Photovoltaic output evaluation method and device, computer equipment and program product

Abstract

The invention relates to a photovoltaic output evaluation method and device, computer equipment and a computer program product. The method comprises the following steps: performing cluster division on nodes in the power distribution network based on comprehensive performance indexes to obtain a cluster division result; based on a cluster division result, establishing a photovoltaic output model taking minimization of light abandoning quantity as an objective function and taking power system power flow constraint, node voltage constraint, branch power flow constraint and injection power constraint as constraint conditions; and determining an evaluation result of the node based on a photovoltaic output model. By adopting the method, the photovoltaic output can be evaluated from multiple aspects, so that the evaluation accuracy of the photovoltaic output is improved.

Description

Technical Field

[0001] The present application relates to the technical field of distributed power sources, and particularly to a method and device for evaluating photovoltaic output, a computer device, a computer-readable storage medium, and a computer program product. Background Art

[0002] With the increasingly severe global energy shortage and environmental problems, the penetration rate of distributed photovoltaics in the distribution network has been continuously increasing. However, the output of photovoltaics is random and uncertain, which brings many challenges to the distribution network, such as increased network losses, voltage over-limit, and power flow fluctuations. Some of these problems are due to the insufficient local consumption capacity after a high proportion of distributed photovoltaics are connected to the distribution network. Especially in low-voltage distribution networks, the installed capacity of distributed photovoltaics is small and the access locations are scattered, which further exacerbates these problems. The electricity consumption scale of the load fluctuates greatly, resulting in a common situation of mismatch between the power source and the load, which further increases the operation and control difficulty of the distribution network with a high proportion of photovoltaics. Accurate evaluation of photovoltaic output can effectively regulate the photovoltaic cluster, improve the consumption rate of distributed photovoltaics, and the stability and reliability of the power system.

[0003] In related technologies, a clustering method considering consumption capacity and modularity combines the characteristics of 5G base stations and photovoltaic output for clustering. These methods have improved the clustering effect to a certain extent, but there are still deficiencies. For example, the multi-section matching of photovoltaic output and load curves is not fully considered, resulting in inaccurate evaluation results. Summary of the Invention

[0004] Based on this, it is necessary to provide a method and device for evaluating photovoltaic output, a computer device, a computer-readable storage medium, and a computer program product that can improve the accuracy of photovoltaic output evaluation for the above technical problems.

[0005] In a first aspect, the present application provides a method for evaluating photovoltaic output, including:

[0006] Based on comprehensive performance indicators, cluster the nodes in the distribution network to obtain a cluster division result; the comprehensive performance indicator is the sum value obtained by multiplying the electrical distance modularity indicator, active power balance indicator, capacity matching indicator, and reactive power compensation indicator of the cluster by preset weights and adding them together; the nodes include load nodes and photovoltaic nodes;

[0007] Based on the cluster division result, establish a photovoltaic output model with minimizing the light curtailment amount as the objective function and power system power flow constraints, node voltage constraints, branch power flow constraints, and injection power constraints as the constraint conditions;

[0008] Based on the photovoltaic output model, determine the evaluation result of the nodes.

[0009] In one embodiment, the method for clustering nodes in a distribution network based on comprehensive performance indicators to obtain a clustering result includes:

[0010] Obtain the target number of clusters, and cluster the nodes in the distribution network according to the target number to obtain an intermediate clustering result;

[0011] Obtain the electrical distance modularity index of the intermediate clustering result;

[0012] Adjust the target number, and return to the step of clustering the nodes in the distribution network according to the target number to obtain an intermediate clustering result and continue to execute until the number of adjustments of the target number reaches a preset number;

[0013] Determine the minimum value from the electrical distance modularity indexes obtained by each adjustment of the target number;

[0014] Construct a similarity matrix, and based on the similarity matrix, determine a normalized Laplacian matrix;

[0015] Perform eigenvalue decomposition on the Laplacian matrix, extract the target number of eigenvectors corresponding to the minimum value to form an eigenmatrix;

[0016] Based on the eigenmatrix and the target number corresponding to the minimum value, cluster the nodes in the distribution network to obtain a target clustering result;

[0017] Determine the target clustering result corresponding to the maximum value of the comprehensive performance index as the clustering result.

[0018] In one embodiment, the calculation formula of the electrical distance modularity index of the distribution network includes:

[0019] ;

[0020] Wherein, is the electrical distance modularity index of the distribution network, represents the degree of node i in the distribution network, that is, the sum of the edge weights of all edges connected to node i, represents the sum of the edge weights of the entire distribution network, represents the edge weight between node i and node j, which is 1 if the two nodes are connected, otherwise 0, is a 0-1 matrix. If node i and node j belong to the same cluster, then δ(i,j)=1, otherwise δ(i,j)=0.

[0021] In one embodiment, the calculation formula of the active power balance index of the distribution network includes:

[0022] ;

[0023] ;

[0024] wherein, is the active power balance degree of cluster c in the distribution network, is the active power balance degree index of the distribution network, T is the time consumed in the whole photovoltaic output evaluation process, is the net power of cluster c at time point t, N c is the number of clusters in the distribution network, and M is the set of clusters in the distribution network.

[0025] In one embodiment, the calculation formula of the capacity matching degree index of the distribution network includes:

[0026] ;

[0027] wherein, is the capacity matching degree index of the distribution network, represents the sum of the maximum active power outputs of all photovoltaic nodes in the hth cluster, that is, the total active power of the photovoltaic nodes in the cluster under the maximum power generation condition, represents the sum of the maximum active power of load nodes in the hth cluster, that is, the total active power of all loads in the cluster under the maximum demand condition, and m is the number of clusters in the distribution network.

[0028] In one embodiment, the calculation formula of the reactive power compensation degree index of the distribution network includes:

[0029] ;

[0030] wherein, is the reactive power compensation degree index of the distribution network, is the intra-cluster reactive power compensation amount of cluster i in the distribution network, and m is the number of clusters in the distribution network.

[0031] In a second aspect, the present application further provides a photovoltaic output evaluation device, including:

[0032] A partitioning module, configured to partition the nodes in the distribution network based on comprehensive performance indexes to obtain a cluster partitioning result; the comprehensive performance indexes are the sum values obtained by multiplying the electrical distance modularity index, active power balance degree index, capacity matching degree index, and reactive power compensation degree index of the cluster by preset weights and adding them together; the nodes include load nodes and photovoltaic nodes;

[0033] A building module, configured to build a PV output model with the objective function of minimizing the PV curtailment amount and with power system power flow constraints, node voltage constraints, branch power flow constraints, and injection power constraints based on the cluster division result;

[0034] A determination module, configured to determine the evaluation result of the node based on the PV output model.

[0035] In a third aspect, the present application further provides a computer device, including a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, the following steps are implemented:

[0036] Based on comprehensive performance indicators, perform cluster division on the nodes in the distribution network to obtain a cluster division result; the comprehensive performance indicator is the sum value obtained by multiplying the electrical distance modularity indicator, active power balance indicator, capacity matching indicator, and reactive power compensation indicator of the cluster by preset weights and adding them together; the nodes include load nodes and PV nodes;

[0037] Based on the cluster division result, build a PV output model with the objective function of minimizing the PV curtailment amount and with power system power flow constraints, node voltage constraints, branch power flow constraints, and injection power constraints as constraints;

[0038] Based on the PV output model, determine the evaluation result of the node.

[0039] In a fourth aspect, the present application further provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the following steps are implemented:

[0040] Based on comprehensive performance indicators, perform cluster division on the nodes in the distribution network to obtain a cluster division result; the comprehensive performance indicator is the sum value obtained by multiplying the electrical distance modularity indicator, active power balance indicator, capacity matching indicator, and reactive power compensation indicator of the cluster by preset weights and adding them together; the nodes include load nodes and PV nodes;

[0041] Based on the cluster division result, build a PV output model with the objective function of minimizing the PV curtailment amount and with power system power flow constraints, node voltage constraints, branch power flow constraints, and injection power constraints as constraints;

[0042] Based on the PV output model, determine the evaluation result of the node.

[0043] In a fifth aspect, the present application further provides a computer program product, including a computer program. When the computer program is executed by a processor, the following steps are implemented:

[0044] Based on comprehensive performance indicators, the nodes in the distribution network are clustered to obtain the clustering result; the comprehensive performance indicator is the sum value obtained by multiplying the electrical distance modularity indicator, active power balance indicator, capacity matching indicator, and reactive power compensation indicator of the cluster by preset weights and adding them together; the nodes include load nodes and photovoltaic nodes;

[0045] Based on the clustering result, a photovoltaic output model is established with minimizing the amount of abandoned light as the objective function and power system power flow constraints, node voltage constraints, branch power flow constraints, and injection power constraints as the constraint conditions;

[0046] Based on the photovoltaic output model, the evaluation result of the node is determined.

[0047] The above photovoltaic output evaluation method, device, computer device, computer-readable storage medium, and computer program product first cluster the nodes in the distribution network based on comprehensive performance indicators to obtain the clustering result; based on the clustering result, establish a photovoltaic output model with minimizing the amount of abandoned light as the objective function and power system power flow constraints, node voltage constraints, branch power flow constraints, and injection power constraints as the constraint conditions; based on the photovoltaic output model, determine the evaluation result of the node. The present invention comprehensively considers multiple indicators such as electrical distance modularity, active power balance, capacity matching, and reactive power compensation during clustering, ensuring that the clustering is not only reasonable in structure but also meets the requirements of reactive power local balance and new energy local consumption in terms of function, thereby making the result of photovoltaic output evaluation more accurate. Description of the Drawings

[0048] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the following will briefly introduce the drawings required to be used in the description of the embodiments of the present application or related technologies. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, other related drawings can be obtained based on these drawings without creative efforts.

[0049] Figure 1 It is an application environment diagram of the photovoltaic output evaluation method in an embodiment;

[0050] Figure 2 It is a flowchart of the photovoltaic output evaluation method in an embodiment;

[0051] Figure 3 It is a schematic diagram of the connection of nodes in the distribution network in an embodiment;

[0052] Figure 4 It is a flowchart of the clustering method in an embodiment;

[0053] Figure 5Schematic diagram of the cluster division result in an embodiment;

[0054] Figure 6 Structural block diagram of a photovoltaic output evaluation device in an embodiment;

[0055] Figure 7 Internal structure diagram of a computer device in an embodiment. Detailed implementation manners

[0056] In order to make the objectives, technical solutions and advantages of the present application clearer and more understandable, the present application will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0057] The photovoltaic output evaluation method provided by the embodiments of the present application can be applied to an application environment as shown in Figure 1 . Among them, the terminal 102 communicates with the server 104 through the network. The data storage system can store the photovoltaic output data that the server 104 needs to process. The data storage system can be integrated on the server 104, or can be placed in the cloud or other network servers. Among them, the terminal 102 can be, but is not limited to, various personal computers, laptop computers, smart phones, tablet computers, Internet of Things devices and portable wearable devices. The Internet of Things devices can be smart speakers, smart TVs, smart air conditioners, smart in-vehicle devices, projection devices, etc. The portable wearable devices can be smart watches, smart bracelets, head-mounted devices, etc. The head-mounted device can be a virtual reality (VR) device, an augmented reality (AR) device, a smart glasses, etc. The server 104 can be an independent physical server, or a server cluster or distributed system composed of multiple physical servers, or a cloud server providing cloud computing services.

[0058] In an exemplary embodiment, as shown in Figure 2 , a photovoltaic output evaluation method is provided. Taking the method applied to the terminal 102 in Figure 1 as an example, the method includes the following steps 202 to 206. Among them:

[0059] Step 202: Based on the comprehensive performance index, perform cluster division on the nodes in the distribution network to obtain the cluster division result.

[0060] Among them, the comprehensive performance index is the sum value obtained by multiplying the electrical distance modularity index, active power balance index, capacity matching index and reactive power compensation index of the cluster by preset weights and adding them together; the nodes include load nodes and photovoltaic nodes.

[0061] Among them, the load node is the power consumption point connected to the power grid in the power system, usually corresponding to various electrical equipment or users. The photovoltaic node refers to the node connected to the photovoltaic panel (solar panel). These nodes convert solar energy into electrical energy through the photovoltaic effect and inject the electrical energy into the power system. The photovoltaic node can be a single photovoltaic panel or a photovoltaic power station composed of multiple photovoltaic panels.

[0062] Exemplarily, the nodes in the distribution network are continuously clustered, and the comprehensive performance index is calculated. The target clustering result corresponding to the maximum value of the comprehensive performance index is used as the clustering result.

[0063] Optionally, the comprehensive performance index is the sum value obtained by multiplying the electrical distance modularity index, active power balance index, capacity matching index, and reactive power compensation index of the cluster by the preset weights respectively and then adding them. The specific calculation formula is as shown in (1).

[0064] (1)

[0065] Where p is the comprehensive performance index of the distribution network, is the electrical distance modularity index of the distribution network, is the active power balance index of the distribution network, is the capacity matching index of the distribution network, is the reactive power compensation index of the distribution network, is the preset weight corresponding to the electrical distance modularity index, is the preset weight corresponding to the active power balance index, is the preset weight corresponding to the capacity matching index, is the preset weight corresponding to the reactive power compensation index.

[0066] Step 204, based on the clustering result, establish a photovoltaic output model with the objective of minimizing the light curtailment amount and with the power system power flow constraint, node voltage constraint, branch power flow constraint, and injection power constraint as the constraint conditions.

[0067] Exemplarily, according to the clustering result, establish a photovoltaic output model with the objective of minimizing the light curtailment amount and with the power system power flow constraint, node voltage constraint, branch power flow constraint, and injection power constraint as the constraint conditions.

[0068] Among them, the specific formula of the objective function is as shown in (2).

[0069] (2)

[0070] Where F is the light curtailment amount, T represents the total number of time periods in the evaluation process, and N represents the total number of clusters in the distribution network. is the photovoltaic power generation of the nth cluster within the time period t. is the actually utilized photovoltaic power generation of the nth cluster within the time period t.

[0071] Optionally, the power flow constraint of the power system is specifically shown in formula (3).

[0072] (3)

[0073] where is the active power injected into node i, is the reactive power injected into node i, is the voltage amplitude of node i, is the real part of the nodal admittance matrix, is the phase angle difference between node i and node j, is the imaginary part of the nodal admittance matrix, is is all the nodes connected to node i in the distribution network.

[0074] Optionally, the node voltage constraint is specifically shown in formula (4).

[0075] (4)

[0076] where is the minimum value of the voltage amplitude of node i, is the voltage amplitude of node i, is the maximum value of the voltage amplitude of node i.

[0077] Optionally, the branch power flow constraint is specifically shown in formula (5).

[0078] (5)

[0079] where is the active power of line c, is the maximum active power of line c, which is determined by the upper limit of the maximum active power of the branch.

[0080] Optionally, the injection power constraint is specifically shown in formula (6).

[0081] (6)

[0082] where is the photovoltaic active power injected into the distribution network, is the maximum photovoltaic active power that can be injected into the distribution network.

[0083] Step 206: Determine the evaluation result of the node based on the photovoltaic output model.

[0084] Exemplarily, the photovoltaic output model is officially put into use to obtain the evaluation results used by the nodes in the distribution network.

[0085] In another embodiment, the photovoltaic output evaluation method further includes the selection of key nodes. Voltage observability is used to evaluate the sensitivity of key nodes to the voltage changes of other load nodes in the cluster, reflecting their importance in maintaining voltage stability within the cluster. Reactive / active controllability characterizes the sensitivity of key nodes to the changes in reactive / active power within the cluster. The higher the voltage observability, the more significant the role of the node in voltage regulation within the cluster. Specifically, as shown in formulas (7) and (8).

[0086] (7)

[0087] (8)

[0088] Wherein, is the reactive / active controllability of the node, is, is the cluster the sensitivity of the reactive power between node i and key node e in the cluster to voltage, is the cluster the sensitivity of the active power between node i and key node e in the cluster to voltage, is the cluster the total number of photovoltaic nodes in the cluster, is the voltage observability of the node, is the cluster the voltage of key node e in the cluster, is the cluster the reactive power of the photovoltaic nodes in the cluster, is the cluster the active power of the photovoltaic nodes in the cluster.

[0089] In the above photovoltaic output evaluation method, based on the comprehensive performance index, the nodes in the distribution network are clustered to obtain the cluster division result; based on the cluster division result, a photovoltaic output model is established with the objective of minimizing the light curtailment amount and with power system power flow constraints, node voltage constraints, branch power flow constraints, and injection power constraints as the constraint conditions; based on the photovoltaic output model, the evaluation results of the nodes are determined. In the cluster division of the present invention, multiple indexes such as electrical distance modularity, active power balance degree, capacity matching degree, and reactive power compensation degree are comprehensively considered to ensure that the cluster division is not only reasonable in structure but also can meet the requirements of reactive power in-situ balance and new energy in-situ consumption in terms of function, thereby making the results of photovoltaic output evaluation more accurate.

[0090] In an exemplary embodiment, based on comprehensive performance indicators, nodes in the distribution network are clustered to obtain a clustering result, including: obtaining the target number of clusters, clustering the nodes in the distribution network according to the target number to obtain an intermediate clustering result; obtaining the electrical distance modularity index of the intermediate clustering result; adjusting the target number, and returning to the step of clustering the nodes in the distribution network according to the target number to obtain an intermediate clustering result and continuing to execute until the number of adjustments of the target number reaches a preset number; determining the minimum value from the electrical distance modularity indicators obtained each time the target number is adjusted; constructing a similarity matrix, and based on the similarity matrix, determining a normalized Laplacian matrix; performing eigenvalue decomposition on the Laplacian matrix, extracting the target number of eigenvectors corresponding to the minimum value to form an eigenmatrix; clustering the nodes in the distribution network based on the eigenmatrix and the target number corresponding to the minimum value to obtain a target clustering result; determining the target clustering result corresponding to the maximum value of the comprehensive performance indicator as the clustering result.

[0091] In actual implementation, initialize the target number of clusters, cluster the nodes in the distribution network according to the target number to obtain an intermediate clustering result, and continuously re-iterate the target number until the number of iterations reaches a preset number, calculate the electrical distance modularity index of each intermediate clustering result, and determine the minimum value from the electrical distance modularity indicators corresponding to all intermediate clustering results.

[0092] Construct a similarity matrix to represent the similarity between nodes, determine a normalized Laplacian matrix according to the similarity matrix, perform eigen-decomposition, extract the eigenvectors corresponding to the first minimum number of eigenvalues, combine them into a new eigenmatrix, divide the nodes into the minimum number of regions, group the nodes within each region into the same cluster, calculate the comprehensive performance indicator of each target clustering result, and use the target clustering result corresponding to the maximum comprehensive performance indicator as the final clustering result.

[0093] In the above embodiment, by evaluating the rationality of each partitioning scheme, the optimal partitioning result is selected, and finally the optimal clustering result is output, improving the operation efficiency and economy of the distribution network. This partitioning result can optimize the operation efficiency of the distribution network, improve the self-consumption ability of distributed photovoltaics, and reduce network losses.

[0094] In an exemplary embodiment, the calculation process of the electrical distance modularity index of the distribution network includes:

[0095] In actual implementation, the calculation formula of the electrical distance modularity index of the distribution network is shown in formula (9).

[0096] (9)

[0097] Among them, is the electrical distance modularity index of the distribution network, represents the degree of node i in the distribution network, that is, the sum of the edge weights of all edges connected to node i, represents the sum of the edge weights of the entire distribution network, represents the edge weight between node i and node j. If the two nodes are connected, it is 1, otherwise it is 0, is a 0-1 matrix. If node i and node j belong to the same cluster, then δ(i,j)=1; otherwise, δ(i,j)=0.

[0098] Among them, in formula (9), represents the actually existing edge weight, and the specific calculation formula is as shown in (10), while represents the expected value of the edge weight between node i and node j in the random network. The difference between the two reflects the difference between the actual network and the random network.

[0099] (10)

[0100] Among them, is the electrical distance between node i and node j, and e is the electrical matrix composed of the electrical distances between all node pairs in the distribution network.

[0101] For example, if node i and node j belong to the same cluster and the actual edge weight is greater than the expected edge weight, it means that the internal connection within the cluster is tight and the electrical distance modularity value increases; otherwise, it decreases.

[0102] In the above embodiment, in the distribution network, the electrical distance modularity index can be used to divide distributed photovoltaic clusters, help identify the tight connection relationship between nodes, optimize the division result of the clusters, and improve the operation efficiency and stability of the network.

[0103] In an exemplary embodiment, the calculation process of the active power balance index of the distribution network includes:

[0104] In actual implementation, the calculation formula of the active power balance index of the distribution network is as shown in formulas (11) and (12).

[0105] (11)

[0106] (12)

[0107] Among them, is the active power balance of cluster c in the distribution network, is the active power balance index of the distribution network, and T is the total number of time periods in the entire photovoltaic output evaluation process. is the net power of cluster c at time point t, N c is the number of clusters in the distribution network, and M is the set of clusters in the distribution network.

[0108] In the above embodiments, the calculation of the active power balance degree aims to measure the matching degree between the power sources and loads within the cluster. The higher its value, the better the matching between the power sources and loads within the cluster, which can significantly reduce the active power transmission between clusters, enhance the self-consumption ability of photovoltaic power generation, and thus reduce the phenomenon of light curtailment.

[0109] In an exemplary embodiment, the calculation process of the capacity matching degree index of the distribution network includes:

[0110] In actual implementation, the calculation formula of the capacity matching degree index of the distribution network is as shown in formula (13).

[0111] (13)

[0112] Among them, is the capacity matching degree index of the distribution network, represents the sum of the maximum active power outputs of all photovoltaic nodes within the h-th cluster, that is, the total active power of the photovoltaic nodes within the cluster under the maximum power generation condition, represents the sum of the maximum active power of the load nodes within the h-th cluster, that is, the total active power of all loads within the cluster under the maximum demand condition, and m is the number of clusters in the distribution network.

[0113] In the above embodiments, by optimizing the capacity matching degree, the energy flow across clusters can be reduced, the line loss rate of the distribution network can be decreased, thereby enhancing the overall economy. The capacity matching degree index helps to maximize the local consumption capacity of photovoltaic power generation and reduce the phenomenon of light curtailment. When the photovoltaic capacity within the cluster matches well with the load demand, the photovoltaic power generation can be fully consumed by the local load, avoiding the problem of light curtailment caused by grid capacity limitations or transmission losses. The application of this index helps to optimize the division of distributed photovoltaic clusters and improve the operation efficiency and economy of the distribution network.

[0114] In an exemplary embodiment, the calculation process of the reactive power compensation degree index of the distribution network includes:

[0115] In actual implementation, the calculation formula of the reactive power compensation degree index of the distribution network is as shown in formula (14).

[0116] (14)

[0117] Among them, is the reactive power compensation degree index of the distribution network, is the amount of reactive power compensation within cluster \(i\) in the distribution network. The specific calculation is shown in formula (15), where \(m\) is the number of clusters in the distribution network.

[0118] (15)

[0119] Among them, is the reactive power supply value of the photovoltaic within the cluster, that is, the reactive power that the photovoltaic node can provide while outputting active power. is the reactive power demand value of the nodes within the cluster, that is, the total demand for reactive power by all load nodes within the cluster.

[0120] In the above embodiments, by optimizing the reactive power compensation index, the operation stability and economy of the cluster can be improved, providing an important functional basis for the division of the cluster.

[0121] To illustrate the photovoltaic output evaluation method in detail in this application, an embodiment is used for illustration below. Exemplarily, this application illustrates the photovoltaic output evaluation method of a distribution network in a specific scenario. As Figure 3 shown, this distribution network contains 108 load nodes and several photovoltaic nodes.

[0122] First, initialize the target number \(k = 2\) for cluster division. According to the target number, divide the nodes in the distribution network to obtain the intermediate result of cluster division, and continuously re-iterate the target number until the number of iterations reaches the preset number. Calculate the electrical distance modularity index \(Q\) VK of each intermediate result of cluster division. Determine the minimum value \(k\) from the electrical distance modularity indexes corresponding to all intermediate results of cluster division.

[0123] Construct a similarity matrix \(S\) to represent the similarity between nodes. Determine the normalized Laplacian matrix \(L\) according to the similarity matrix, and perform eigenvalue decomposition. Extract the eigenvectors corresponding to the first minimum number of eigenvalues, combine them into a new eigenmatrix, divide the nodes into the minimum number of regions, and classify the nodes within each region into the same cluster. Calculate the comprehensive performance index \(p\) of each target cluster division result, and take the target cluster division result corresponding to the maximum comprehensive performance index as the final cluster division result. The specific flowchart is as Figure 4 shown, and the specific cluster division result is as Figure 5 shown.

[0124] The photovoltaic output evaluation method also includes the selection of key nodes. Voltage observability is used to evaluate the sensitivity of key nodes to the voltage changes of other load nodes in the cluster, reflecting their importance in maintaining voltage stability within the cluster. Reactive / active controllability characterizes the sensitivity of key nodes to the changes in reactive / active power within the cluster. The higher the voltage observability, the more significant the role of the node in voltage regulation within the cluster. Specifically, as shown in Formulas (7) and (8).

[0125] According to the cluster division results, a photovoltaic output model is established with the objective of minimizing the curtailment of photovoltaic power, subject to power system power flow constraints, node voltage constraints, branch power flow constraints, and injection power constraints. The specific formula of the objective function is as shown in (2), the power system power flow constraints are specifically as shown in Formula (3), the node voltage constraints are specifically as shown in Formula (4), the branch power flow constraints are specifically as shown in Formula (5), and the injection power constraints are specifically as shown in Formula (6).

[0126] Put the photovoltaic output model into formal use to obtain the evaluation results of the nodes used in the distribution network.

[0127] Through aspects such as the flexibility of cluster division, comprehensive multi-index optimization, refinement of reactive power compensation and voltage control, and optimization of internal coordination within the cluster, this application can better adapt to the complex scenarios of high-penetration distributed photovoltaic access to the distribution network, and improve the operation efficiency and economy of the distribution network.

[0128] It should be understood that although the steps in the flowcharts involved in the above-described embodiments are sequentially shown according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless there is a clear indication in this article, the execution of these steps has no strict order limit, and these steps can be executed in other orders. Moreover, at least some of the steps in the flowcharts involved in the above-described embodiments may include multiple steps or multiple stages. These steps or stages are not necessarily executed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be executed alternately or in turn with at least some of the steps or stages in other steps or other steps.

[0129] Based on the same inventive concept, the embodiments of this application also provide a photovoltaic output evaluation device for implementing the above-mentioned photovoltaic output evaluation method. The implementation solutions provided by this device to solve problems are similar to the implementation solutions described in the above method. Therefore, the specific limitations in one or more embodiments of the photovoltaic output evaluation device provided below can refer to the limitations on the photovoltaic output evaluation method in the above text, and will not be repeated here.

[0130] In an exemplary embodiment, as Figure 6As shown in the figure, a photovoltaic output evaluation device is provided, including: a partitioning module 601, a building module 602, and a determining module 603, where:

[0131] The partitioning module is configured to perform cluster partitioning on the nodes in the distribution network based on comprehensive performance indicators to obtain a cluster partitioning result; the comprehensive performance indicator is the sum value obtained by multiplying the electrical distance modularity indicator, active power balance indicator, capacity matching indicator, and reactive power compensation indicator of the cluster by preset weights and adding them together; the nodes include load nodes and photovoltaic nodes.

[0132] The building module is configured to build a photovoltaic output model with minimizing the light curtailment amount as the objective function and power system power flow constraints, node voltage constraints, branch power flow constraints, and injection power constraints as the constraint conditions based on the cluster partitioning result.

[0133] The determining module is configured to determine the evaluation result of the nodes based on the photovoltaic output model.

[0134] In some embodiments, the above-mentioned partitioning module is further configured to obtain the target number of cluster partitions, perform cluster partitioning on the nodes in the distribution network according to the target number to obtain an intermediate result of cluster partitioning;

[0135] Obtain the electrical distance modularity indicator of the intermediate result of cluster partitioning;

[0136] Adjust the target number, and return to the step of performing cluster partitioning on the nodes in the distribution network according to the target number to obtain an intermediate result of cluster partitioning and continue to execute until the number of adjustments of the target number reaches a preset number;

[0137] Determine the minimum value from the electrical distance modularity indicators obtained each time the target number is adjusted;

[0138] Construct a similarity matrix, and determine a normalized Laplacian matrix based on the similarity matrix;

[0139] Perform eigenvalue decomposition on the Laplacian matrix, extract the target number of eigenvectors corresponding to the minimum value to form an eigenmatrix;

[0140] Perform cluster partitioning on the nodes in the distribution network based on the eigenmatrix and the target number corresponding to the minimum value to obtain a target cluster partitioning result;

[0141] Determine the target cluster partitioning result corresponding to the maximum value of the comprehensive performance indicator as the cluster partitioning result.

[0142] In some embodiments, the device further includes a calculation module for calculating the electrical distance modularity indicator of the distribution network, including:

[0143] ;

[0144] Among them, is the electrical distance modularity index of the distribution network, represents the degree of node i in the distribution network, that is, the sum of the edge weights of all edges connected to node i, represents the sum of the edge weights of the entire distribution network, represents the edge weight between node i and node j, which is 1 if the two nodes are connected, otherwise 0, is a 0-1 matrix. If node i and node j belong to the same cluster, then δ(i,j)=1, otherwise δ(i,j)=0.

[0145] In some embodiments, the device further includes a calculation module for calculating the active power balance index of the distribution network, including:

[0146] ;

[0147] ;

[0148] Among them, is the active power balance of cluster c in the distribution network, is the active power balance index of the distribution network, T is the time consumed during the entire photovoltaic output evaluation process, is the net power of cluster c at time point t, N c is the number of clusters in the distribution network, and M is the set of clusters in the distribution network.

[0149] In some embodiments, the device further includes a calculation module for calculating the capacity matching index of the distribution network, including:

[0150] ;

[0151] Among them, is the capacity matching index of the distribution network, represents the sum of the maximum active power outputs of all photovoltaic nodes in the hth cluster, that is, the total active power of the photovoltaic nodes in the cluster under the maximum power generation condition, represents the sum of the maximum active power of the load nodes in the hth cluster, that is, the total active power of all loads in the cluster under the maximum demand condition, and m is the number of clusters in the distribution network.

[0152] In some embodiments, the device further includes a calculation module for calculating the capacity matching index of the distribution network, including:

[0153] ;

[0154] Among them, is the reactive power compensation index of the distribution network is the in-group reactive power compensation amount of cluster i in the distribution network, and m is the number of clusters in the distribution network.

[0155] Each module in the above photovoltaic output evaluation device can be implemented in whole or in part by software, hardware, and their combination. Each of the above modules can be embedded in the processor of the computer device in hardware form or independent of it, or stored in the memory of the computer device in software form, so that the processor can call and execute the operations corresponding to each of the above modules.

[0156] In an exemplary embodiment, a computer device is provided. The computer device can be a server, and its internal structure diagram can be as Figure 7 shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, abbreviated as I / O), a communication interface, a display unit, and an input device. Among them, the processor, the memory, and the input / output interface are connected through the system bus, and the communication interface, the display unit, and the input device are connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store photovoltaic output data. The input / output interface of the computer device is used to exchange information between the processor and external devices. The communication interface of the computer device is used to communicate with external terminals through a network connection. When the computer program is executed by the processor, it implements a photovoltaic output evaluation method.

[0157] The display unit of the computer device is used to form a visually visible picture, which can be a display screen, a projection device, or a virtual reality imaging device. The display screen can be a liquid crystal display screen or an electronic ink display screen. The input device of the computer device can be a touch layer covering the display screen, or a button, a trackball, or a touchpad provided on the housing of the computer device, or an external keyboard, touchpad, or mouse, etc.

[0158] Those skilled in the art can understand that Figure 7 the structure shown in

[0159] In an exemplary embodiment, a computer device is provided, including a memory and a processor. A computer program is stored in the memory. When the processor executes the computer program, the following steps are implemented:

[0160] Based on the comprehensive performance index, cluster the nodes in the distribution network to obtain the cluster division result; the comprehensive performance index is the sum value obtained by multiplying the electrical distance modularity index, active power balance index, capacity matching index, and reactive power compensation index of the cluster by preset weights and adding them together; the nodes include load nodes and photovoltaic nodes;

[0161] Based on the cluster division result, establish a photovoltaic output model with minimizing the light curtailment amount as the objective function and power system power flow constraints, node voltage constraints, branch power flow constraints, and injection power constraints as the constraint conditions;

[0162] Based on the photovoltaic output model, determine the evaluation result of the nodes.

[0163] In an embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the following steps are implemented:

[0164] Based on the comprehensive performance index, cluster the nodes in the distribution network to obtain the cluster division result; the comprehensive performance index is the sum value obtained by multiplying the electrical distance modularity index, active power balance index, capacity matching index, and reactive power compensation index of the cluster by preset weights and adding them together; the nodes include load nodes and photovoltaic nodes;

[0165] Based on the cluster division result, establish a photovoltaic output model with minimizing the light curtailment amount as the objective function and power system power flow constraints, node voltage constraints, branch power flow constraints, and injection power constraints as the constraint conditions;

[0166] Based on the photovoltaic output model, determine the evaluation result of the nodes.

[0167] In an embodiment, a computer program product is provided, including a computer program. When the computer program is executed by a processor, the following steps are implemented:

[0168] Based on the comprehensive performance index, cluster the nodes in the distribution network to obtain the cluster division result; the comprehensive performance index is the sum value obtained by multiplying the electrical distance modularity index, active power balance index, capacity matching index, and reactive power compensation index of the cluster by preset weights and adding them together; the nodes include load nodes and photovoltaic nodes;

[0169] Based on the cluster division result, a photovoltaic output model is established with the objective function of minimizing the curtailment of photovoltaic power and the constraint conditions of power system power flow constraint, node voltage constraint, branch power flow constraint, and injection power constraint;

[0170] Based on the photovoltaic output model, the evaluation result of the node is determined.

[0171] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use, and processing of relevant data need to comply with relevant regulations.

[0172] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above methods. Among them, any reference to a memory, database, or other medium used in the embodiments provided in this application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc. The databases involved in the embodiments provided in this application can include at least one of relational databases and non-relational databases. Non-relational databases can include distributed databases based on blockchain, etc., without limitation. The processors involved in the embodiments provided in this application can be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, data processing logics based on quantum computing, artificial intelligence (AI) processors, etc., without limitation.

[0173] The technical features of the above embodiments can be combined arbitrarily. For the sake of brevity of description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered as the scope recorded in this application.

[0174] The above-described embodiments merely represent several implementation manners of the present application. The description thereof is relatively specific and detailed, but it should not be construed as a limitation on the patent scope of the present application. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present application, several modifications and improvements can still be made, and these all fall within the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the appended claims.

Claims

1. A method for evaluating photovoltaic power output, characterized in that The method includes: Based on comprehensive performance indicators, clustering the nodes in the distribution network to obtain a clustering result; the comprehensive performance indicator is the sum value obtained by multiplying the electrical distance modularity indicator, active power balance indicator, capacity matching indicator, and reactive power compensation indicator of the cluster by preset weights and adding them together; the nodes include load nodes and photovoltaic nodes; Based on the clustering result, establish a photovoltaic output model with the objective of minimizing the amount of abandoned light and subject to power system power flow constraints, node voltage constraints, branch power flow constraints, and injection power constraints; Based on the photovoltaic output model, determine the evaluation result of the nodes.

2. The method according to claim 1, characterized in that, The step of clustering the nodes in the distribution network based on comprehensive performance indicators to obtain a clustering result includes: Obtain the target number of clustering, and cluster the nodes in the distribution network according to the target number to obtain an intermediate clustering result; Obtain the electrical distance modularity indicator of the intermediate clustering result; Adjust the target number, and return to the step of clustering the nodes in the distribution network according to the target number to obtain an intermediate clustering result and continue to execute until the number of adjustments of the target number reaches a preset number; Determine the minimum value from the electrical distance modularity indicators obtained by each adjustment of the target number; Construct a similarity matrix, and based on the similarity matrix, determine a normalized Laplacian matrix; Perform eigenvalue decomposition on the Laplacian matrix, extract the target number of eigenvectors corresponding to the minimum value to form an eigenmatrix; Based on the eigenmatrix and the target number corresponding to the minimum value, cluster the nodes in the distribution network to obtain a target clustering result; Determine the target clustering result corresponding to the maximum value of the comprehensive performance indicator as the clustering result.

3. The method according to claim 2, wherein The calculation formula for the electrical distance modularity indicator of the distribution network includes: ； Among them, is the modularity index of the electrical distance of the distribution network, represents the degree of node i in the distribution network, that is, the sum of the edge weights of all edges connected to node i, represents the sum of the edge weights of the entire distribution network, represents the edge weight between node i and node j. If the two nodes are connected, it is 1; otherwise, it is 0. is a 0-1 matrix. If node i and node j belong to the same cluster, then δ(i,j)=1; otherwise, δ(i,j)=0.

4. The method according to claim 2, characterized in that, The calculation formula for the active power balance indicator of the distribution network includes: ； ； Among them, is the active power balance degree of cluster c in the distribution network, is the active power balance degree index of the distribution network, T is the total number of time periods in the entire photovoltaic output evaluation process, is the net power of cluster c at time point t, N c is the number of clusters in the distribution network, and M is the set of clusters in the distribution network.

5. The method according to claim 2, characterized in that, The calculation formula for the capacity matching indicator of the distribution network includes: ； Among them, is the capacity matching degree index of the distribution network, represents the sum of the maximum active power outputs of all photovoltaic nodes in the hth cluster, that is, the total active power of the photovoltaic nodes in the cluster under the maximum power generation condition, represents the sum of the maximum active power of the load nodes in the hth cluster, that is, the total active power of all loads in the cluster under the maximum demand condition, and m is the number of clusters in the distribution network.

6. The method according to claim 2, characterized in that, The calculation formula for the reactive power compensation indicator of the distribution network includes: ； Among them, is the reactive power compensation index of the distribution network, is the reactive power compensation amount within cluster i in the distribution network, and m is the number of clusters in the distribution network.

7. A photovoltaic output evaluation device, characterized in that, The device includes: A partitioning module for clustering the nodes in the distribution network based on comprehensive performance indicators to obtain a clustering result; the comprehensive performance indicator is the sum value obtained by multiplying the electrical distance modularity indicator, active power balance indicator, capacity matching indicator, and reactive power compensation indicator of the cluster by preset weights and adding them together; the nodes include load nodes and photovoltaic nodes; An establishment module for establishing a photovoltaic output model with the objective of minimizing the amount of abandoned light and subject to power system power flow constraints, node voltage constraints, branch power flow constraints, and injection power constraints based on the clustering result; A determination module for determining the evaluation result of the nodes based on the photovoltaic output model.

8. A computer device, comprising a memory and a processor, the memory storing a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the method according to any one of claims 1 to 6.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.

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