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CN-122021980-A - Integrated energy capacity configuration method, device, equipment, storage medium and computer program product

CN122021980ACN 122021980 ACN122021980 ACN 122021980ACN-122021980-A

Abstract

The present application relates to the field of intelligent micro-grid comprehensive energy capacity configuration application technologies, and in particular, to a comprehensive energy capacity configuration method, apparatus, device, storage medium, and computer program product. The method comprises the steps of constructing a multi-objective optimization function based on historical wind power data, historical photovoltaic power generation data, historical user load data, a preset cost strategy and a preset green power duty ratio strategy, determining constraint conditions according to preset green power duty ratio constraint, preset wind and light rejection rate constraint and preset return on investment constraint, constructing a linear programming model based on the multi-objective optimization function and the constraint conditions, and solving the linear programming model by adopting a simplex method to obtain a target configuration scheme. The method can effectively improve the utilization rate of renewable energy sources, reduce energy waste, ensure the economical efficiency and environmental protection benefit of projects and optimize comprehensive energy capacity configuration.

Inventors

  • HE XIONG
  • Gu tianchen

Assignees

  • 中石油深圳新能源研究院有限公司
  • 中国石油天然气股份有限公司

Dates

Publication Date
20260512
Application Date
20241112

Claims (10)

  1. 1. A method for configuring integrated energy capacity, the method comprising: Constructing a multi-objective optimization function based on historical wind power data, historical photovoltaic power generation data, historical user load data, a preset cost strategy and a preset green power duty ratio strategy; determining constraint conditions according to preset green electricity duty ratio constraint, preset wind-abandoning light-abandoning rate constraint and preset return on investment constraint; Constructing a linear programming model based on the multi-objective optimization function and the constraint condition; And solving the linear programming model by adopting a simplex method to obtain a target configuration scheme.
  2. 2. The method of claim 1, wherein the step of constructing a linear programming model based on the multi-objective optimization function and the constraints comprises: determining an optimization target according to the multi-target optimization function and the constraint condition; carrying out weight distribution on the optimization targets, and merging the multi-target optimization functions into a single target function based on a weight distribution result; And carrying out standardization processing on the constraint conditions, and combining the constraint conditions subjected to the standardization processing with the single objective function to obtain the linear programming model.
  3. 3. The method of claim 2, further comprising, prior to the step of normalizing the constraints and combining the normalized constraints with the single objective function to obtain the linear programming model: Arranging the constraint condition and the single objective function into a matrix form; Determining a variable value range according to the constraint condition; and based on the variable value range, carrying out conflict or redundancy verification between the single objective function and the constraint condition.
  4. 4. The method of claim 1, wherein the step of solving the linear programming model using a simplex method to obtain a target configuration scheme comprises: initializing the linear programming model to obtain a current feasible solution; Judging whether the current feasible solution is an optimal solution or not; if not, determining an entering base variable and an exiting base variable based on the simplex table; updating the current solution by a Gaussian elimination method based on the entering base variable and the leaving base variable until an optimal solution is obtained; and obtaining optimal capacity configuration based on the optimal solution, and generating the target configuration scheme based on the optimal capacity configuration.
  5. 5. The method of claim 1, further comprising, after the step of solving the linear programming model using the simplex method to obtain a target configuration scheme: verifying whether the target configuration scheme meets the constraint condition; if yes, the influence of the adjustment on the target configuration scheme is evaluated by adjusting the parameters of the linear programming model, and an alternative scheme is generated.
  6. 6. The method of claim 1, further comprising, prior to the step of determining the constraint conditions based on a preset green duty cycle constraint, a preset waste-to-waste rate constraint, and a preset return-on-investment constraint: sorting and quantifying the preset green electricity duty ratio constraint, the preset wind-abandoning light-abandoning rate constraint and the preset return on investment constraint; Converting the preset green electricity duty ratio constraint, the preset wind-discarding light-discarding rate constraint and the preset return on investment constraint after arrangement and quantification into a model expression form; judging whether conflicts exist among the preset green electricity duty ratio constraint, the preset wind-abandoning light-abandoning rate constraint and the preset return on investment constraint; If yes, adding a corresponding relaxation variable.
  7. 7. An integrated energy capacity allocation apparatus, the apparatus comprising: the multi-objective optimization module is used for constructing a multi-objective optimization function based on historical wind power data, historical photovoltaic power generation data, historical user load data, a preset cost strategy and a preset green power duty ratio strategy; The constraint condition module is used for determining constraint conditions according to preset green electricity duty ratio constraint, preset wind-abandoning light-abandoning rate constraint and preset return on investment constraint; the model construction module is used for constructing a linear programming model based on the multi-objective optimization function and the constraint condition; And the target module is used for solving the linear programming model by adopting a simplex method to obtain a target configuration scheme.
  8. 8. A computer device, characterized in that the device comprises a memory, a processor and a comprehensive energy capacity allocation program stored on the memory and executable on the processor, the comprehensive energy capacity allocation program being configured to implement the steps of the comprehensive energy capacity allocation method according to any one of claims 1 to 6.
  9. 9. A storage medium having stored thereon a comprehensive energy capacity allocation program which, when executed by a processor, implements the steps of the comprehensive energy capacity allocation method according to any one of claims 1 to 6.
  10. 10. A computer program product, characterized in that the computer program product comprises a computer program which, when executed by a processor, implements the steps of the integrated energy capacity configuration method according to any one of claims 1 to 6.

Description

Integrated energy capacity configuration method, device, equipment, storage medium and computer program product Technical Field The present application relates to the field of intelligent micro-grid comprehensive energy capacity configuration application technologies, and in particular, to a comprehensive energy capacity configuration method, apparatus, device, storage medium, and computer program product. Background With the ever-increasing global energy demand and increasing environmental requirements, the proportion of renewable energy in energy supply is increasing. The application of clean energy sources such as wind power generation, photovoltaic power generation and the like has become an important direction of global energy transformation, and particularly in intelligent micro-networks and distributed energy systems, capacity allocation and optimal use of renewable energy sources become one of key technical challenges. The intelligent micro-grid is a localized energy management system integrating various energy forms (such as wind power, photovoltaic and energy storage), and can realize flexible scheduling and management of energy. Due to randomness and uncertainty of wind power and photovoltaic power generation, how to reasonably configure the capacity of renewable energy sources on the premise of meeting the power load becomes a key factor affecting the economy and stability of the micro-grid. Conventional capacity allocation methods typically focus on optimization of a single objective, such as cost minimization or power generation efficiency maximization, ignoring a balance between various practical constraints, such as green duty cycle, waste-to-wind waste rate, return-on-investment, etc. In the prior art, some capacity allocation schemes fail to fully consider the complexity of multiple targets in the optimization process, and only local economy or environmental protection is often concerned, so that over-allocation or under-allocation of renewable energy sources is caused. For example, too large wind power and photovoltaic power generation capacity can cause a large amount of wind and light discarding phenomena, so that the economic benefit of the system is reduced, and too small configuration is difficult to meet the use ratio requirement of clean energy. Therefore, how to optimize the comprehensive energy capacity allocation to achieve multiple objectives such as cost, environmental benefit, and return on investment becomes an important difficulty in the current comprehensive energy system design. Disclosure of Invention The application mainly aims to provide a comprehensive energy capacity configuration method, a device, equipment, a storage medium and a computer program product, which aim to solve the technical problem of how to optimize the comprehensive energy capacity configuration. In order to achieve the above object, the present application provides a comprehensive energy capacity configuration method, which includes the steps of: Constructing a multi-objective optimization function based on historical wind power data, historical photovoltaic power generation data, historical user load data, a preset cost strategy and a preset green power duty ratio strategy; determining constraint conditions according to preset green electricity duty ratio constraint, preset wind-abandoning light-abandoning rate constraint and preset return on investment constraint; Constructing a linear programming model based on the multi-objective optimization function and the constraint condition; And solving the linear programming model by adopting a simplex method to obtain a target configuration scheme. In an embodiment, the step of constructing a linear programming model based on the multi-objective optimization function and the constraint condition includes: determining an optimization target according to the multi-target optimization function and the constraint condition; carrying out weight distribution on the optimization targets, and merging the multi-target optimization functions into a single target function based on a weight distribution result; And carrying out standardization processing on the constraint conditions, and combining the constraint conditions subjected to the standardization processing with the single objective function to obtain the linear programming model. In an embodiment, before the step of normalizing the constraint condition and combining the normalized constraint condition with the single objective function to obtain the linear programming model, the method further includes: Arranging the constraint condition and the single objective function into a matrix form; Determining a variable value range according to the constraint condition; and based on the variable value range, carrying out conflict or redundancy verification between the single objective function and the constraint condition. In an embodiment, the step of solving the linear programming model by adopting a simplex m