Energy storage candidate node identification method and device, electronic equipment and storage medium

Abstract

The invention discloses an energy storage candidate node identification method and device, electronic equipment and a storage medium, and the energy storage candidate node identification method proposes an energy storage candidate node identification method based on graph theory centrality analysis and operation load data fusion, and is suitable for scientific site selection configuration of power grid side energy storage. According to the method, on the basis of a power grid topological structure and a historical power flow load rate, a set of energy storage candidate node identification process which is automatic, quantitative and high in interpretability is formed by constructing a load rate weighted graph, calculating node centrality and fusing electrical operation indexes, so that scientific deployment of power grid side energy storage is supported, the calculation complexity is reduced, and the power grid side energy storage efficiency is improved. The identification efficiency of the energy storage site selection strategy is improved; the method is suitable for any-scale and complex-structure power grids, and the problem of high locality in the prior art is solved; according to the invention, the expandability is improved; the image table is visually presented, so that the visualization is strong, and the intuitive interpretation is good; the method is easy to integrate and has a general graph model integration capability.

Description

Technical Field

[0001] The present invention relates to the technical field of power system planning and intelligent optimization, and in particular to a method, device, equipment and storage medium for identifying candidate energy storage nodes. Background Art

[0002] With the integration of large-scale renewable energy and the increasing decentralization of source and load distribution, the demand for energy storage devices in power systems continues to grow. Grid-side energy storage plays a key role in peak and frequency regulation, delaying transmission expansion, and improving system resilience. However, current energy storage configuration decisions generally rely on experience or single-point power flow analysis, lacking a systematic and automated site selection mechanism, making it difficult to fully tap the grid-level comprehensive value of energy storage. Energy storage site selection is primarily achieved through the following methods: empirical site placement based on "load concentration" or "voltage-exceeding nodes"; power flow sensitivity analysis to quantify the degree to which injection into each node improves target indicators (such as voltage and frequency); and heuristic algorithms (such as PSO and GA) to optimize energy storage site selection and capacity. As can be seen, due to the lack of consideration of the overall structural characteristics of the network, there are deviations from the global optimality, resulting in strong localization and low efficiency in site selection strategies. These strategies also rely heavily on power flow simulations for specific operating conditions and lack the use of historical operational data. Sensitivity analysis is computationally complex, making it unsuitable for large-scale node scanning. Graph integration with central control systems is difficult, and the lack of general graph model integration capabilities limits real-time performance and scalability. Summary of the Invention

[0003] On the one hand, the present application provides a method for identifying candidate energy storage nodes, which is used to solve the technical problems that existing energy storage configuration decisions lack systematic and automated site selection mechanisms, are highly complex, inefficient, poorly scalable, and lack the ability to integrate general graph models.

[0004] This application is implemented through the following scheme:

[0005] A method for identifying candidate energy storage nodes, comprising the steps of:

[0006] S1. The power grid structure is topologically transformed into an undirected graph to obtain a power grid topology structure, and the operating data in the power grid topology structure is calculated to obtain a historical power flow load rate of each branch;

[0007] S2. Based on the grid topology and historical power flow load rates, a load rate weighted graph model is constructed. The weight of each edge in the load rate weighted graph model is the inverse of the historical power flow load rate of the corresponding branch.

[0008] S3. Calculate the graph theory centrality index of nodes in the load rate weighted graph model to measure the importance of power grid nodes in the network, including proximity centrality and betweenness centrality. The proximity centrality represents the inverse of the average distance of the shortest path from a node to all other nodes, and the betweenness centrality represents the proportion of the node appearing in all shortest paths.

[0009] S4. Construct physical layer features based on the load rate weighted undirected graph model integrating the electrical operation indicators of the power grid, including power flow intersection intensity and short-term voltage / frequency sensitivity indicators;

[0010] S5. Predict the future load evolution trend indicators for each node based on the historical flow load rate of each branch. Areas with rapid load growth and potential bottlenecks will be prioritized as candidate locations for early deployment of energy storage.

[0011] S6. Construct a multi-factor comprehensive scoring function based on proximity centrality, betweenness centrality, flow intersection strength, short-term voltage / frequency sensitivity index, and load evolution trend index, and screen candidate energy storage nodes based on the calculation results of the multi-factor comprehensive scoring function;

[0012] S7. Output the selected energy storage candidate nodes through images and tables for visual presentation.

[0013] Furthermore, the step S1 specifically includes the steps of:

[0014] S11. Node and branch extraction: Assume that the power grid topology is an undirected graph G = (V, E), where: V = v1, v2, ..., v n Represents nodes in the power grid, including busbars and substation nodes; E = e ij |(v i ,v j )∈V×V represents the line or transformer connection branch between nodes;

[0015] S12. Extraction of branch flow data: For each branch e ij , its load rate data at T time steps in the past year is known: The time step is day, hour or 15 minutes;

[0016] S13. Calculate the annual average load rate for each branch:

[0017]

[0018] Among them, if there are multiple loops in the branch, the equivalent load rate is taken.

[0019] Furthermore, the step S2 specifically includes the steps of:

[0020] S21. Graph structure construction: construct an initial undirected graph G based on the nodes V and branches E of the power grid topology;

[0021] S22. Define edge weights: To make high-load lines shorter in the path, the inverse of the historical flow load rate of the corresponding branch is used as the edge weight:

[0022]

[0023] Where, ò = 0.01 to avoid division by 0. If reactance / impedance needs to be considered, it can be expanded to:

[0024]

[0025] Among them, Z ij represents the branch impedance between node i and node j.

[0026] Furthermore, in step S3, the proximity centrality represents the inverse of the average distance of the shortest path from a node to all other nodes, specifically:

[0027]

[0028] Where: d(v,u) is the edge weight w ij The shortest path length, v represents the target node to be calculated for centrality, and u represents all other nodes in the graph except v;

[0029] The betweenness centrality represents the proportion of nodes appearing in all shortest paths:

[0030]

[0031] Among them, σ st is the number of shortest paths from node s to node t; σ st (v) is the number of paths that pass through node v.

[0032] Furthermore, in step S4, the power flow intersection intensity is obtained by calculating the sum of the absolute values ​​of the power flows of the branches connected to the node v in T time steps:

[0033]

[0034] Short-time voltage / frequency sensitivity index S v The sensitivity coefficient of node voltage or frequency to the injected power is obtained through short-time disturbance simulation (PSD-BPA, DIgSILENT).

[0035] Furthermore, the step S5 specifically includes the steps of:

[0036] S51. Definition of load growth rate index: Based on historical load data, assume that the maximum annual load of node v in year t is (past five years), the future forecast value is

[0037] S52. Define the annual growth rate of load (compound growth rate CAGR), including:

[0038] The historical growth rates are:

[0039]

[0040] The future forecast growth rate is:

[0041]

[0042] The total growth rate is (weighting optional):

[0043]

[0044] Among them, λ = 0.7, emphasizing future growth;

[0045] The total growth rate r for all nodes v v Normalize to form the priority factor:

[0046]

[0047] Where max(r) and min(r) are the maximum and minimum growth rates of all nodes v;

[0048] or,

[0049] Taking the absolute forecast growth as the priority factor (rather than the proportion):

[0050]

[0051] Furthermore, the step S6 specifically includes the steps of:

[0052] S61, Betweenness Centrality C B (v) Closeness centrality C C (v) Tidal current intersection intensity P cross (v) Short-time voltage / frequency sensitivity index S v Normalize them separately:

[0053]

[0054] Among them, X(v) represents a certain original indicator value of node v, such as graph centrality, node load rate, etc.; X max Indicates the maximum value X of this indicator among all nodes max=max u∈V X(u);X min Indicates the minimum value X of this indicator among all nodes min =min u∈V X(u).

[0055] S62, according to the betweenness centrality C B (v) Closeness centrality C C (v) Tidal current intersection intensity P cross (v) Short-time voltage / frequency sensitivity index S v , load evolution trend index to construct a multi-factor comprehensive scoring function:

[0056]

[0057] Among them, α is the betweenness importance weight; β is the proximity influence weight; γ is the traffic intersection weight; δ is the fault sensitivity weight (if the fault impact is not considered, it can be set to 0); μ is the weight parameter of the newly added factor, which is adjusted through sensitivity analysis (for example, μ = 0.2 means giving a 20% weight to the growth rate);

[0058] S63. Set a threshold θ and select all nodes with Score(v)>θ as energy storage candidate nodes; or sort the nodes from high to low by Score(v) and select the top N nodes as energy storage candidate nodes.

[0059] Another embodiment of the present application further provides a device for identifying candidate energy storage nodes, including:

[0060] The grid structure and operation data preprocessing module is used to convert the grid structure into an undirected graph to obtain the grid topology structure, and calculate the operation data in the grid topology structure to obtain the historical flow load rate of each branch;

[0061] A load-rate weighted undirected graph model construction module is used to construct a load-rate weighted graph model based on the grid topology and historical power flow load rates. The weight of each edge in the load-rate weighted graph model is the inverse of the historical power flow load rate of the corresponding branch.

[0062] A node graph centrality index calculation module is used to calculate the node graph centrality index in the load rate weighted graph model to measure the importance of power grid nodes in the network, including proximity centrality and betweenness centrality. The proximity centrality represents the inverse of the average distance of the shortest path from a node to all other nodes, and the betweenness centrality represents the proportion of a node appearing in all shortest paths.

[0063] The physical layer feature construction module is used to construct physical layer features based on the load rate weighted undirected graph model and the electrical operation indicators of the power grid, including the flow intersection strength and short-time voltage / frequency sensitivity indicators;

[0064] The load evolution trend indicator calculation module is used to predict the future load evolution trend indicators of each node based on the historical flow load rate of each branch. Among them, areas with rapid node load growth and potential bottlenecks in the future will be selected as priority candidates for early deployment of energy storage;

[0065] The candidate node screening module is used to construct a multi-factor comprehensive scoring function based on proximity centrality, betweenness centrality, power flow intersection strength, short-term voltage / frequency sensitivity index, and load evolution trend index, and to screen candidate energy storage nodes based on the calculation results of the multi-factor comprehensive scoring function;

[0066] The output and visualization presentation module is used to output the selected energy storage candidate nodes for visualization through images and tables.

[0067] Another preferred embodiment of the present application further provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the energy storage candidate node identification method when executing the computer program.

[0068] Another preferred embodiment of the present application further provides a storage medium, which includes a stored program, and when the program is executed, controls the device where the storage medium is located to execute the steps of the energy storage candidate node identification method.

[0069] Compared with the existing technology, this application has the following beneficial effects:

[0070] The present application provides a method, device, electronic device, and storage medium for identifying candidate energy storage nodes. The method proposes a method for identifying candidate energy storage nodes based on graph centrality analysis and operational load data fusion, which is suitable for the scientific site selection and configuration of grid-side energy storage. The method is based on the grid topology and historical flow load rate, uses flow load rate data to construct a weighted graph model, calculates node centrality based on a graph centrality algorithm to implement node importance assessment, and is no longer limited to current bottlenecks but can predict future peak areas in advance. A multidimensional scoring function is constructed to integrate electrical characteristics and network structure characteristics, and a comprehensive scoring function construction and screening mechanism is constructed to quickly screen candidate energy storage deployment nodes and visualize them. This supports large-scale deployment planning and forms a set of automated, quantifiable, and highly interpretable energy storage candidate node identification processes. This method solves the problems of existing site selection strategies such as strong locality, low efficiency, and lack of general graph model integration capabilities, thereby supporting the scientific deployment of new grid-side energy storage. The main advantages include the following:

[0071] (1) Fast: Only one-time historical flow data is required, without multiple rounds of flow sensitivity simulation, which reduces the computational complexity and improves the efficiency of energy storage site selection strategy identification;

[0072] (2) Strong versatility: It is applicable to power grids of any scale and complex structure, overcoming the problem of strong localization of existing technologies and improving scalability;

[0073] (3) Strong visualization: It is presented visually through images and tables, and the graph structure naturally supports heat maps and has good intuitive interpretation;

[0074] (4) Easy to integrate: It is suitable as a pre-screening module for energy storage optimization configuration systems and has the ability to integrate general graphical models.

[0075] In addition to the above-described purposes, features and advantages, the present application also has other purposes, features and advantages. The present application will be further described in detail below with reference to the drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0076] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0077] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, a person skilled in the art can derive other drawings based on these drawings without inventive work, among which:

[0078] Figure 1 This is a flow chart of a method for identifying candidate energy storage nodes according to a preferred embodiment of the present application;

[0079] Figure 2 This is a schematic diagram of a module of an energy storage candidate node identification device according to a preferred embodiment of the present application;

[0080] Figure 3 This is a schematic block diagram of an electronic device according to a preferred embodiment of the present application;

[0081] Figure 4 It is a diagram of the internal structure of a computer device according to a preferred embodiment of the present application. DETAILED DESCRIPTION

[0082] It should be understood that the specific embodiments described herein are merely used to explain the technical solutions of the present application and are not intended to limit the present application.

[0083] In order to better understand the technical solution of the present application, a detailed description will be given below in conjunction with the accompanying drawings and specific implementation methods.

[0084] Definitions of Abbreviations and Key Terms:

[0085] Energy storage: refers to new electrochemical energy storage devices, such as lithium-ion batteries, flow batteries, sodium-ion batteries, etc., which are used for peak shaving, valley filling, frequency regulation and voltage regulation in power systems.

[0086] Line Loading Rate: The ratio of the actual flow of a power line to its rated capacity.

[0087] Centrality: An indicator used in graph theory to measure the importance of power grid nodes in the network. Commonly used indicators include Betweenness and Closeness.

[0088] Weighted Graph: The edges in the network are assigned numerical weights, reflecting properties such as line load and impedance.

[0089] GNN: Graph Neural Network, which is not yet involved in this method, but is a subsequent scalable path.

[0090] Score(v): A comprehensive scoring function for candidate energy storage sites, measuring their priority in site selection.

[0091] It should be noted that the execution subject of this embodiment can be a computing service device with data processing, network communication, and program execution capabilities, such as a tablet computer, personal computer, mobile phone, etc., or an energy storage candidate node identification device capable of performing the above functions. The following describes this embodiment and the following embodiments using the energy storage candidate node identification device as the execution subject.

[0092] like Figure 1 As shown, a preferred embodiment of the present application provides a method for identifying candidate energy storage nodes, comprising the steps of:

[0093] S1. Preprocessing of grid structure and operation data: The grid structure is topologically transformed into an undirected graph to obtain the grid topology. The operation data in the grid topology is calculated to obtain the historical power flow load rate of each branch.

[0094] S2. Construct a load-rate weighted undirected graph model: Based on the grid topology and historical power flow load rates, construct a load-rate weighted graph model. The weight of each edge in the load-rate weighted graph model is the inverse of the historical power flow load rate of the corresponding branch.

[0095] S3. Calculation of Node Graph Centrality Indicators: Calculation of node graph centrality indicators in the load-rate weighted graph model to measure the importance of power grid nodes in the network, including proximity centrality and betweenness centrality. The proximity centrality represents the inverse of the average distance of the shortest path from a node to all other nodes, and the betweenness centrality represents the proportion of a node appearing in all shortest paths.

[0096] S4. Construct physical-level features by integrating electrical operation indicators: Based on the load-rate weighted undirected graph model, the electrical operation indicators of the power grid are integrated to construct physical-level features, including power flow intersection strength and short-term voltage / frequency sensitivity indicators.

[0097] S5. Load evolution trend indicators: Based on the historical flow load rate of each branch, the future load evolution trend indicators of each node are predicted. Among them, areas with rapid node load growth and potential bottlenecks in the future will be selected as priority candidates for early deployment of energy storage;

[0098] S6. Construct a comprehensive scoring function and screen candidate nodes: Construct a multi-factor comprehensive scoring function based on proximity centrality, betweenness centrality, flow intersection strength, short-term voltage / frequency sensitivity index, and load evolution trend index. Screen candidate energy storage nodes based on the calculation results of the multi-factor comprehensive scoring function.

[0099] S7. Output and visualization: The selected energy storage candidate nodes are output and visualized through images and tables.

[0100] This embodiment provides a method for identifying candidate energy storage nodes. The method proposes a method for identifying candidate energy storage nodes based on graph centrality analysis and the fusion of operating load data. The method is suitable for the scientific site selection and configuration of grid-side energy storage. Based on the grid topology and historical power flow load rate, the method uses power flow load rate data to construct a weighted graph model. Node centrality is calculated based on a graph centrality algorithm to implement node importance assessment. Furthermore, the method is no longer limited to current bottlenecks but can predict future peak areas in advance. A multidimensional scoring function is constructed to integrate electrical characteristics and network structure features. The construction and screening mechanism of the comprehensive scoring function is used to quickly screen candidate energy storage deployment nodes and visualize them. This method supports large-scale deployment planning and forms an automated, quantifiable, and highly interpretable energy storage candidate node identification process. This method solves the problems of existing site selection strategies, such as strong locality, low efficiency, and lack of general graph model integration capabilities, thereby supporting the scientific deployment of new grid-side energy storage. The method has the following main advantages:

[0101] (1) Fast: Only one-time historical flow data is required, without multiple rounds of flow sensitivity simulation, which reduces the computational complexity and improves the efficiency of energy storage site selection strategy identification;

[0102] (2) Strong versatility: It is applicable to power grids of any scale and complex structure, overcoming the problem of strong localization of existing technologies and improving scalability;

[0103] (3) Strong visualization: It is presented visually through images and tables, and the graph structure naturally supports heat maps and has good intuitive interpretation;

[0104] (4) Easy to integrate: It is suitable as a pre-screening module for energy storage optimization configuration systems and has the ability to integrate general graphical models.

[0105] In a preferred embodiment of the present application, step S1 specifically includes the following steps:

[0106] S11. Node and branch extraction: Assume that the power grid topology is an undirected graph G = (V, E), where: V = v1, v2, ..., v n Represents nodes in the power grid, including busbars and substation nodes; E = e ij |(v i ,v j )∈V×V represents the line or transformer connection branch between nodes;

[0107] S12. Extraction of branch flow data: For each branch e ij , its load rate data at T time steps in the past year is known: The time step is day, hour or 15 minutes;

[0108] S13. Calculate the annual average load rate for each branch:

[0109]

[0110] Among them, if there are multiple loops in the branch, the equivalent load rate is taken.

[0111] Steps S11 to S13 of this embodiment describe a specific process for converting the grid structure into an undirected graph to obtain a grid topology, and calculating the operating data in the grid topology to obtain the historical power flow load rate of each branch. This includes extracting nodes and branches to construct a grid topology diagram, extracting branch power flow data, and calculating the annual average load rate of each branch. The benefits and purposes of this process include: the average load rate can capture long-standing power flow bottlenecks; a higher value indicates a more "busy and congested" branch, and can be used to identify areas with heavy operating burdens and greater need for energy storage regulation.

[0112] In a preferred embodiment of the present application, step S2 specifically includes the following steps:

[0113] S21. Graph structure construction: construct an initial undirected graph G based on the nodes V and branches E of the power grid topology;

[0114] S22. Define edge weights: To make high-load lines shorter in the path, the inverse of the historical flow load rate of the corresponding branch is used as the edge weight:

[0115]

[0116] Where, ò = 0.01 to avoid division by 0. If reactance / impedance needs to be considered, it can be expanded to:

[0117]

[0118] Among them, Z ij represents the branch impedance between node i and node j.

[0119] Steps S21 to S22 of this embodiment provide a specific process for constructing a load rate weighted graph model based on the grid topology and historical flow load rate, such as graph structure construction and edge weight definition. The benefits and purposes of this process include: reflecting the "resistance" or "criticality" between nodes. The higher the branch load rate, the smaller its weight and the "shorter" the path, making the high-load rate path a critical path.

[0120] In a preferred embodiment of the present application, in step S3, the proximity centrality represents the inverse of the average distance of the shortest path from node v to all other nodes, specifically:

[0121]

[0122] Where: d(v,u) is the edge weight w ij The shortest path length, v represents the target node to be calculated for centrality, and u represents all other nodes in the graph except v;

[0123] The betweenness centrality represents the proportion of node v in all shortest paths:

[0124]

[0125] Among them, σ st is the number of shortest paths from node s to node t; σ st (v) is the number of paths that pass through node v.

[0126] This embodiment provides specific calculation formulas for the proximity centrality and betweenness centrality. For example, proximity centrality is represented by the inverse of the average distance of the shortest paths from node v to all other nodes, and betweenness centrality is represented by the proportion of node v appearing in all shortest paths. The benefits and purposes of these formulas include: proximity centrality reflects the service coverage and global regulation capabilities of energy storage nodes, and betweenness centrality reflects the flow channel control and fault response capabilities of energy storage nodes.

[0127] In a preferred embodiment of the present application, in step S4, the power flow intersection intensity is obtained by summing the absolute values ​​of the power flows of the branches connected to the node v in T time steps:

[0128]

[0129] Short-time voltage / frequency sensitivity index S v The sensitivity coefficient of node voltage or frequency to the injected power is obtained through short-time disturbance simulation (PSD-BPA, DIgSILENT).

[0130] This embodiment provides a specific calculation process for constructing physical-level characteristics by integrating the electrical operation indicators of the power grid based on a load rate weighted undirected graph model, such as the flow intersection strength and short-time voltage / frequency sensitivity index. Its benefits and purposes include: the flow intersection strength measures the actual flow activity and power regulation hub properties of the node, and the short-time voltage / frequency sensitivity index identifies the node's response capability and regulation sensitivity to disturbances / support, thereby integrating electrical operation characteristics into graph theory analysis.

[0131] In a preferred embodiment of the present application, step S5 specifically includes the following steps:

[0132] S51. Definition of load growth rate index: Based on historical load data, assume that the maximum annual load of node v in year t is (past five years), the future forecast value is

[0133] S52. Define the annual growth rate of load (compound growth rate CAGR), including:

[0134] The historical growth rates are:

[0135]

[0136] The future forecast growth rate is:

[0137]

[0138] The total growth rate is (weighting optional):

[0139]

[0140] Among them, λ = 0.7, emphasizing future growth;

[0141] The total growth rate r for all nodes v v Normalize to form the priority factor:

[0142]

[0143] Where max(r) and min(r) are the maximum and minimum growth rates of all nodes v;

[0144] or,

[0145] Taking the absolute forecast growth as the priority factor (rather than the proportion):

[0146]

[0147] Steps S51 to S52 of this embodiment provide a specific process for predicting the future load evolution trend index of each node based on the historical flow load rate of each branch, including the definition of the load growth rate index, the definition of the annual load growth rate, the annual load growth rate is based on the historical growth rate and the future predicted growth rate to obtain the total growth rate, and the total growth rate r of all nodes v v Normalization forms a priority factor, with benefits and purposes including: introducing a dynamic, future-oriented perspective that focuses not only on the current operating conditions of the grid but also on the evolutionary trends of medium- and long-term load demand growth, thereby enabling a forward-looking, proactive, and planning-guided energy storage deployment strategy.

[0148] In a preferred embodiment of the present application, step S6 specifically includes the following steps:

[0149] S61, Betweenness Centrality C B (v) Closeness centrality C C (v) Tidal current intersection intensity P cross (v) Short-time voltage / frequency sensitivity index S v Normalize them separately:

[0150]

[0151] Among them, X(v) represents a certain original indicator value of node v, such as graph centrality, node load rate, etc.; X max Indicates the maximum value X of this indicator among all nodes max =max u∈V X(u);X min Indicates the minimum value X of this indicator among all nodes min =min u∈V X(u);

[0152] S62, according to the betweenness centrality C B (v) Closeness centrality C C (v) Tidal current intersection intensity P cross (v) Short-time voltage / frequency sensitivity index S v , load evolution trend index to construct a multi-factor comprehensive scoring function:

[0153]

[0154] Among them, α is the betweenness importance weight; β is the proximity influence weight; γ is the traffic intersection weight; δ is the fault sensitivity weight (if the fault impact is not considered, it can be set to 0); μ is the weight parameter of the newly added factor, which is adjusted through sensitivity analysis (for example, μ = 0.2 means giving a 20% weight to the growth rate);

[0155] S63. Set a threshold θ and select all nodes with Score(v)>θ as energy storage candidate nodes; or sort the nodes from high to low by Score(v) and select the top N nodes as energy storage candidate nodes.

[0156] Steps S61-S63 of this embodiment describe the specific process of constructing a multi-factor comprehensive scoring function and screening candidate energy storage nodes based on the results of the multi-factor comprehensive scoring function. This includes normalizing relevant parameters, constructing the multi-factor comprehensive scoring function, and developing two logics for energy storage candidate nodes. This approach offers the following benefits and objectives: a model for identifying candidate energy storage nodes that integrates topological features, electrical operating characteristics, and load trends. This approach offers significant advantages, including multi-dimensional information fusion, adjustable and interpretable functionality, and support for project implementation.

[0157] In a preferred embodiment of the present application, step S7 specifically includes:

[0158] 1. Layer heat map drawing: Map the scores to node color and size, and use visualization tools to draw a power grid diagram; the darker the node color, the higher the energy storage priority;

[0159] 2. Candidate point output report: The output format includes node number, proximity centrality, betweenness centrality, flow intersection strength, multi-factor comprehensive scoring function score, and whether it is recommended;

[0160] 3. Linkage with optimization configuration: The output candidate point set can be used as input constraints in subsequent capacity configuration optimization models (such as MILP and NSGA-II).

[0161] This embodiment uses a heat map to represent energy storage priority. The graph structure naturally supports heat maps and has good intuitive interpretation.

[0162] like Figure 2 As shown, another preferred embodiment of the present application further provides an energy storage candidate node identification device, comprising:

[0163] The grid structure and operation data preprocessing module is used to convert the grid structure into an undirected graph to obtain the grid topology structure, and calculate the operation data in the grid topology structure to obtain the historical flow load rate of each branch;

[0164] A load-rate weighted undirected graph model construction module is used to construct a load-rate weighted graph model based on the grid topology and historical power flow load rates. The weight of each edge in the load-rate weighted graph model is the inverse of the historical power flow load rate of the corresponding branch.

[0165] A node graph centrality index calculation module is used to calculate the node graph centrality index in the load rate weighted graph model to measure the importance of power grid nodes in the network, including proximity centrality and betweenness centrality. The proximity centrality represents the inverse of the average distance of the shortest path from a node to all other nodes, and the betweenness centrality represents the proportion of a node appearing in all shortest paths.

[0166] The physical layer feature construction module is used to construct physical layer features based on the load rate weighted undirected graph model and the electrical operation indicators of the power grid, including the flow intersection strength and short-time voltage / frequency sensitivity indicators;

[0167] The load evolution trend indicator calculation module is used to predict the future load evolution trend indicators of each node based on the historical flow load rate of each branch. Among them, areas with rapid node load growth and potential bottlenecks in the future will be selected as priority candidates for early deployment of energy storage;

[0168] The candidate node screening module is used to construct a multi-factor comprehensive scoring function based on proximity centrality, betweenness centrality, power flow intersection strength, short-term voltage / frequency sensitivity index, and load evolution trend index, and to screen candidate energy storage nodes based on the calculation results of the multi-factor comprehensive scoring function;

[0169] The output and visualization presentation module is used to output the selected energy storage candidate nodes for visualization through images and tables.

[0170] The energy storage candidate node identification device provided in this application adopts the energy storage candidate node identification method in the above-mentioned embodiment, which can solve the technical problems that the existing energy storage configuration decision-making lacks a systematic and automated site selection mechanism, is highly complex, has low efficiency and poor scalability, and does not have the ability to integrate a general graph model. Compared with the existing technology, the beneficial effects of the energy storage candidate node identification device provided in this application are the same as the beneficial effects of the energy storage candidate node identification method provided in the above-mentioned embodiment, and the other technical features of the energy storage candidate node identification device are the same as the features disclosed in the above-mentioned embodiment method, which will not be repeated here.

[0171] like Figure 3 As shown, a preferred embodiment of the present application also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the energy storage candidate node identification method in the above embodiment when executing the computer program.

[0172] The application provides an electronic device that uses the energy storage candidate node identification method in the above-mentioned embodiment to solve the technical problems of existing energy storage configuration decisions, such as the lack of systematic and automated site selection mechanisms, high complexity, low efficiency and poor scalability, and the lack of general graph model integration capabilities. Compared with the existing technology, the beneficial effects of the electronic device provided by this application are the same as the beneficial effects of the energy storage candidate node identification method provided by the above-mentioned embodiment, and the other technical features of the electronic device are the same as the features disclosed in the above-mentioned embodiment method, which will not be repeated here.

[0173] like Figure 4 As shown, the preferred embodiment of the present application further provides a computer device, which can be a terminal or a liveness detection server, and its internal structure diagram can be as shown in FIG. Figure 4 As shown. The computer device includes a processor, a memory and a network interface connected via a system bus. 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 and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The network interface of the computer device is used to communicate with other external computer devices via a network connection. When the computer program is executed by the processor, the steps of the above-mentioned energy storage candidate node identification method are implemented.

[0174] Those skilled in the art will understand that Figure 4 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0175] The computer device provided in the application, using the energy storage candidate node identification method described in the above-mentioned embodiments, can address the technical issues of existing energy storage configuration decision-making, which lack a systematic and automated site selection mechanism, are highly complex, inefficient, and poorly scalable, and lack the ability to integrate general graph models. Compared to the prior art, the beneficial effects of the computer device provided in this application are the same as those of the energy storage candidate node identification method described in the above-mentioned embodiments, and the other technical features of the electronic device are the same as those disclosed in the above-mentioned embodiments, which are not further described here.

[0176] A preferred embodiment of the present application further provides a storage medium, which includes a stored program, and when the program is executed, controls the device where the storage medium is located to execute the steps of the energy storage candidate node identification method in the above embodiment.

[0177] It should be noted that the steps shown in the flowcharts of the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and that, although a logical order is shown in the flowcharts, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0178] If the functions described in the method of this embodiment are implemented in the form of a software functional unit and sold or used as an independent product, they can be stored in a storage medium readable by one or more computing devices. Based on this understanding, the part of the embodiment of the present application that contributes to the prior art or the part of the technical solution can be embodied in the form of a software product, which is stored in a storage medium and includes a number of instructions for enabling a computing device (which can be a personal computer, server, mobile computing device or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk and other media that can store program code.

[0179] Those skilled in the art will appreciate that the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the application can adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the application can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code. The scheme in the embodiment of the present application can be implemented in various computer languages, for example, object-oriented programming language Java and literal translation scripting language JavaScript, etc.

[0180] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the steps in the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0181] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0182] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0183] The present application also provides a computer program product, including a computer program, which implements the steps of the above-mentioned energy storage candidate node identification method when executed by a processor.

[0184] The computer program product provided in this application can address the technical issues of existing energy storage configuration decision-making, which lack a systematic and automated site selection mechanism, are highly complex, inefficient, and poorly scalable, and lack the ability to integrate general graph models. Compared to existing technologies, the beneficial effects of the computer program product provided in this application are similar to those of the energy storage candidate node identification method provided in the aforementioned embodiments, and are not further elaborated here.

[0185] Although the preferred embodiments of the present application have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present application.

[0186] Obviously, those skilled in the art may make various changes and modifications to this application without departing from the spirit and scope of this application. Thus, if these modifications and variations of this application fall within the scope of the claims of this application and their equivalents, this application is intended to include these modifications and variations.

Claims

1. A method for identifying candidate energy storage nodes, characterized in that: Including steps: S1. The power grid structure is topologically transformed into an undirected graph to obtain a power grid topology structure, and the operating data in the power grid topology structure is calculated to obtain a historical power flow load rate of each branch; S2. Based on the grid topology and historical power flow load rates, a load rate weighted graph model is constructed. The weight of each edge in the load rate weighted graph model is the inverse of the historical power flow load rate of the corresponding branch. S3. Calculate the graph theory centrality index of nodes in the load rate weighted graph model to measure the importance of power grid nodes in the network, including proximity centrality and betweenness centrality. The proximity centrality represents the inverse of the average distance of the shortest path from a node to all other nodes, and the betweenness centrality represents the proportion of the node appearing in all shortest paths. S4. Construct physical layer features based on the load rate weighted undirected graph model integrating the electrical operation indicators of the power grid, including power flow intersection intensity and short-term voltage / frequency sensitivity indicators; S5. Predict the future load evolution trend indicators for each node based on the historical flow load rate of each branch. Areas with rapid load growth and potential bottlenecks will be prioritized as candidate locations for early deployment of energy storage. S6. Construct a multi-factor comprehensive scoring function based on proximity centrality, betweenness centrality, flow intersection strength, short-term voltage / frequency sensitivity index, and load evolution trend index, and screen candidate energy storage nodes based on the calculation results of the multi-factor comprehensive scoring function; S7. Output the selected energy storage candidate nodes through images and tables for visual presentation.

2. The energy storage candidate node identification method according to claim 1, characterized in that: The step S1 specifically includes the following steps: S11. Node and branch extraction: Assume that the power grid topology is an undirected graph G = (V, E), where: V = v1, v2, ..., v n Represents nodes in the power grid, including busbars and substation nodes; E = e ij |(v i ,v j )∈V×V represents the line or transformer connection branch between nodes; S12. Extraction of branch flow data: For each branch e ij , its load rate data at T time steps in the past year is known: The time step is day, hour or 15 minutes; S13. Calculate the annual average load rate for each branch:

3. The energy storage candidate node identification method according to claim 2, characterized in that: The step S2 specifically includes the following steps: S21. Graph structure construction: construct an initial undirected graph G based on the nodes V and branches E of the power grid topology; S22. Define edge weights: To make high-load lines shorter in the path, the inverse of the historical flow load rate of the corresponding branch is used as the edge weight: Where, ò = 0.

01. If reactance / impedance needs to be considered, it can be expanded to: Among them, Z ij represents the branch impedance between node i and node j.

4. The energy storage candidate node identification method according to claim 3, characterized in that: In step S3, the proximity centrality represents the inverse of the average distance of the shortest path from a node to all other nodes, specifically: Where: d(v,u) is the edge weight w ij The shortest path length, v represents the target node to be calculated for centrality, and u represents all other nodes in the graph except v; The betweenness centrality represents the proportion of nodes appearing in all shortest paths: Among them, σ st is the number of shortest paths from node s to node t; σ st (v) is the number of paths that pass through node v.

5. The energy storage candidate node identification method according to claim 4, characterized in that: In step S4, the flow intersection intensity is obtained by summing the absolute values ​​of the flow of the branches connected to the node v in T time steps: Short-time voltage / frequency sensitivity index S v The sensitivity coefficient of node voltage or frequency to injected power is obtained through short-time disturbance simulation.

6. The energy storage candidate node identification method according to claim 5, characterized in that: The step S5 specifically includes the following steps: S51. Definition of load growth rate index: Based on historical load data, assume that the maximum annual load of node v in year t is The future forecast value is S52. Define the annual load growth rate, including: The historical growth rates are: The future forecast growth rate is: The total growth rate is: Among them, λ = 0.7, emphasizing future growth; The total growth rate r for all nodes v v Normalize to form the priority factor: Where max(r) and min(r) are the maximum and minimum growth rates of all nodes v; or, Taking the absolute forecast growth as the priority factor:

7. The energy storage candidate node identification method according to claim 6, characterized in that: The step S6 specifically includes the following steps: S61, Betweenness Centrality C B (v) Closeness centrality C C (v) Tidal current intersection intensity P cross (v) Short-time voltage / frequency sensitivity index S v Normalize them separately: Among them, X(v) represents the original index value of node v, X max Indicates the maximum value of the indicator among all nodes, X min Indicates the minimum value of the indicator among all nodes; S62, according to the betweenness centrality C B (v) Closeness centrality C C (v) Tidal current intersection intensity P cross (v) Short-time voltage / frequency sensitivity index S v , load evolution trend index to construct a multi-factor comprehensive scoring function: Among them, α is the betweenness importance weight; β is the proximity influence weight; γ is the flow intersection weight; δ is the fault sensitivity weight, and μ is the weight parameter of the newly added factor, which is adjusted through sensitivity analysis; S63. Set a threshold θ and select all nodes with Score(v)>θ as energy storage candidate nodes; or sort the nodes from high to low by Score(v) and select the top N nodes as energy storage candidate nodes.

8. A device for identifying candidate energy storage nodes, characterized in that: include: The grid structure and operation data preprocessing module is used to convert the grid structure into an undirected graph to obtain the grid topology structure, and calculate the operation data in the grid topology structure to obtain the historical flow load rate of each branch; A load-rate weighted undirected graph model construction module is used to construct a load-rate weighted graph model based on the grid topology and historical power flow load rates. The weight of each edge in the load-rate weighted graph model is the inverse of the historical power flow load rate of the corresponding branch. A node graph centrality index calculation module is used to calculate the node graph centrality index in the load rate weighted graph model to measure the importance of power grid nodes in the network, including proximity centrality and betweenness centrality. The proximity centrality represents the inverse of the average distance of the shortest path from a node to all other nodes, and the betweenness centrality represents the proportion of a node appearing in all shortest paths. The physical layer feature construction module is used to construct physical layer features based on the load rate weighted undirected graph model and the electrical operation indicators of the power grid, including the flow intersection strength and short-time voltage / frequency sensitivity indicators; The load evolution trend indicator calculation module is used to predict the future load evolution trend indicators of each node based on the historical flow load rate of each branch. Among them, areas with rapid node load growth and potential bottlenecks in the future will be selected as priority candidates for early deployment of energy storage; The candidate node screening module is used to construct a multi-factor comprehensive scoring function based on proximity centrality, betweenness centrality, power flow intersection strength, short-term voltage / frequency sensitivity index, and load evolution trend index, and to screen candidate energy storage nodes based on the calculation results of the multi-factor comprehensive scoring function; The output and visualization presentation module is used to output the selected energy storage candidate nodes for visualization through images and tables.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the steps of the energy storage candidate node identification method according to any one of claims 1 to 7 are implemented.

10. A storage medium comprising a stored program, which controls a device where the storage medium is located to execute the steps of the energy storage candidate node identification method according to any one of claims 1 to 7 when the program is executed.

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