A method for planning power grid
Through a grid grid planning method combining data collection, grid division, power demand forecasting and intelligent algorithm solution, the inefficiency and high cost encountered by traditional methods in grid planning is solved, and a more efficient and reliable grid grid planning is achieved.
Patent Information
- Application Number
- CN202411581739.6
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-11-07
- Publication Date
- 2025-04-08
- Estimated Expiration
- Not applicable · inactive patent
AI Technical Summary
When traditional grid grid planning methods deal with large-scale, nonlinear, multi-objective grid planning problems that contain a large number of uncertain factors, they often face problems such as search direction errors, iterative divergence and "combination explosion", and it is difficult to obtain optimization plans within a reasonable time.
A grid grid planning method is adopted, including data collection and current status analysis, grid division, power demand forecasting, establishment of multi-objective optimization models, intelligent algorithm solution, plan evaluation and adjustment and planning reports are prepared. Solve multi-objective optimization models through intelligent algorithms such as genetic algorithms, particle swarm optimization algorithms, and simulated annealing algorithms to find the optimal grid grid planning solution.
It improves planning efficiency, reduces unnecessary investment, reduces grid construction and operation costs, improves power supply reliability of the power grid, and can flexibly adjust according to the development positioning and needs of different regions.
Smart Images
Figure CN119783866A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the technical field of power grid planning, and in particular to a power grid framework planning method. Background Art
[0002] Traditional grid planning methods are mostly based on operations research methods such as linear programming and integer programming. However, when dealing with large-scale, nonlinear, multi-objective grid planning problems that contain a large number of uncertain factors, these methods often face problems such as wrong search direction, iterative divergence, and "combinatorial explosion", making it difficult to obtain an optimization solution within a reasonable time. With the continuous development of the power system, how to reduce the cost of grid construction and operation while ensuring safe and reliable power transmission has become an urgent problem that the power industry needs to solve. Summary of the invention
[0003] The main purpose of the present invention is to provide a power grid planning method, which can effectively solve the technical problems raised by the background technology.
[0004] To achieve the above object, the technical solution adopted by the present invention is:
[0005] A power grid planning method includes data collection and status analysis, grid division, power demand forecasting, establishment of multi-objective optimization model, intelligent algorithm solution, scheme evaluation and adjustment, and preparation and output of planning report.
[0006] As a further solution of the present invention, the data collected include original data such as socio-economic data, grid scale data, grid operation data and load data, and the indicators of the current situation analysis include but are not limited to uneven load distribution, line aging, and low power supply reliability.
[0007] As a further solution of the present invention, the grid division is used to divide the planning area into multiple grids, and compile a grid division detailed list, including but not limited to grid number, power supply area, power supply partition and grid profile information.
[0008] As a further solution of the present invention, the power demand forecast is used to make short-term load forecasts and long-term saturated load forecasts for the planning area. Combined with the grid division results, differentiated load density indicators are formulated according to the development positioning and direction of different areas.
[0009] As a further solution of the present invention, the model for establishing a multi-objective optimization model includes but is not limited to minimizing economic costs, maximizing power supply reliability, and minimizing operating losses, and the constraints include but are not limited to flow constraints, branch transmission power constraints, power plant output constraints, and new line number constraints.
[0010] As a further solution of the present invention, the intelligent algorithm includes but is not limited to a genetic algorithm, a particle swarm optimization algorithm, and a simulated annealing algorithm, which solves a multi-objective optimization model. The intelligent algorithm searches for the optimal power grid planning scheme through iterative search while satisfying constraints.
[0011] As a further solution of the present invention, the scheme evaluation and adjustment is used to conduct technical and economic evaluation of the preliminary planning scheme, including but not limited to cost estimation, power supply capacity verification, and reliability analysis. According to the evaluation results, the planning scheme is adjusted and optimized to ensure the scientificity and feasibility of the scheme.
[0012] As a further solution of the present invention, the planning report is prepared and output for writing a detailed power grid framework planning report, including but not limited to planning background, planning objectives, planning methods, planning schemes, investment estimates and planning effectiveness analysis, and outputting report results.
[0013] Compared with the prior art, the present invention has the following beneficial effects:
[0014] 1. Intelligent algorithms are used to solve multi-objective optimization models, which improves planning efficiency.
[0015] 2. By optimizing the planning scheme, unnecessary investment is reduced and the cost of power grid construction and operation is lowered.
[0016] 3. The power supply reliability of the power grid is improved by rationally laying out the power grid structure.
[0017] 4. The planning method can be flexibly adjusted according to the development positioning and needs of different regions. BRIEF DESCRIPTION OF THE DRAWINGS
[0018] Figure 1 The present invention is a flowchart of a method for planning a power grid structure. DETAILED DESCRIPTION
[0019] In order to make the technical means, creative features, objectives and effects achieved by the present invention easy to understand, the present invention is further explained below in conjunction with specific implementation methods.
[0020] like Figure 1 As shown, a power grid planning method includes data collection and status analysis, grid division, power demand forecasting, establishment of multi-objective optimization model, intelligent algorithm solution, scheme evaluation and adjustment, and preparation and output of planning report.
[0021] The data collected include original data such as socio-economic data, grid size data, grid operation data and load data. The indicators for status quo analysis include but are not limited to uneven load distribution, line aging and low power supply reliability.
[0022] Specifically, big data technology and data mining algorithms are used to comprehensively collect and process various types of data related to power grid planning. Based on the data analysis results, key issues and bottlenecks in power grid planning are identified to provide data support for subsequent planning.
[0023] Grid division is used to divide the planning area into multiple grids and compile a grid division detail table, including but not limited to grid number, power supply area, power supply zone and grid profile information.
[0024] Specifically, GIS technology is used, combined with topography, administrative divisions, and transportation network factors, to carry out fine grid division of the planning area, analyze the load density and electricity consumption characteristics within each grid, and provide a basis for subsequent power demand forecasting and grid planning.
[0025] The power demand forecast is used to make short-term load forecasts and long-term saturated load forecasts for the planning area. Combined with the grid division results, differentiated load density indicators are formulated according to the development positioning and direction of different areas.
[0026] Specifically, we use historical load data and adopt time series analysis methods to predict future load trends. Combined with the grid division results, we use spatial load prediction models to predict the load growth in each grid.
[0027] The multi-objective optimization model includes but is not limited to minimizing economic costs, maximizing power supply reliability, and minimizing operating losses. The constraints include but are not limited to power flow constraints, branch transmission power constraints, power plant output constraints, and new line number constraints.
[0028] Specifically, according to the basic analysis results and planning objectives, a multi-objective optimization model is constructed, including objective functions such as economic cost, power supply reliability and operation efficiency and corresponding constraints. Multiple objectives are converted into quantifiable function forms, and multi-objective optimization is performed through weighted summation and hierarchical analysis method.
[0029] Intelligent algorithms include but are not limited to genetic algorithms, particle swarm optimization algorithms, and simulated annealing algorithms to solve multi-objective optimization models. Intelligent algorithms use iterative searches to find the optimal grid planning solution while satisfying constraints.
[0030] Specifically, the initial parameters and initial solution of the algorithm are set, and through iterative calculation, a better solution in the solution space is continuously searched. According to the preset convergence conditions, whether the algorithm converges is determined, and finally the optimal solution is output.
[0031] Scheme evaluation and adjustment is used to conduct technical and economic evaluation of the preliminary planning scheme, including but not limited to cost estimation, power supply capacity verification, and reliability analysis. According to the evaluation results, the planning scheme is adjusted and optimized to ensure the scientific nature and feasibility of the scheme.
[0032] Specifically, evaluate the technical feasibility of the planning scheme, calculate the economic indicators of the planning scheme, make necessary adjustments and optimizations to the planning scheme based on the evaluation results and actual conditions, and conduct technical evaluation of the planning scheme using the technical and economic comparison method.
[0033] Prepare planning reports and output them for writing detailed power grid planning reports, including but not limited to planning background, planning objectives, planning methods, planning schemes, investment estimates and planning effectiveness analysis, and output report results.
[0034] The above shows and describes the basic principles and main features of the present invention and the advantages of the present invention. It should be understood by those skilled in the art that the present invention is not limited to the above embodiments. The above embodiments and descriptions are only for explaining the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, which fall within the scope of the present invention to be protected. The scope of protection of the present invention is defined by the attached claims and their equivalents.
Claims
1. A method for planning a power grid, characterized in that: It includes data collection and current situation analysis, grid division, power demand forecasting, establishment of multi-objective optimization models, intelligent algorithm solving, program evaluation and adjustment, and preparation and output of planning reports.
2. A method for planning a power grid according to claim 1, characterized in that: The data collected include original data such as social and economic data, power grid scale data, power grid operation data and load data. The indicators of the current situation analysis include but are not limited to uneven load distribution, line aging and low power supply reliability.
3. A method for planning a power grid according to claim 1, characterized in that: The grid division is used to divide the planning area into multiple grids and compile a grid division detail table, including but not limited to grid number, power supply area, power supply zone and grid brief information.
4. A method for planning a power grid according to claim 1, characterized in that: The electricity demand forecast is used to make short-term load forecasts and long-term saturated load forecasts for the planning area. Combined with the grid division results, differentiated load density indicators are formulated according to the development positioning and direction of different areas.
5. A method for planning a power grid according to claim 1, characterized in that: The model for establishing the multi-objective optimization model includes but is not limited to minimizing economic costs, maximizing power supply reliability, and minimizing operating losses. The constraints include but are not limited to flow constraints, branch transmission power constraints, power plant output constraints, and new line number constraints.
6. A method for planning a power grid according to claim 1, characterized in that: The intelligent algorithms include but are not limited to genetic algorithms, particle swarm optimization algorithms, and simulated annealing algorithms, which solve multi-objective optimization models. The intelligent algorithms use iterative searches to find the optimal grid planning solution while satisfying constraints.
7. A method for planning a power grid according to claim 1, characterized in that: The scheme evaluation and adjustment is used to conduct technical and economic evaluation of the preliminary planning scheme, including but not limited to cost estimation, power supply capacity verification, and reliability analysis. According to the evaluation results, the planning scheme is adjusted and optimized to ensure the scientificity and feasibility of the scheme.
8. A method for planning a power grid according to claim 1, characterized in that: The preparation of the planning report and output thereof is used to prepare a detailed power grid framework planning report, including but not limited to planning background, planning objectives, planning methods, planning schemes, investment estimates and planning effectiveness analysis, and output report results.