Intelligent power dispatching optimization method and system

Abstract

The invention relates to the field of intelligent power dispatching optimization, in particular to an intelligent power dispatching optimization method and system, and the method comprises the steps: collecting multi-source data, and carrying out the construction of a power grid diagram; performing evaluation analysis according to the power grid diagram, and performing comprehensive evaluation on an analysis result; according to a comprehensive evaluation result, judging an equipment risk level; and performing dynamic adjustment according to the risk level. The method comprises the following steps of: acquiring real-time state data of power equipment, topological structure data of a power grid, power load data and meteorological environment data, and constructing a weighted power grid graph containing equipment operation characteristics, load requirements and environmental influences; and based on the power grid diagram, respectively carrying out equipment redundancy assessment, power grid topology stability analysis and load adaptability analysis to form a multi-angle comprehensive assessment result.

Description

Technical Field

[0001] The present invention relates to the field of intelligent power dispatching optimization, and in particular to an intelligent power dispatching optimization method and system. Background Art

[0002] As power systems continue to expand in size and become more complex, traditional power dispatch methods are no longer able to meet the growing demands for intelligent and electrified systems. Power equipment is widely distributed, operates in complex conditions, and is significantly affected by factors such as weather conditions and load fluctuations. Consequently, power systems are increasingly demanding dispatch optimization and fault risk management. Existing dispatch methods mostly rely on static data or empirical rules, lacking in-depth integrated analysis of the grid's real-time status, environmental changes, and dynamic load response capabilities. This results in insufficient flexibility and fault tolerance in system dispatch when faced with sudden load fluctuations, equipment failures, or extreme weather events, easily leading to localized power outages or cascading failures.

[0003] At the same time, existing technologies for assessing power equipment risk often rely on a single metric (such as failure rate or load factor), lacking a comprehensive analysis mechanism for multi-source data. This makes it difficult to accurately reflect the comprehensive fault tolerance capabilities of equipment under different operating environments. Therefore, there is an urgent need for an intelligent power dispatching method that integrates multi-source data, can dynamically perceive the grid status, comprehensively assess the grid's fault tolerance capabilities, and dynamically optimize dispatch based on risk levels, in order to improve the safety, reliability, and economic efficiency of the power system. Summary of the Invention

[0004] In view of the above problems in the prior art, the present invention is proposed.

[0005] To solve the above technical problems, the present invention provides the following technical solutions: an intelligent power dispatch optimization method, comprising: collecting multi-source data and constructing a power grid diagram;

[0006] Conduct evaluation and analysis based on the power grid diagram and make a comprehensive assessment of the analysis results;

[0007] Determine the equipment risk level based on the comprehensive assessment results;

[0008] Dynamic adjustments are made based on risk levels.

[0009] By collecting multi-source data to construct a power grid map, conducting evaluation and analysis based on the power grid map, judging the equipment risk level after comprehensive evaluation and dynamically adjusting the dispatch, the power dispatch process can perceive and adaptively optimize the real-time status changes of the power grid and equipment operation risks, overcoming the problems of static, passive and delayed response of traditional power dispatch.

[0010] As a preferred solution of the intelligent power dispatch optimization method described in the present invention, the multi-source data includes real-time status data of power equipment, power grid topology data, power load data, and meteorological environment data.

[0011] By introducing multi-source information such as real-time status data of power equipment, grid topology data, power load data and meteorological environment data, the comprehensive perception capability of the grid status is improved, and multi-dimensional analysis of equipment operation, network connection, load changes and external environmental impact is achieved, solving the problems of single data source and incomplete monitoring in existing technologies.

[0012] As a preferred embodiment of the intelligent power dispatch optimization method described in the present invention, the power grid diagram construction includes analyzing the real-time status of power equipment and power load data, identifying key nodes in the power grid, each key equipment corresponding to a node in the power grid diagram, determining the connection relationship between nodes based on the power grid topology data, each connection corresponding to an edge in the power grid diagram, and setting a comprehensive weight for each connection based on the equipment operating status, historical failure rate, and meteorological environment changes. Based on the node set and edge set, a weighted power grid diagram with physical, electrical, and environmental attributes is generated;

[0013] The key nodes include generators, substations, transmission line nodes and load centers.

[0014] By parsing multi-source data, identifying key nodes and constructing a weighted power grid diagram with physical, electrical and environmental attributes, the visualization and quantitative modeling of the power grid structure and its operating characteristics are achieved, the ability to accurately describe the overall operating status of the power grid is enhanced, and subsequent fault tolerance assessment and risk identification are effectively supported. It solves the problems of rough power grid structure modeling and missing attribute information in existing methods.

[0015] As a preferred solution of the intelligent power dispatch optimization method described in the present invention, the evaluation and analysis based on the power grid diagram includes equipment redundancy evaluation, power grid topology stability analysis, and load adaptability analysis.

[0016] By conducting equipment redundancy assessment, grid topology stability analysis, and load adaptability analysis based on the power grid diagram, the grid operation status is comprehensively evaluated from three perspectives: structural redundancy, network connectivity, and load dynamic adaptability. This improves the accuracy of fault prevention and load regulation, and overcomes the problems of the existing single evaluation method and insufficient analysis dimensions.

[0017] As a preferred solution of the intelligent power dispatching optimization method described in the present invention, the equipment redundancy evaluation includes calculating the maximum flow from the source node to the sink, the maximum flow is equal to the size of the minimum cut in the power grid, and evaluating the redundancy according to the minimum cut theorem.

[0018] The grid topology stability analysis includes:

[0019] Calculate the reachability value of each node, and through DFS traversal, dynamically update the reachability value of the node based on the connection between nodes and the weight of the edge, and make a judgment;

[0020] The load adaptability analysis includes:

[0021] A load balance equation is established to ensure that each node in the power grid meets the power flow conservation constraint. Load adjustment factors and task priority factors are introduced. The load demand of each node is dynamically corrected based on real-time environmental data. The simulated annealing algorithm is used to optimize the scheduling scheme. Through step-by-step search and temperature decay strategies, the optimal solution is found globally and the most adaptable scheduling scheme is output.

[0022] The maximum flow and minimum cut theories are used to quantitatively analyze equipment redundancy, the grid topology stability is dynamically evaluated based on node reachability, and the simulated annealing algorithm is combined to optimize load adaptive scheduling. This effectively realizes fault-tolerant evaluation and rapid adaptation of the grid under faults or load disturbances, solving the technical bottlenecks of fault assessment lag and lack of global optimality in load scheduling in traditional methods.

[0023] As a preferred solution of the intelligent power dispatch optimization method described in the present invention, the comprehensive evaluation of the analysis results is expressed as:

[0024] F=w1R+w2T+w3A

[0025] Among them, F is the final comprehensive fault tolerance score, F∈[0,1]; w1, w2, w3 are the weight coefficients of each indicator, satisfying:

[0026] w1+w2+w3=1,w1,w2,w3≥0.

[0027] By setting up a unified comprehensive fault tolerance scoring model and integrating redundancy, topology stability and load adaptability indicators according to weights, a quantifiable and unified comprehensive evaluation system is formed, which improves the objectivity and accuracy of the judgment of the power grid operation status and overcomes the problems of scattered evaluation results and difficulty in unified decision-making in existing technologies.

[0028] As a preferred solution of the intelligent power dispatch optimization method described in the present invention, the risk level of the equipment is determined as follows:

[0029] When A2≤F≤A1, it is judged as a low-risk device;

[0030] When A3≤F<A2, it is judged as medium-risk equipment;

[0031] When F<A3, it is judged as high-risk equipment.

[0032] By setting risk level classification standards based on comprehensive fault tolerance scores, equipment is divided into low-risk, medium-risk and high-risk levels, achieving accurate identification and classification of equipment risks, providing a clear basis for dynamic scheduling optimization, solving the problems of extensive and slow response of existing risk assessment methods, and improving the accuracy and foresight of scheduling decisions.

[0033] As a preferred solution of the intelligent power dispatching optimization method described in the present invention, an intelligent power dispatching optimization system includes: an acquisition module, an analysis module, a judgment module, and an adjustment module.

[0034] A computer device includes a memory and a processor, wherein the memory stores a computer program, and is characterized in that when the processor executes the computer program, the steps of the intelligent power dispatch optimization method described above are implemented.

[0035] A computer-readable storage medium having a computer program stored thereon, characterized in that when the computer program is executed by a processor, the steps of the intelligent power dispatch optimization method as described above are implemented.

[0036] The present invention has the following beneficial effects: By collecting real-time data on power equipment status, grid topology, power load, and meteorological environment data, a weighted power grid diagram is constructed that incorporates equipment operating characteristics, load demands, and environmental impacts. Based on this diagram, equipment redundancy assessment, grid topology stability analysis, and load adaptability analysis are performed, resulting in a comprehensive, multi-faceted assessment.

[0037] This method determines equipment risk levels based on comprehensive fault tolerance scores and dynamically adjusts grid dispatch plans based on these risk levels. This enables dispatch decisions to adaptively respond to real-time grid state changes, potential equipment risks, and dynamic load fluctuations. Compared to existing methods, this method more accurately reflects the overall and local fault tolerance levels of the grid, improving fault prevention capabilities and emergency dispatch efficiency, further enhancing the operational stability and economic efficiency of the power system. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0039] Figure 1 A flowchart of an intelligent power dispatch optimization method provided by one embodiment of the present invention. DETAILED DESCRIPTION

[0040] To make the above-mentioned objects, features, and advantages of the present invention more clearly understood, the following detailed description of the specific embodiments of the present invention is given in conjunction with the accompanying drawings. It is obvious that the described embodiments are only part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in this field without creative work should fall within the scope of protection of the present invention.

[0041] Example 1, with reference to Figure 1 , is an embodiment of the present invention, which provides an intelligent power dispatch optimization method, comprising:

[0042] S1: Collect multi-source data and construct power grid diagram

[0043] By introducing multi-source data such as real-time status data of power equipment, power grid topology data, power load data, meteorological environment data, etc., and integrating physical, electrical and environmental properties, a weighted power grid map is constructed. This not only comprehensively reflects the operating status of the equipment, but also takes into account the impact of the external environment on the power grid, greatly improving the integrity and real-time performance of status perception.

[0044] Through detailed modeling of nodes, edges, and weights, the physical structure and operating characteristics of the power grid are reflected dynamically in real time, overcoming the shortcomings of traditional static topology models and laying the foundation for subsequent accurate analysis.

[0045] S2: Conduct evaluation and analysis based on the power grid diagram and conduct a comprehensive evaluation of the analysis results

[0046] Based on the power grid diagram, the maximum flow-minimum cut theory is combined to evaluate the equipment redundancy, the DFS traversal is used to evaluate the power grid topology stability, and the load adaptability is analyzed through the load balance equation and simulated annealing algorithm.

[0047] Then, various indicators are integrated through a unified weighted comprehensive evaluation model to form a consistent and highly reliable comprehensive score for power grid operation, avoiding the problems of traditional evaluations being independent and with inconsistent standards, and achieving an integrated and standardized status assessment.

[0048] S3: Determine the equipment risk level based on the comprehensive assessment results

[0049] By setting clear comprehensive fault tolerance score ranges (low risk / medium risk / high risk) for classification, we break away from the traditional model of relying on experience and subjective judgment, achieve quantitative and standardized identification of equipment risks, and provide an accurate basis for scheduling decisions.

[0050] S4: Dynamic adjustment based on risk level

[0051] Based on the risk level, the system can adjust the dispatch plan in real time, such as distributing load at high-risk nodes, reconfiguring routes, or increasing redundancy, forming a closed-loop, real-time dynamic optimization mechanism. This ensures that potential faults are effectively addressed in the bud, significantly improving the grid's self-healing and robustness.

[0052] Example 2, reference Figure 1 , is an embodiment of the present invention. Based on the above embodiment, an intelligent power dispatching optimization method is provided.

[0053] S1: Collect multi-source data and construct power grid diagram.

[0054] It should be noted that the multi-source data includes real-time status data of power equipment, power grid topology data, power load data, and meteorological environment data.

[0055] Furthermore, real-time status data of power equipment includes operating parameters of generators, transformers, circuit breakers, switches, and transmission lines; voltage, current, active power, reactive power; temperature, humidity, vibration, partial discharge; equipment open and closed status, protection action records; and equipment aging indicators (such as insulation life estimation).

[0056] Furthermore, the grid topology data includes a list of nodes (power stations, substations, load centers, etc.); edge connection relationships (transmission lines, switch connections); edge / node attributes: connectivity status; connection reliability (historical failure rate); and maintenance records.

[0057] Furthermore, the power load data includes real-time load power demand (MW / MVAR); daily load curve (24-hour load changes); electricity consumption category of each load point (residential, industrial, commercial); and load priority / interruptibility identification.

[0058] Furthermore, meteorological environmental data include temperature, humidity, wind speed, wind direction, rainfall, thunderstorm probability; meteorological disaster warnings (such as typhoons, freezing, and heavy rain); solar irradiance (affecting photovoltaic power generation); and temperature extreme value predictions (affecting load demand).

[0059] It should be noted that the construction of the power grid diagram includes analyzing the real-time status of power equipment and power load data, identifying key nodes in the power grid, each key device corresponding to a node in the power grid diagram, determining the connection relationship between nodes based on the power grid topology data, each connection corresponding to an edge in the power grid diagram, and setting a comprehensive weight for each connection based on the equipment operating status, historical failure rate and meteorological environment changes. Based on the node set and edge set, a weighted power grid diagram with physical, electrical and environmental attributes is generated;

[0060] The key nodes include generators, substations, transmission line nodes and load centers.

[0061] S2: Conduct evaluation and analysis based on the power grid diagram and make a comprehensive evaluation of the analysis results.

[0062] It should be noted that the evaluation and analysis based on the power grid diagram includes equipment redundancy evaluation, power grid topology stability analysis, and load adaptability analysis.

[0063] Furthermore, the device redundancy evaluation includes calculating the maximum flow from the source node to the sink node, the maximum flow is equal to the size of the minimum cut in the power grid, and evaluating the redundancy according to the minimum cut theorem.

[0064] The grid topology stability analysis includes:

[0065] Calculate the reachability value of each node, and through DFS traversal, dynamically update the reachability value of the node based on the connection between nodes and the weight of the edge, and make a judgment;

[0066] The load adaptability analysis includes:

[0067] A load balance equation is established to ensure that each node in the power grid meets the power flow conservation constraint. Load adjustment factors and task priority factors are introduced. The load demand of each node is dynamically corrected based on real-time environmental data. The simulated annealing algorithm is used to optimize the scheduling scheme. Through step-by-step search and temperature decay strategies, the optimal solution is found globally and the most adaptable scheduling scheme is output.

[0068] S3: Determine the equipment risk level based on the comprehensive assessment results.

[0069] The comprehensive evaluation of the analysis results is expressed as:

[0070] F=w1R+w2T+w3A

[0071] Among them, F is the final comprehensive fault tolerance score, F∈[0,1]; w1, w2, w3 are the weight coefficients of each indicator, satisfying:

[0072] w1+w2+w3=1,w1,w2,w3≥0.

[0073] The determination of equipment risk level includes:

[0074] When A2≤F≤A1, it is judged as a low-risk device;

[0075] When A3≤F<A2, it is judged as medium-risk equipment;

[0076] When F<A3, it is judged as high-risk equipment.

[0077] S4: Dynamic adjustment based on risk level.

[0078] When low-risk equipment is assessed to be stable and reliable, no major adjustments to its original operating status are required to maintain normal operation. Only when overall system optimization (such as load redistribution) is required will the load or output power of low-risk equipment be adjusted appropriately based on the principle of minimal change to ensure maximum overall grid efficiency. In the event of a localized fault or sudden load change, temporary support for low-risk equipment will be prioritized to ensure power supply continuity.

[0079] When equipment is identified as medium-risk, the system will appropriately reduce its load based on its current load, or divert some of the load to adjacent low-risk equipment or alternative paths to reduce operating pressure. Medium-risk equipment will be assigned a more frequent data collection and status monitoring cycle to track operational status changes in real time, ensuring timely detection and intervention before risks escalate. Based on the current power grid map, local network connections will be dynamically reconfigured, such as by activating alternative lines and adjusting power flow directions, to optimize the network structure for medium-risk equipment and improve its stability.

[0080] When it is judged to be a high-risk device, priority will be given to forced load migration measures for the high-risk equipment, and the load it bears will be quickly distributed to low-risk or medium-risk equipment to avoid cascading failures caused by equipment failure. When it is judged that there is a high possibility of failure in high-risk equipment, isolation or decoupling operations will be performed in advance based on preset logical conditions (such as excessive load, abnormal status, etc.), and the equipment will be actively separated from the main power grid to block the fault propagation path. Synchronously start the backup generator, backup line or backup load center, and ensure that the power supply reliability is not affected by physical switching or virtual switching (such as load readjustment). After the high-risk equipment is adjusted, the power grid diagram model will be updated immediately, and the node weights, connection relationships and system redundancy will be recalculated to ensure that the next scheduling decision is based on the latest power grid status.

[0081] Example 3, an embodiment of the present invention, provides an intelligent power dispatch optimization system, including: a collection module, an analysis module, a judgment module, and an adjustment module;

[0082] Acquisition module, collects multi-source data and constructs power grid diagram;

[0083] The analysis module performs evaluation and analysis based on the power grid diagram and makes a comprehensive evaluation of the analysis results;

[0084] The judgment module determines the risk level of the equipment based on the comprehensive assessment results;

[0085] Adjust the module and make dynamic adjustments based on the risk level.

[0086] This embodiment further provides a computing device applicable to a method for optimizing intelligent power dispatching, including:

[0087] Memory and processor; the memory is used to store computer-executable instructions, and the processor is used to execute computer-executable instructions to implement an intelligent power dispatch optimization method proposed in the above embodiment.

[0088] This embodiment further provides a storage medium on which a computer program is stored. When the program is executed by a processor, an intelligent power dispatch optimization method proposed in the above embodiment is implemented.

[0089] The storage medium proposed in this embodiment and the intelligent power dispatching optimization method proposed in the above embodiment belong to the same inventive concept. The technical details not fully described in this embodiment can be referred to the above embodiment, and this embodiment has the same beneficial effects as the above embodiment.

[0090] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0091] The logic and / or steps represented in the flowcharts or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing the logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (e.g., a computer-based system, a system including a processor, or other system that can fetch and execute instructions from an instruction execution system, apparatus, or device). For purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0092] It should be understood that various parts of the present invention can be implemented using hardware, software, firmware, or a combination thereof. In the above-described embodiments, multiple steps or methods can be implemented using software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented using hardware, as in another embodiment, any one of the following technologies known in the art or a combination thereof can be used: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application-specific integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0093] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. An intelligent power dispatch optimization method, characterized by: include: Collect multi-source data and construct power grid diagrams; Conduct evaluation and analysis based on the power grid diagram and make a comprehensive assessment of the analysis results; Determine the equipment risk level based on the comprehensive assessment results; Dynamic adjustments are made based on risk levels.

2. The intelligent power dispatch optimization method according to claim 1, characterized in that: The multi-source data includes real-time status data of power equipment, power grid topology data, power load data, and meteorological environment data.

3. The intelligent power dispatch optimization method according to claim 2, characterized in that: The power grid diagram construction includes analyzing the real-time status of power equipment and power load data, identifying key nodes in the power grid, each key device corresponding to a node in the power grid diagram, determining the connection relationship between nodes based on the power grid topology data, each connection corresponding to an edge in the power grid diagram, and setting a comprehensive weight for each connection based on the equipment operating status, historical failure rate and meteorological environment changes. Based on the node set and edge set, a weighted power grid diagram with physical, electrical and environmental attributes is generated; The key nodes include generators, substations, transmission line nodes and load centers.

4. The intelligent power dispatch optimization method according to claim 3, characterized in that: The evaluation and analysis based on the power grid diagram includes equipment redundancy evaluation, power grid topology stability analysis, and load adaptability analysis.

5. The intelligent power dispatch optimization method according to claim 4, characterized in that: The device redundancy evaluation includes calculating the maximum flow from the source node to the sink node, where the maximum flow is equal to the size of the minimum cut in the power grid, and evaluating the redundancy according to the minimum cut theorem. The grid topology stability analysis includes: Calculate the reachability value of each node, and through DFS traversal, dynamically update the reachability value of the node based on the connection between nodes and the weight of the edge, and make a judgment; The load adaptability analysis includes: A load balance equation is established to ensure that each node in the power grid meets the power flow conservation constraint. Load adjustment factors and task priority factors are introduced. The load demand of each node is dynamically corrected based on real-time environmental data. The simulated annealing algorithm is used to optimize the scheduling scheme. Through step-by-step search and temperature decay strategies, the optimal solution is found globally and the most adaptable scheduling scheme is output.

6. The intelligent power dispatch optimization method according to claim 5, characterized in that: The comprehensive evaluation of the analysis results is expressed as: F=w1R+w2T+w3A Among them, F is the final comprehensive fault tolerance score, F∈[0,1]; w1, w2, w3 are the weight coefficients of each indicator, satisfying: w1+w2+w3=1,w1,w2,w3≥0.

7. The intelligent power dispatch optimization method according to claim 6, characterized in that: The determination of equipment risk level includes: When A2≤F≤A1, it is judged as a low-risk device; When A3≤F<A2, it is judged as medium-risk equipment; When F<A3, it is judged as high-risk equipment.

8. A system based on the intelligent power dispatch optimization method according to any one of claims 1 to 7, characterized in that: include: Acquisition module, analysis module, judgment module, and adjustment module.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the intelligent power dispatch optimization method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of an intelligent power dispatch optimization method according to any one of claims 1 to 7 are implemented.

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