CN-115940267-B - Regional distribution network distributed photovoltaic cluster division method

CN115940267BCN 115940267 BCN115940267 BCN 115940267BCN-115940267-B

Abstract

The invention belongs to the technical field of photovoltaic power generation control, and discloses a regional power distribution network distributed photovoltaic cluster division method, wherein the electrical distance between nodes is calculated based on a frequency-active sensitivity matrix, and an electrical distance matrix is obtained; the method comprises the steps of obtaining an optimized initial centroid through a PSO algorithm, carrying out node clustering analysis on a power distribution network according to the initial centroid by using a K-means algorithm to finish cluster division on energy storage nodes, carrying out cluster division on residual photovoltaic nodes without energy storage and load nodes by taking the energy storage nodes as centroids, carrying out weighted calculation on three indexes of active balance degree, energy storage balance degree and modularity to obtain comprehensive performance indexes, and taking a division scheme with the highest comprehensive performance indexes as the optimal cluster division. The invention uses the energy storage of the transformer area as the center to divide the transformer area containing the distributed photovoltaic into clusters, and provides necessary inertia and frequency support for the power grid.

Inventors

XIONG JUNJIE
ZHOU SUYANG
LUO LIZI
RAO ZHEN
XIAO CHAOXIA
FANG HONGWEI
ZHENG YAMING
GU WEI
ZHENG SHU
HUANG SHAOZHEN
ZHANG GUOQIN
LU XIAOJUN
TANG CHENGHONG
WU ZHI

Assignees

国网江西省电力有限公司电力科学研究院
国家电网有限公司
东南大学
国电南瑞科技股份有限公司
天津大学

Dates

Publication Date: 20260505
Application Date: 20221213

Claims (9)

1. The regional distribution network distributed photovoltaic cluster division method is characterized by comprising the following steps of: Calculating the electrical distance between nodes based on a frequency-active sensitivity matrix, and obtaining an electrical distance matrix; obtaining an optimized initial centroid through a PSO algorithm, performing cluster analysis on nodes of the power distribution network by applying a K-means algorithm according to the initial centroid, and performing cluster division on energy storage nodes; the energy storage node is used as a centroid to conduct cluster division on the rest photovoltaic nodes without energy storage and the load nodes; weighting calculation is carried out on three indexes of active balance degree, energy storage balance degree and modularity degree to obtain comprehensive performance indexes, and the cluster division result is evaluated according to the comprehensive performance indexes; Step five, repeating the step two to the step four to finish the traversal of different number of cluster division, wherein the division scheme with the highest comprehensive performance index is used as the optimal cluster division; the specific process of the third step is as follows: s21, in all n nodes, the number of the energy storage nodes is z, and the energy storage nodes are assembled as follows: ; b represents the set of all energy storage nodes, Represents an s-th energy storage node, s=1, 2,..z; S22, according to the energy storage nodes, establishing the node set with the rest energy storage as follows: ; H represents the remaining set of non-energy storage nodes, Represent the first The energy storage node is not arranged, ; Respectively calculating the electrical distance from the non-energy storage node to the energy storage node: ; represent the first The electrical distance from the non-energy storage node to the s-th energy storage node; s23, forming an energy storage node-free electric distance matrix D by using electric distances from the energy storage nodes to the energy storage nodes, classifying the rest energy storage nodes into clusters where the energy storage nodes closest to the rest energy storage nodes are located, and finally, representing the clustering result as a set: ; C x represents an x-th cluster, which comprises an energy storage node and no energy storage node, and the number of the clusters after division is N c , wherein x=1, 2, and N c ; The method for calculating the mass center comprises the following steps of calculating the sum D Tr of the electrical distances between the (r) th energy storage node and other nodes in the clusters, wherein the (x) th cluster C x is provided with m nodes, the number of the energy storage nodes is g, the number of the non-energy storage nodes is m-g, and the sum D Tr of the electrical distances between the (r) th energy storage node and the other nodes in the clusters is calculated: ; Representing the energy storage node at the r-th, Represent the first The energy storage node is not arranged, ; ; Selecting a node with a minimum sum of electrical distances D Tk =min(D Tr ) from other nodes And repeating the step S22 and the step S23 until the iteration is finished.
2. The method for dividing a distributed photovoltaic cluster of a regional distribution network according to claim 1, wherein in the first step, a distance between two nodes is defined according to a frequency-active sensitivity matrix: ; Wherein S δP is a frequency-active sensitivity matrix between the node j and the node i; The maximum element of all numbers of the j-th column in the frequency-active sensitivity matrix between the node j and the node i is d ij which is the distance between the node j and the node i; the electrical distance between node i and node j is defined by the Euclidean distance: ; Wherein d i1 , d i2 … d in represents the distance between node i and node 1, 2..n, d j1 , d j2 … d jn represents the distance between node j and node 1, 2..n, and n is the number of nodes; The edge weight A ij of the connection between the node i and the node j is as follows: ; And e is an electrical distance matrix formed by electrical distances between any two nodes in the power distribution network.
3. The method for dividing the regional distribution network distributed photovoltaic clusters according to claim 1, wherein in the second step, the PSO algorithm generates z particles according to z energy storage nodes, gives the initial position and speed of each particle, and then starts iteration, the individual extremum P best of each particle and the global extremum G best of the population are recorded while the particle continuously and iteratively updates the self speed and position, the speed and position update of the particle are influenced by the two extremums, and the optimal solution is found in the continuous iteration process, wherein the formula is as follows: ; ; wherein k is the iteration number; The position of the node i at the kth iteration is the position of the node i; the speed of the node i at the kth iteration is given; Is an inertial weight; 、 The first learning factor and the second learning factor are respectively; 、 is a randomly generated parameter between 0 and 1; Optimizing a K-means clustering algorithm according to a PSO algorithm, and defining a deviation function F of particles as follows: ; Wherein K is the number of clusters, a i is the data vector of the node i, C q is the mass center of the cluster q, K q is a subset of the cluster q, and d is the distance from the node to the mass center.
4. The method for dividing regional distribution network distributed photovoltaic clusters according to claim 3, wherein in the second step, the module degree is used as a fitness function of particle optimization, and a PSO algorithm optimization improvement K-means clustering algorithm is applied to divide the energy storage clusters.
5. The regional power distribution network distributed photovoltaic cluster division method according to claim 1, wherein the specific process of the second step is as follows: s11, inputting node parameters and PSO algorithm parameters of the power distribution network; S12, initializing the speed and the position of the particles according to an electric distance matrix between the nodes; S13, calculating a cluster to which each node belongs, selecting a node with the smallest sum of electrical distances from the node to other energy storage nodes in the cluster as a centroid of a new cluster, re-dividing the cluster, and calculating the adaptability of particles and the extreme value of the particle; s14, updating the positions corresponding to the local optimal solution and the global optimal solution according to the extremum of all particles; S15, recalculating the speed of the particles, and determining the positions of the particles through a relation formula of the positions and the speeds; s16, judging whether an iteration ending condition is met, if so, obtaining the position of the optimal particle, otherwise, continuing iteration, wherein the obtained optimal particle is the initial centroid; and S17, clustering the energy storage nodes by using a K-means algorithm according to the initial centroid optimized by PSO, and completing cluster division of the energy storage nodes.
6. The regional power distribution network distributed photovoltaic cluster division method according to claim 1, wherein the energy storage balance degree calculation process is as follows: The x-th cluster C x has m nodes in total, wherein g nodes are provided with energy storage systems, and the total discharging power of the energy storage devices of g energy storage nodes in the x-th cluster C x is as follows: ; Wherein, the Representing the discharge rated power of the energy storage device on the r energy storage node in the x-th cluster; the h period net power of the x-th cluster C x is: ; Wherein, the 、 Representing the instantaneous power of the load or photovoltaic of the nth period of the nth node, respectively The average net power for the x-th cluster C x is: ; t is the duration of the selected typical scene; Obtaining energy storage configuration coefficients in the x-th cluster ; ; ; ; Wherein: representing the number of cluster divisions, based on the number of cluster divisions, The prediction value of the integral energy storage configuration coefficient is adopted, S is the standard deviation of the integral energy storage configuration of the power distribution network, Representing the energy storage balance of the energy storage arrangement.
7. The regional power distribution network distributed photovoltaic cluster division method according to claim 6, wherein the active balance degree calculation mode is as follows: ; ; wherein: C represents the set of all clusters; representing the active balance of the x-th cluster C x ; Representing the net power of the xth cluster C x during the h period; representing the active balance that is seen by all clusters as a whole.
8. The method for dividing regional distribution network distributed photovoltaic clusters according to claim 7, wherein the modularity of all clusters is Is defined as: ; ; ; Of all n nodes, A ij represents the edge weight of node i to node j, when node Sum node Taking 1 when directly connected and 0 when not connected, k i represents the sum of the weights of all the edges connected with the node i, k j represents the sum of the weights of all the edges connected with the node j, W represents the sum of the weights of the whole network; 0-1 matrix, if node Sum node Within the same cluster, then If not in the same cluster 。
9. The regional power distribution network distributed photovoltaic cluster division method according to claim 8, wherein the comprehensive performance index ρ is: ; wherein: , And The module degree, the active balance degree and the energy storage balance degree weight are determined by a hierarchical analysis method.

Description

Regional distribution network distributed photovoltaic cluster division method Technical Field The invention belongs to the technical field of photovoltaic power generation control, and relates to a distributed photovoltaic cluster division method of a regional power distribution network. Background With the continuous development of distributed photovoltaic, a district with a high distributed photovoltaic ratio in a power grid generally selects to configure distributed energy storage. The access of the distributed energy storage of the station area increases the capacity of the renewable energy sources for configuring the energy storage station area, and simultaneously has the advantages of quickly adjusting the inertia and the frequency of the system, improving the tide of the system and reducing the network loss. The mass distributed photovoltaic large-scale application also has some technical problems, such as high requirements on secondary equipment and a communication system, and the like, because a distribution network side energy management system needs to be constructed. When the permeability of mass distributed photovoltaic in the regional distribution network reaches a certain proportion and is widely accessed, the distributed power supply under the transformer area and the energy storage configured by the distributed power supply need to provide more support for the power grid, including inertia and frequency support and voltage support. Meanwhile, in order to facilitate the dispatching and management of the power distribution network to the mass distributed power supply, the power distribution network uses the energy storage of the power distribution network as the center to divide the power distribution network into clusters, so as to realize the balance between the network and the source and the load and the storage in the clusters, and provide necessary inertia and frequency support for the power grid. Disclosure of Invention In order to improve inertia-frequency supporting capability, the invention provides a distributed photovoltaic cluster division method for a regional distribution network. The technical scheme adopted by the invention is that the regional power distribution network distributed photovoltaic cluster division method comprises the following steps: Calculating the electrical distance between nodes based on a frequency-active sensitivity matrix, and obtaining an electrical distance matrix; obtaining an optimized initial centroid through a PSO algorithm, performing cluster analysis on nodes of the power distribution network by applying a K-means algorithm according to the initial centroid, and performing cluster division on energy storage nodes; the energy storage node is used as a centroid to conduct cluster division on the rest photovoltaic nodes without energy storage and the load nodes; weighting calculation is carried out on three indexes of active balance degree, energy storage balance degree and modularity degree to obtain comprehensive performance indexes, and the cluster division result is evaluated according to the comprehensive performance indexes; And fifthly, repeating the second step and the fourth step to finish the traversal of different numbers of cluster partitions, and taking a partition scheme with the highest comprehensive performance index as the optimal cluster partition. Further preferably, in step one, the distance between two nodes is defined according to a frequency-active sensitivity matrix: Wherein S δP is a frequency-active sensitivity matrix between the node j and the node i; The maximum element of all numbers of the j-th column in the frequency-active sensitivity matrix between the node j and the node i is d ij which is the distance between the node j and the node i; the electrical distance between node i and node j is defined by the Euclidean distance: Wherein d i1,di2…din represents the distance between node i and node 1, 2..n, d j1,dj2…djn represents the distance between node j and node 1, 2..n, and n is the number of nodes; The edge weight A ij of the connection between the node i and the node j is as follows: And e is an electrical distance matrix formed by electrical distances between any two nodes in the power distribution network. In the second step, the PSO algorithm generates z particles according to the z energy storage nodes, gives the initial position and speed to each particle, and then starts iteration, records an individual extremum P best of each particle and a global extremum G best of the population while the particle continuously and iteratively updates the self speed and position, and the speed and position updating of the particle are influenced by the two extremums, and the optimal solution is found in the continuous iteration process, wherein the formula is as follows: wherein k is the iteration number; The method comprises the steps of determining a position of a node i in a kth iteration, determining a speed of the no