Medium and low voltage distribution network optical storage cluster division method, apparatus and device, and storage medium

Abstract

The invention provides a medium and low voltage power distribution network optical storage cluster division method and device, equipment and a storage medium. The method comprises the following steps: firstly, dividing nodes in a power distribution network into groups with different cluster numbers, and constructing a source-load storage power optimization model taking maximum total energy storage rated power as a target; and calculating a difference value between the distributed photovoltaic power and the load power in each cluster through the model, and determining an adjustment potential value. And carrying out multi-objective optimization by combining a preset electrical modularity index to obtain a comprehensive performance division index, and selecting a group with an optimal cluster number. Then, calculating the sensitivity of the voltage of each node to the power change, and dividing the nodes into the optimal cluster number according to the sensitivity; according to the method, through the comprehensive division of the adjustment potential value and the electrical modularity index, the node division is optimized by using the voltage sensitivity, the reasonable division of the optical storage cluster is realized, and the voltage adjustment capability and the operation flexibility of the power grid are improved.

Description

Technical Field

[0001] The present application belongs to the field of photovoltaic storage cluster division, and in particular, relates to a method, device, equipment and storage medium for dividing photovoltaic storage clusters in medium and low voltage distribution networks. Background Art

[0002] In the medium and low voltage distribution network, with the large-scale access of distributed photovoltaic power sources (DPV), the operation characteristics of the power grid have changed significantly. Especially when the power grid is in a long line light load operation state, problems such as voltage limit exceeding and large-scale abandonment of light often occur. These problems not only affect the stable operation of the power grid, but also cause energy waste.

[0003] Traditional distribution network management strategies often seem inadequate when faced with a high proportion of distributed photovoltaic access. This is because traditional strategies mainly focus on the safety and reliability of the power grid, while ignoring the flexibility and adjustability of distributed photovoltaics. In addition, traditional distribution network voltage regulation methods, such as transformer tap adjustment and reactive power compensation device switching, have shortcomings such as slow regulation speed and limited regulation range, and cannot meet the voltage regulation needs of medium and low voltage distribution networks with a high proportion of distributed photovoltaics.

[0004] In order to solve this problem, the photovoltaic storage cluster division method came into being. The photovoltaic storage cluster refers to the combination of distributed photovoltaic and energy storage devices in a certain way to form a unit that can be independently adjusted and controlled. Through reasonable photovoltaic storage cluster division, the flexibility and adjustability of distributed photovoltaic can be fully utilized to achieve flexible adjustment of grid voltage.

[0005] However, most of the existing methods for dividing PV and energy storage clusters are based on experience or simple rules, lacking systematicity and scientificity. These methods often cannot accurately evaluate the impact of different cluster division schemes on the voltage regulation effect of the grid, nor can they optimize cluster division. Summary of the invention

[0006] The purpose of the present application is to overcome the above-mentioned prior art and provide a method, device, equipment and storage medium for dividing photovoltaic storage clusters in medium and low voltage distribution networks.

[0007] The present application provides a method for dividing a medium- and low-voltage distribution network photovoltaic storage cluster, comprising:

[0008] Divide each node in the medium and low voltage distribution network into cluster groups with different cluster numbers;

[0009] A source-load-storage power optimization model of the medium and low voltage distribution network is constructed with the maximum total rated power of energy storage as the objective function;

[0010] Obtain the difference between the distributed photovoltaic and load power in each cluster group of the medium and low voltage distribution network according to the source-load-storage power optimization model;

[0011] determining a regulation potential value of the cluster group according to the difference;

[0012] Performing multi-objective optimization calculation on the regulation potential value and the preset electrical modularity index of the medium and low voltage distribution network to obtain the optimal comprehensive performance division index;

[0013] According to the optimal comprehensive performance partitioning index, selecting a cluster group with an optimal number of clusters from the cluster groups with different numbers of clusters;

[0014] Obtaining the sensitivity of the voltage on each of the nodes relative to the power change of the node;

[0015] According to the sensitivity, each node in the medium and low voltage distribution network is divided into cluster groups with the optimal number of clusters as a division result.

[0016] Optionally, the difference between the distributed photovoltaic and load power in each cluster group of the medium and low voltage distribution network is obtained according to the source-load-storage power optimization model, and the expression is as follows:

[0017]

[0018] is the regulation potential value of the jth cluster; is the rated charging power of the energy storage in the jth cluster; is the difference between the distributed photovoltaic and load power in the jth cluster at time t; At time t, the jth cluster does not have the ability to adjust.

[0019] Optionally, the multi-objective optimization calculation of the regulation potential value and the preset electrical modularity index of the medium and low voltage distribution network is performed to obtain the optimal comprehensive performance division index, including:

[0020] A particle swarm algorithm or other optimization algorithms are used to iteratively optimize the regulation potential value and the preset electrical modularity index of the medium and low voltage distribution network to obtain the optimal comprehensive performance division index.

[0021] Optionally, selecting a cluster group with an optimal number of clusters from the cluster groups with different numbers of clusters includes:

[0022] The comprehensive performance partitioning indicators under different numbers of clusters are compared, and the cluster group with the best comprehensive performance partitioning indicator is selected as the cluster group with the optimal number of clusters.

[0023] Optionally, obtaining the sensitivity of the voltage on each node relative to the power change of the node includes:

[0024] Based on the line data of the medium and low voltage distribution networks and the photovoltaic storage output data, the sensitivity of the voltage at each node to the power change of the node is calculated.

[0025] Optionally, the dividing of each node in the medium and low voltage distribution network into the optimal number of clusters according to the sensitivity includes:

[0026] Nodes whose sensitivity is within a preset threshold are divided into the same cluster group.

[0027] Optionally, it includes: after dividing the cluster groups, controlling the charging and discharging of the energy storage devices in the cluster groups, and realizing flexible voltage adjustment according to the regulation potential value and comprehensive performance division index of the cluster groups.

[0028] The present application also provides a medium and low voltage distribution network photovoltaic storage cluster division device, comprising:

[0029] A partitioning module divides each node in the medium and low voltage distribution network into cluster groups with different numbers of clusters;

[0030] A model module, which constructs a source-load-storage power optimization model of the medium and low voltage distribution network with the maximum total rated power of energy storage as the objective function;

[0031] A difference module, which obtains the difference between the distributed photovoltaic and load power in each cluster group of the medium and low voltage distribution network according to the source-load-storage power optimization model;

[0032] An indicator module determines the regulation potential value of the cluster group according to the difference; performs multi-objective optimization calculation on the regulation potential value and the preset electrical modularity index of the medium and low voltage distribution network to obtain the optimal comprehensive performance division index;

[0033] A screening module, which selects a cluster group with an optimal number of clusters from the cluster groups with different numbers of clusters according to the optimal comprehensive performance partitioning index;

[0034] An acquisition module is used to acquire the sensitivity of the voltage on each node relative to the power change of the node;

[0035] The result module divides each node in the medium and low voltage distribution network into cluster groups with the optimal number of clusters according to the sensitivity as a division result.

[0036] The present application also provides a medium and low voltage distribution network photovoltaic storage cluster division device, including:

[0037] Memory;

[0038] A processor, for retrieving from the memory a computer executable program of the above-mentioned method for dividing photovoltaic and storage clusters in a medium and low voltage distribution network, and executing: dividing each node in the medium and low voltage distribution network into cluster groups with different numbers of clusters; constructing a source-load-storage power optimization model of the medium and low voltage distribution network with the maximum total rated power of energy storage as the objective function; obtaining the difference between the distributed photovoltaic and load power in each cluster group of the medium and low voltage distribution network according to the source-load-storage power optimization model; determining the regulation potential value of the cluster group according to the difference; performing multi-objective optimization calculation on the regulation potential value and a preset electrical modularity index of the medium and low voltage distribution network to obtain an optimal comprehensive performance division index; selecting a cluster group with an optimal number of clusters from the cluster groups with different numbers of clusters according to the optimal comprehensive performance division index; obtaining the sensitivity of the voltage on each node relative to the power change of the node; and dividing each node in the medium and low voltage distribution network into cluster groups with the optimal number of clusters according to the sensitivity as a division result.

[0039] The present application also provides a storage medium, including a computer executable program stored therein, wherein the computer executable program is used to be called by a processor to execute the steps of the above-mentioned method for dividing a medium- and low-voltage distribution network photovoltaic storage cluster.

[0040] The beneficial effects of this application are:

[0041] The present application provides a method for dividing photovoltaic storage clusters in a medium and low voltage distribution network, including: dividing each node in the medium and low voltage distribution network into cluster groups with different cluster numbers; constructing a source-load-storage power optimization model of the medium and low voltage distribution network with the maximum total rated power of energy storage as the objective function; obtaining the difference between the distributed photovoltaic and load power in each cluster group of the medium and low voltage distribution network according to the source-load-storage power optimization model; determining the regulation potential value of the cluster group according to the difference; performing multi-objective optimization calculation on the regulation potential value and the preset electrical modularity index of the medium and low voltage distribution network to obtain the optimal comprehensive performance division index; according to the optimal comprehensive performance division index, selecting the cluster group with the optimal number of clusters from the cluster groups with different cluster numbers; obtaining the sensitivity of the voltage on each node relative to the power change of the node; according to the sensitivity, dividing each node in the medium and low voltage distribution network into the cluster group with the optimal number of clusters as the division result. The present application divides each node in the power grid into the division result of the optimal number of clusters according to the sensitivity of voltage to power change. It not only achieves the rational division of photovoltaic and storage clusters, but also improves the voltage regulation capability and operational flexibility of the power grid. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 This is a schematic diagram of the process of dividing the photovoltaic storage clusters in the medium and low voltage distribution network in this application;

[0043] Figure 2 It is the 33-node distribution line topology in this application;

[0044] Figure 3 It is the photovoltaic and load data diagram of each node in this application for 24 hours;

[0045] Figure 4 It is a schematic diagram of the total rated power of energy storage under different numbers of cluster divisions solved in this application;

[0046] Figure 5 It is a schematic diagram of the cluster regulation potential index curve in this application;

[0047] Figure 6 It is a schematic diagram of the division results under different division weights set in this application;

[0048] Figure 7 It is a schematic diagram of voltage comparison under different division methods used in this application;

[0049] Figure 8 It is a schematic diagram of the specific division results in this application. DETAILED DESCRIPTION

[0050] Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided in order to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art.

[0051] Please refer to Figure 1 As shown, the present application provides a method for dividing a medium- and low-voltage distribution network photovoltaic storage cluster, the steps of which include:

[0052] S101 divides each node in the medium and low voltage distribution network into cluster groups with different cluster numbers.

[0053] like Figure 2 As shown in the figure, 33 nodes are clearly marked on the distribution line topology diagram, and each node is connected to its adjacent nodes through lines, forming a complex network structure.

[0054] According to actual needs, these nodes are divided into different cluster groups.

[0055] In the above process, multiple factors need to be considered, such as the geographical location of the node, electrical distance, the impact of power changes on voltage, etc. Especially in medium and low voltage distribution networks, since the resistance and reactance are not much different, the impact of active power changes on voltage cannot be ignored. Therefore, when dividing the cluster, special attention needs to be paid to the active power sensitivity between nodes.

[0056] For example, nodes 5, 7, 8, 10, 15, 18, 20, 23, 25, 29, and 31 can be incorporated into the photovoltaic system. These nodes usually have higher power generation capacity and better geographical location, and are suitable as the central nodes of the photovoltaic cluster. At the same time, nodes 13 and 26 can be incorporated into the energy storage system. These nodes can be used as the central nodes of the energy storage cluster to balance and adjust the power and voltage of the entire distribution network.

[0057] In the specific division process, multiple cluster groups can be set as needed, and each cluster group contains clusters with different numbers of clusters. For example, one cluster group can contain 2 clusters, and another cluster group can contain 5 clusters, etc. In each cluster group, the nodes need to be divided based on their active power sensitivity, so that nodes with higher mutual sensitivity are divided into the same cluster, and nodes with weaker mutual sensitivity are divided into different clusters.

[0058] This division method helps to effectively implement the voltage control strategy. By implementing different voltage regulation strategies for different clusters, the voltage regulation strategy can be effectively implemented within the cluster, while having little impact on the outside of the cluster. At the same time, this division method can also improve the reliability and stability of the entire distribution network and reduce voltage fluctuations and power losses.

[0059] S102, constructing a source-load-storage power optimization model of the medium and low voltage distribution network based on energy storage, taking the maximum total rated power of energy storage as the objective function.

[0060] This application uses responsive loads and distributed photovoltaics to adjust power, and sets the maximum total rated power of energy storage in all clusters as the objective function:

[0061]

[0062]

[0063] in, is the difference between the distributed photovoltaic and load power in the jth cluster at time t; It is the sum of the power generation of all distributed photovoltaics in the jth cluster at time t; It is the sum of the power values ​​of all loads in the jth cluster at time t; j represents the sequence number of multiple clusters.

[0064] like Figure 3 As shown in the figure, in order to realize this objective function, a 24-hour period is selected for analysis. During this period, the power of photovoltaic and responsive loads is adjusted according to the source-load-storage power optimization model. This adjustment is based on real-time data to ensure that the total rated power of energy storage is maximized while meeting the load demand.

[0065] In particular, within the selected 24-hour period, a specific time point (e.g., t=12) can be selected to observe the total energy storage rated power under different cluster division numbers. By comparing the results under different cluster division numbers, the impact of different division schemes on the total energy storage rated power can be evaluated, so as to select the optimal cluster division scheme.

[0066] S103. Obtain the difference between photovoltaic and load power in each cluster group of the medium and low voltage distribution network according to the source-load-storage power optimization model.

[0067] In the above content, a source-load-storage power optimization model with the maximum total rated power of energy storage as the objective function has been established. This model adjusts power by responsive loads and distributed photovoltaics, aiming to optimize the energy configuration of the entire medium and low voltage distribution network.

[0068] Based on the optimization model, the difference between the distributed photovoltaic power generation and the load power in each cluster can be calculated. This difference reflects the mismatch between photovoltaic power generation and load demand in the cluster, and is an important basic data for the subsequent evaluation of the cluster regulation potential.

[0069] Specifically, for the jth cluster at time t, the difference between its distributed photovoltaic and load powers can be calculated. This difference is obtained by subtracting the sum of the power values ​​of all loads in the cluster from the sum of the power generation powers of all distributed photovoltaics in the cluster.

[0070] S104: Determine the adjustment potential value of the cluster group according to the difference.

[0071] When the difference between distributed photovoltaic and load power The voltage can be adjusted by increasing the energy storage charging power. The voltage can be adjusted by increasing the energy storage discharge power.

[0072] In this application, for the purpose of voltage suppression, we only focus on Forward regulation voltage indicator at:

[0073]

[0074]

[0075] in, is the regulation potential value of the jth cluster; is the rated charging power of the energy storage in the jth cluster; That is, at time t, cluster j does not contain any energy storage system under this division result.

[0076] Please refer to Figure 4 and Figure 5As shown in the figure, when the number of cluster divisions is 1, 2, and 5, the cluster adjustment potential reaches the maximum value. However, when the clusters are not divided, the nodes tend to be concentrated, the number of clusters is small, the scale is large, and the cluster structure performance is low, which does not meet the requirements. Only when the number of clusters is 2 and 5, they are used as alternatives.

[0077] S105, performing multi-objective optimization calculation on the regulation potential value and the preset electrical modularity index of the medium and low voltage distribution network to obtain the optimal comprehensive performance division index.

[0078] Establish the optimal comprehensive performance partitioning index.

[0079] The optimal comprehensive performance partitioning index belongs to a multi-objective optimization problem, and its expression is as follows:

[0080] γ=ω1η1+ω2η2

[0081] Among them, ω1, ω2 are the weight values ​​of each indicator in the comprehensive division index, and η1, η2 are indicator 1 and indicator 2 input under different optimization objectives respectively.

[0082] The comprehensive performance index of cluster groups includes two elements: structural performance and regulatory performance. Different weights can be set according to actual needs to achieve different emphases of cluster division. Different weight combinations will produce different cluster group division results.

[0083] The embodiment of the present application selects a preset electrical modularity index and a cluster regulation potential index as comprehensive performance indicators.

[0084] It should be clear that the optimal comprehensive performance partitioning index is a multi-objective optimization problem, which involves the trade-offs of multiple indicators. The expression is as follows:

[0085] γ=ω1η1+ω2η2

[0086] Among them, ω1, ω2 are the weight values ​​of each indicator in the comprehensive division index, and η1, η2 are indicator 1 and indicator 2 input under different optimization objectives respectively.

[0087] In this application, the expression of the optimal comprehensive performance partitioning index is in the form of a weighted sum, in which the weight value of each index is determined according to the index 1 and index 2 input under different optimization objectives. These weight values ​​can be set according to actual needs to achieve different focuses of cluster partitioning. Different weight combinations will produce different cluster partitioning results, thereby meeting the needs of different scenarios.

[0088] The comprehensive performance index of a cluster includes two main elements: structural performance and regulatory performance.

[0089] The structural performance mainly reflects the internal organizational structure and connection mode of the cluster, while the regulation performance mainly reflects the cluster's ability to regulate the grid voltage. By comprehensively considering these two elements, a comprehensive evaluation of the cluster's performance can be achieved.

[0090] In the specific implementation, this application selects the preset electrical modularity index and cluster regulation potential index as comprehensive performance indicators. The electrical modularity index is mainly used to measure the degree of electrical connection and modularity within the cluster, which reflects the compactness and stability of the cluster structure. The cluster regulation potential index is mainly used to measure the cluster's ability to regulate the grid voltage, which reflects the cluster's response speed and regulation effect when dealing with grid voltage fluctuations.

[0091] By performing multi-objective optimization calculations on these two indicators, a comprehensive performance partitioning indicator can be obtained, which can be used as an important basis for cluster partitioning. In practical applications, the weight values ​​can be adjusted and optimized according to different needs and scenarios to obtain a cluster partitioning result that is more in line with the actual situation.

[0092] S106: Selecting a cluster group with an optimal number of clusters from the cluster groups with different numbers of clusters according to the optimal comprehensive performance division index.

[0093] The above-mentioned comprehensive performance division index is obtained through multi-objective optimization calculation, which comprehensively considers the regulation potential value and electrical modularity index of the cluster.

[0094] Based on this comprehensive performance partitioning index, the cluster group with the optimal number of clusters will be selected from cluster groups with different numbers of clusters.

[0095] Specifically, we can first set a series of possible cluster numbers (for example, from 2 clusters to N clusters, where N is a reasonable upper limit of the total number of nodes), and then run the cluster partitioning algorithm once for each cluster number to obtain the corresponding cluster group. Next, we calculate the comprehensive performance partitioning index of each cluster group and compare the sizes of these indexes. Finally, we select the cluster group with the best comprehensive performance partitioning index (i.e., the index value is the largest or meets a specific optimization goal) as the final partitioning result.

[0096] S107: Obtain sensitivity of the voltage on each node relative to power change of the node.

[0097] The goal of cluster division is voltage control. Based on the sensitivity of voltage to power changes, the voltages with higher mutual sensitivity are divided into the same cluster, and the voltages with weaker mutual sensitivity are divided into different clusters. This allows the voltage regulation strategy to be effectively implemented within the cluster with less impact on the outside of the cluster.

[0098] When controlling the voltage of a high-voltage power grid, since the reactance in the grid is much greater than the resistance, the impact of active power fluctuations on the system voltage is usually ignored, and only the impact of reactive power flow on the system voltage is considered. However, in medium and low voltage distribution networks, since the resistance and reactance are not much different, the impact of active power changes on voltage cannot be ignored.

[0099]

[0100]

[0101] Among them, S PV is the active voltage sensitivity matrix; the element in the i-th row and j-th column of the matrix is It represents the relationship between the change of unit active power injected into node j and the change of voltage amplitude at node i. SQV is the active voltage sensitivity matrix; the element in the i-th row and j-th column of the matrix is It represents the relationship between the change in unit reactive power injected into node j and the change in voltage amplitude at node i.

[0102]

[0103] S108. According to the sensitivity, divide each node in the medium and low voltage distribution network into cluster groups with the optimal number of clusters as a division result.

[0104] L ij The output partitioning algorithm is used as the basis for cluster division.

[0105] Divide N samples into k non-overlapping clusters, where each sample will be divided. The sample data is [x1,…x N ], where each sample is an n-dimensional vector that represents the characteristics of the sample. The algorithm uses formula (9) as the objective function to cluster N samples into k clusters [c1,…c k ].

[0106]

[0107] where |c i | represents the number of samples in cluster i, ε i Represents the center of cluster i, which is generally the average value of the sample data in the cluster, that is:

[0108]

[0109] The specified initial cluster center is used to avoid the problem of unstable division results and falling into the local optimum. The specific steps are as follows:

[0110] (1) For each node i, calculate its distance to all other nodes to form the node distance vector LS of node ii :

[0111] LS i ={L i1 ,L i2 …,L ij ,…L iN ,j≠i}

[0112] (2) Calculate the node density index. Set the parameter M, sort the elements in ascending order from small to large, and select the value of the Mth element after sorting as the density index of node i, which can represent the node density around node i. The more nodes there are around node i, the smaller the element in the node distance vector that is close to the parameter M value, that is, the smaller the node density index.

[0113] (3) Select high-density nodes. Set the parameter T, sort the elements in index = {index1, index2, ... indexN} in ascending order, and select the value of the Tth element after sorting as the threshold. Nodes with an index less than the threshold are high-density nodes. The smaller the value, the more likely the node is to become the center of the cluster. Determine the high-density nodes and select the cluster center from these nodes to avoid the situation where the initial cluster center appears on an isolated edge node.

[0114] (4) Select the first cluster center. Select the node with the highest node density as the first cluster center ε1. Then select the remaining k-1 cluster centers. Among the remaining nodes in the set index = {index1, index2, ... indexN}, select the node farthest from all existing cluster centers as the next cluster center. That is:

[0115]

[0116] Please refer to Figure 6 As shown in the figure, for different weight combinations, increasing the weight of the index ω1 increases the structure of the cluster and decreases the coordination ability within the group; increasing the weight of ω2 is the opposite, but the magnitude of the change of the two with the weight is different, and generally the appropriate weight combination can be selected as needed. Under different weight combinations, the comprehensive performance is optimal when the number of cluster divisions is 2 or 5.

[0117] Please refer to Figure 7 As shown, the node voltage of the distribution line has been reduced to a certain extent.

[0118] The present application also provides a medium and low voltage distribution network photovoltaic storage cluster division device, comprising:

[0119] A partitioning module divides each node in the medium and low voltage distribution network into cluster groups with different numbers of clusters;

[0120] A model module is used to construct a source-load-storage power optimization model of the medium and low voltage distribution network with the maximum total rated power of energy storage as the objective function;

[0121] A difference module, which obtains the difference between the distributed photovoltaic and load power in each cluster of the medium and low voltage distribution network according to the source-load-storage power optimization model;

[0122] An indicator module determines the regulation potential value of the cluster according to the difference; performs multi-objective optimization calculation on the regulation potential value and the preset electrical modularity index of the medium and low voltage distribution network to obtain a comprehensive performance division index;

[0123] A screening module, which selects a cluster group with an optimal number of clusters from the cluster groups with different numbers of clusters according to the comprehensive performance classification index;

[0124] An acquisition module is used to acquire the sensitivity of the voltage on each node relative to the power change of the node;

[0125] The result module divides each node in the medium and low voltage distribution network into the optimal number of cluster division results according to the sensitivity.

[0126] The present application also provides a medium and low voltage distribution network photovoltaic storage cluster division device, including:

[0127] Memory;

[0128] A processor is used to retrieve from the memory a computer executable program of the above-mentioned method for dividing photovoltaic and energy storage clusters in a medium and low voltage distribution network, and execute: dividing each node in the medium and low voltage distribution network into cluster groups with different numbers of clusters; constructing a source-load-storage power optimization model of the medium and low voltage distribution network with the maximum total rated power of energy storage as the objective function; obtaining the difference between the distributed photovoltaic and load power in each cluster of the medium and low voltage distribution network according to the source-load-storage power optimization model; determining the regulation potential value of the cluster according to the difference; performing multi-objective optimization calculation on the regulation potential value and a preset electrical modularity index of the medium and low voltage distribution network to obtain a comprehensive performance division index; selecting a cluster group with an optimal number of clusters from the cluster groups with different numbers of clusters according to the comprehensive performance division index; obtaining the sensitivity of the voltage on each of the nodes relative to the power change of the node; and dividing each node in the medium and low voltage distribution network into the division results of the optimal number of clusters according to the sensitivity.

[0129] The present application also provides a storage medium, including a computer executable program stored therein, wherein the computer executable program is used to be called by a processor to execute the steps of the above-mentioned method for dividing a medium- and low-voltage distribution network photovoltaic storage cluster.

Claims

1. A method for dividing photovoltaic and storage clusters in medium and low voltage distribution networks, characterized in that: include: Divide each node in the medium and low voltage distribution network into cluster groups with different cluster numbers; A source-load-storage power optimization model of the medium and low voltage distribution network is constructed with the maximum total rated power of energy storage as the objective function; Obtain the difference between the distributed photovoltaic and load power in each cluster group of the medium and low voltage distribution network according to the source-load-storage power optimization model; determining a regulation potential value of the cluster group according to the difference; Performing multi-objective optimization calculation on the regulation potential value and the preset electrical modularity index of the medium and low voltage distribution network to obtain the optimal comprehensive performance division index; According to the optimal comprehensive performance partitioning index, selecting a cluster group with an optimal number of clusters from the cluster groups with different numbers of clusters; Obtaining the sensitivity of the voltage on each of the nodes relative to the power change of the node; According to the sensitivity, each node in the medium and low voltage distribution network is divided into cluster groups with the optimal number of clusters as a division result.

2. According to the method for dividing the photovoltaic storage clusters in the medium and low voltage distribution network according to claim 1, it is characterized in that: The difference between the distributed photovoltaic and load power in each cluster group of the medium and low voltage distribution network is obtained according to the source-load-storage power optimization model, and the expression is as follows: is the adjustment potential value of the jth cluster; is the rated charging power of the energy storage in the jth cluster; is the difference between the distributed photovoltaic and load power in the jth cluster at time t; At time t, the jth cluster does not have the ability to adjust.

3. According to the method for dividing the photovoltaic storage clusters in the medium and low voltage distribution network according to claim 1, it is characterized in that: The multi-objective optimization calculation of the regulation potential value and the preset electrical modularity index of the medium and low voltage distribution network is performed to obtain the optimal comprehensive performance division index, including: A particle swarm algorithm or other optimization algorithms are used to iteratively optimize the regulation potential value and the preset electrical modularity index of the medium and low voltage distribution network to obtain the optimal comprehensive performance division index.

4. According to the method for dividing the photovoltaic storage clusters in the medium and low voltage distribution network according to claim 1, it is characterized in that: Selecting a cluster group with an optimal number of clusters from the cluster groups with different numbers of clusters includes: The comprehensive performance partitioning indicators under different numbers of clusters are compared, and the cluster group with the best comprehensive performance partitioning indicator is selected as the cluster group with the optimal number of clusters.

5. According to the method for dividing the photovoltaic storage clusters in the medium and low voltage distribution network according to claim 1, it is characterized in that: The obtaining of the sensitivity of the voltage on each node relative to the power change of the node comprises: Based on the line data of the medium and low voltage distribution networks and the photovoltaic storage output data, the sensitivity of the voltage at each node to the power change of the node is calculated.

6. According to claim 1, a method for dividing photovoltaic storage clusters in a medium and low voltage distribution network is characterized in that: The division result of dividing each node in the medium and low voltage distribution network into the optimal number of clusters according to the sensitivity includes: Nodes whose sensitivity is within a preset threshold are divided into the same cluster group.

7. According to claim 1, a method for dividing photovoltaic and storage clusters in a medium and low voltage distribution network is characterized in that: include: After the cluster groups are divided, the charging and discharging of the energy storage devices in the cluster groups are controlled, and the voltage is flexibly adjusted according to the regulation potential value and comprehensive performance classification index of the cluster groups.

8. A device for dividing photovoltaic and storage clusters in medium and low voltage distribution networks, characterized in that: include: A partitioning module divides each node in the medium and low voltage distribution network into cluster groups with different numbers of clusters; A model module, which constructs a source-load-storage power optimization model of the medium and low voltage distribution network with the maximum total rated power of energy storage as the objective function; A difference module, which obtains the difference between the distributed photovoltaic and load power in each cluster group of the medium and low voltage distribution network according to the source-load-storage power optimization model; an indicator module, determining a regulation potential value of the cluster group according to the difference; Performing multi-objective optimization calculation on the regulation potential value and the preset electrical modularity index of the medium and low voltage distribution network to obtain the optimal comprehensive performance division index; A screening module, which selects a cluster group with an optimal number of clusters from the cluster groups with different numbers of clusters according to the optimal comprehensive performance partitioning index; An acquisition module is used to acquire the sensitivity of the voltage on each node relative to the power change of the node; The result module divides each node in the medium and low voltage distribution network into cluster groups with the optimal number of clusters according to the sensitivity as a division result.

9. A device for dividing photovoltaic and storage clusters in medium and low voltage distribution networks, characterized in that: include: Memory; A processor, for retrieving from the memory a computer executable program of a method for dividing photovoltaic and storage clusters in a medium- and low-voltage distribution network as described in any one of claims 1 to 7, and executing: dividing each node in the medium- and low-voltage distribution network into cluster groups with different numbers of clusters; constructing a source-load-storage power optimization model of the medium- and low-voltage distribution network with the maximum total rated power of energy storage as the objective function; obtaining the difference between the distributed photovoltaic and load power in each of the cluster groups of the medium- and low-voltage distribution network according to the source-load-storage power optimization model; and determining the regulation potential value of the cluster group according to the difference; Perform multi-objective optimization calculation on the regulation potential value and the preset electrical modularity index of the medium and low voltage distribution network to obtain the optimal comprehensive performance division index; select the cluster group with the optimal number of clusters from the cluster groups with different numbers of clusters according to the optimal comprehensive performance division index; Obtaining the sensitivity of the voltage on each of the nodes relative to the power change of the node; According to the sensitivity, each node in the medium and low voltage distribution network is divided into cluster groups with the optimal number of clusters as a division result.

10. A storage medium, characterized in that: The invention comprises a computer executable program stored therein, wherein the computer executable program is used to be called by a processor to execute the steps of a method for dividing a medium- and low-voltage distribution network photovoltaic storage cluster as described in any one of claims 1 to 7.

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