Power system optimization method and device based on dynamic meshing, equipment and medium

Abstract

The application discloses a power system optimization method and device based on dynamic meshing, equipment and medium, applied to the field of distribution network regulation and control, comprising: based on the input power system original parameter, the initial grid division of the power system is carried out, according to the load prediction data and the initial operation state, the system operation state prediction is carried out, and based on the prediction result and the initial grid division scheme, the dynamic grid adjustment criterion is started, the grid dynamic adjustment instruction is output, the initial grid division scheme is adjusted in real time according to the grid dynamic adjustment instruction, the dynamic optimization grid is generated, the power system is partitioned and decoupled, the system model of each sub-region after decoupling is output, the optimal power flow problem of each sub-region is solved in parallel, and the optimization results of each sub-region are coordinated, and the global optimal power system scheduling scheme is output. The application improves the optimization calculation efficiency of large-scale power grid, significantly reduces the calculation time, and meets the timeliness requirement of real-time optimization of the power system.

Description

Technical Field

[0001] This invention relates to the field of power distribution network control technology, and in particular to a power system optimization method, device, equipment and medium based on dynamic gridding. Background Technology

[0002] As the scale of the power system continues to expand and the penetration rate of renewable energy continues to increase, the complexity and uncertainty of power grid operation have increased significantly.

[0003] In existing technologies, traditional centralized optimization methods struggle to meet the real-time optimization needs of large-scale power grids, exhibiting low computational efficiency and difficulty in guaranteeing convergence. Fixed-partition parallel computing methods lack dynamic adjustment capabilities and cannot adapt to the time-varying characteristics of system operation, often leading to inaccurate optimization results or constraint violations during periods of drastic load fluctuations or network topology changes. Furthermore, existing distributed coordination algorithms often have slow convergence speeds and fail to guarantee a globally optimal solution.

[0004] Therefore, how to achieve efficient parallel solution and dynamic optimization coordination of large-scale power systems while ensuring computational accuracy has become a technical problem that urgently needs to be solved by those skilled in the art. Summary of the Invention

[0005] This invention provides a power system optimization method, apparatus, equipment, and medium based on dynamic gridding to achieve dynamic and collaborative optimization of the power grid's operating status.

[0006] To address the aforementioned technical problems, embodiments of the present invention provide a power system optimization method based on dynamic gridding, comprising:

[0007] Input the original parameters of the power system, which include network topology, load forecast data and initial operating status.

[0008] Based on the input network topology and the initial operating state, the power system is initially divided into grids, and the initial grid division scheme is output.

[0009] Based on the load forecast data and the initial operating state, the system operating state is predicted, and based on the prediction results and the initial grid partitioning scheme, the dynamic grid adjustment criterion is activated, and the grid dynamic adjustment command is output.

[0010] According to the grid dynamic adjustment command, the initial grid division scheme is adjusted in real time to generate a dynamically optimized grid, and the power system is partitioned and decoupled based on the dynamically optimized grid, and the decoupled sub-region system models are output.

[0011] Based on the decoupled sub-region system models, the optimal power flow problem of each sub-region is solved in parallel, and the optimization results of each sub-region are coordinated to output the globally optimal power system dispatching scheme.

[0012] Furthermore, the initial grid partitioning of the power system based on the input network topology and the initial operating state, and the output of the initial grid partitioning scheme, includes:

[0013] The initial number of grid regions is determined based on the total number of nodes and connection density in the network topology.

[0014] Based on the network topology, a power grid connection diagram based on nodes and lines is established.

[0015] The electrical distance between nodes is calculated based on the power flow data in the initial operating state.

[0016] The power system is initially divided into grids based on the number of grid regions, the power grid connection diagram, and the electrical distance, and the initial grid division scheme is output.

[0017] Further, the step of predicting the system operating state based on the load forecast data and the initial operating state, and initiating a dynamic grid adjustment criterion based on the prediction results and the initial grid partitioning scheme, and outputting a grid dynamic adjustment command, includes:

[0018] Based on the load forecast data and the initial operating status, the power change trend of the interconnection lines between regions is predicted for future periods.

[0019] Based on the power change trend, determine whether the power of each tie line exceeds the corresponding transmission capacity limit in the initial grid partitioning scheme.

[0020] Based on the judgment results, a grid adjustment command is generated and output for the area where the power exceeds the limit of the tie line.

[0021] Further, the step of adjusting the initial grid partitioning scheme in real time according to the grid dynamic adjustment command to generate a dynamically optimized grid, and performing partitioning and decoupling of the power system based on the dynamically optimized grid to output the decoupled sub-region system models, includes:

[0022] Based on the adjustment area and direction specified in the mesh dynamic adjustment command, the initial mesh division scheme is modified, and nodes with similar electrical distances are re-divided into the same mesh area to generate a dynamically optimized mesh.

[0023] Based on the dynamic optimization grid, the power system is decoupled into several independent sub-region systems, and the decoupled sub-region system model is output, which includes the node composition, parameters and boundary conditions of each sub-region.

[0024] Furthermore, based on the decoupled sub-region system models, the optimal power flow problem of each sub-region is solved in parallel, and the optimization results of each sub-region are coordinated to output a globally optimal power system dispatching scheme, including:

[0025] Based on the decoupled sub-region system models, the optimal power flow distribution within each sub-region is calculated in parallel.

[0026] Based on the optimal power flow distribution described above, obtain the boundary node voltage and power exchange information of the corresponding sub-region.

[0027] Based on the boundary node voltage and the power exchange information, the power generation output of each sub-region is iteratively corrected by the alternating direction multiplier method, so that each sub-region achieves power exchange balance.

[0028] Based on the power generation output that achieves power exchange balance, a power system dispatching scheme is generated and output.

[0029] Another embodiment of the present invention provides a power system optimization device based on dynamic gridding, comprising:

[0030] The parameter input module is used to input the original parameters of the power system, including network topology, load forecast data, and initial operating status.

[0031] The grid partitioning module is used to perform initial grid partitioning of the power system based on the input network topology and the initial operating state, and output the initial grid partitioning scheme.

[0032] The dynamic adjustment module is used to predict the system operating status based on the load forecast data and the initial operating status, and to initiate dynamic grid adjustment criteria and output grid dynamic adjustment instructions based on the prediction results and the initial grid partitioning scheme.

[0033] The system partitioning module is used to adjust the initial grid division scheme in real time according to the grid dynamic adjustment command, generate a dynamically optimized grid, and perform partitioning and decoupling of the power system based on the dynamically optimized grid, and output the decoupled sub-region system models.

[0034] The scheme optimization module is used to solve the optimal power flow problem of each sub-region in parallel based on the decoupled sub-region system model, coordinate the optimization results of each sub-region, and output the globally optimal power system dispatch scheme.

[0035] Furthermore, the mesh generation module is used for:

[0036] The initial number of grid regions is determined based on the total number of nodes and connection density in the network topology.

[0037] Based on the network topology, a power grid connection diagram based on nodes and lines is established.

[0038] The electrical distance between nodes is calculated based on the power flow data in the initial operating state.

[0039] The power system is initially divided into grids based on the number of grid regions, the power grid connection diagram, and the electrical distance, and the initial grid division scheme is output.

[0040] Furthermore, the dynamic adjustment module is used for:

[0041] Based on the load forecast data and the initial operating status, the power change trend of the interconnection lines between regions is predicted for future periods.

[0042] Based on the power change trend, determine whether the power of each tie line exceeds the corresponding transmission capacity limit in the initial grid partitioning scheme.

[0043] Based on the judgment results, a grid adjustment command is generated and output for the area where the power exceeds the limit of the tie line.

[0044] Another embodiment of the present invention provides a computer device including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein the processor executes the computer program to implement the power system optimization method based on dynamic gridding as described above.

[0045] In another embodiment of the present invention, a computer-readable storage medium is provided, the computer-readable storage medium storing a computer program, wherein when the device containing the computer-readable storage medium executes the computer program, it implements the power system optimization method based on dynamic gridding as described above.

[0046] Compared with the prior art, the beneficial effects of the embodiments of the present invention are at least one of the following:

[0047] By employing a dynamic grid partitioning and real-time adjustment mechanism, the system's adaptability to load fluctuations and network topology changes is significantly improved. The grid partitioning is automatically adjusted based on real-time operating status prediction results, ensuring that each sub-region always maintains optimal electrical coupling characteristics. This effectively avoids the optimization accuracy degradation problem that occurs when the system operating status changes in traditional fixed partitioning methods. The adoption of a partitioned parallel computing architecture greatly improves the optimization computation efficiency of large-scale power grids. By decoupling the entire system into multiple electrically connected sub-regions and solving the optimal power flow in parallel, the complex large-scale optimization problem is decomposed into multiple smaller-scale sub-problems, significantly reducing computation time and meeting the timeliness requirements of real-time optimization of power systems. Attached Figure Description

[0048] Figure 1 This is a flowchart illustrating the steps of a power system optimization method based on dynamic gridding in one embodiment of the present invention.

[0049] Figure 2 This is a structural block diagram of a power system optimization device based on dynamic gridding in one embodiment of the present invention;

[0050] Figure 3 A structural diagram of a computer device provided in an embodiment of the present invention. Detailed Implementation

[0051] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. The purpose of providing these embodiments is to make the disclosure of the present invention more thorough and comprehensive. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0052] In the description of this application, the terms "first," "second," "third," etc., are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Therefore, a feature defined with "first," "second," "third," etc., may explicitly or implicitly include one or more of that feature. In the description of this application, unless otherwise stated, "a plurality of" means two or more.

[0053] In the description of this application, it should be noted that, unless otherwise expressly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to fixed connections, detachable connections, or integral connections; they can refer to mechanical connections or electrical connections; they can refer to direct connections or indirect connections through an intermediate medium; and they can refer to the internal communication between two components. The terms "vertical," "horizontal," "left," "right," "upper," "lower," and similar expressions used herein are for illustrative purposes only and do not indicate or imply that the device or component referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as limiting the invention. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items. Those skilled in the art can understand the specific meaning of the above terms in this application based on the specific circumstances.

[0054] In the description of this application, it should be noted that, unless otherwise defined, all technical and scientific terms used in this invention have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. The terminology used in this specification is for the purpose of describing specific embodiments only and is not intended to limit the invention. Those skilled in the art can understand the specific meaning of the above terms in this application based on the specific circumstances.

[0055] One embodiment of the present invention provides a power system optimization method based on dynamic gridding. For details, please refer to [link to relevant documentation]. Figure 1 , Figure 1 The flowchart shown is a step-by-step diagram of a power system optimization method based on dynamic gridding in one embodiment of the present invention, including the following steps:

[0056] Step S11: Input the original parameters of the power system, including the network topology, load forecast data and initial operating status.

[0057] With the rapid development of society and the economy and the increasing demand for electricity, the safe, stable, and efficient operation of the power system has become crucial. The power system is a complex engineering system, and its operating state is affected by a variety of factors, including network topology, load forecasting data, and initial operating conditions.

[0058] The raw parameters mainly include three aspects: network topology, load forecast data, and initial operating status. Network topology is the basic framework of a power system, determining the paths and connections of power transmission. By clearly defining the network topology, the connection relationships between power plants, substations, and users can be clearly understood. For example, a city power grid with a complex network structure requires detailed records of the connection relationships between various transmission lines, transformers, and switching equipment to accurately simulate the power transmission process during system planning and operation control.

[0059] Load forecasting data is a crucial basis for power system operation and planning. Accurate load forecasting provides reliable data support for power system generation planning, equipment maintenance scheduling, and market operation. Specifically, the load forecasting in this embodiment comprehensively considers various factors, including seasonal variations, weather conditions, social activities, and economic development. For example, during periods of high summer temperatures or low winter temperatures, the electricity load may increase significantly due to the extensive use of appliances such as air conditioners; and during holidays or special events, the load distribution may also change.

[0060] The initial operating state reflects the actual operating condition of the power system at a specific point in time, including the output of each generating unit, the bus voltage level, and the power flow distribution of transmission lines. When conducting transient stability analysis of a power system, it is necessary to simulate the dynamic response process of the system after being subjected to sudden fault disturbances, based on the initial operating state. Furthermore, in the electricity market environment, the initial operating state also determines the starting point of electricity trading, influencing the setting of generation costs and user electricity prices.

[0061] Step S12: Based on the input network topology and the initial operating state, perform initial grid partitioning on the power system and output the initial grid partitioning scheme.

[0062] The initial number of grid regions is determined based on the total number of nodes and connection density in the network topology. The network topology of a power system is a crucial indicator of its physical connections and operational characteristics. The total number of nodes and connection density are key factors in determining the initial number of grid regions. The total number of nodes reflects the scale of the power system, while connection density reflects the tightness of connections between nodes. By analyzing the total number of nodes and connection density, the initial number of grid regions can be reasonably determined.

[0063] Specifically, power systems with a large number of nodes and high connection density typically require more grid areas for more granular management and optimization. Conversely, systems with fewer nodes and lower connection density may only need fewer grid areas. For example, a large urban power grid, due to its numerous nodes and complex connections, may require multiple grid areas, each responsible for managing a certain number of nodes and lines. In contrast, a small rural power grid, with relatively fewer nodes and lines and simpler connections, may only require fewer grid areas.

[0064] Based on the aforementioned network topology, a power grid connection diagram is established, using nodes and lines as its foundation. This diagram is a visual representation of the power system network topology, clearly showing the connections between nodes within the power system. Nodes can represent power plants, substations, users, etc., while lines represent transmission lines. In this embodiment, the location, type, and function of each node, as well as the start and end points and transmission capacity of each line, are recorded in detail when establishing the power grid connection diagram. For example, for a power system containing multiple power plants and substations, the power grid connection diagram can clearly show the connections between each power plant and substation, as well as the connections between substations and users.

[0065] Based on the power flow data in the initial operating state, the electrical distances between nodes are calculated. The power flow data in the initial operating state is a crucial indicator of the power system's operating status. This data includes information such as voltage, current, and power at each node, which can be used to calculate the electrical distances between nodes. Electrical distance is an important indicator of the tightness of the electrical connection between two nodes, reflecting the ease with which current can be transmitted between them. Methods for calculating electrical distances are typically based on node admittance matrices or impedance matrices. By analyzing the power flow data, the node admittance or impedance matrices can be obtained, and thus the electrical distances between nodes can be calculated. For example, in a power system containing multiple nodes, the electrical distance between each node and other nodes can be calculated by analyzing the power flow data.

[0066] The power system is initially gridded based on the number of grid regions, the power grid connection diagram, and the electrical distances, and an initial grid partitioning scheme is output. After determining the number of grid regions, establishing the power grid connection diagram, and calculating the electrical distances between nodes, the power system can be initially gridded. The goal of the initial grid partitioning is to divide the power system into multiple relatively independent grid regions, where nodes and lines within each region have high electrical connectivity, while electrical connections between different regions are relatively few. Various algorithms can be used for the initial grid partitioning, such as graph theory algorithms and clustering algorithms. These algorithms can divide the nodes and lines in the power system into multiple grid regions based on the power grid connection diagram and electrical distances. For example, the minimum spanning tree algorithm in graph theory or the K-means algorithm in clustering can be used to divide nodes into multiple grid regions based on electrical distances, ensuring high electrical connectivity between nodes and lines within each grid region, while electrical connections between different regions are relatively few.

[0067] Step S13: Based on the load forecast data and the initial operating state, predict the system operating state, and based on the prediction results and the initial grid partitioning scheme, initiate the dynamic grid adjustment criterion and output the grid dynamic adjustment command.

[0068] Based on the load forecast data and the initial operating status, the power change trend of interconnections between regions is predicted for future periods. The operating status of the power system is affected by a variety of factors, among which load forecast data and initial operating status are two key factors. Load forecast data provides the load change trend of each region in the future period, while the initial operating status reflects the actual operating condition of the current power system.

[0069] Specifically, load forecasting data typically includes load demand, load growth trends, and load fluctuations in various regions over the future. This data can be obtained through comprehensive analysis of multiple data sources, such as historical load data, meteorological data, and social activity information. Initial operating status includes information such as the current output of each generator unit, bus voltage levels, and power flow distribution of transmission lines. By analyzing this data, the power change trends of inter-regional tie lines can be predicted over the future.

[0070] For example, in a power system comprising multiple regions, by analyzing load forecast data and initial operating conditions, the power changes of the interconnections between regions can be predicted over future periods. If the load demand of a certain region increases significantly in the future, while the region's generating capacity is limited, then the power of the interconnections between that region and other regions may increase accordingly.

[0071] Based on the power change trend, it is necessary to determine whether the power of each tie line exceeds the transmission capacity limit corresponding to the initial grid partitioning scheme. After predicting the power change trend of tie lines between regions in the future, it is necessary to further determine whether these power changes exceed the transmission capacity limit corresponding to the initial grid partitioning scheme. The transmission capacity limit refers to the maximum power that a tie line can transmit under safe operating conditions. This limit is usually determined by the physical characteristics of the tie line (such as line length, conductor cross-sectional area, etc.) and operating conditions (such as voltage level, temperature, etc.). If the tie line power exceeds the transmission capacity limit, it may lead to line overload, voltage drop, or even system stability problems. Therefore, it is necessary to make timely judgments based on the predicted power change trend.

[0072] Suppose that the transmission capacity limit of a certain tie line is 500MW, but according to the forecast, the power of the tie line will increase to 600MW in the future. In this case, the power of the tie line exceeds the transmission capacity limit. In this case, corresponding measures need to be taken to adjust the grid operation mode to ensure the safe operation of the power system.

[0073] Based on the judgment results, a grid adjustment command is generated and output for the area where the power exceeds the limit of the tie line.

[0074] Once it is determined that the power of a certain tie line exceeds the transmission capacity limit, a grid adjustment command needs to be generated for the area where the tie line with the excessive power is located. The grid adjustment command is a specific operation command for dynamically adjusting the power grid operation mode, and it can take different adjustment measures according to different situations.

[0075] For example, if the power of a certain tie line exceeds the limit, the following adjustment measures can be taken:

[0076] Adjusting power generation plans: By adjusting the output of generator units in relevant areas, the power transmission of over-limit interconnection lines can be reduced.

[0077] Adjusting power flow distribution: By adjusting the power flow distribution of the power grid, some loads are transferred to other tie lines to reduce the burden on overloaded tie lines.

[0078] Adjusting the power grid structure: By adjusting the topology of the power grid, such as changing the switch status, connecting or disconnecting certain lines, the operation mode of the power grid can be optimized.

[0079] The generated grid adjustment instructions need to clearly specify the details of the adjustment and the operational steps so that grid operators can execute them quickly. These instructions can be sent directly to relevant equipment through automated systems or implemented manually by dispatchers.

[0080] For example, if the power of a certain tie line exceeds the limit, the generated grid adjustment command may include the following:

[0081] Adjust the output of generator set A to reduce it by 100MW.

[0082] Put standby line B into operation and transfer part of the load to line B.

[0083] Cut off non-critical load C to reduce the burden on overloaded tie lines.

[0084] Step S14: According to the grid dynamic adjustment instruction, the initial grid division scheme is adjusted in real time to generate a dynamically optimized grid, and the power system is partitioned and decoupled based on the dynamically optimized grid, and the decoupled sub-region system models are output.

[0085] Based on the adjustment area and direction specified in the mesh dynamic adjustment command, the initial mesh division scheme is modified, and nodes with similar electrical distances are re-divided into the same mesh area to generate a dynamically optimized mesh.

[0086] The initial grid partitioning scheme of a power system is based on a certain network topology and operating status. However, in actual operation, due to factors such as load changes, equipment failures, and maintenance work, the initial grid partitioning scheme needs to be dynamically adjusted.

[0087] Specifically, the grid dynamic adjustment command will clearly indicate which areas of the grid need adjustment, and the specific direction of the adjustment. For example, if the power of a tie line exceeds its limit, the adjustment command may require the area containing that tie line to be re-divided to reduce the load on the tie line. When making adjustments, the electrical distance between nodes needs to be considered, and nodes with similar electrical distances should be re-divided into the same grid area.

[0088] Suppose a power grid in a certain area initially included multiple nodes, but according to dynamic adjustment instructions, some nodes in this area need to be reassigned to adjacent areas. By calculating the electrical distances between these nodes, it can be determined which nodes should be reassigned. If the electrical distances between some nodes are relatively short, it indicates that their electrical connections are relatively tight. Reassigning these nodes to the same grid area can better optimize the operation of the power grid.

[0089] Based on the dynamic optimization grid, the power system is decoupled into several independent sub-region systems, and the decoupled sub-region system model is output, which includes the node composition, parameters and boundary conditions of each sub-region.

[0090] After generating the dynamic optimization grid, the next step is to decouple the power system into several independent sub-regional systems based on these grids. The purpose of decoupling is to break down the complex power system into multiple relatively independent subsystems for more effective management and optimization. Each sub-regional system contains a certain number of nodes, lines, and devices, and these subsystems are interconnected through boundary conditions.

[0091] Specifically, the decoupled sub-region system model needs to include the following:

[0092] Node composition: A list of nodes in each sub-region system, including generation nodes, load nodes, substation nodes, etc. The composition of these nodes reflects the basic structure and function of the sub-region system.

[0093] Parameters: Equipment parameters for each sub-region system, such as generator output capacity, line impedance, and transformer turns ratio. These parameters are the basic data for the operation and optimization of the sub-region system.

[0094] Boundary conditions: These are the connection boundary conditions between sub-regional systems, including the power transmission direction and power transmission limits of tie lines. Boundary conditions are a crucial factor in the mutual influence between sub-regional systems; by setting appropriate boundary conditions, the coordinated operation of the entire power system can be ensured.

[0095] Suppose a power system is decoupled into three sub-regional systems, each containing a certain number of nodes and devices. The first sub-regional system primarily supplies power to the city center, including multiple substations and load nodes; the second sub-regional system primarily supplies power to the industrial area, including multiple power plants and large load nodes; and the third sub-regional system primarily supplies power to the suburbs, including some small power plants and dispersed load nodes. Each sub-regional system has its unique node composition and device parameters. These sub-regional systems are interconnected via tie lines, with the power transmission direction and limits of the tie lines serving as boundary conditions.

[0096] This decoupling approach allows complex power systems to be broken down into multiple relatively independent sub-regional systems, each of which can be managed and optimized independently. This partitioned decoupling method not only improves the operational efficiency of the power system but also enhances its reliability and flexibility, providing strong support for optimized power system scheduling and fault analysis.

[0097] Step S15: Based on the decoupled sub-region system models, solve the optimal power flow problem of each sub-region in parallel, coordinate the optimization results of each sub-region, and output the globally optimal power system dispatching scheme.

[0098] Based on the decoupled sub-region system models, the optimal power flow distribution within each sub-region is calculated in parallel. A power system is a vast and complex network, and its operation is affected by various factors, such as load changes, generation plans, and equipment failures. To improve the operating efficiency and reliability of the power system, it is necessary to partition and decouple the power system, dividing it into multiple relatively independent sub-region systems. These sub-region system models contain key information such as the node composition, parameters, and boundary conditions of each sub-region.

[0099] The goal of the optimal power flow problem is to minimize the operating cost of the power system by adjusting the generation plan and controlling the status of equipment, while satisfying the system's security and reliability constraints. For each sub-regional system, the optimal power flow problem can be solved independently based on its own system model. Parallel computing can make full use of computing resources and improve computing efficiency.

[0100] When performing parallel calculations of the optimal power flow distribution within each subregion, it is necessary to consider the internal structure and operating characteristics of each subregion. For example, for a subregional system containing multiple power plants and substations, the optimal power flow calculation needs to comprehensively consider factors such as generation costs, line losses, and equipment capacity limitations. By establishing corresponding mathematical models and optimization algorithms, the optimal power flow distribution within the subregional system can be solved, including information such as the output of each generator, the voltage level of each node, and the power flow of each line.

[0101] Based on the optimal power flow distributions described, the boundary node voltage and power exchange information of the corresponding sub-regions are obtained. In the decoupled sub-region systems, although each sub-region system can independently perform optimal power flow calculations, the sub-region systems are not completely isolated. They are interconnected through boundary nodes, resulting in power exchange. Therefore, after solving for the optimal power flow distribution within each sub-region, it is necessary to further obtain the boundary node voltage and power exchange information of the corresponding sub-regions.

[0102] Boundary node voltage directly reflects the electrical state of the sub-region system at that node, while power exchange information indicates the power transfer between sub-region systems through the boundary node.

[0103] Obtaining voltage and power exchange information at a boundary node requires comprehensive consideration of the optimal power flow distribution within the sub-region system and the connectivity between sub-region systems. Assume sub-region A and sub-region B are connected by a boundary node. The optimal power flow calculation for sub-region A shows that the voltage at this boundary node is 1.0 pu, and the power transferred to sub-region B is 100 MW. Conversely, the optimal power flow calculation for sub-region B shows that the voltage at this boundary node is 0.98 pu, and the power received from sub-region A is 100 MW. By comparing this information, it can be observed that there is a voltage difference and a balanced power exchange between sub-regions A and B at this boundary node.

[0104] Based on the boundary node voltage and the power exchange information, the power generation output of each sub-region is iteratively corrected by the alternating direction multiplier method so that each sub-region achieves power exchange balance.

[0105] After obtaining the boundary node voltage and power exchange information of each sub-region, in order to ensure the coordinated operation of the entire power system, it is necessary to coordinate and correct the power generation output of each sub-region to achieve a balance in power exchange between them. This embodiment achieves this by applying the Alternating Direction Multiplier Method (ADMM) algorithm.

[0106] The alternating direction multiplier method is an effective distributed optimization algorithm suitable for handling multi-region coordinated optimization problems. By introducing auxiliary variables and multipliers, it decomposes the complex multi-region coordinated optimization problem into several relatively simple subproblems, which are solved separately in each sub-region system. In each iteration, each sub-region system updates its own power generation output based on the current boundary node voltage and power exchange information, and gradually corrects the power exchange imbalance between sub-regions by exchanging boundary information.

[0107] In the scenario of sub-regions A and B described above, initially, the boundary node voltage of sub-region A is 1.0 pu, and the power transmitted to sub-region B is 100 MW. Meanwhile, the boundary node voltage of sub-region B is 0.98 pu, and the power received from sub-region A is 100 MW. Through the first iteration of the alternating direction multiplier method, sub-region A may adjust its power output based on the boundary information of sub-region B, causing its boundary node voltage to drop to 0.99 pu. Conversely, sub-region B may adjust its power output based on the boundary information of sub-region A, causing its boundary node voltage to rise to 0.99 pu. After multiple iterations, the boundary node voltages of the two sub-regions gradually converge, and the power exchange reaches equilibrium.

[0108] Based on the power generation output that achieves power exchange balance, a power system dispatching scheme is generated and output.

[0109] After the coordination and correction steps described above, the power generation output of each sub-region has reached a power exchange balance. At this point, a globally optimal power system dispatching scheme can be generated based on these balanced power generation outputs.

[0110] When generating the scheduling scheme, this embodiment integrates information such as power generation output, equipment status, and power flow distribution of each sub-region to form a complete and coordinated power system operation scheme.

[0111] After coordinating and correcting sub-regions A and B, the final power system dispatch scheme might include: adjusting the output of generator unit 1 in sub-region A to 500 MW and generator unit 2 to 300 MW; adjusting the output of generator unit 3 in sub-region B to 400 MW; and adjusting the reactive power compensation equipment capacity of substation 1 to 20 Mvar, etc. By sending these detailed dispatch instructions to the operating units of various power plants, substations, etc., it can be ensured that the power system operates according to the globally optimal scheme, thereby improving the operating efficiency and reliability of the power system.

[0112] Preferably, in the process of generating scheduling schemes based on the decoupled sub-region system models, this embodiment introduces master-slave game theory to further improve the efficiency and stability of power grid operation. Specifically, the global dispatch center of the power system is regarded as the "master," while the dispatch centers of each sub-region are regarded as the "slave." The master-slave game model can effectively simulate the coordination and competition relationship between the master and slave, realizing the optimal dispatch of the power system.

[0113] In the master-slave game framework, the global dispatch center (master) is responsible for formulating the global dispatch strategy and guiding the dispatch decisions of each sub-region. Its goal is to achieve globally optimal dispatch for the entire power system, considering factors such as system operating costs, reliability, and stability. Each sub-region dispatch center (slave) aims to achieve optimal dispatch for its own sub-region while satisfying the master's global dispatch strategy. The slave's goal is to minimize the operating costs of its sub-region and ensure the safety and stability of equipment within its sub-region. Through the coordination mechanism of the master-slave game model, the master and slave can effectively exchange information and adjust strategies, achieving a balance between global and local optimization.

[0114] In the master-slave game, the master can set constraints on power generation output and power flow distribution for each sub-region based on the overall power grid load forecast and operating status. The slave, in turn, adjusts its own generator output and reactive power compensation equipment based on these constraints and the optimal power flow calculation results within the sub-region, aiming to achieve the optimal operating state within that sub-region. Simultaneously, the slave feeds back the adjusted boundary node voltage and power exchange information to the master, which then further adjusts the global dispatch strategy based on this information. Through this interactive master-slave game, the power exchange balance between sub-regions can be better coordinated, ensuring the stable operation of the entire power system and improving the grid's operating efficiency and reliability.

[0115] The power system optimization method based on dynamic gridding in this embodiment significantly improves the system's adaptability to load fluctuations and network topology changes through dynamic grid partitioning and real-time adjustment mechanisms. It automatically adjusts the grid partitioning based on real-time operating status prediction results to ensure that each sub-region always maintains optimal electrical coupling characteristics, thereby effectively avoiding the optimization accuracy degradation problem that occurs when the system operating status changes in traditional fixed partitioning methods. The partitioned parallel computing architecture greatly improves the optimization computation efficiency of large-scale power grids. By decoupling the entire system into multiple electrically connected sub-regions to solve the optimal power flow in parallel, the complex large-scale optimization problem is decomposed into multiple smaller-scale sub-problems, significantly reducing computation time and meeting the timeliness requirements of real-time power system optimization.

[0116] This invention also provides a power system optimization device based on dynamic gridding, used to execute the power system optimization method based on dynamic gridding as described above. Figure 2 This is a structural block diagram of a power system optimization device based on dynamic gridding according to an embodiment of the present invention. The device includes:

[0117] Parameter input module 21 is used to input raw parameters of the power system, including network topology, load forecast data and initial operating status;

[0118] The mesh partitioning module 22 is used to perform initial mesh partitioning of the power system based on the input network topology and the initial operating state, and output the initial mesh partitioning scheme;

[0119] The dynamic adjustment module 23 is used to predict the system operating status based on the load forecast data and the initial operating status, and to initiate the dynamic grid adjustment criterion and output the grid dynamic adjustment command based on the prediction results and the initial grid partitioning scheme.

[0120] The system partitioning module 24 is used to adjust the initial grid division scheme in real time according to the grid dynamic adjustment instruction, generate a dynamically optimized grid, and perform partitioning and decoupling of the power system based on the dynamically optimized grid, and output the decoupled sub-region system model.

[0121] The scheme optimization module 25 is used to solve the optimal power flow problem of each sub-region in parallel based on the decoupled sub-region system model, coordinate the optimization results of each sub-region, and output the globally optimal power system dispatch scheme.

[0122] Furthermore, the mesh generation module is used for:

[0123] The initial number of grid regions is determined based on the total number of nodes and connection density in the network topology.

[0124] Based on the aforementioned network topology, a power grid connection diagram based on nodes and lines is established;

[0125] Calculate the electrical distance between nodes based on the power flow data in the initial operating state;

[0126] The power system is initially divided into grids based on the number of grid regions, the power grid connection diagram, and the electrical distance, and the initial grid division scheme is output.

[0127] Furthermore, the dynamic adjustment module is used for:

[0128] Based on the load forecast data and the initial operating status, predict the power change trend of the interconnection lines between regions in the future period;

[0129] Based on the power change trend, determine whether the power of each tie line exceeds the corresponding transmission capacity limit in the initial grid partitioning scheme;

[0130] Based on the judgment results, a grid adjustment command is generated and output for the area where the power exceeds the limit of the tie line.

[0131] The technical features and effects of the device proposed in the embodiments of the present invention are the same as those of the method proposed in the embodiments of the present invention, and will not be repeated here. Each module in the above-described device can be implemented entirely or partially through software, hardware, or a combination thereof. Each module can be embedded in or independent of the processor in a computer device in hardware form, or stored in the memory of a computer device in software form, so that the processor can call and execute the operations corresponding to each module.

[0132] See Figure 3This is a structural block diagram of a computer device provided in an embodiment of the present invention. The computer device includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements the steps described in the above embodiment of the power system optimization method based on dynamic gridding. Figure 1 Steps S11 to S15 as described above; or, when the processor executes the computer program, it implements the functions of each module in the above-described device embodiments, such as modules 21 to 25 of the power system optimization device based on dynamic gridding.

[0133] For example, the computer program may be divided into one or more modules, which are stored in the memory and executed by the processor to complete the present invention. The one or more modules may be a series of computer program instruction segments capable of performing a specific function, which describe the execution process of the computer program in the computer device.

[0134] The computer device may include, but is not limited to, a processor and memory. Those skilled in the art will understand that the schematic diagram is merely an example of a computer device and does not constitute a limitation on the computer device. It may include more or fewer components than illustrated, or combine certain components, or different components. For example, the computer device may also include input / output devices, network access devices, buses, etc.

[0135] The processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or any conventional processor. The processor is the control center of the computer device, connecting various parts of the computer device via various interfaces and lines.

[0136] The memory can be used to store the computer programs and / or modules. The processor implements various functions of the computer device by running or executing the computer programs and / or modules stored in the memory and by calling data stored in the memory. The memory may mainly include a program storage area and a data storage area. The program storage area may store the operating system, at least one application program required for a function (such as sound playback function, image playback function, etc.), etc.; the data storage area may store data created according to the use of the mobile phone (such as audio data, phonebook, etc.). In addition, the memory may include high-speed random access memory, and may also include non-volatile memory, such as hard disk, memory, plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, at least one disk storage device, flash memory device, or other volatile solid-state storage device.

[0137] If the modules integrated into the computer device are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc.

[0138] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. The storage medium can be a magnetic disk, optical disk, read-only memory (ROM), or random access memory (RAM), etc.

[0139] Accordingly, embodiments of the present invention provide a computer-readable storage medium comprising a stored computer program, wherein, when the computer program is executed, it controls the device containing the computer-readable storage medium to perform steps in the power system optimization method based on dynamic gridding as described in the above embodiments, for example... Figure 1 Steps S11 to S15 as described above.

[0140] In summary, compared with the prior art, the power system optimization method, apparatus, computer equipment, and computer-readable storage medium based on dynamic gridding provided by the embodiments of the present invention have the following beneficial effects:

[0141] By employing a dynamic grid partitioning and real-time adjustment mechanism, the system's adaptability to load fluctuations and network topology changes is significantly improved. The grid partitioning is automatically adjusted based on real-time operating status prediction results, ensuring that each sub-region always maintains optimal electrical coupling characteristics. This effectively avoids the optimization accuracy degradation problem that occurs when the system operating status changes in traditional fixed partitioning methods. The adoption of a partitioned parallel computing architecture greatly improves the optimization computation efficiency of large-scale power grids. By decoupling the entire system into multiple electrically connected sub-regions and solving the optimal power flow in parallel, the complex large-scale optimization problem is decomposed into multiple smaller-scale sub-problems, significantly reducing computation time and meeting the timeliness requirements of real-time optimization of power systems.

[0142] The embodiments described above are merely illustrative of several implementations of the present invention, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these modifications and improvements all fall within the scope of protection of the present invention. Therefore, the scope of protection of this patent should be determined by the appended claims.

Claims

1. A power system optimization method based on dynamic gridding, characterized in that, include: Input the original parameters of the power system, including network topology, load forecast data, and initial operating status; Based on the input network topology and the initial operating state, the power system is initially meshed, and the initial meshing scheme is output. Based on the load forecast data and the initial operating status, the system operating status is predicted, the power changes of the interconnection lines between regions are predicted in the future period, and based on the prediction results and the initial grid division scheme, the dynamic grid adjustment criterion is activated and the grid dynamic adjustment command is output. According to the grid dynamic adjustment command, the initial grid division scheme is adjusted in real time to generate a dynamically optimized grid, and the power system is partitioned and decoupled based on the dynamically optimized grid, and the decoupled sub-region system model is output. Based on the decoupled sub-region system models, the optimal power flow problem of each sub-region is solved in parallel, and the optimization results of each sub-region are coordinated to output the globally optimal power system dispatching scheme.

2. The power system optimization method based on dynamic gridding as described in claim 1, characterized in that, Based on the input network topology and the initial operating state, the power system is initially meshed, and an initial meshing scheme is output, including: The initial number of grid regions is determined based on the total number of nodes and connection density in the network topology. Based on the aforementioned network topology, a power grid connection diagram based on nodes and lines is established; Calculate the electrical distance between nodes based on the power flow data in the initial operating state; The power system is initially divided into grids based on the number of grid regions, the power grid connection diagram, and the electrical distance, and the initial grid division scheme is output.

3. The power system optimization method based on dynamic gridding as described in claim 1, characterized in that, The process of predicting the system operating status based on the load forecast data and the initial operating status, and then, based on the prediction results and the initial grid partitioning scheme, initiating a dynamic grid adjustment criterion and outputting a grid dynamic adjustment command, includes: Based on the load forecast data and the initial operating status, predict the power change trend of the interconnection lines between regions in the future period; Based on the power change trend, determine whether the power of each tie line exceeds the corresponding transmission capacity limit in the initial grid partitioning scheme; Based on the judgment results, a grid adjustment command is generated and output for the area where the power exceeds the limit of the tie line.

4. The power system optimization method based on dynamic gridding as described in claim 1, characterized in that, The process of adjusting the initial grid partitioning scheme in real time according to the grid dynamic adjustment command to generate a dynamically optimized grid, and performing partitioning and decoupling of the power system based on the dynamically optimized grid, outputting the decoupled system models of each sub-region, includes: Based on the adjustment area and adjustment direction specified in the mesh dynamic adjustment instruction, the initial mesh division scheme is modified, and nodes with similar electrical distances are re-divided into the same mesh area to generate a dynamically optimized mesh. Based on the dynamic optimization grid, the power system is decoupled into several independent sub-region systems, and the decoupled sub-region system model is output, which includes the node composition, parameters and boundary conditions of each sub-region.

5. The power system optimization method based on dynamic gridding as described in claim 1, characterized in that, Based on the decoupled sub-region system models, the optimal power flow problem of each sub-region is solved in parallel, and the optimization results of each sub-region are coordinated to output a globally optimal power system dispatching scheme, including: Based on the decoupled sub-region system models, the optimal power flow distribution within each sub-region is calculated in parallel. Based on the optimal power flow distributions described above, obtain the boundary node voltage and power exchange information of the corresponding sub-regions; Based on the boundary node voltage and the power exchange information, the power generation output of each sub-region is iteratively corrected by the alternating direction multiplier method so that each sub-region achieves power exchange balance. Based on the power generation output that achieves power exchange balance, a power system dispatching scheme is generated and output.

6. A power system optimization device based on dynamic gridding, characterized in that, include: The parameter input module is used to input the original parameters of the power system, including network topology, load forecast data, and initial operating status. The grid partitioning module is used to perform initial grid partitioning of the power system based on the input network topology and the initial operating state, and output the initial grid partitioning scheme. The dynamic adjustment module is used to predict the system operating status based on the load forecast data and the initial operating status, predict the power changes of the tie lines between regions in the future period, and based on the prediction results and the initial grid division scheme, initiate the dynamic grid adjustment criteria and output the grid dynamic adjustment command. The system partitioning module is used to adjust the initial grid division scheme in real time according to the grid dynamic adjustment instruction, generate a dynamically optimized grid, and partition and decouple the power system based on the dynamically optimized grid, and output the decoupled sub-region system model. The scheme optimization module is used to solve the optimal power flow problem of each sub-region in parallel based on the decoupled sub-region system model, coordinate the optimization results of each sub-region, and output the globally optimal power system dispatch scheme.

7. The power system optimization device based on dynamic gridding as described in claim 6, characterized in that, The grid division module is used for: The initial number of grid regions is determined based on the total number of nodes and connection density in the network topology. Based on the aforementioned network topology, a power grid connection diagram based on nodes and lines is established; Calculate the electrical distance between nodes based on the power flow data in the initial operating state; The power system is initially divided into grids based on the number of grid regions, the power grid connection diagram, and the electrical distance, and the initial grid division scheme is output.

8. The power system optimization device based on dynamic gridding as described in claim 6, characterized in that, The dynamic adjustment module is used for: Based on the load forecast data and the initial operating status, predict the power change trend of the interconnection lines between regions in the future period; Based on the power change trend, determine whether the power of each tie line exceeds the corresponding transmission capacity limit in the initial grid partitioning scheme; Based on the judgment results, a grid adjustment command is generated and output for the area where the power exceeds the limit of the tie line.

9. A computer device, characterized in that, It includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein the processor executes the computer program to implement the power system optimization method based on dynamic gridding as described in any one of claims 1 to 5.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, wherein when the device containing the computer-readable storage medium executes the computer program, it implements the power system optimization method based on dynamic gridding as described in any one of claims 1 to 5.

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