Distributed energy cluster voltage regulation control method, system and equipment

Abstract

The invention relates to the technical field of distributed energy voltage regulation, and discloses a distributed energy cluster voltage regulation control method, system and device, and the method comprises the steps: obtaining all distributed energy nodes in a power distribution network; performing group division on all the distributed energy nodes based on the sensitivity of the voltage of the distributed energy nodes to power change in combination with a K-means clustering algorithm to form a plurality of clusters; respectively establishing an adjustment control model for each cluster, wherein the adjustment control model comprises an objective function taking reduction of power fluctuation and voltage deviation as an optimization objective and a constraint condition taking safe operation of the power distribution network as a premise; and solving the regulation control model corresponding to each cluster through an improved particle swarm algorithm, thereby obtaining a control strategy for voltage regulation of each distributed energy node in each cluster. Quantitative evaluation indexes are established to evaluate the node voltage stability, a method for improving calculation precision is combined, power grid changes are quickly responded, and the voltage is accurately adjusted.

Description

Technical Field

[0001] The present invention relates to the technical field of distributed energy voltage regulation, and in particular to a distributed energy cluster voltage regulation control method, system and device. Background Art

[0002] As the global energy crisis and environmental problems continue to escalate, the development and utilization of renewable energy has become the focus of attention of all countries. As one of the key technologies to promote energy transformation, distributed energy systems are gradually changing the energy supply model. Compared with centralized energy systems, distributed energy systems have shown significant economic and environmental benefits due to their advantages such as being close to load centers, reducing long-distance transmission losses, supporting cascade energy utilization, and improving energy utilization efficiency. However, the access of a large number of distributed power sources has brought significant challenges to the operation and control of traditional distribution networks, among which voltage fluctuations and over-limit problems are particularly prominent, seriously affecting power quality and system stability.

[0003] In response to this problem, existing studies have proposed a distribution network cluster partitioning method, which can overcome the drawbacks of traditional centralized control, improve regulation efficiency, achieve internal autonomy of the cluster, and then coordinate the entire network to achieve effective control of the entire distribution network. Cluster control refers to a control method with the same power characteristics in the power network with node grouping. The system takes the decomposition of each cluster as the overall control goal for the control purpose, so that the cluster control working nodes have the same use function, and each node is assigned to each cluster. Ding Ming gave the definition of clusters and cluster partitioning in the power system, and proposed a scheme to connect to the electric field compatible with the power system through the distance between the power and the power system. Lu Lingzhi defined the electrical distance based on the voltage-voltage sensitivity between nodes and designed the partition partitioning index based on the reactive power network, and finally used the optimal particle swarm optimization to realize the reactive power partitioning. Considering the voltage regulation capability of photovoltaic inverters and energy storage equipment, Yu Lin designed a comprehensive performance index based on electrical distance and local voltage regulation capability. Li Guowu designed a comprehensive index system based on electrical distance, active power balance and reactive power balance, and used an improved genetic algorithm to divide the clusters. In response to the problem of uneven distribution of voltage regulation tasks in cluster voltage regulation, Huang Dongmei proposed a distributed photovoltaic cluster voltage regulation strategy that considers balanced net load.

[0004] Although the above research has made progress in the reactive power regulation capability of distributed power sources, the impact of active power changes on voltage levels has been generally ignored, limiting the comprehensiveness and effectiveness of the regulation strategy. Existing methods fail to fully consider the root cause of voltage fluctuations - active power changes - in cluster division and voltage regulation strategies, which may result in the regulation method not being able to achieve the best effect in complex network environments, especially in distributed power access scenarios with high penetration rates. Summary of the invention

[0005] To solve this technical bottleneck, the present invention proposes a distributed energy cluster voltage regulation control method, system and equipment. This method optimizes the electrical distance and power characteristics to form a distribution network voltage regulation cluster, aiming to minimize voltage fluctuations while ensuring power quality and system stability. Through experimental verification, this method shows superior performance in high DG penetration scenarios, providing strong technical support for the efficient and stable operation of distributed energy systems.

[0006] The present invention is achieved through the following technical solutions:

[0007] A distributed energy cluster voltage regulation control method, comprising:

[0008] Get all distributed energy nodes in the distribution network;

[0009] Based on the sensitivity of the voltage of the distributed energy nodes to power changes and combined with the K-means clustering algorithm, all the distributed energy nodes are grouped to form multiple clusters;

[0010] Establishing a regulation control model for each cluster respectively, wherein the regulation control model includes an objective function with the reduction of power fluctuation and voltage deviation as the optimization goal and a constraint condition with the safe operation of the distribution network as the premise;

[0011] The regulation control model corresponding to each cluster is solved respectively by using an improved particle swarm algorithm, so as to obtain a control strategy for voltage regulation of each distributed energy node in each cluster.

[0012] As an optimization, the sensitivity of the voltage of the distributed energy node to power changes can be obtained by changing the power injection situation of the distributed energy node in the channel where the distributed energy node is located, and then measuring the power flow in the distribution network, so as to obtain a quantitative index of the voltage sensitivity of the distributed energy node, and then obtain the sensitivity of the voltage of the distributed energy node to power changes.

[0013] As an optimization, the sensitivity of the voltage of the distributed energy node to the power change can be obtained by defining the electrical distance through the voltage sensitivity matrix, and the sensitivity of the voltage to the power change is reflected by the electrical distance, wherein the voltage sensitivity matrix includes a voltage active power sensitivity matrix and a voltage reactive power sensitivity matrix, and the specific calculation process of the electrical distance is:

[0014] A1. Set the voltage sensitivity matrix:

[0015]

[0016] △U=(N-HJ -1 L)-1 △P+(L-JH -1 N) -1 △Q=S P △P+S Q △Q;

[0017]

[0018] Among them, △P, △Q, △θ and △U are the active power change, reactive power change, voltage phase angle change and voltage amplitude change of the distributed energy node respectively; H, N, J and L are the Jacobian matrix elements respectively, H is the sensitivity matrix of phase angle to active power change; N is the sensitivity matrix of voltage amplitude to active power change; J is the sensitivity matrix of phase angle to reactive power change.

[0019] The sensitivity matrix of reactive power change; L is the sensitivity matrix of voltage amplitude to reactive power change; S P is the voltage active power sensitivity matrix, and the elements in the voltage sensitivity matrix represent the influence of the change of the active power injected by the distributed energy node on the node voltage change. Q is the voltage reactive power sensitivity matrix, n represents the number of distributed energy nodes, Respectively represent the voltage and reactive power sensitivity matrix S Q And voltage active power sensitivity matrix S P is an n×n matrix;

[0020] A2. The electrical distance between the distributed energy nodes is expressed as:

[0021]

[0022] D ij =S Pii -S Pij +S Qii -S Qij ;

[0023] Among them, L ij , L ji Respectively represent the distance between distributed energy node i and distributed energy node j; D ij represents the influence of the power change injected into node j on the voltage of node i, where power includes active power and reactive power; S Pii , S Pij , S Qii , S Qij They represent the influence of active power, reactive power change and voltage between nodes i and j, respectively. Pii The matrix elements represent the influence of the active power change on the voltage amplitude of node i; S PijThe matrix elements represent the degree of influence of active power change at node i on the voltage amplitude at node j; S Qii The matrix elements represent the influence of reactive power change on the voltage amplitude of node i; where S Qij The matrix elements represent the degree of influence on the voltage amplitude of node j when the reactive power changes at node i.

[0024] As an optimization, based on the sensitivity of the voltage of the distributed energy nodes to power changes combined with the K-means clustering algorithm, all the distributed energy nodes are grouped to form multiple clusters. Specifically, based on the electrical distance between the distributed energy nodes combined with the K-means clustering algorithm, all the distributed energy nodes are grouped to form multiple clusters. The specific process is:

[0025] B1. For each distributed energy node, the electrical distance between the distributed energy node and all distributed energy nodes other than the distributed energy node is calculated, wherein the electrical distance set formed by the electrical distances between the distributed energy node i and all nodes other than the distributed energy node i is expressed as:

[0026] LS i ={L i1 ,L i2 ,···,L ij ,···,L in}, j≠i;

[0027] B2. Calculate the node index of each distributed energy node: sort the electrical distances in the electrical distance set of each distributed energy node in ascending order from small to large, and select the Ath electrical distance after sorting as the index of the corresponding distributed energy node, where the index of distributed energy node i is represented by index i ;

[0028] B3. Sort the indexes in the index set index formed by the indexes of all the distributed energy nodes in ascending order from small to large, select the Tth index after sorting as the threshold, and take the distributed energy nodes corresponding to the indexes less than the threshold as high index nodes, thereby forming a high index node set, and sort the high index nodes in the high index node set from large to small according to the node index;

[0029] B4. Select the distributed energy node with the highest node index as the clustering center;

[0030] B5. Selecting a high-index node with the farthest total distance to the existing cluster center from the high-index node set to form a new cluster center, and repeating this step until a preset number of cluster centers are found;

[0031] B6. Form corresponding clusters based on the existing cluster centers based on the K-Means algorithm;

[0032] B7. Calculate the electrical distance between each distributed energy node and the distributed energy node in the formed multiple clusters respectively, and select the distributed energy node with the smallest sum of the electrical distances as the new clustering center of the cluster.

[0033] As an optimization, in B5, the preset number is set based on the SSE index, and the SSE index is the sum of the squares of the distances between each distributed energy node in each of the clusters and the cluster center of the corresponding cluster.

[0034] As an optimization, the objective function is specifically:

[0035] minf=λ1f1+λ2f2+cf3;

[0036]

[0037] f2=max|V i -V need |, i = 1, 2, ..., N′;

[0038] f3=max{V1,V2,…,V M , V need}-V need ;

[0039] Where f is the objective function; f1 represents the total power change caused by the voltage regulation within the cluster; f2 represents the maximum deviation between the voltage regulation requirements and all distributed energy nodes in the cluster; f3 is the penalty function; λ1 and λ2 represent the weight coefficients respectively; c represents the penalty factor of the penalty function; M represents the number of all distributed energy nodes in the cluster, ΔP i , ΔQ i They represent the active power change and reactive power change generated by distributed energy node i, N′ represents the number of all nodes in the cluster, V i Represents the voltage amplitude of distributed energy node i in the cluster, V need Indicates the voltage amplitude required for voltage regulation within the cluster.

[0040] As an optimization, the constraints are specifically:

[0041] 0≤ΔP i ≤ΔP imax ;

[0042] 0≤ΔQ i ≤ΔQ imax ;

[0043] U imin ≤U i ≤U imax ;

[0044] Where ΔP imax , ΔQ imax They represent the upper limit of active power change and reactive power change of distributed energy node i, respectively. imin , U imax They represent the lower and upper voltage limits of distributed energy node i, respectively, and U i Represents the actual voltage on distributed energy node i.

[0045] As an optimization, when solving the regulation control model corresponding to each cluster separately by using the improved particle swarm algorithm, specifically, the active power change and the reactive power change are regarded as particles together, and the particle swarm algorithm is used to search for the optimal solution, and finally the optimal solution of the active power change and the reactive power change is found, and then the optimal solution of the voltage amplitude change is obtained.

[0046] The present invention also discloses a distributed energy cluster voltage regulation control system, which is used to execute the above-mentioned distributed energy cluster voltage regulation control method, including:

[0047] The acquisition module is used to obtain all distributed energy nodes in the distribution network;

[0048] A cluster generation module, used to group all the distributed energy nodes to form multiple clusters based on the sensitivity of the voltage of the distributed energy nodes to power changes in combination with a K-means clustering algorithm;

[0049] A model building module, used to establish a regulation control model for each of the clusters, wherein the regulation control model includes an objective function with the reduction of power fluctuation and voltage deviation as the optimization goal and a constraint condition with the safe operation of the distribution network as the premise;

[0050] The solving module is used to solve the regulation control model corresponding to each cluster respectively through the improved particle swarm algorithm, so as to obtain the control strategy for voltage regulation of each distributed energy node in each cluster.

[0051] The present invention also discloses an electronic device, characterized in that it includes at least one processor and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute a distributed energy cluster voltage regulation control method as described above.

[0052] Compared with the prior art, the present invention has the following advantages and beneficial effects:

[0053] (1) The present invention divides the voltage regulation of the distribution network into groups. The nodes are divided into groups according to their sensitivity to power changes, and the nodes with high voltage sensitivity are concentrated to achieve refined control of the power grid.

[0054] (2) The present invention establishes a quantitative evaluation index to evaluate the node voltage stability, combines it with a method to improve the calculation accuracy, quickly responds to grid changes, and accurately adjusts the voltage.

[0055] (3) The present invention establishes a cluster voltage regulation model and improves the clustering algorithm. The main reasons for voltage over-limit when distributed energy is connected to the distribution network are analyzed, and the reduction of power fluctuation and voltage deviation is taken as the optimization goal, and the safe operation of the distribution network is taken as the constraint condition to ensure the effective utilization of distributed energy and the improvement of voltage quality. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] The drawings described herein are used to provide a further understanding of the embodiments of the present invention, constitute a part of this application, and do not constitute a limitation of the embodiments of the present invention. In the drawings:

[0057] Figure 1 This is a flow chart of a distributed energy cluster voltage regulation control method according to the present invention;

[0058] Figure 2 A flow chart of solving a regulating control model by using a particle swarm algorithm according to the present invention;

[0059] Figure 3 IEEE33 node system diagram in an embodiment of the present invention;

[0060] Figure 4 The distributed energy output curve of each node in the embodiment;

[0061] Figure 5 It is the load output curve diagram;

[0062] Figure 6 It is the SSE curve graph;

[0063] Figure 7 Result graph for cluster partitioning;

[0064] Figure 8 This is the voltage distribution diagram when distributed photovoltaics are not connected;

[0065] Fig. 9 This is the voltage distribution diagram when distributed photovoltaic is connected;

[0066] Fig.10 This is the voltage distribution diagram after cluster voltage regulation. DETAILED DESCRIPTION

[0067] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with embodiments and drawings. The exemplary embodiments of the present invention and their description are only used to explain the present invention and are not intended to limit the present invention.

[0068] In view of the voltage over-limit problem that occurs when large-scale distributed energy is connected to the distribution network, and the limitations of traditional centralized voltage control methods, the present invention aims to develop a new distributed energy cluster voltage regulation control strategy. First, the k-means clustering algorithm based on electrical distance is innovatively used to intelligently divide the distribution network to form multiple voltage regulation clusters to improve the flexibility and efficiency of voltage regulation. Then, the active power reduction and reactive power regulation methods are combined with the improved particle swarm optimization algorithm to adjust the voltage within the cluster, minimize the power regulation amount and voltage fluctuation, and ensure that the voltage level is stable within a safe range. Finally, a cluster voltage autonomy strategy is proposed, which improves the flexibility and efficiency of voltage regulation, especially in the case of large-scale distributed energy access, further enhancing the adaptability and stability of the distribution network.

[0069] First, this technology divides the voltage regulation of the distribution network into groups. This technology divides the nodes into groups based on the sensitivity of the node voltage to power changes, concentrates the nodes with high voltage sensitivity, and realizes refined control of the power grid. The node voltage stability is evaluated by quantitative indicators, combined with methods to improve calculation accuracy, to quickly respond to power grid changes and accurately adjust the voltage. According to the different mobilization points, the K-means clustering algorithm is used to optimize the group division, reduce the tunnel effect between the cluster position and the vicinity of the cluster, reduce the local optimal phenomenon, and improve the clustering quality.

[0070] Next, the main reasons for voltage over-limit when distributed energy is connected to the distribution network are analyzed, and a strategy model is established. Taking reducing power fluctuations and voltage deviations as the optimization goal and the safe operation of the distribution network as the constraint condition, the effective utilization of distributed energy and the improvement of voltage quality are ensured. The difference between the most serious voltage over-limit amplitude and the voltage regulation requirement is used as a penalty function to enhance the performance of the optimization algorithm on the voltage over-limit problem.

[0071] Finally, an improved particle swarm optimization algorithm is used to solve the problem and a cluster voltage autonomy strategy is proposed to enhance the adaptability and stability of the distribution network and achieve effective control of the voltage overlimit problem.

[0072] Next, the method of the present invention is described by means of specific examples.

[0073] This embodiment 1 provides a distributed energy cluster voltage regulation control method, such as Figure 1 As shown, including:

[0074] S1, obtain all distributed energy nodes in the distribution network;

[0075] In some embodiments, the distributed energy nodes may include distributed photovoltaic nodes, distributed wind power nodes, distributed energy storage nodes, etc. In the subsequent description, the distributed energy nodes may be referred to as nodes.

[0076] S2. Based on the sensitivity of the voltage of the distributed energy nodes to power changes and in combination with the K-means clustering algorithm, all the distributed energy nodes are grouped to form a plurality of clusters;

[0077] In some embodiments, the sensitivity of the voltage of the distributed energy node to power changes can be obtained by changing the power injection situation of the distributed energy node in the channel where the distributed energy node is located, and then measuring the power flow in the distribution network, so as to obtain a quantitative index of the voltage sensitivity of the distributed energy node, and then obtain the sensitivity of the voltage of the distributed energy node to power changes.

[0078] The distribution network cluster partitioning strategy proposed in this invention is a partitioning method that takes control resources as the basic consideration and starts from the unique perspective of voltage sensitivity to power changes. Under the guidance of this method, nodes with high sensitivity to voltage changes are concentrated together, while nodes with weak mutual sensitivity are assigned to different clusters. This partitioning method aims to explore and utilize the differences in mutual sensitivity between nodes to achieve more refined management of the power grid.

[0079] In actual grid operation, planned grid adjustments and reactive power changes will affect the voltage level of nodes. This effect is consistent with the difference in voltage effects between different nodes, and the present invention provides a method for analyzing and optimizing grid performance through voltage effects.

[0080] In terms of calculation method, the present invention first reveals and evaluates the mobilization capability of nodes in the power grid by changing the power injection of nodes in their channels. Specifically, by measuring the power flow in the power grid, a quantitative index of node voltage sensitivity can be obtained, providing a basis for evaluating the voltage stability contribution of nodes.

[0081] In some embodiments, the sensitivity of the voltage of the distributed energy node to power changes can be obtained by defining the electrical distance through a voltage sensitivity matrix, and the sensitivity of the voltage to power changes is reflected by the electrical distance.

[0082] In addition, in order to quickly respond to changes in the power grid, in addition to quickly measuring and adjusting the voltage by changing the power injection of the node, the present invention also adopts a method to improve the calculation accuracy. Through this method, the present invention can accurately adjust the voltage of each node while ensuring the optimization speed, thereby achieving a dual improvement in the operating efficiency and stability of the entire distribution network. This cluster division method not only enriches the control method of the distribution network in theory, but also has important application value in practice.

[0083] From the perspective of voltage control of distributed controllable resources, the present invention uses a voltage sensitivity matrix to define electrical distance, wherein the node voltage sensitivity matrix can be obtained by power flow calculation. In order to quickly calculate the voltage of each node according to the power change of each node and speed up the optimization speed, a modified equation is used to update the voltage to replace the traditional power flow calculation.

[0084] Specifically, the voltage sensitivity matrix includes a voltage active power sensitivity matrix and a voltage reactive power sensitivity matrix, and the specific calculation process of the electrical distance is:

[0085] A1. Set the voltage sensitivity matrix:

[0086]

[0087]

[0088] △U=(N-HJ -1 L) -1 △P+(L-JH -1 N) -1 △Q=S P △P+S Q △Q;

[0089]

[0090] Among them, △P, △Q, △θ and △U are the active power change, reactive power change, voltage phase angle change and voltage amplitude change of the distributed energy node respectively; H, N, J and L are the Jacobian matrix elements respectively, H is the sensitivity matrix of phase angle to active power change; N is the sensitivity matrix of voltage amplitude to active power change; J is the sensitivity matrix of phase angle to reactive power change.

[0091] The sensitivity matrix of reactive power change; L is the sensitivity matrix of voltage amplitude to reactive power change; S P is the voltage active power sensitivity matrix, and the elements in the voltage sensitivity matrix represent the influence of the change of the active power injected by the distributed energy node on the node voltage change. Q is the voltage reactive power sensitivity matrix, n represents the number of distributed energy nodes, Respectively represent the voltage and reactive power sensitivity matrix S Q And voltage active power sensitivity matrix S P is an n×n matrix.

[0092] A2. The electrical distance between the distributed energy nodes is expressed as:

[0093]

[0094] D ij =S Pii -S Pij +S Qii -S Qij ;

[0095] Among them, L ij , L ji Respectively represent the distance between distributed energy node i and distributed energy node j; D ij represents the influence of the power change injected into node j on the voltage of node i, where power includes active power and reactive power; S Pii , S Pij , S Qii , S Qij They represent the influence of active power, reactive power change and voltage between nodes i and j, respectively. Pii The matrix elements represent the influence of the active power change on the voltage amplitude of node i; S Pij The matrix elements represent the degree of influence of active power change at node i on the voltage amplitude at node j; S Qii The matrix elements represent the influence of reactive power change on the voltage amplitude of node i; where S Qij The matrix elements represent the degree of influence on the voltage amplitude of node j when the reactive power changes at node i.

[0096] Based on the sensitivity of the voltage of the distributed energy nodes to power changes combined with the K-means clustering algorithm, all the distributed energy nodes are grouped to form multiple clusters. Specifically, based on the electrical distance between the distributed energy nodes combined with the K-means clustering algorithm, means The clustering algorithm groups all the distributed energy nodes to form multiple clusters.

[0097] The continuous development of cluster technology has driven the development of clusters, which are also widely used in different activities. The algorithm of cluster partitioning also has array advantages and variability. According to the different mobilization points, the clustering part of the present invention adopts the K-means algorithm. In order to reduce the tunnel effect between the cluster position and the vicinity of the cluster, the objective function of cluster partitioning is simplified to:

[0098]

[0099] in, is the distance from node j to cluster center ε i The distance, |C i | represents the number of samples in cluster i, and K represents the number of clusters.

[0100] Because the selection of the first clustering plant is usually a necessary condition to determine the quality of the clustering results. Improper selection of the first cluster center is more likely to make K- means The clustering algorithm reaches a local optimum. The present invention specifies the initial cluster center to avoid unstable partitioning results and local optimum results. For the initialization of the cluster center, the node index is first defined according to the density around the node, and then the cluster center is selected from the high index nodes according to the principle of dispersing the initial cluster center as much as possible.

[0101] The specific process is:

[0102] B1. For each distributed energy node, the electrical distance between the distributed energy node and all distributed energy nodes other than the distributed energy node is calculated, wherein the electrical distance set formed by the electrical distances between the distributed energy node i and all nodes other than the distributed energy node i is expressed as:

[0103] LS i ={L i1 , L i2 ,…,L ij ,…,L in}j≠i (7)

[0104] B2. Calculate the node index of each distributed energy node: sort the electrical distances in the electrical distance set of each distributed energy node in ascending order from small to large, and select the Ath electrical distance after sorting as the index of the corresponding distributed energy node, where the index of distributed energy node i is represented by index i ;

[0105] The indicator requirement can characterize the node density around node i. Set parameter A to i The elements of are arranged in ascending order from small to large, and the value of the Ath element after sorting is selected as the index of the node. Here, A is a positive integer.

[0106] B3. Sort the indexes in the index set index formed by the indexes of all the distributed energy nodes in ascending order from small to large, select the Tth index after sorting as the threshold, and take the distributed energy nodes corresponding to the indexes less than the threshold as high index nodes, thereby forming a high index node set, and sort the high index nodes in the high index node set from large to small according to the node index;

[0107] Set parameter T, and set index = {index1, index2, ..., index n The elements in} are arranged in ascending order from small to large, and the value of the Tth element after sorting is selected as the threshold. Nodes with an index less than the threshold belong to high-index nodes. The smaller the index, the more nodes there are around the node, and the greater the probability of becoming the center of the cluster. Therefore, a set of high-index nodes is determined, and the cluster center is determined from this set to avoid the situation where the initial cluster center is an isolated node at the edge.

[0108] B4. Select the distributed energy node with the highest node index as the clustering center;

[0109] B5. Selecting a high-index node with the longest total distance to the existing cluster center from the high-index node set to form a new cluster center, and repeating this step until a preset number K of cluster centers are found;

[0110] B6. Form corresponding clusters based on the existing cluster centers based on the K-Means algorithm;

[0111] B7. Calculate the electrical distance between each distributed energy node and the distributed energy node in the cluster in the formed multiple clusters, and select the distributed energy node with the smallest sum of the electrical distances as the new cluster center of the cluster, that is:

[0112]

[0113] Among them, C k represents the kth cluster.

[0114] Afterwards, the node with the smallest sum of distances to other nodes in the newly formed cluster is used as the new cluster center.

[0115] In some embodiments, in B5, the preset number is set based on an SSE indicator, and the SSE indicator is the sum of the squares of the distances between each distributed energy node in each of the clusters and the cluster center of the corresponding cluster.

[0116] In addition, when clustering, it is often impossible to determine the appropriate number of clusters in advance, and the value of the number of clusters often affects the quality of the clustering results. Therefore, a quantitative indicator is needed to objectively evaluate the impact of the K value on the clustering effect. The present invention uses the sum of the squares of the distances between the internal nodes of each cluster and the cluster center as the indicator for evaluating the quality of the clustering result, the sum of the squared errors (SSE), that is:

[0117]

[0118] The smaller the SSE, the better the clustering result. The selection criteria of threshold K are: obtain the SSE values ​​corresponding to different cluster numbers, and take the minimum value of the number of clusters that satisfies the difference between the SSE values ​​corresponding to two adjacent cluster numbers is less than the set change threshold as threshold K.

[0119] S3. Establishing a regulation control model for each cluster respectively, wherein the regulation control model includes an objective function with the reduction of power fluctuation and voltage deviation as the optimization goal and a constraint condition with the safe operation of the distribution network as the premise;

[0120] In the context of voltage over-limit, the reduction of active power output and the absorption of reactive power are the main factors. In order to ensure the effective use of distributed power sources and improve the voltage quality as much as possible, voltage regulation is performed by minimizing voltage fluctuations and power regulation on the basis of controlling the voltage range. The present invention takes minimizing power changes and voltage deviations as the optimization goals, and takes the difference between the most serious voltage over-limit amplitude and the voltage regulation requirement as the penalty function. On the basis of satisfying the constraints, voltage regulation within the cluster is achieved.

[0121] The original purpose of clustering is to divide similar individuals into the same cluster according to a certain feature. The goal of the present invention is voltage control. According to the sensitivity of voltage to power changes, those with high mutual sensitivity are divided into the same cluster, and those with weak mutual sensitivity are divided into different clusters. Therefore, the voltage regulation strategy can be effectively implemented within the cluster with little impact on the outside of the cluster. The active and reactive power in the distributed energy network has an impact on the node voltage, and the voltage effect between different nodes is significantly different. It is initially considered to define the electrical distance by the impact of the change of node injection power on other nodes.

[0122] The objective function is specifically:

[0123] minf=λ1f1+λ2f2+cf3 (10)

[0124]

[0125] f2=max|V i -V need|,i=1,2,…,N′ (12)

[0126] f3=max{V1,V2,…,V M , V need}-V need (13)

[0127] Among them, f is the objective function; f1 represents the total power change caused by the voltage regulation within the cluster; f2 represents the maximum deviation between all distributed energy nodes in the cluster and the voltage regulation requirements; the smaller f2 is, the smaller the voltage fluctuation in the cluster is and the better the voltage quality is; f3 is the penalty function, which represents the degree of voltage exceeding the limit in the cluster. It is added to the objective function as a penalty function, and a good voltage regulation effect is obtained by adjusting the size of the penalty factor; it is a penalty function, which represents the degree of voltage exceeding the limit in the cluster; λ1 and λ2 represent weight coefficients respectively. Since the power regulation amount and the voltage regulation amount may be of different orders of magnitude, by adjusting the weight coefficient, the realization of the two voltage regulation goals is taken into account, and the power change is minimized while obtaining good voltage quality; c represents the penalty factor of the penalty function. Since solving the voltage exceeding the limit problem is the focus of the voltage regulation strategy design, that is, the voltage regulation indicator to be completed first, a larger penalty factor can be set to obtain better results; M represents the number of all distributed energy nodes in the cluster, ΔP i , ΔQ i They represent the active power reduction (active power change) and reactive power absorption (reactive power change) generated by distributed energy node i, N′ represents the number of all nodes in the cluster, V i Represents the voltage amplitude of distributed energy node i in the cluster, V need Indicates the voltage amplitude required for voltage regulation within the cluster.

[0128] As variables in the voltage regulation strategy, due to the limitation of distributed energy regulation capacity, it is impossible to increase indefinitely. Each type of distributed energy has its own reactive power regulation capacity, and the active power reduction of each type of distributed power source cannot exceed the existing active power output range. When the voltage exceeds the limit, the active power of each node is specified based on voltage control.

[0129] As the variable ΔP i , ΔQ i In the voltage regulation strategy, due to the limitation of the regulation capacity of distributed wind power, it is impossible to increase it indefinitely. Various types of distributed wind power have their own reactive power regulation capabilities. The active power reduction of various types of distributed power generation cannot exceed the existing active power output range. According to the voltage control under the voltage limit, the active power reduction and reactive power absorption of each node are stipulated to be positive to avoid reverse voltage regulation between nodes, which leads to deviation in voltage regulation results. Therefore, there is a constraint ΔP on the variable i , ΔQ i, as shown below:

[0130] 0≤ΔP i ≤ΔP imax (14)

[0131] 0≤ΔQ i ≤ΔQ imax (15)

[0132] The voltage in the cluster is controlled within the standard range, so the distribution network voltage needs to be limited as follows:

[0133] U imin ≤U i ≤U imax (16)

[0134] Where ΔP imax , ΔQ imax They represent the upper limit of active power change and reactive power change of distributed energy node i, respectively. imin , U imax They represent the lower voltage limit and upper voltage limit of distributed energy node i, respectively. i Represents the actual voltage on distributed energy node i.

[0135] S4. Solve the regulation control model corresponding to each cluster respectively through the improved particle swarm algorithm, so as to obtain the control strategy for voltage regulation of each distributed energy node in each cluster.

[0136] Specifically, the active power change ΔP i And reactive power change ΔQ i The particles are collectively regarded as particles, and the particle swarm algorithm is used to search for the optimal solution, and finally the optimal solution of the active power change and the reactive power change is found, and then the optimal solution of the voltage amplitude change is obtained.

[0137] In the present invention, an improved particle swarm optimization algorithm is used to control the voltage of the cluster. Figure 2 As shown in Figure 1, the main idea of ​​the particle swarm optimization algorithm is to randomly initialize a group of particles, each particle represents the solution of an optimization problem, and corresponds to a function value F of a fitness function. i Each particle i (a particle includes ΔP i and ΔQ i ) have two attributes x i 、v i, representing their own position and speed respectively, each particle carries two pieces of information. pbest is the best position recorded according to the historical trajectory of its own actions, and the corresponding fitness function value is optimal for the function value of the particle's position; the corresponding fitness function value gbest is the best position among all particle positions, that is, the optimal position of all particles. Particles update their own speed and position based on the information they carry in order to better search, and then update pbest and gbest for the searched positions in turn. Based on multiple updates, the optimal solution is found. Among them, the formula for updating the particle's speed and position is:

[0138]

[0139] In order to verify the effectiveness of the cluster voltage regulation control strategy and the improvement of the energy storage system on the operation of the distribution network, this patent uses an actual distribution network system as an example for analysis.

[0140] 1. Scenario description:

[0141] The present invention uses the IEEE33 node model as a simulation example to verify the effectiveness of the proposed cluster division method and the optimization effect on the voltage regulation of the distributed energy network. The IEEE33 node distribution network is taken as the example system. The system reference capacity is 10MVA and the voltage level is 12.66kV. Nodes 7, 10, 12, 15, 18, 23, 29, and 31 are connected to distributed photovoltaics (PV), nodes 5 and 20 are connected to distributed wind power (WT), and node 21 is connected to distributed energy storage (ESS). The rated voltage of the distribution network is 12.66kV, and the allowable voltage deviation range is ±5%. The system diagram of the calculation example is shown in the figure below. Figure 3 The curves of distributed energy output and load output of each node are shown in the figure.

[0142] 2. Results and analysis:

[0143] 2.1 Clustering results:

[0144] according to Figure 6 In the curve graph, K=6 is the inflection point of the curve. When K increases, the SSE index decreases and the clustering performance gradually improves. However, when K is greater than 6, as K further increases, the rate of decrease of the SSE index gradually slows down. That is, when the number of clusters exceeds 6, continuing to increase the number of clusters can no longer bring about a significant improvement in the SSE index. Therefore, the method of selecting a cluster number of 6 is the best. When K=6, the 33-node system can be divided into 6 clusters. The division results are shown in Figure 2. Figure 7 And as shown in Table 1.

[0145] Table 1 Clustering results

[0146]

[0147] 2.2 Cluster voltage control results:

[0148] Different voltage regulation clusters are formed according to the clustering results. Large-scale distributed power access to the distribution network will cause voltage over-limit and other problems, and the cluster voltage regulation method of the present invention can be performed separately in the cluster, effectively solving this problem.

[0149] When distributed photovoltaics are not connected, the voltage distribution of each node in the distribution network in each time period is as follows: Figure 8 As shown in the figure; when distributed photovoltaic is connected, the voltage distribution of each node in the distribution network in each time period is as follows Fig. 9 As shown; after cluster voltage regulation, the voltage distribution of the system is as follows Fig.10 shown.

[0150] contrast Figure 8 and Fig. 9 After the distributed energy is connected, at t=20, the voltage of nodes 30 and 20 increases significantly, while the voltage of nodes 16 and 17 decreases, approaching 0.9pu. It can be inferred that the connection of a large number of distributed energy sources will cause voltage fluctuations in the system, and even exceed the limit in severe cases.

[0151] contrast Fig. 9 and Fig.10 After cluster voltage regulation, at t=20, the voltages of nodes 16 and 17 close to the limit increase, and the fluctuations of each node in the system decrease. Therefore, after cluster voltage is adjusted by this method, the system voltage fluctuation decreases, avoiding the voltage exceeding the limit. It can be seen that the method proposed in the present invention has certain effectiveness.

[0152] Embodiment 2 further discloses a distributed energy cluster voltage regulation control system, which is used to execute a distributed energy cluster voltage regulation control method of embodiment 1, including:

[0153] The acquisition module is used to obtain all distributed energy nodes in the distribution network;

[0154] A cluster generation module, used to group all the distributed energy nodes to form multiple clusters based on the sensitivity of the voltage of the distributed energy nodes to power changes in combination with a K-means clustering algorithm;

[0155] A model building module, used to establish a regulation control model for each of the clusters, wherein the regulation control model includes an objective function with the reduction of power fluctuation and voltage deviation as the optimization goal and a constraint condition with the safe operation of the distribution network as the premise;

[0156] The solving module is used to solve the regulation control model corresponding to each cluster respectively through the improved particle swarm algorithm, so as to obtain the control strategy for voltage regulation of each distributed energy node in each cluster.

[0157] Example 3 also discloses an electronic device, comprising at least one processor, and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute a distributed energy cluster voltage regulation control method as described in Example 1.

[0158] The specific implementation methods described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific implementation method of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A distributed energy cluster voltage regulation control method, characterized in that: include: Get all distributed energy nodes in the distribution network; Based on the sensitivity of the voltage of the distributed energy nodes to power changes and combined with the K-means clustering algorithm, all the distributed energy nodes are grouped to form multiple clusters; Establishing a regulation control model for each cluster respectively, wherein the regulation control model includes an objective function with the reduction of power fluctuation and voltage deviation as the optimization goal and a constraint condition with the safe operation of the distribution network as the premise; The regulation control model corresponding to each cluster is solved respectively by using an improved particle swarm algorithm, so as to obtain a control strategy for voltage regulation of each distributed energy node in each cluster.

2. A distributed energy cluster voltage regulation control method according to claim 1, characterized in that: The sensitivity of the voltage of the distributed energy node to power changes can be obtained by changing the power injection situation of the distributed energy node in the channel where the distributed energy node is located, and then measuring the power flow in the distribution network, so as to obtain a quantitative index of the voltage sensitivity of the distributed energy node, and then obtain the sensitivity of the voltage of the distributed energy node to power changes.

3. A distributed energy cluster voltage regulation control method according to claim 1, characterized in that: The sensitivity of the voltage of the distributed energy node to the power change can be obtained by defining the electrical distance through the voltage sensitivity matrix, and the electrical distance is used to reflect the sensitivity of the voltage to the power change, wherein the voltage sensitivity matrix includes a voltage active power sensitivity matrix and a voltage reactive power sensitivity matrix, and the specific calculation process of the electrical distance is: A1. Set the voltage sensitivity matrix: △U=(N-HJ -1 L) -1 △P+(L-JH -1 N) -1 △Q=S P △P+S Q △Q; Among them, △P, △Q, △θ and △U are respectively the active power change, reactive power change, voltage phase angle change and voltage amplitude change of the distributed energy node; H, N, J and L are respectively Jacobian matrix elements, H is the sensitivity matrix of phase angle to active power change; N is the sensitivity matrix of voltage amplitude to active power change; J is the sensitivity matrix of phase angle to reactive power change; L is the sensitivity matrix of voltage amplitude to reactive power change; S P is the voltage active power sensitivity matrix, and the elements in the voltage sensitivity matrix represent the influence of the change of the active power injected by the distributed energy node on the node voltage change. Q is the voltage reactive power sensitivity matrix, n represents the number of distributed energy nodes, Respectively represent the voltage and reactive power sensitivity matrix S Q And voltage active power sensitivity matrix S P is an n×n matrix; A2. The electrical distance between the distributed energy nodes is expressed as: D ij =S Pii -S Pij +S Qii -S Qij ; Among them, L ij , L ji Respectively represent the distance between distributed energy node i and distributed energy node j; D ij represents the influence of the power change injected into node j on the voltage of node i, where power includes active power and reactive power; S Pii , S Pij , S Qii , S Qij Respectively represent the influence degree between active power, reactive power change and voltage between node i and node j, where S Pii The matrix elements represent the influence of the active power change on the voltage amplitude of node i; S Pij The matrix elements represent the degree of influence of active power change at node i on the voltage amplitude at node j; S Qii The matrix elements represent the influence of reactive power change on the voltage amplitude of node i; where S Qij The matrix elements represent the degree of influence on the voltage amplitude of node j when the reactive power changes at node i.

4. A distributed energy cluster voltage regulation control method according to claim 3, characterized in that: Based on the sensitivity of the voltage of the distributed energy nodes to power changes, combined with the K-means clustering algorithm, all the distributed energy nodes are grouped to form multiple clusters. Specifically, based on the electrical distance between the distributed energy nodes, combined with the K-means clustering algorithm, all the distributed energy nodes are grouped to form multiple clusters. The specific process is: B1. For each distributed energy node, the electrical distance between the distributed energy node and all distributed energy nodes other than the distributed energy node is calculated, wherein the electrical distance set formed by the electrical distances between the distributed energy node i and all nodes other than the distributed energy node i is expressed as: LS i ={L i1 ,L i2 ,···,L ij ,···,L in }, j≠i; B2. Calculate the node index of each distributed energy node: sort the electrical distances in the electrical distance set of each distributed energy node in ascending order from small to large, and select the Ath electrical distance after sorting as the index of the corresponding distributed energy node, where the index of distributed energy node i is represented by index i ; B3. Sort the indexes in the index set index formed by the indexes of all the distributed energy nodes in ascending order from small to large, select the Tth index after sorting as the threshold, and take the distributed energy nodes corresponding to the indexes less than the threshold as high index nodes, thereby forming a high index node set, and sort the high index nodes in the high index node set from large to small according to the node index; B4. Select the distributed energy node with the highest node index as the clustering center; B5. Selecting a high-index node with the farthest total distance to the existing cluster center from the high-index node set to form a new cluster center, and repeating this step until a preset number of cluster centers are found; B6. Form corresponding clusters based on the existing cluster centers based on the K-Means algorithm; B7. Calculate the electrical distance between each distributed energy node and the distributed energy node in the formed multiple clusters respectively, and select the distributed energy node with the smallest sum of the electrical distances as the new clustering center of the cluster.

5. A distributed energy cluster voltage regulation control method according to claim 4, characterized in that: In B5, the preset number is set based on the SSE index, and the SSE index is the sum of the squares of the distances between each distributed energy node in each of the clusters and the cluster center of the corresponding cluster.

6. A distributed energy cluster voltage regulation control method according to claim 1, characterized in that: The objective function is specifically: minf=λ1f1+λ2f2+cf3; f2=max|V i -V need |,i=1,2,···,N′; f3=max{V1,V2,···,V M ,V need }-V need ; Where f is the objective function; f1 represents the total power change caused by the voltage regulation within the cluster; f2 represents the maximum deviation between the voltage regulation requirements and all distributed energy nodes in the cluster; f3 is the penalty function; λ1 and λ2 represent the weight coefficients respectively; c represents the penalty factor of the penalty function; M represents the number of all distributed energy nodes in the cluster, ΔP i , ΔQ i They represent the active power change and reactive power change generated by distributed energy node i, N′ represents the number of all nodes in the cluster, V i Represents the voltage amplitude of distributed energy node i in the cluster, V need Indicates the voltage amplitude required for voltage regulation within the cluster.

7. A distributed energy cluster voltage regulation control method according to claim 1, characterized in that: The constraints are specifically: 0≤ΔP i ≤ΔP imax ; 0≤ΔQ i ≤ΔQ imax ; IN imin ≤U i ≤U imax ; Among them, ΔP imax , ΔQ imax They represent the upper limit of active power change and reactive power change of distributed energy node i, respectively. imin , U imax They represent the lower and upper voltage limits of distributed energy node i, respectively, and U i Represents the actual voltage on distributed energy node i.

8. A distributed energy cluster voltage regulation control method according to claim 1, characterized in that: When solving the regulation control model corresponding to each cluster separately by using the improved particle swarm algorithm, specifically, the active power change and the reactive power change are regarded as particles, and the particle swarm algorithm is used to search for the optimal solution, and finally the optimal solution of the active power change and the reactive power change is found, and then the optimal solution of the voltage amplitude change is obtained.

9. A distributed energy cluster voltage regulation control system, used to execute a distributed energy cluster voltage regulation control method according to any one of claims 1 to 8, characterized in that: include: The acquisition module is used to obtain all distributed energy nodes in the distribution network; A cluster generation module, used to group all the distributed energy nodes to form multiple clusters based on the sensitivity of the voltage of the distributed energy nodes to power changes in combination with a K-means clustering algorithm; A model building module, used to establish a regulation control model for each of the clusters, wherein the regulation control model includes an objective function with the reduction of power fluctuation and voltage deviation as the optimization goal and a constraint condition with the safe operation of the distribution network as the premise; The solving module is used to solve the regulation control model corresponding to each cluster respectively through the improved particle swarm algorithm, so as to obtain the control strategy for voltage regulation of each distributed energy node in each cluster.

10. An electronic device, characterized in that: It includes at least one processor and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute a distributed energy cluster voltage regulation control method as described in any one of claims 1 to 8.

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