Power system topological structure optimization method and device, computer equipment and medium

Abstract

The invention relates to a power system topological structure optimization method and device, computer equipment, a computer readable storage medium and a computer program product. The method comprises the following steps: determining an optimization index of a power grid architecture according to the power grid architecture of a power system and distributed new energy power supply access data; according to the optimization index of the power grid architecture, establishing an optimization planning model of a power system topological structure; improving the ant colony algorithm by using a pheromone updating mode; and calculating and generating an optimization result of the topological structure of the power system according to the optimization planning model by using an improved ant colony algorithm. By adopting the method, the optimization result of the topological structure of the power system is calculated and generated based on the improved ant colony algorithm and the optimization planning model; the result meets the power grid operation constraint, the electric energy quality, the power supply reliability, the economical efficiency and the flexibility are remarkably improved, and meanwhile, the method adapts to distributed new energy power supply access proportion change and load dynamic change.

Description

Technical Field

[0001] The present application relates to the technical field of power systems, and in particular, to a method, device, computer device, computer-readable storage medium, and computer program product for optimizing the topological structure of a power system. Background Art

[0002] With the rapid economic development and continuous social progress, high-quality power supply is becoming increasingly important in modern society. From a macroscopic perspective, the global energy transition is accelerating, and traditional energy is gradually being transformed into clean energy, which poses new requirements for the power grid architecture. Against the backdrop of the accelerating urbanization process, the electricity demand in cities continues to grow, and the distribution of electricity loads is changing significantly. In densely populated urban areas, the requirements for the reliability and quality of power supply are extremely high, which prompts the power grid architecture planning to consider how to optimize the power supply network, reduce the risk of power outages, and improve the power quality. In addition, with the gradual liberalization of the power market, the topological structure planning of the power grid architecture also needs to take into account various factors such as market competition and cost-effectiveness.

[0003] In this context, current technologies have constructed topological analysis rules for power system networks, and then obtained real measurement data within a specific interval of the power grid. By using the threshold of bad data to determine the change in bus voltage over-limit, the topological analysis results of the entire power grid are finally obtained. However, harmonics may interfere with the extraction of negative sequence voltage components, resulting in a decrease in analysis accuracy. In other technologies, the power system grid model is abstracted into a compressed array form, and based on the previous drive array method, a whole-grid topological analysis strategy relying on GPU acceleration is proposed. However, in complex and changing power system operation scenarios, this method may need to be further optimized and improved to improve the accuracy of the analysis results.

[0004] Therefore, there is an urgent need for a method, device, computer device, computer-readable storage medium, and computer program product for optimizing the topological structure of a power system, which can solve the problems of poor power grid transmission flexibility and low power quality existing in current technologies during power grid planning and operation. Summary of the Invention

[0005] Based on this, it is necessary to provide a method, device, computer device, computer-readable storage medium, and computer program product for optimizing the topological structure of a power system, which can solve the problems of poor power grid transmission flexibility and low power quality existing in current technologies.

[0006] In a first aspect, the present application provides a method for optimizing the topological structure of a power system, including:

[0007] Determine the optimization index of the power grid architecture according to the power grid architecture of the power system and the access data of distributed new energy power sources;

[0008] Establish an optimization planning model for the power system topology structure according to the optimization indexes of the power grid architecture;

[0009] Improve the ant colony algorithm by using the pheromone update method;

[0010] Use the improved ant colony algorithm to calculate and generate the optimization result of the power system topology structure according to the optimization planning model.

[0011] In one embodiment, determining the optimization indexes of the power grid architecture according to the power grid architecture of the power system and the access data of distributed new energy power sources includes:

[0012] Divide the power grid architecture of the power system into multiple calculation sub-modules, and obtain the correlation function between any two calculation sub-modules;

[0013] Conduct boundary coordination network analysis on the power grid architecture according to the access data of distributed new energy power sources and the correlation function to obtain bus numbers;

[0014] Determine the optimization indexes of the power grid architecture according to the bus numbers and the preset node-branch calculation model.

[0015] In one embodiment, establishing an optimization planning model for the power system topology structure according to the optimization indexes of the power grid architecture includes:

[0016] Collect the real-time status data of each power device in the power grid architecture;

[0017] Adopt the wavelet transform method to clean the collected real-time status data;

[0018] Based on the cleaned real-time status data and according to the optimization indexes of the power grid architecture, establish an optimization planning model for the power system topology structure.

[0019] In one embodiment, establishing an optimization planning model for the power system topology structure includes:

[0020] Use the indexing algorithm of the graph engine to determine the connectivity relationship between any two adjacent nodes in the optimization planning model;

[0021] Assign weights to the connection edges of the nodes according to the connectivity relationship between any two adjacent nodes;

[0022] Use the breadth-first search algorithm, starting from the vertex, take the connection edge with the maximum weight as the search path, traverse each node in the search path, and add the visited nodes to the spanning tree until all nodes are visited, and establish an optimization planning model for the power system topology structure.

[0023] In one embodiment, the ant colony algorithm is improved by using the pheromone update method, including:

[0024] Obtain the constraints in the power grid architecture;

[0025] According to the specific constraints in the power grid architecture, adjust the concentration of pheromone in the ant colony algorithm to improve the ant colony algorithm.

[0026] In one embodiment, the adjustment formula for the concentration of pheromone in the ant colony algorithm is:

[0027]

[0028] where, v represents the pheromone evaporation factor; both represent the pheromone increment, represent the stationary factor, the smoothing factor and the path factor respectively.

[0029] In a second aspect, the present application also provides an optimization device for the power system topology structure, including:

[0030] An index determination module, configured to determine the optimization index of the power grid architecture according to the power grid architecture of the power system and the distributed new energy power source access data;

[0031] An optimization planning model construction module, configured to establish an optimization planning model for the power system topology structure according to the optimization index of the power grid architecture;

[0032] An algorithm improvement module, configured to improve the ant colony algorithm by using the pheromone update method;

[0033] An optimization result generation module, configured to use the improved ant colony algorithm to calculate and generate the optimization result of the power system topology structure according to the optimization planning model.

[0034] In a third aspect, the present application also provides a computer device, including a memory and a processor, where the memory stores a computer program, and when the processor executes the computer program, the following steps are implemented:

[0035] Determine the optimization index of the power grid architecture according to the power grid architecture of the power system and the distributed new energy power source access data;

[0036] Establish an optimization planning model for the power system topology structure according to the optimization index of the power grid architecture;

[0037] Improve the ant colony algorithm by using the pheromone update method;

[0038] Use the improved ant colony algorithm to calculate and generate the optimization result of the power system topology structure according to the optimization planning model.

[0039] In a fourth aspect, the present application also 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] Determine the optimization index of the power grid architecture according to the power grid architecture of the power system and the access data of distributed new energy power sources;

[0041] Establish an optimization planning model for the power system topology structure according to the optimization index of the power grid architecture;

[0042] Improve the ant colony algorithm by using a pheromone update method;

[0043] Use the improved ant colony algorithm to calculate and generate the optimization result of the power system topology structure according to the optimization planning model.

[0044] In a fifth aspect, the present application also provides a computer program product, including a computer program. When the computer program is executed by a processor, the following steps are implemented:

[0045] Determine the optimization index of the power grid architecture according to the power grid architecture of the power system and the access data of distributed new energy power sources;

[0046] Establish an optimization planning model for the power system topology structure according to the optimization index of the power grid architecture;

[0047] Improve the ant colony algorithm by using a pheromone update method;

[0048] Use the improved ant colony algorithm to calculate and generate the optimization result of the power system topology structure according to the optimization planning model.

[0049] The above-mentioned power system topology structure optimization method, device, computer equipment, computer-readable storage medium and computer program product analyze the power grid architecture and distributed new energy power source access data to clarify optimization indicators such as power quality, power supply reliability, network loss and economy. This makes the optimization process more targeted and avoids blindness, thereby improving the power grid performance and operation efficiency. An optimization planning model for the power system topology structure is established based on the optimization indicators, comprehensively considering the physical characteristics of the power grid, operation constraints and the impact of distributed new energy power source access. This model is comprehensive, adaptable and guiding, providing a clear direction and constraint conditions for the optimization algorithm to ensure the feasibility and effectiveness of the optimization results. The ant colony algorithm is improved using the pheromone update method to enhance the global search ability and avoid falling into local optima. At the same time, the convergence speed is increased, the calculation time is reduced, and the algorithm robustness is improved. The improved algorithm performs more stably and has stronger adaptability under complex power grids and multiple constraint conditions. Based on the improved ant colony algorithm and the optimization planning model, the optimization results of the power system topology structure are calculated and generated. The results meet the power grid operation constraints, significantly improve the power quality, power supply reliability, economy and flexibility, and at the same time adapt to the changes in the access ratio of distributed new energy power sources and the dynamic changes of loads. Description of the Drawings

[0050] 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 for the description in 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, without creative efforts, other related drawings can also be obtained based on these drawings.

[0051] Figure 1 It is an application environment diagram of the power system topology structure optimization method in an embodiment;

[0052] Figure 2 It is a flowchart of the power system topology structure optimization method in an embodiment;

[0053] Figure 3 It is a flowchart of the power system topology structure optimization method in another embodiment;

[0054] Figure 4 It is a flowchart of the objective function solution of the improved ant colony algorithm in another embodiment;

[0055] Figure 5 It is a comparison diagram of the node connectivity rates obtained by the embodiments of the present application and traditional methods;

[0056] Figure 6 It is a structural block diagram of the power system topology structure optimization device in an embodiment;

[0057] Figure 7 It is the internal structure diagram of a computer device in an embodiment. Detailed implementation manners

[0058] 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 in conjunction with 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.

[0059] The power system topology optimization method provided by the embodiments of the present application can be applied to, for example Figure 1 the application environment shown in the figure. Among them, the terminal 102 communicates with the server 104 through a network. The data storage system can store the 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 on other network servers.

[0060] The terminal 102 obtains the grid architecture of the power system and the access data of distributed new energy power sources. The server 104 determines the optimization indexes of the grid architecture according to the grid architecture of the power system and the access data of distributed new energy power sources; according to the optimization indexes of the grid architecture, an optimization planning model of the power system topology structure is established; the ant colony algorithm is improved by using the pheromone update method; by using the improved ant colony algorithm, according to the optimization planning model, the optimization result of the power system topology structure is calculated and generated.

[0061] 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 vehicle-mounted 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.

[0062] In an exemplary embodiment, as Figure 2 shown in the figure, a power system topology optimization method is provided. Taking the method applied to Figure 1 the server 104 in the figure as an example, the following steps S202 to step S208 are included. Among them:

[0063] Step S202, determine the optimization indexes of the grid architecture according to the grid architecture of the power system and the access data of distributed new energy power sources.

[0064] Specifically, the power grid architecture refers to the network structure used for transmitting and distributing electric energy in the power system, including components such as power generation stations, substations, transmission lines, distribution networks, and user terminals. The performance of the power grid architecture directly affects the operation efficiency, reliability, and economy of the power system.

[0065] Distributed new energy power sources refer to small-scale power generation equipment distributed near users, such as solar photovoltaics, wind power generation, small hydropower, biomass power generation, etc. The access data of distributed new energy power sources includes access location, access capacity, and output characteristics.

[0066] Optimization indicators are key parameters for measuring the performance of the power grid architecture and are used to guide the optimization direction of the power grid. The selection of optimization indicators needs to comprehensively consider the operation requirements, economy, reliability, and environmental impact of the power grid. Common optimization indicators include power quality, power supply reliability, and network losses, etc.

[0067] The process of determining optimization indicators requires the following steps:

[0068] Data collection: Collect detailed information of the power grid architecture (such as line parameters, substation capacity, topological structure) and the access data of distributed new energy power sources (such as access location, capacity, output characteristics).

[0069] Current situation analysis: Analyze the operation of the existing power grid architecture and find out existing problems, such as power quality problems, insufficient power supply reliability, excessive network losses, etc.

[0070] Requirement analysis: Determine the optimization objectives according to the development plan of the power grid, the access plan of distributed new energy power sources, and the electricity consumption requirements of users.

[0071] Indicator setting: Select appropriate optimization indicators according to the current situation and requirements. For example, if there are voltage deviation problems in the power grid, then voltage stability is taken as an important optimization indicator; if the access of distributed new energy power sources leads to a decline in power quality, then harmonic suppression is taken as the optimization indicator.

[0072] Weight assignment: Assign weights according to the importance of each indicator for comprehensive evaluation during the optimization process.

[0073] Step S204, establish an optimization planning model for the power system topological structure according to the optimization indicators of the power grid architecture.

[0074] Specifically, according to the optimization indicators of the power grid architecture, collect the real-time status data of power grid equipment from business systems such as the SCADA system, GIS system, and PMS system. Use wavelet transform technology to clean the original data and remove outliers and noise.

[0075] Regard the power grid as a graph structure, where substations, power generation stations, etc. are nodes and transmission lines are edges to construct a graph model of the power grid. Use the indexing algorithm of the graph engine to determine the connectivity between nodes, and define the objective function according to the optimization index. Then, set the constraint conditions, which include the physical constraints of power grid operation (such as voltage and current limits), the constraints of distributed new energy power source access (such as access capacity limits), and the economic constraints (such as budget limits). Next, combine the above objective function and constraint conditions to form an optimization planning model.

[0076] Step S206: Improve the ant colony algorithm by using the pheromone update method.

[0077] Specifically, in the ant colony algorithm, pheromone is a chemical substance released by ants on the path to guide other ants to choose paths. The higher the pheromone concentration, the greater the probability that the path will be selected. The functions of pheromone include:

[0078] Memory of paths: Record the search history of ants to help the group remember which paths are better.

[0079] Guide the search: Guide ants to choose better paths through the pheromone concentration, thus accelerating the search for the global optimal solution.

[0080] In the traditional ant colony algorithm, when updating pheromone, simple evaporation and enhancement strategies are usually adopted. However, this method has the following problems: It is easy to fall into local optimality: Excessive concentration of pheromone may cause ants to concentrate on local optimal paths and it is difficult to explore the global optimal solution. Slow convergence speed: The pheromone update is not flexible enough, which may lead to a long search process and it is difficult to quickly converge to the optimal solution.

[0081] To overcome the deficiencies of the traditional ant colony algorithm, the pheromone update mechanism can be improved in the following ways:

[0082] (1) Dynamically adjust the pheromone evaporation factor: The pheromone evaporation factor (ρ) determines the evaporation speed of pheromone. By dynamically adjusting the evaporation factor, the evaporation of pheromone can be enhanced in the initial stage of the search to promote global exploration; and the evaporation factor can be reduced in the later stage of the search to enhance the local search ability.

[0083] (2) Introduce multi-factor pheromone: In the power grid topology optimization, in addition to the path length (such as network loss), other optimization indexes (such as power supply reliability and economy) also need to be considered. Therefore, multi-factor pheromone can be introduced, which corresponds to different optimization objectives respectively.

[0084] (3) Pheromone update based on quality: Dynamically adjust the pheromone increment according to the quality of the solution. For example, for a better solution, a higher pheromone increment is given; for a worse solution, a lower pheromone increment is given.

[0085] (4)Combination of local update and global update: During the search process, combine local update (updating pheromone on the current path) and global update (updating pheromone in the entire solution space). Local update helps enhance the exploration ability of the current path, while global update helps avoid local optima.

[0086] Step S208, using the improved ant colony algorithm, calculate and generate the optimization result of the power system topology structure according to the optimization planning model.

[0087] Specifically, using the improved ant colony algorithm, calculate according to the optimization planning model to generate the optimization result of the power system topology structure. The specific steps include:

[0088] (1)Initialization: Set parameters such as the number of ants and the number of iterations. Randomly generate an initial solution, that is, the initial topology structure of the power grid.

[0089] (2)Iterative calculation: Each ant selects a path according to the pheromone concentration and heuristic factor, that is, selects the topology structure of the power grid; calculate the quality of the solution of each ant, that is, the performance index of the power grid topology structure; update the pheromone according to the quality of the solution, and better solutions release more pheromone; repeat the above process until the maximum number of iterations is reached or the convergence condition is satisfied.

[0090] (3)Result output: Select the optimal solution from all iterations, that is, the optimal topology structure of the power grid; output the detailed information of the optimal solution, such as cost, network loss, power supply reliability, etc.

[0091] In the above power system topology structure optimization method, by analyzing the power grid architecture and distributed new energy power source access data, clarify the optimization indicators, such as power quality, power supply reliability, network loss, and economy, etc.; this makes the optimization process more targeted, avoids blindness, and thus improves the power grid performance and operation efficiency; establish an optimization planning model of the power system topology structure based on the optimization indicators, comprehensively considering the physical characteristics of the power grid, operation constraints, and the influence of distributed new energy power source access; this model has comprehensiveness, adaptability, and guidance, provides a clear direction and constraint conditions for the optimization algorithm, and ensures the feasibility and effectiveness of the optimization result; use the pheromone update method to improve the ant colony algorithm, enhance the global search ability, and avoid falling into local optima; at the same time, improve the convergence speed, reduce the calculation time, and enhance the robustness of the algorithm; the improved algorithm performs more stably and has stronger adaptability under complex power grids and multi-constraint conditions; based on the improved ant colony algorithm and the optimization planning model, calculate and generate the optimization result of the power system topology structure; this result meets the power grid operation constraints, significantly improves power quality, power supply reliability, economy, and flexibility, and at the same time adapts to the changes in the access ratio of distributed new energy power sources and the dynamic changes of load.

[0092] In an exemplary embodiment, such as Figure 3As shown, according to the grid architecture of the power system and the access data of distributed new energy power sources, determine the optimization indicators of the grid architecture, including:

[0093] Step S302, divide the grid architecture of the power system into multiple calculation sub-modules, and obtain the correlation function between any two calculation sub-modules;

[0094] Step S304, based on the access data of distributed new energy power sources and the correlation function, conduct boundary coordination network analysis on the grid architecture to obtain bus numbers;

[0095] Step S306, determine the optimization indicators of the grid architecture according to the bus numbers and the preset node-branch calculation model.

[0096] Specifically, decompose the complex grid architecture into multiple relatively independent calculation sub-modules to more carefully analyze the characteristics of each local area and simplify the complexity of the overall calculation. Among them, divide the grid into multiple calculation sub-modules according to the topological structure, functional partition or geographical area of the grid. Each sub-module can be a substation, a power supply area or a specific set of transmission lines. Collect detailed information of each sub-module, including parameters of nodes (such as substations, power generation stations, load points) and connecting edges (transmission lines).

[0097] The correlation function is used to describe the electrical connection strength or information transfer ability between two sub-modules. It can be a function of current, power or impedance. Calculate the correlation function value between any two sub-modules according to the electrical connection relationship between the sub-modules.

[0098] Through boundary coordination network analysis, identify the positions and roles of key nodes (buses) in the grid, providing a basis for determining the optimization indicators. Specifically, combine the access location, capacity and output characteristics of distributed new energy power sources, analyze their impacts on the grid, and use the correlation function to analyze the boundary connection relationship between sub-modules to determine the numbers of key nodes (buses).

[0099] Based on the bus numbers and the node-branch calculation model, quantify the performance indicators of the grid, thereby clarifying the optimization objectives. Specifically, preset a calculation model for evaluating the performance of the grid, such as power quality, power supply reliability, network loss, etc. Calculate the optimization indicators of the grid according to the bus numbers and the node-branch calculation model.

[0100] Specifically, first divide the power grid network into n calculation sub-modules, and obtain the correlation function Y(n) between the calculation sub-modules based on the system real-time database, that is:

[0101]

[0102] Wherein, I represents the sum of all injected currents in an electrical island; e represents the truncation error; U represents an empirical constant. According to the correlation function, boundary coordination network analysis is performed on the network to obtain the bus number χ, that is:

[0103]

[0104] Wherein, represents the decision function. Combining the bus number, the node-branch calculation model is used to correct the local architecture. The expression of the directed graph search path ψ of the local architecture information of the network is as follows:

[0105]

[0106] Wherein, δ represents the branch parameter information; represents the attribute constraint; represents the scalar proportionality coefficient; κ represents the distance function. The correction of the local architecture of the power grid is completed according to the above steps.

[0107] In this embodiment, according to the power grid architecture of the power system and the access data of distributed new energy power sources, the optimization index of the power grid architecture is determined, which can accurately locate the optimization target, enhance the adaptability of the power grid, quantify the optimization index, improve the overall performance of the power grid, and lay a foundation for subsequent optimization work. This process not only improves the operation efficiency and stability of the power grid, but also provides a scientific basis for the upgrade and transformation of the power grid and the access of distributed new energy power sources.

[0108] In an exemplary embodiment, according to the optimization index of the power grid architecture, an optimization planning model of the power system topology structure is established, including:

[0109] Collect the real-time status data of each power equipment in the power grid architecture;

[0110] Adopt the wavelet transform method to clean the collected real-time status data;

[0111] Based on the cleaned real-time status data and according to the optimization index of the power grid architecture, an optimization planning model of the power system topology structure is established.

[0112] Specifically, first, collect the real-time status data of each power equipment in the power grid architecture from multiple business systems of the power grid, including: SCADA system (Supervisory Control and Data Acquisition system): provides the real-time operation parameters of power grid equipment, such as voltage, current, power, etc. GIS system (Geographic Information System): provides the geographic information and topological relationship of power grid equipment. PMS system (Production Management System): provides the maintenance records and operation status information of equipment.

[0113] Next, perform wavelet decomposition on the collected real-time status data. By setting a threshold, remove the noise components in the wavelet coefficients, and perform wavelet reconstruction on the processed data to obtain the cleaned data.

[0114] Secondly, transform the cleaned data and the optimization metrics into a mathematical model to provide a solvable form for the optimization algorithm. Specific steps: Define the optimization objective: According to the optimization metrics of the power grid architecture (such as power quality, power supply reliability, network loss, economy, etc.), define the optimization objective function. Set the constraint conditions: According to the physical constraints of power grid operation (such as voltage and current limits), the access constraints of distributed new energy power sources (such as capacity limits), and the economic constraints (such as budget limits), set the constraint conditions of the optimization model. Construct the graph model: Regard the power grid as a graph structure, where the nodes represent power equipment (such as substations, power generation stations, load points), and the edges represent transmission lines. Based on the cleaned data, construct the graph model G=(V, E) of the power grid, where V is the set of nodes and E is the set of edges. Node connectivity analysis: Use the indexing algorithm of the graph engine to determine the connectivity between nodes and provide the topological relationship for the optimization model. Integrate the model: Integrate the objective function and the constraint conditions into a complete optimization planning model.

[0115] Specifically, the data cleaning expression is:

[0116]

[0117] In the formula, represents the cleaned data set; represents the original data set; m represents the number of data points; M represents the maximum value of the number of data points; μ represents the correlation function formed by the change of the node structure; γ represents the wavelet basis function; R represents the wavelet decomposition coefficient; V represents the filtering function.

[0118] Based on the preprocessed data, construct the graph model G of the power grid, and the expression is:

[0119]

[0120] In the formula, Q represents the set of power grid nodes; ξ represents the set of connecting edges. Use the indexing algorithm of the graph engine to determine whether two adjacent nodes are connected. The formula is:

[0121]

[0122] In the formula, represents the indexing factor; H represents the series of the connectivity matrix; η represents the dimension of the adjacency matrix; τ represents the connectivity factor. When Z takes a positive value, the two nodes sought are connected nodes; otherwise, they are unconnected nodes.

[0123] In this embodiment, data is cleaned through wavelet transform to remove noise and outliers, ensuring that the input data of the optimization model is accurate and reliable, thereby improving the scientificity and practicality of the model. The model established based on the optimization index can accurately locate the weak links of the power grid, optimize the power grid topology structure, reduce network losses, improve power quality, enhance power supply reliability, adapt to the access of distributed new energy power sources, and improve the operation efficiency and stability of the power grid.

[0124] In an exemplary embodiment, an optimization planning model for the power system topology structure is established, including:

[0125] Using the indexing algorithm of the graph engine to determine the connectivity relationship between any two adjacent nodes in the optimization planning model;

[0126] According to the connectivity relationship between any two adjacent nodes, assign weights to the connecting edges of the nodes;

[0127] Using the breadth-first search algorithm, starting from the vertex, taking the connecting edge with the maximum weight as the search path, traversing each node in the search path, and adding the visited nodes to the spanning tree until all nodes are visited, to establish an optimization planning model for the power system topology structure.

[0128] Specifically, through the indexing algorithm of the graph engine, quickly determine whether any two nodes in the power grid are connected. The specific steps are as follows: regard the power grid as a graph, where nodes (such as substations, power generation stations, load points) represent the key equipment of the power grid, and edges (transmission lines) represent the connection relationships between nodes. Use the indexing algorithm of the graph engine (such as adjacency matrix or adjacency list) to quickly determine the connectivity between any two nodes. By calculating the indexing factor, determine whether two nodes are connected.

[0129] By assigning weights to the connecting edges, quantify the connection strength between nodes. The specific steps are as follows: according to the actual operation requirements and optimization goals of the power grid, assign weights to each connecting edge. The weights can be based on factors such as the transmission capacity, loss, and reliability of the line. According to the optimization goal, calculate the weight of each edge. For example, the weight can be the transmission efficiency, reliability index, or cost-benefit ratio of the line.

[0130] Select a starting node as the starting point of the search. Use the breadth-first search algorithm, starting from the starting point, traverse the nodes layer by layer, and preferentially select the connecting edge with the maximum weight as the search path. Add the visited nodes to the spanning tree in sequence until all nodes are visited. Based on the path of the spanning tree, construct an optimization planning model for the power system topology structure to ensure that the model can reflect the optimal topology structure of the power grid. If there are nodes that have never been visited, mark them as isolated points, otherwise mark them as loops.

[0131] In this embodiment, a graph engine and a breadth-first search algorithm are used to quickly complete topological analysis. The optimal path is selected through weights to ensure that the model reflects the optimal topological structure of the power grid, and the model can adapt to the dynamic changes of the power grid.

[0132] In an exemplary embodiment, the ant colony algorithm is improved by using a pheromone update method, including:

[0133] Obtain the constraint conditions in the power grid architecture;

[0134] According to the specific constraint conditions in the power grid architecture, adjust the concentration of pheromone in the ant colony algorithm to improve the ant colony algorithm.

[0135] Specifically, in the optimization of the power grid topology structure, the constraint conditions are the key factors to ensure that the optimization results meet the actual operation requirements of the power grid. These constraint conditions include physical constraints (such as voltage constraints, current constraints, and power constraints), economic constraints (such as construction costs and operation and maintenance costs), and constraints on the access of distributed new energy power sources (such as access capacity limits and access location limits).

[0136] Such as Figure 4 shown, the ant colony algorithm guides ants to select paths through pheromones to search for the optimal solution. The traditional ant colony algorithm may fall into local optimality or have a slow convergence rate due to a single pheromone update method. By dynamically adjusting the pheromone concentration according to the specific constraint conditions of the power grid architecture, the global search ability and convergence rate of the algorithm can be enhanced.

[0137] In the initial stage of the search, increase the pheromone evaporation factor to promote global exploration. In the later stage of the search, reduce the pheromone evaporation factor to enhance the local search ability. Adjust the pheromone increment according to the degree to which the solution satisfies the constraint conditions. For example, if a solution satisfies all constraint conditions, a higher pheromone increment is given; if not, the pheromone increment is reduced. Introduce multiple pheromone factors, each corresponding to a different optimization goal (such as cost, reliability, loss). By adjusting the weights of each factor, guide the ants to search for solutions that better meet the power grid requirements. The pheromone can be dynamically adjusted on the current path to enhance the local search ability. Or update the pheromone in the entire solution space to avoid falling into local optimality.

[0138] Such as Figure 4As shown, the process of solving the objective function based on the improved ant colony algorithm starts with the initialization of parameters. Subsequently, the ants start from the starting point, select paths according to the heuristic values, move to the end point, and generate the initial paths. Then, the pheromone on the generated paths is updated and optimized. In the path search stage, the ants continuously update the pheromone on the paths until they reach the end point. This process is repeated until all paths are generated. Subsequently, the algorithm collects all paths to form a set of planning solutions and executes the genetic algorithm to further optimize these solutions. In the genetic algorithm stage, the algorithm obtains the current and global optimal solutions and conducts fitness comparison to select the optimal solution. Finally, the algorithm outputs the optimal solution and ends the entire process. This coherent process ensures that the ant colony algorithm can efficiently search the solution space and find the optimal solution for the optimization of the power system topology structure.

[0139] In this embodiment, by dynamically adjusting the pheromone concentration according to the constraint conditions, the improved ant colony algorithm can search for the global optimal solution more efficiently, avoid falling into the local optimum, and at the same time improve the convergence speed and stability of the algorithm.

[0140] In an exemplary embodiment, the adjustment formula for the concentration of pheromone in the ant colony algorithm is:

[0141]

[0142] Among them, ν represents the pheromone evaporation factor; Both represent the pheromone increment, respectively represent the stationary factor, the smoothing factor, and the path factor.

[0143] Specifically, the pheromone evaporation factor is a constant between 0 and 1, indicating the degree of pheromone evaporation over time. A higher value of the pheromone evaporation factor means that the pheromone evaporates faster, which helps the algorithm maintain diversity during the search process and avoid premature convergence to the local optimal solution. A lower value of the pheromone evaporation factor means that the pheromone evaporates slower, which helps the algorithm maintain stability during the search process and accelerate convergence to the optimal solution.

[0144] In this embodiment, through this way of adjusting the pheromone concentration, the ant colony algorithm can more effectively search for the optimal solution of the power grid topology structure, improve the convergence speed and the quality of the solution, and at the same time avoid falling into the local optimal solution.

[0145] The specific embodiment of this application is:

[0146] Such as Figure 4As shown in the figure, it presents the power grid architecture diagram of the IEEE 9-bus system. Verification was carried out in the IEEE 9-bus system test environment, which includes a total of 3 generator nodes, 3 load nodes, and 3 transmission lines. In terms of generator nodes, the rated power of Generator 1 (i.e., Station 1) is 100 MW, the rated power of Generator 2 (i.e., Station 2) is 80 MW, and the rated power of Generator 3 (i.e., Station 3) is 60 MW. The rated voltage of Generator 1 is 13.8 kV, the rated voltage of Generator 2 is 13.8 kV, and the rated voltage of Generator 3 is 13.8 kV. For load nodes, Load Node 1 is 50 MW + 20 Mvar, Load Node 2 is 40 MW + 15 Mvar, and Load Node 3 is 30 MW + 10 Mvar. In terms of transmission lines, the resistance of Branch 1 is 0.05 Ω and the reactance is 0.2 Ω; the resistance of Branch 2 is 0.04 Ω and the reactance is 0.18 Ω; the resistance of Branch 3 is 0.06 Ω and the reactance is 0.22 Ω. The setting of these parameters provides the basic conditions for the experiment to verify the effectiveness of the power grid architecture planning method in the IEEE 9-bus system environment.

[0147] To verify the effectiveness of the power grid architecture planning method proposed in the present invention, the node connectivity rate index is used to complete the verification. The comparison methods are Traditional Method 1 and Traditional Method 2 introduced in the introduction. The node connectivity rate reflects the connectivity of the power grid architecture. Comparing the node connectivity rates output by the three methods with the actual values, if the two are consistent, it indicates that the connectivity between the analyzed power grid structure and the actual power grid structure is the same, the consistency of the architecture structure is relatively high, and the power grid architecture planning effect is good. The comparison results are as Figure 5 shown.

[0148] According to Figure 5 it can be seen that compared with Traditional Method 1 and Traditional Method 2, the power grid nodes directly have a higher connectivity rate under the application of the power grid architecture planning method proposed in this application. A higher node connectivity rate provides more options for the optimized transmission of electricity. In the case where there are differences in power demand and generation capacity in different regions, electricity can be transmitted from regions with excess power generation to regions with strong power demand through more efficient paths. For example, in a regional power grid containing multiple power plants and power consumption areas, nodes with high connectivity can make power dispatching more flexible and reduce power loss during transmission.

[0149] The implementation effect of this embodiment is that by modifying the local power grid architecture and completing the planning with the help of a graph engine, and then using the optimized ant colony algorithm to search for solutions, this method effectively improves the accuracy of power grid architecture planning. The power grid has a stronger ability to respond when facing transmission line or substation failures, can ensure the continuous transmission of electric energy, and avoid the occurrence of large-scale power outages. This method not only helps to improve the reliability of power grid operation, but also provides a strong support for ensuring social stability and meeting the power demand of economic development.

[0150] It should be understood that although the steps in the flowcharts involved in the above-described embodiments are sequentially shown according to the indications of the arrows, these steps are not necessarily executed sequentially in the order indicated by the arrows. Unless there is a clear indication in this document, the execution of these steps has no strict order limit, and these steps can be executed in other orders. Moreover, at least a part 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 moment, but can be executed at different moments. The execution order of these steps or stages is not necessarily sequential, but can be executed alternately or in rotation with at least a part of other steps or steps or stages in other steps.

[0151] Based on the same inventive concept, an embodiment of the present application also provides a power system topology structure optimization device for implementing the above-mentioned power system topology structure optimization method. The solution provided by this device to solve the problem is similar to the solution described in the above method. Therefore, the specific limitations in one or more embodiments of the power system topology structure optimization device provided below can refer to the limitations on the power system topology structure optimization method in the above text, and will not be repeated here.

[0152] In an exemplary embodiment, as Figure 6 shown, a power system topology structure optimization device is provided, including:

[0153] An index determination module 602, configured to determine an optimization index of the grid architecture according to the grid architecture of the power system and the access data of distributed new energy power sources;

[0154] An optimization planning model construction module 604, configured to establish an optimization planning model of the power system topology structure according to the optimization index of the grid architecture;

[0155] An algorithm improvement module 606, configured to improve the ant colony algorithm by using a pheromone update method;

[0156] An optimization result generation module 608, configured to use the improved ant colony algorithm to calculate and generate an optimization result of the power system topology structure according to the optimization planning model.

[0157] In an exemplary embodiment, the index determination module 602 is specifically configured to divide the grid architecture of the power system into multiple calculation sub-modules, and obtain the correlation function between any two calculation sub-modules; perform boundary coordination network analysis on the grid architecture according to the access data of distributed new energy power sources and the correlation function to obtain the bus numbers; and determine the optimization index of the grid architecture according to the bus numbers and a preset node-branch calculation model.

[0158] In an exemplary embodiment, the optimization planning model construction module 604 is further configured to collect real-time status data of each power device in the power grid architecture; perform cleaning processing on the collected real-time status data by using a wavelet transform method; and establish an optimization planning model for the topology structure of the power system based on the cleaned real-time status data and the optimization indexes of the power grid architecture.

[0159] In an exemplary embodiment, the optimization planning model construction module 604 is specifically configured to use the indexing algorithm of the graph engine to determine the connectivity relationship between any two adjacent nodes in the optimization planning model; assign weights to the connection edges of the nodes according to the connectivity relationship between any two adjacent nodes; use the breadth-first search algorithm to start from the vertex, take the connection edge with the maximum weight as the search path, traverse each node in the search path, and add the accessed nodes to the spanning tree until all nodes are accessed, so as to establish an optimization planning model for the topology structure of the power system.

[0160] In an exemplary embodiment, the algorithm improvement module 606 is specifically configured to obtain the constraint conditions in the power grid architecture; and improve the ant colony algorithm by adjusting the concentration of pheromone in the ant colony algorithm according to the specific constraint conditions in the power grid architecture.

[0161] In an exemplary embodiment, the adjustment formula for the concentration of pheromone in the ant colony algorithm is:

[0162]

[0163] where ν represents the pheromone evaporation factor; both represent the pheromone increment, respectively represent the stationary factor, the smoothing factor, and the distance factor.

[0164] Each module in the above power system topology structure optimization device can be implemented in whole or in part by software, hardware, and their combination. The above modules can be embedded in the processor in the computer device in the form of hardware or be independent of it, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above respective modules.

[0165] 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 7As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, abbreviated as I / O), and a communication interface. Among them, the processor, the memory, and the input / output interface are connected through a system bus, and the communication interface is 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 the grid architecture of the power system and the access data of distributed new energy power sources. 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 method for optimizing the topology structure of a power system.

[0166] Those skilled in the art can understand that Figure 7 the structure shown in is only a block diagram of some structures 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 those shown in the figure, or combine some components, or have different component arrangements.

[0167] 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:

[0168] Determine the optimization index of the grid architecture according to the grid architecture of the power system and the access data of distributed new energy power sources;

[0169] Establish an optimization planning model for the topology structure of the power system according to the optimization index of the grid architecture;

[0170] Improve the ant colony algorithm by using the pheromone update method;

[0171] Use the improved ant colony algorithm to calculate and generate the optimization result of the topology structure of the power system according to the optimization planning model.

[0172] In one embodiment, when the processor executes the computer program, the following steps are also implemented:

[0173] Divide the grid architecture of the power system into multiple calculation sub-modules, and obtain the correlation function between any two calculation sub-modules;

[0174] Perform boundary coordination network analysis on the grid architecture according to the access data of distributed new energy power sources and the correlation function to obtain the bus numbers;

[0175] Determine the optimization index of the power grid architecture according to the bus number and the preset node-branch calculation model.

[0176] In one embodiment, when the processor executes the computer program, the following steps are further implemented:

[0177] Collect the real-time status data of each power device in the power grid architecture;

[0178] Adopt the wavelet transform method to clean the collected real-time status data;

[0179] Based on the real-time status data after cleaning and according to the optimization index of the power grid architecture, establish an optimization planning model for the power system topology structure.

[0180] In one embodiment, when the processor executes the computer program, the following steps are further implemented:

[0181] Use the indexing algorithm of the graph engine to determine the connectivity relationship between any two adjacent nodes in the optimization planning model;

[0182] According to the connectivity relationship between any two adjacent nodes, assign weights to the connection edges of the nodes;

[0183] Use the breadth-first search algorithm, starting from the vertex, take the connection edge with the maximum weight as the search path, traverse each node in the search path, and add the visited nodes to the spanning tree until all nodes are visited, and establish an optimization planning model for the power system topology structure.

[0184] In one embodiment, when the processor executes the computer program, the following steps are further implemented:

[0185] Obtain the constraint conditions in the power grid architecture;

[0186] According to the specific constraint conditions in the power grid architecture, adjust the concentration of pheromone in the ant colony algorithm to improve the ant colony algorithm.

[0187] In one embodiment, when the processor executes the computer program, the following steps are further implemented:

[0188]

[0189] Wherein, v represents the pheromone evaporation factor; Both represent the pheromone increment, respectively represent the stationary factor, the smoothing factor and the distance factor.

[0190] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, the following steps are implemented:

[0191] Determine the optimization index of the power grid architecture according to the power grid architecture of the power system and the access data of distributed new energy power sources;

[0192] Establish an optimization planning model for the power system topology structure according to the optimization index of the power grid architecture;

[0193] Improve the ant colony algorithm by using the pheromone update method;

[0194] Use the improved ant colony algorithm to calculate and generate the optimization result of the power system topology structure according to the optimization planning model.

[0195] In one embodiment, when the computer program is executed by the processor, the following steps are further implemented:

[0196] Divide the power grid architecture of the power system into multiple calculation sub-modules, and obtain the correlation function between any two calculation sub-modules;

[0197] Conduct boundary coordination network analysis on the power grid architecture according to the access data of distributed new energy power sources and the correlation function, and obtain the bus numbers;

[0198] Determine the optimization index of the power grid architecture according to the bus numbers and the preset node-branch calculation model.

[0199] In one embodiment, when the computer program is executed by the processor, the following steps are further implemented:

[0200] Collect the real-time status data of each power equipment in the power grid architecture;

[0201] Adopt the wavelet transform method to clean the collected real-time status data;

[0202] Based on the cleaned real-time status data and according to the optimization index of the power grid architecture, establish an optimization planning model for the power system topology structure.

[0203] In one embodiment, when the computer program is executed by the processor, the following steps are further implemented:

[0204] Use the indexing algorithm of the graph engine to determine the connectivity relationship between any two adjacent nodes in the optimization planning model;

[0205] Assign weights to the connection edges of the nodes according to the connectivity relationship between any two adjacent nodes;

[0206] Use the breadth-first search algorithm, starting from the vertex, take the connection edge with the maximum weight as the search path, traverse each node in the search path, and add the visited nodes to the spanning tree until all nodes are visited, and establish an optimization planning model for the power system topology structure.

[0207] In one embodiment, when the computer program is executed by a processor, the following steps are further implemented:

[0208] Obtain the constraints in the power grid architecture;

[0209] According to the specific constraints in the power grid architecture, adjust the concentration of pheromone in the ant colony algorithm to improve the ant colony algorithm.

[0210] In one embodiment, when the computer program is executed by a processor, the following steps are further implemented:

[0211]

[0212] Among them, v represents the pheromone evaporation factor; Both represent the pheromone increment, respectively represent the stationary factor, the smoothing factor, and the path factor.

[0213] In one embodiment, a computer program product is provided, including a computer program, which when executed by a processor implements the following steps:

[0214] According to the power grid architecture of the power system and the distributed new energy power source access data, determine the optimization index of the power grid architecture;

[0215] According to the optimization index of the power grid architecture, establish an optimization planning model for the power system topology structure;

[0216] Use the pheromone update method to improve the ant colony algorithm;

[0217] Use the improved ant colony algorithm to calculate and generate the optimization result of the power system topology structure according to the optimization planning model.

[0218] In one embodiment, when the computer program is executed by a processor, the following steps are further implemented:

[0219] Divide the power grid architecture of the power system into multiple calculation sub-modules, and obtain the correlation function between any two calculation sub-modules;

[0220] According to the distributed new energy power source access data and the correlation function, conduct boundary coordination network analysis on the power grid architecture to obtain the bus numbers;

[0221] According to the bus numbers and the preset node-branch calculation model, determine the optimization index of the power grid architecture.

[0222] In one embodiment, when the computer program is executed by a processor, the following steps are further implemented:

[0223] Collect the real-time status data of each power device in the power grid architecture;

[0224] Adopt the wavelet transform method to clean the collected real-time status data;

[0225] Based on the cleaned real-time status data and the optimization indexes according to the power grid architecture, establish an optimization planning model for the power system topology structure.

[0226] In one embodiment, when the computer program is executed by the processor, the following steps are also implemented:

[0227] Use the indexing algorithm of the graph engine to determine the connectivity relationship between any two adjacent nodes in the optimization planning model;

[0228] According to the connectivity relationship between any two adjacent nodes, assign weights to the connection edges of the nodes;

[0229] Use the breadth-first search algorithm. Starting from the vertex, take the connection edge with the maximum weight as the search path, traverse each node in the search path, and add the visited nodes to the spanning tree until all nodes are visited, and establish an optimization planning model for the power system topology structure.

[0230] In one embodiment, when the computer program is executed by the processor, the following steps are also implemented:

[0231] Obtain the constraint conditions in the power grid architecture;

[0232] According to the specific constraint conditions in the power grid architecture, adjust the concentration of pheromone in the ant colony algorithm to improve the ant colony algorithm.

[0233] In one embodiment, when the computer program is executed by the processor, the following steps are also implemented:

[0234]

[0235] Wherein, v represents the pheromone evaporation factor; Both represent the pheromone increment, respectively represent the stationary factor, the smoothing factor and the path factor.

[0236] 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.

[0237] 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 the present 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 the present 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 the present 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.

[0238] The technical features of the above embodiments can be combined arbitrarily. For the sake of concise 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 the present application.

[0239] 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 optimizing the topology structure of a power system, characterized in that, The method includes: determining an optimization index of the grid architecture according to the grid architecture of the power system and the access data of distributed new energy power sources; establishing an optimization planning model for the topology structure of the power system according to the optimization index of the grid architecture; improving the ant colony algorithm by using a pheromone update method; using the improved ant colony algorithm to calculate and generate an optimization result of the topology structure of the power system according to the optimization planning model.

2. The method according to claim 1, characterized in that, The determining an optimization index of the grid architecture according to the grid architecture of the power system and the access data of distributed new energy power sources includes: dividing the grid architecture of the power system into multiple calculation sub-modules, and obtaining a correlation function between any two calculation sub-modules; performing boundary coordination network analysis on the grid architecture according to the access data of the distributed new energy power sources and the correlation function to obtain bus numbers; determining an optimization index of the grid architecture according to the bus numbers and a preset node-branch calculation model.

3. The method according to claim 1, characterized in that, The establishing an optimization planning model for the topology structure of the power system according to the optimization index of the grid architecture includes: collecting real-time status data of each power device in the grid architecture; adopting a wavelet transform method to clean the collected real-time status data; establishing an optimization planning model for the topology structure of the power system based on the cleaned real-time status data and according to the optimization index of the grid architecture.

4. The method according to claim 1, characterized in that The establishing an optimization planning model for the topology structure of the power system includes: using an indexing algorithm of a graph engine to determine the connectivity relationship between any two adjacent nodes in the optimization planning model; assigning weights to the connection edges of the nodes according to the connectivity relationship between any two adjacent nodes; using a breadth-first search algorithm, starting from the vertex, taking the connection edge with the maximum weight as the search path, traversing each node in the search path, and adding the visited nodes to the spanning tree until all nodes are visited, to establish an optimization planning model for the topology structure of the power system.

5. The method according to claim 1, wherein The improving the ant colony algorithm by using a pheromone update method includes: obtaining the constraint conditions in the grid architecture; adjusting the concentration of pheromone in the ant colony algorithm according to the specific constraint conditions in the grid architecture to improve the ant colony algorithm.

6. The method according to claim 5, wherein The adjustment formula for the concentration of pheromone in the ant colony algorithm is: Among them, v represents the pheromone evaporation factor; both represent the pheromone increment, represent the stationary factor, the smoothing factor, and the path factor respectively.

7. An apparatus for optimizing the topology of a power system, characterized in that, The device includes: an index determination module for determining an optimization index of the grid architecture according to the grid architecture of the power system and the access data of distributed new energy power sources; an optimization planning model construction module for establishing an optimization planning model for the topology structure of the power system according to the optimization index of the grid architecture; an algorithm improvement module for improving the ant colony algorithm by using a pheromone update method; an optimization result generation module for calculating and generating an optimization result of the topology structure of the power system by using the improved ant colony algorithm according to the optimization planning 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, the steps of the method according to any one of claims 1 to 6 are implemented.

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

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

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