A power distribution network partition voltage optimization method based on a distributed photovoltaic cluster

Abstract

The present application relates to power distribution network voltage control technical field, disclose a kind of based on distributed photovoltaic cluster's power distribution network partition voltage optimization method, comprising: obtaining the reactive power voltage sensitivity between each node, the electrical distance between each node is calculated, cluster division is carried out to power distribution network, obtains the cluster of power distribution network;With cluster as basic control unit, voltage optimization is carried out, when reactive power is sufficient in cluster, based on the area reactive power-voltage sensitivity of the cluster of voltage out-of-limit node, the reactive power support amount of photovoltaic in cluster is calculated;When reactive power is insufficient in cluster, the optimization target of original cluster voltage out-of-limit node is mapped to the boundary node of adjacent cluster, only exchange the information of boundary node with adjacent cluster, suppress voltage out-of-limit by adjusting the voltage of boundary node of adjacent cluster;The method can effectively utilize reactive power resource and suppress voltage out-of-limit, so as to realize voltage optimization, while optimizing network loss, more reactive power reserve can be retained, and voltage control time is significantly reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of distribution network voltage control, and in particular to a distribution network partition voltage optimization method based on a distributed photovoltaic cluster. Background Art

[0002] As the scale of distributed photovoltaic power generation integrated into distribution networks continues to increase, the volatility and intermittency of photovoltaic power generation are leading to serious voltage overshooting issues in distribution networks. At the same time, the rapid reactive power regulation capabilities of distributed photovoltaic inverters offer the potential to optimize distribution network voltage and mitigate the risk of voltage overshooting. Therefore, fully leveraging the regulatory potential of distributed photovoltaic power generation and developing distribution network voltage optimization methods suitable for large-scale distributed photovoltaic integration have become a key research focus.

[0003] Currently, distribution network voltage optimization mostly relies on centralized control. This approach involves a central controller collecting and analyzing global distribution network information, which then transmits control commands to each controllable unit. This requires all controllable devices to be connected to a master distribution network control station, placing high demands on communication capabilities. Due to the lack of robust communication capabilities within distribution networks, centralized control is challenging to implement. Summary of the Invention

[0004] In view of the above-mentioned deficiencies in the prior art, the present invention provides a distribution network partition voltage optimization method based on a distributed photovoltaic cluster, which is used to solve the problem of voltage optimization difficulties caused by incomplete distribution network communication conditions in the prior art.

[0005] In order to achieve the above-mentioned object of the invention, the technical solution adopted by the present invention is:

[0006] A method for optimizing voltage in distribution network zones based on distributed photovoltaic clusters includes the following steps:

[0007] S1. Obtain the reactive voltage sensitivity between nodes in the distribution network and calculate the electrical distance between nodes;

[0008] S2. Cluster the distribution network according to the electrical distance between nodes to obtain the distribution network clusters;

[0009] S3, determine whether there is a voltage over-limit and the voltage over-limit duration exceeds the set threshold. If so, execute step S4; otherwise, return to step S2;

[0010] S4. Sort the voltages of all nodes whose voltage over-limit duration exceeds a set threshold from small to large, obtain the node with the most serious voltage over-limit, obtain the cluster to which the node with the most serious voltage over-limit belongs and the numbers of all nodes in the cluster, use the cubic exponential smoothing method to predict the voltage over-limit duration of the node with the most serious voltage over-limit and the cubic exponential smoothing value of the voltage of the node with the most serious voltage over-limit, and calculate the voltage prediction value of the node with the most serious voltage over-limit during the voltage over-limit duration;

[0011] S5. Determine whether the voltage prediction value of the node with the most serious voltage over-limit duration is less than the first set value or greater than the second set value. If so, execute step S6; otherwise, return to step S2.

[0012] S6. Receive a voltage optimization start signal, and based on the reactive voltage sensitivity between nodes in the distribution network, obtain the reactive voltage sensitivities of the adjustable photovoltaic nodes in the cluster to which the node with the most severe voltage over-limit belongs and the node with the most severe voltage over-limit, and sequentially adjust the reactive power of the node with the most severe voltage over-limit using the adjustable photovoltaic nodes with decreasing reactive sensitivities.

[0013] S7. Determine whether the reactive power of the adjustable photovoltaic node in the cluster to which the node with the most serious voltage over-limit belongs is exhausted. If so, map the node with the most serious voltage over-limit to the boundary node of the adjacent cluster, and use the boundary node of the adjacent cluster as the node with the most serious voltage over-limit, and execute step S6. Otherwise, continue to execute step S6 to implement reactive power regulation of the node with the most serious voltage over-limit.

[0014] Furthermore, in step S1, the reactive voltage sensitivity between nodes in the distribution network is:

[0015] S VQ,ij =-[(G ij -P ij )(B ij +Q ij ) -1 (G ij +P ij )+(B ij -Q ij )] -1

[0016] Among them, S VQ,ij represents the reactive voltage sensitivity of node i to node j, G ij represents the line conductance from node i to node j, B ij represents the susceptance from node i to node j, P ij represents the active power flowing on the line from node i to node j, Q ij Represents the reactive power flowing on the line from node i to node j.

[0017] Furthermore, the calculation formula for the electrical distance between nodes in step S1 is:

[0018]

[0019] Among them, N represents the total number of nodes, L ij represents the electrical distance between node i and node j, D ij It represents the sensitivity ratio of node i and node j to the voltage change of node j, lg represents the logarithmic function, S VQ,jj represents the reactive voltage sensitivity of node j to node j, S VQ,ij represents the reactive voltage sensitivity of node i to node j, D i1 The sensitivity ratio of node i to node 1 to the voltage change of node 1 is represented by D j1 The sensitivity ratio of node j to node 1 to the voltage change of node 1 is represented by D iN It represents the sensitivity ratio of node i to node N to the voltage change of node N, D jN It represents the sensitivity ratio of node j to node N to the voltage change of node N.

[0020] Furthermore, step S2 specifically includes:

[0021] S21. Set the total number of clusters to K and the total number of iterations to T, randomly select K nodes as cluster centers and initialize them;

[0022] S22, sequentially comparing the electrical distances of each node to the center of each cluster, and grouping each node into the cluster closest to the center of each cluster, to obtain K clusters;

[0023] S23. Randomly reselect the cluster center based on all the nodes of the K clusters, repeat step S22, and determine whether the total number of iterations has been reached. If so, the cluster of the distribution network is obtained; otherwise, continue to execute step S22.

[0024] Furthermore, in step S3, the threshold is set to 3 minutes.

[0025] Furthermore, the triple exponential smoothing value of the voltage of the node with the most serious voltage limit violation in step S4 is:

[0026]

[0027] Among them, W t (1) 、W t (2) 、W t (3) They represent the first, second, and third exponential smoothing values ​​of the node voltage with the most serious voltage limit violation at time t, They represent the first, second, and third exponential smoothing values ​​of the node voltage with the most serious voltage limit at time t, α represents the smoothing coefficient, V t Indicates the voltage amplitude of the node with the most serious voltage limit violation at time t.

[0028] Furthermore, the calculation formula for the voltage prediction value of the node with the most serious voltage over-limit during the voltage over-limit duration in step S4 is:

[0029]

[0030] Where k represents the duration of voltage exceeding the limit, V t+k A represents the voltage prediction value of the node with the most serious voltage over-limit at time t+k, that is, the voltage amplitude of the node with the most serious voltage over-limit during the voltage over-limit duration. t 、B t 、C t Both represent intermediate variables.

[0031] Furthermore, in step S5, the first set value and the second set value are both voltage optimization start thresholds.

[0032] Furthermore, in step S7, the mapping formula for mapping the node with the most serious voltage over-limit to the boundary node of the adjacent cluster is:

[0033]

[0034] Where ΔV represents the voltage difference that needs to be adjusted at the node with the most serious voltage over-limit, V obj,a Indicates the target voltage of node a, which has the most serious voltage limit violation, V obj,b represents the target voltage mapped from the boundary node b in the adjacent cluster, k ′ 、i ′ All represent the nodes between node a, where the voltage exceeds the limit the most seriously, and node b, which is the boundary node in the adjacent cluster. n represents the nodes between node a and node k. ′ All nodes connected to the node, x, r represent the nodes connected to k ′ The resistance and reactance of the branches connected to the node, Represents k ′ The equivalent active power and reactive power of the node, V i′-1 Represents node i ′ -1 voltage.

[0035] The present invention has the following beneficial effects:

[0036] The present invention proposes a distribution network partition voltage optimization method based on distributed photovoltaic clusters, which can effectively utilize reactive resources and effectively suppress voltage over-limit problems, thereby achieving voltage optimization. It can also retain more reactive reserves while optimizing network losses and significantly reduce the voltage control time during the voltage optimization process. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 This is a flow chart of a method for optimizing voltage in distribution network zones based on distributed photovoltaic clusters proposed by the present invention;

[0038] Figure 2 This is a schematic diagram of the cluster division results of the IEEE33 standard distribution network nodes in the embodiment;

[0039] Figure 3 Schematic diagram of comparison between the triple exponential smoothing value of the voltage at a node where the voltage exceeds the limit and the actual voltage value in an embodiment;

[0040] Figure 4 Schematic diagram showing changes in the voltage of the node 18 and the reactive margin in the cluster with the number of control times in the embodiment;

[0041] Figure 5 Schematic diagram of reactive power and voltage changes of cluster B during inter-cluster coordinated control in the embodiment;

[0042] Figure 6 Schematic diagram of voltage comparison after intra-cluster voltage control and inter-cluster reactive power support in the embodiment. DETAILED DESCRIPTION

[0043] The specific embodiments of the present invention are described below to facilitate understanding of the present invention by those skilled in the art. However, it should be clear that the present invention is not limited to the scope of the specific embodiments. For those skilled in the art, as long as various changes are within the spirit and scope of the present invention as defined and determined by the appended claims, these changes are obvious, and all inventions and creations utilizing the concepts of the present invention are protected.

[0044] like Figure 1 As shown, a distribution network partition voltage optimization method based on a distributed photovoltaic cluster includes the following steps S1-S7:

[0045] S1. Obtain the reactive voltage sensitivity between nodes in the distribution network and calculate the electrical distance between nodes.

[0046] In this embodiment, the purpose of calculating the electrical distance between each node is to facilitate clustering of each node in subsequent steps.

[0047] Specifically, the reactive voltage sensitivity between nodes in the distribution network in step S1 is:

[0048] S VQ,ij = -[(G ij -P ij )(B ij -Q ij ) -1 (G ij +P ij )+(B ij -Q ij )] -1

[0049] wherein, S VQ,ij represents the reactive voltage sensitivity of node i to node j, G ij represents the line conductance from node i to node j, B ij represents the susceptance from node i to node j, P ij represents the active power flowing on the line from node i to node j, and Q ij represents the reactive power flowing on the line from node i to node j.

[0050] Specifically, the calculation formula of the electrical distance between nodes in step S1 is as follows:

[0051]

[0052] wherein, N represents the total number of nodes, L ij represents the electrical distance between node i and node j, D ij represents the sensitivity ratio of node i and node j to the voltage change of node j, lg represents the logarithmic function, S VQ,jj represents the reactive voltage sensitivity of node j to node j, S VQ,ij represents the reactive voltage sensitivity of node i to node j, D i1 represents the sensitivity ratio of node i and node 1 to the voltage change of node 1, D j1 represents the sensitivity ratio of node j and node 1 to the voltage change of node 1, D iN represents the sensitivity ratio of node i and node N to the voltage change of node N, and D jN represents the sensitivity ratio of node j and node N to the voltage change of node N.

[0053] S2, clustering the distribution network according to the electrical distance between nodes to obtain the cluster of the distribution network.

[0054] In this embodiment, the purpose of clustering the distribution network is to overcome the inherent communication bottleneck and calculation burden limitations of the centralized control method currently used for voltage regulation of the distribution network.

[0055] Specifically, step S2 specifically includes S21-S23:

[0056] S21. Set the total number of clusters to K and the total number of iterations to T, randomly select K nodes as cluster centers and initialize them.

[0057] In this embodiment, T is greater than 50 times to ensure the correctness of cluster selection.

[0058] S22. Compare the electrical distances from each node to the center of each cluster in turn, and group each node into the cluster closest to the center of each cluster, to obtain K clusters.

[0059] S23. Randomly reselect the cluster center based on all the nodes of the K clusters, repeat step S22, and determine whether the total number of iterations has been reached. If so, the cluster of the distribution network is obtained; otherwise, continue to execute step S22.

[0060] In this embodiment, taking the IEEE33 standard distribution network node as an example, the reference voltage is 12.66kV, the total photovoltaic access capacity is 5600kVA, and there are a total of 13 350kVA photovoltaic units. The cluster division results are as follows: Figure 2 As shown, cluster A is {1-2, 19-22}, cluster B is {3-4, 23-25}, and cluster C is {6-18, 26-33}.

[0061] S3. Determine whether the voltage exceeds the limit and the duration of the voltage exceeding the limit exceeds the set threshold. If so, execute step S4; otherwise, return to step S2.

[0062] Specifically, in step S3, the threshold is set to 3 minutes.

[0063] In this embodiment, in order to ensure the safety of power grid operation, voltage over-limit detection is required; at the same time, it is not necessary to immediately adjust the reactive equipment when voltage over-limit occurs. That is, in the actual operation of the power system, while ensuring safety, it is also necessary to take into account economy. Therefore, voltage control is required only when the voltage over-limit lasts for a certain duration. That is, when the voltage over-limit duration exceeds 3 minutes, it can be judged by calculating the voltage amplitude after three smoothings to start reactive compensation, that is, executing step S4.

[0064] S4. Sort the voltages of all nodes whose voltage over-limit duration exceeds the set threshold from small to large to obtain the node with the most serious voltage over-limit, obtain the cluster to which the node with the most serious voltage over-limit belongs and the numbers of all nodes in the cluster, use the cubic exponential smoothing method to predict the voltage over-limit duration of the node with the most serious voltage over-limit and the cubic exponential smoothing value of the voltage of the node with the most serious voltage over-limit, and calculate the voltage prediction value of the node with the most serious voltage over-limit during the voltage over-limit duration.

[0065] Specifically, the triple exponential smoothing value of the voltage of the node with the most serious voltage limit violation in step S4 is:

[0066]

[0067] Among them, W t (1) 、W t (2) 、W t (3) They represent the first, second, and third exponential smoothing values ​​of the node voltage with the most serious voltage limit violation at time t, They represent the first, second, and third exponential smoothing values ​​of the node voltage with the most serious voltage limit at time t, α represents the smoothing coefficient, V t Indicates the voltage amplitude of the node with the most serious voltage limit violation at time t.

[0068] In this embodiment, the value of α is 0.1.

[0069] Specifically, the calculation formula for the voltage prediction value of the node with the most serious voltage over-limit during the voltage over-limit duration in step S4 is:

[0070]

[0071] Where k represents the duration of voltage exceeding the limit, V t+k A represents the voltage prediction value of the node with the most serious voltage over-limit at time t+k, that is, the voltage amplitude of the node with the most serious voltage over-limit during the voltage over-limit duration. t 、B t 、C t They all represent intermediate variables, that is, they have no practical meaning.

[0072] In this embodiment, the IEEE33 standard distribution network node is still taken as an example. According to the real-time load and photovoltaic data provided in Table 1, it is found that at 18:57, the voltage of nodes 12 to 18 exceeds the limit. At the same time, according to the real-time data provided by the distribution network measurement and previous historical data, it is predicted that this limit exceeding will last for more than 3 minutes. At this time, the cluster voltage autonomy strategy is activated, that is, the triple exponential smoothing method is used to predict the triple exponential smoothing value of the node voltage exceeding the limit. The triple exponential smoothing result is as follows: Figure 3 As shown. Among them, Table 1 is as follows:

[0073] Table 1 Real-time load and photovoltaic data of IEEE33 standard distribution network nodes

[0074]

[0075]

[0076] S5. Determine whether the voltage prediction value of the node with the most serious voltage over-limit duration is less than the first set value or greater than the second set value. If so, execute step S6; otherwise, return to step S2.

[0077] In this embodiment, the first set value is 0.95 pu, and the second set value is 1.05 pu.

[0078] Specifically, in step S5 , the first set value and the second set value are both voltage optimization start thresholds.

[0079] S6. Receive a voltage optimization start signal, and obtain the reactive voltage sensitivity of the adjustable photovoltaic node in the cluster to which the node with the most severe voltage over-limit belongs and the node with the most severe voltage over-limit according to the reactive voltage sensitivity between each node in the distribution network, and adjust the reactive power of the node with the most severe voltage over-limit in sequence using the adjustable photovoltaic nodes with decreasing reactive sensitivity.

[0080] In this embodiment, voltage exceeding the limit is avoided by regulating reactive voltage.

[0081] S7. Determine whether the reactive power of the adjustable photovoltaic node in the cluster to which the node with the most serious voltage over-limit belongs is exhausted. If so, map the node with the most serious voltage over-limit to the boundary node of the adjacent cluster, and use the boundary node of the adjacent cluster as the node with the most serious voltage over-limit, and execute step S6. Otherwise, continue to execute step S6 to implement reactive power regulation of the node with the most serious voltage over-limit.

[0082] In this embodiment, by detecting the reactive surplus capacity within the cluster, it is determined whether reactive power needs to be used from adjacent clusters to suppress voltage exceeding the limit within the cluster.

[0083] Specifically, in step S7, the mapping formula for mapping the node with the most serious voltage over-limit to the boundary node of the adjacent cluster is:

[0084]

[0085] Where ΔV represents the voltage difference that needs to be adjusted at the node with the most serious voltage over-limit, V obj,a Indicates the target voltage of node a, which has the most serious voltage limit violation, V obj,b represents the target voltage mapped from the boundary node b in the adjacent cluster, k ′ 、i ′ All represent the nodes between node a, where the voltage exceeds the limit the most seriously, and node b, which is the boundary node in the adjacent cluster. n represents the nodes between node a and node k. ′ All nodes connected to the node, x, r represent the nodes connected to k ′ The resistance and reactance of the branches connected to the node, Represents k ′The equivalent active power and reactive power of the node, V i′-1 Represents node i ′ -1 voltage.

[0086] In this embodiment, taking the IEEE33 standard distribution network node as an example, it is found that the voltage of node 18 exceeds the limit most seriously, and the method proposed in step S5 is used for adjustment, as shown in the following example: Figure 4 As shown in FIG, the variation process of the voltage at node 18 and the reactive power margin within the cluster with the number of control times is shown. It can be seen that when the autonomous method within the cluster is adopted, the photovoltaic nodes 18, 17, 13 and 11, which are closer to the node with the most serious limit violation, take on the voltage regulation task in turn until they are power-free and exhausted after 6 controls. At the same time, the photovoltaic nodes 9, 28, 31 and 33, which are farther away, are power-free and exhausted after 14 controls. Therefore, it is necessary to request reactive power support from the adjacent cluster (cluster B), that is, to execute the operation of step S6, as shown in FIG. Figure 5 As shown in the figure, the reactive power and voltage changes of cluster B during inter-cluster collaborative control are shown. When cluster C is short of reactive power, the reactive power support task is transferred to cluster B. Calculation shows that cluster B needs to adjust the voltage of boundary node 5 to 1.0086 pu. At this time, the problem of reactive power support between clusters is transformed into an intra-cluster autonomy problem based on cluster B, thus starting step S5. That is, the node that needs to be adjusted in cluster B is node 5, and the photovoltaics at nodes 4, 25, and 24 provide reactive power support for it in turn, finally completing the reactive power support between clusters. In addition, Figure 6 The voltage changes of each node before and after cluster control are shown. It can be seen that the method proposed in the present invention can effectively suppress the voltage over-limit problem, thereby achieving voltage optimization.

[0087] At the same time, the reactive power margin and control duration under centralized control obtained by the traditional centralized control method are compared with the reactive power margin and control duration under partitioned control obtained by the method proposed in the present invention, as shown in Table 2:

[0088] Table 2 Comparison of reactive power margin and control time under centralized control and partition control modes

[0089]

[0090] As shown in Table 2, the partitioned control method proposed in this invention can more effectively utilize reactive resources, retaining more reactive power reserves while optimizing network losses. Furthermore, the control time of this partitioned control method is only 1.682 seconds, while the control time of the centralized control method is 3.237 seconds, indicating that the method proposed in this invention can significantly reduce control time.

[0091] In summary, the present invention proposes a distribution network partition voltage optimization method based on distributed photovoltaic clusters. According to the electrical distance between nodes and the cluster voltage control capability, a distributed photovoltaic cluster (distribution network cluster) with local autonomy is constructed, and the photovoltaic cluster is used as the basic control unit for voltage optimization. When the reactive power in the cluster is sufficient, the reactive support of the distributed photovoltaic in the cluster is calculated based on the regional reactive power-voltage sensitivity of the cluster to which the voltage-over-limit node belongs without the need for inter-cluster communication; when the reactive power in the cluster is insufficient, the optimization target of the original cluster voltage-over-limit node is mapped to the boundary node of the adjacent cluster, and only the information of the boundary node is exchanged with the adjacent cluster. By adjusting the voltage of the boundary node of the adjacent cluster, the voltage over-limit is indirectly suppressed. It can more effectively utilize reactive resources and effectively suppress the voltage over-limit problem, thereby achieving voltage optimization, and can retain more reactive power reserves while optimizing network losses, and significantly reduce the voltage control time during the voltage optimization process.

[0092] Specific embodiments are used in the present invention to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core ideas. At the same time, for those skilled in the art, according to the ideas of the present invention, there may be changes in the specific implementation methods and application scopes. In summary, the contents of this specification should not be understood as limiting the present invention.

[0093] Those skilled in the art will appreciate that the embodiments described herein are intended to help readers understand the principles of the present invention, and it should be understood that the scope of protection of the present invention is not limited to such specific descriptions and embodiments. Those skilled in the art can make various other specific variations and combinations based on the technical teachings disclosed in the present invention without departing from the essence of the present invention, and such variations and combinations are still within the scope of protection of the present invention.

Claims

1. A distribution network partition voltage optimization method based on distributed photovoltaic clusters, characterized in that: The following steps are involved: S1. Obtain the reactive voltage sensitivity between nodes in the distribution network and calculate the electrical distance between nodes; S2. Cluster the distribution network according to the electrical distance between nodes to obtain the distribution network clusters; S3, determine whether there is a voltage over-limit and the voltage over-limit duration exceeds the set threshold. If so, execute step S4; otherwise, return to step S2; S4. Sort the voltages of all nodes whose voltage over-limit duration exceeds a set threshold from small to large, obtain the node with the most serious voltage over-limit, obtain the cluster to which the node with the most serious voltage over-limit belongs and the numbers of all nodes in the cluster, use the cubic exponential smoothing method to predict the voltage over-limit duration of the node with the most serious voltage over-limit and the cubic exponential smoothing value of the voltage of the node with the most serious voltage over-limit, and calculate the voltage prediction value of the node with the most serious voltage over-limit during the voltage over-limit duration; S5. Determine whether the voltage prediction value of the node with the most serious voltage over-limit duration is less than the first set value or greater than the second set value. If so, execute step S6; otherwise, return to step S2. S6. Receive a voltage optimization start signal, and based on the reactive voltage sensitivity between nodes in the distribution network, obtain the reactive voltage sensitivities of the adjustable photovoltaic nodes in the cluster to which the node with the most severe voltage over-limit belongs and the node with the most severe voltage over-limit, and sequentially adjust the reactive power of the node with the most severe voltage over-limit using the adjustable photovoltaic nodes with decreasing reactive sensitivities. S7. Determine whether the reactive power of the adjustable photovoltaic node in the cluster to which the node with the most serious voltage over-limit belongs is exhausted. If so, map the node with the most serious voltage over-limit to the boundary node of the adjacent cluster, and use the boundary node of the adjacent cluster as the node with the most serious voltage over-limit, and execute step S6. Otherwise, continue to execute step S6 to implement reactive power regulation of the node with the most serious voltage over-limit.

2. The method for optimizing voltage in distribution network partitions based on distributed photovoltaic clusters according to claim 1, characterized in that: The reactive voltage sensitivity between nodes in the distribution network in step S1 is: S VQ,ij =-[(G ij -P ij )(B ij +Q ij ) -1 (G ij +P ij )+(B ij -Q ij )] -1 Among them, S VQ,ij represents the reactive voltage sensitivity of node i to node j, G ij represents the line conductance from node i to node j, B ij represents the susceptance from node i to node j, P ij represents the active power flowing on the line from node i to node j, Q ij Represents the reactive power flowing on the line from node i to node j.

3. The method for optimizing voltage in distribution network partitions based on distributed photovoltaic clusters according to claim 2, characterized in that: The calculation formula for the electrical distance between each node in step S1 is: Among them, N represents the total number of nodes, L ij represents the electrical distance between node i and node j, D ij It represents the sensitivity ratio of node i and node j to the voltage change of node j, lg represents the logarithmic function, S VQ,jj represents the reactive voltage sensitivity of node j to node j, S VQ,ij represents the reactive voltage sensitivity of node i to node j, D i1 The sensitivity ratio of node i to node 1 to the voltage change of node 1 is represented by D j1 The sensitivity ratio of node j to node 1 to the voltage change of node 1 is represented by D iN It represents the sensitivity ratio of node i to node N to the voltage change of node N, D jN It represents the sensitivity ratio of node j to node N to the voltage change of node N.

4. The method for optimizing voltage in distribution network zones based on distributed photovoltaic clusters according to claim 3, characterized in that: Step S2 specifically includes: S21. Set the total number of clusters to K and the total number of iterations to T, randomly select K nodes as cluster centers and initialize them; S22, sequentially comparing the electrical distances of each node to the center of each cluster, and grouping each node into the cluster closest to the center of each cluster, to obtain K clusters; S23. Randomly reselect the cluster center based on all the nodes of the K clusters, repeat step S22, and determine whether the total number of iterations has been reached. If so, the cluster of the distribution network is obtained; otherwise, continue to execute step S22.

5. The method for optimizing voltage in distribution network zones based on distributed photovoltaic clusters according to claim 4, characterized in that: In step S3, the threshold is set to 3 minutes.

6. The method for optimizing voltage in distribution network zones based on distributed photovoltaic clusters according to claim 5, characterized in that: The triple exponential smoothing value of the node voltage with the most serious voltage limit violation in step S4 is: Among them, W t (1) 、W t (2) 、W t (3) They represent the first, second, and third exponential smoothing values ​​of the node voltage with the most serious voltage over-limit at time t, They represent the first, second, and third exponential smoothing values ​​of the node voltage with the most serious voltage limit at time t, α represents the smoothing coefficient, V t Indicates the voltage amplitude of the node with the most serious voltage limit violation at time t.

7. The method for optimizing voltage in distribution network zones based on distributed photovoltaic clusters according to claim 6, characterized in that: The calculation formula for the voltage prediction value of the node with the most serious voltage over-limit during the voltage over-limit duration in step S4 is: Where k represents the duration of voltage exceeding the limit, V t+k A represents the voltage prediction value of the node with the most serious voltage over-limit at time t+k, that is, the voltage amplitude of the node with the most serious voltage over-limit during the voltage over-limit duration. t 、B t 、C t Both represent intermediate variables.

8. The method for optimizing voltage in distribution network zones based on distributed photovoltaic clusters according to claim 7, characterized in that: In step S5 , the first set value and the second set value are both voltage optimization start thresholds.

9. The method for optimizing voltage in distribution network zones based on distributed photovoltaic clusters according to claim 8, characterized in that: In step S7, the mapping formula for mapping the node with the most serious voltage over-limit to the boundary node of the adjacent cluster is: Where ΔV represents the voltage difference that needs to be adjusted at the node with the most serious voltage over-limit, V obj,a Indicates the target voltage of node a, which has the most serious voltage limit violation, V obj,b represents the target voltage mapped from the boundary node b in the adjacent cluster, k ′ 、i ′ All represent the nodes between node a, where the voltage exceeds the limit the most seriously, and node b, which is the boundary node in the adjacent cluster. n represents the nodes between node a and node k. ′ All nodes connected to the node, x, r represent the nodes connected to k ′ The resistance and reactance of the branches connected to the node, Represents k ′ The equivalent active power and reactive power of the node, V i′-1 Represents node i ′ -1 voltage.

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