective gold panning algorithm, characterized in that: The steps include: The K-means clustering algorithm, which takes into account spatiotemporal characteristics, is used to cluster the output scenarios of different types of energy in the distribution network and obtain typical output scenario data. The corresponding operating mode of the solid-state transformer (SST) is selected based on its external characteristics, and a reactive power optimization model of the distribution network including the SST is established. The input data of the reactive power optimization model of the distribution network including the SST includes typical output scenario data. Call the multi-objective gold mining algorithm MOGRO, and introduce adaptive fuzzy coefficient, two-way guidance and secondary cross update to improve the multi-objective gold mining algorithm MOGRO; The improved multi-objective gold mining algorithm MOGRO is used to solve the reactive power optimization model of the distribution network containing SST, and the reactive power optimization scheduling strategy set of the distribution network is obtained; The improved multi-objective gold panning algorithm MOGRO is used to solve the reactive power optimization model of the distribution network containing SST, and the reactive power optimization scheduling strategy set of the distribution network is obtained, which is specifically: Initialize the individual positions and Archive set of the population; the positions include the output of each distributed energy source and equipment parameters; Perform non-dominated relationship screening on individuals in the current population, update the Archive set according to the congestion degree, and select the global optimal individual; If the maximum number of iterations is not reached, the current determination factor, determination coefficient, fuzzy coefficient and distance coefficient are calculated respectively, and the fuzzy coefficient of the optimal update is improved by the adaptive fuzzy coefficient. Secondly, the update method of the cooperative update is improved by the two-way guidance. Randomly select one of the three updating methods of the improved GRO to update the position, update the corresponding fitness, use binary simulated crossover to perform a secondary update, and screen the non-dominated relationship after the update; The binary crossover principle of the genetic algorithm is used to update the population twice, and non-dominated relationships are screened, and the updated population is mixed with the archive to form a new population; Select the reactive power optimization scheduling strategy that meets actual needs from the Pareto optimal solution of the final Archive set; During the solution process, the current population is adaptively gridded every several generations, the target space is divided into several small areas, and then the number of individuals in each grid is counted. Grids with too many individuals are clustered, and a representative individual is selected from each cluster to retain, and the rest are discarded. For grids with too few individuals, new individuals are generated by interpolating the individuals in adjacent grids to fill the grid. The diversity and uniformity of the population are maintained by dynamically adjusting the grid granularity.

2. The reactive power optimization method for the port distribution network based on the improved multi-objective gold panning algorithm according to claim 1 is characterized in that: The K-means clustering algorithm considering spatiotemporal characteristics is used to cluster the output scenarios of different types of energy in the distribution network to obtain typical output scenario data. The clustered typical scenarios are combined with the constant power factor mode of the generator to constrain the output of different distributed power sources in the established mathematical model. Specifically: Taking active power output as the indicator, k samples are randomly selected as the initial cluster centers; k is a natural number greater than 0; Calculate the distance between each sample in the data set and the centers of k clusters, and classify it into the cluster with the smallest distance; For each cluster, recalculate the cluster center position; Repeat the above steps until the set number of iterations is reached to obtain a typical output scenario that can represent different spatiotemporal characteristics.

3. The reactive power optimization method for the port distribution network based on the improved multi-objective gold panning algorithm according to claim 1 is characterized in that: The corresponding operating mode is selected according to the external characteristics of the solid-state transformer SST, and a reactive power optimization model of the distribution network including the SST is established, specifically: Considering whether the secondary side of the SST is connected to an active network, select the operating mode of constant AC voltage control, constant power control, or a combination of the two; Select the equivalent node type corresponding to the SST node according to the operation mode; A dual-objective optimization model is established with minimizing network loss and voltage deviation as the objective function; Model constraints include power balance constraints, distributed energy output constraints, reactive power compensator constraints, OLTC gear constraints, energy storage state of charge constraints, and SST capacity constraints.

4. The reactive power optimization method for the port distribution network based on the improved multi-objective gold panning algorithm according to claim 1 is characterized in that: The multi-objective gold mining algorithm MOGRO is called and the adaptive fuzzy coefficient, bidirectional guidance and secondary cross update are introduced to improve the multi-objective gold mining algorithm MOGRO, specifically: An adaptive fuzzy coefficient C1 is introduced into the optimal update, which changes dynamically from large to small during iteration, and the search range is narrowed in the later stage. t is the current iteration number, T is the maximum iteration number; r1, r2 are random numbers in [0, 1]; Introducing bidirectional guidance for cooperative updating, using the vector sum of individual position changes between two iterations to guide the update; X i (t+1)=X i (t)+r1·D3(t)+(1-r1)·D3(t-1); X i (t+1), X i (t) is the position of the i-th gold digger before and after the update; D3 is the distance coefficient; After each iterative update, a secondary crossover operation is performed on the population. Where: pc is the crossover probability, pc1 and pc2 are the crossover start and end probabilities, f is the maximum fitness of any objective function of any two particles, f average is the average value of the fitness of the objective function corresponding to f, f max , f min is the maximum and minimum value of the corresponding objective function fitness; Adaptive crossover probability and simulated binary crossover are introduced to enhance local search capabilities; Where mu is the cross coefficient, m is a random number in [0, 1], and X worker1 (t), X worker2 (t) for any two gold diggers; Among them, the adjustment strategy of the adaptive fuzzy coefficient is: In each iteration, calculate the information entropy H(t) of the current population: H(t) = -sum i p i (t)logp i (t); Among them, p i (t) is the selection probability of individual i, which is proportional to the number of its dominant solutions; Then, let the fuzzy coefficient C be negatively correlated with the entropy H(t): C(t) = C max -kH(t) / logN; Where C max is the maximum fuzzy coefficient, k is the proportional coefficient, and N is the population size.

5. The reactive power optimization method for the port distribution network based on the improved multi-objective gold panning algorithm according to claim 1 is characterized in that: It also includes selecting a reactive power optimization scheduling strategy that meets actual needs from the Pareto optimal solution of the distribution network reactive power optimization scheduling strategy set, specifically: Construct an evaluation index system, including balance index, robustness index and preference matching index, BAL=1-sqrt(sum((F i -mean(F)) 2 ) / n); where F i To solve the function value on the i-th target, mean(F) is the average value of all target function values, n is the number of targets, and the value range of BAL is [0, 1]; R=1 / (1+sum(abs(F'-F)) / n); where F and F' are the objective function value vectors of the original solution and the perturbation solution respectively, and the value range of R is (0, 1]; PREF=sum(w i ·(F i -F i min ) / (F i max -F i min )); where w i is the weight coefficient of the i-th goal, reflecting its relative importance; F i min and F i max are the minimum and maximum values of the i-th objective function respectively; Retrieve Pareto non-dominated solutions, extract the eigenvector of each solution, and perform a weighted sum of their qualities); select the top a% of solutions as positive samples and the remaining solutions as negative samples to train the machine model; the value of a ranges from 1 to 100; The newly generated Pareto solutions of the optimization algorithm are evaluated and screened in real time, and the optimal scheduling strategy that meets actual needs is automatically and efficiently selected to obtain the optimization results. The optimization results are then fed back to the optimization algorithm to guide the evolution direction of the population. Based on the optimization results, the active power output, reactive power, and energy storage charging and discharging power of various types of distributed energy are coordinated and controlled; the reactive power of various devices, including SST and distributed energy inverters, is coordinated, and transformer tap adjustment is coordinated to maintain voltage stability; Continuously monitor changes in distribution network load and fluctuations in distributed energy output, and initiate rolling adjustment of reactive power optimization.

6. The reactive power optimization method for the port distribution network based on the improved multi-objective gold panning algorithm according to claim 1 is characterized in that: The method utilizes a K-means clustering algorithm that considers spatiotemporal characteristics to cluster the output scenarios of different types of energy in the distribution network, obtains typical output scenario data, operates the generator at a constant power factor, and constrains the output of distributed energy resources to correspond to the constraint function in the mathematical model, and further includes: Take the membership degrees of each load node to all cluster centers as its characteristic attributes. For the \(i\)-th load node, extract its multi-dimensional attribute vector \(P(i)=[P(i,1),P(i,2),\cdots,P(i,d)]\) including active power load, reactive power load, voltage amplitude and spatial coordinates. Use the fuzzy membership function \(\mu\) to calculate the membership degree of node \(i\) to the \(j\)-th cluster center \(C(j)\), \(\mu(i,j) = 1 / (\sum_{k = 1}^{c}(|P(i)-C(j)| / |P(i)-C(k)|))\) 2 / (m-1) ), where \(m\) is the fuzzy weighted exponent and \(c\) is the number of clusters; Based on the Euclidean distance, the attribute weight vector W = [w(1), w(2), ..., w(d)] is introduced to construct the weighted distance D(i, j) = ∑ k＝1 d (w(k)·(P(i, k)-C(j, k)) 2 ), the attribute weight vector W is iteratively optimized using the gradient descent method, and each iteration is based on m i n(∑ i＝1 n ∑ j＝1 c (μ(i, j) m· D(i, j))) to update the weight until convergence, n is the number of load nodes; When calculating the membership of node i to cluster center j, μ(i, j), not only the distance factor is considered, but also the mutual influence factor α(i, k) = exp(-|P(i)-P(k)| / β) between nodes is introduced to characterize the intensity of the influence of node i on other nodes k. k≠i (α(i, k)·μ(k, j))) / (∑ l＝1 c ((D(i, j) / D(i, l)) (2 / (m-1) ))) Modify the membership calculation formula, and then normalize the membership matrix by row and column respectively; The cluster center is updated using the gradient descent method. Based on the current membership matrix, let C(j) = ∑ i＝1 n (μ(i, j) m ·P(i)) / ∑ i＝1 n (μ(i, j) m ) Update the cluster center. To overcome the limitation of fixed step size, each iteration is done by Adjust the cluster center, and the step size η is dynamically updated using the decay strategy η = η0·exp(-t / τ), where t is the number of iterations, and the hyperparameters η0 and τ control the initial step size and decay rate respectively; Automatically determine the optimal number of clusters. Initially, set the number of clusters c equal to the number of load nodes n, and perform iterative optimization. After each round of iteration, calculate the validity index XB(c) of the current clustering: XB(c) = ∑ i＝1 n ∑ j＝1 c (μ(i, j) m ·|P(i) - C(j)| 2 ) / (n·m i n(k≠l)(|C(k) - C(l)| 2 ))). If XB(c) > XB(c - 1), then set c = c - 1, merge the two closest cluster centers, and perform re-iteration; otherwise, set c best [[ID= 7. The reactive power optimization method for the port distribution network based on the improved multi-objective gold panning algorithm according to claim 1 is characterized in that: The distribution network reactive power optimization model includes: Distribution Network Flow Equations with Interval and Fuzzy Variables The power balance equation of node i is: P Gi -P Li =U i *sum(U j *(G ij cos(theta ij )+B ij s i n(theta ij ))),j=1::N; Q Gi -Q Li =U i *sum(U j *(G ij s i n(theta ij )-B ij cos(theta ij ))),j=1::N; Among them, P Gi , Q Gi are the active and reactive power generation intervals of node i, U i is the triangular fuzzy number of the voltage amplitude of node i; Introducing the tie line switch state variable matr i x{x} and continuous variable matr i x{f}, coupling network topology optimization with power flow solution; x ij ∈{0, 1}, indicating the on-off state of the tie line between nodes i and j; f ij ∈[0,f ij max], indicating the tie line flow; Topological variables and power flow variables must satisfy the following constraints: f ij -B ij *(theta i -theta j )=0; |f ij |≤x ij *f ij max; Among them B ij is the tie line susceptance, f ij max is the upper limit of the flow.

8. The reactive power optimization method for the port distribution network based on the improved multi-objective gold panning algorithm according to claim 1 is characterized in that: A two-stage optimization strategy is adopted. The outer layer performs combinatorial optimization on the topological variable matrix {x}, while the inner layer performs interval-fuzzy iterative solution of the power flow equations under a given topology. The outer layer uses a tabu search algorithm to minimize the network loss interval width and voltage deviation fuzziness as the optimization goal, searching for the optimal topological combination matrix {x}. The inner layer combines the interval Newton-Raphson method with the fuzzy expansion operator to iteratively solve the mixed uncertainty power flow equations, obtaining the power flow interval and voltage fuzzy distribution. The power flow calculation results under different topologies are evaluated from the two dimensions of interval width and fuzziness. Interval width indicator: W = sum((P imax -P imin ) 2 +(Q imax -Q imin ) 2 , i=1::N) / N; Model degree index: :D=sum((U ic -U ia ) 2 +(U ic -U ib ) 2 , i=1::N) / (2*N); The final optimal topology and corresponding power flow calculation results are selected on the Pareto front of the above two indicators.

9. A reactive power optimization system for port distribution network based on improved MOGRO, characterized in that: include: at least one processor; as well as, a memory communicatively connected to at least one of the processors; wherein, The memory stores instructions that can be executed by the processor, and the instructions are used to be executed by the processor to implement the reactive power optimization method of the port distribution network based on the improved multi-objective gold panning algorithm as described in any one of claims 1 to 8.

Images