Dispatching function optimization method based on multi-objective genetic algorithm

A multi-objective genetic and function optimization technology, applied in the field of scheduling function optimization, can solve problems such as difficulty in guaranteeing output, more damage to normal operation of reservoirs, and calculation results that deviate from the real optimal solution, etc., achieving fewer algorithm parameters and faster convergence speed Effect

Inactive Publication Date: 2012-10-03
STATE GRID HUBEI ELECTRIC POWER COMPANY +1
View PDF0 Cites 21 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Because this method decomposes the unified optimization problem into two independent processes to optimize separately, the calculation results often deviate from the real optimal solution of the original problem; at the same time, because it is difficult to consider cons

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Dispatching function optimization method based on multi-objective genetic algorithm
  • Dispatching function optimization method based on multi-objective genetic algorithm
  • Dispatching function optimization method based on multi-objective genetic algorithm

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0030] This embodiment is a scheduling function optimization method based on a multi-objective genetic algorithm. The basic principles are as follows:

[0031] 1) Scheduling function form

[0032] In the reservoir group, the decision of any reservoir in a certain period depends not only on the natural inflow and initial state of the reservoir at that period, but also on the natural inflow and initial state of other reservoirs in the reservoir group at that period, and it is nonlinear Functional relationship. In-depth research found that the decision-making (output, end-of-period water level and power generation flow) is not significantly affected by high-order nonlinearity, and can be regarded as a linear relationship within the allowable calculation error range of the project. From a computational point of view, if the decision function is identified from a set of data, if there are too many parameters to be identified and the data is limited, a unique function may not be o...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

PUM

No PUM Login to view more

Abstract

The invention relates to a dispatching function optimization method based on a multi-objective genetic algorithm. The method comprises the following steps of: setting the form of a dispatching function; setting decision variables; and calculating dispatching rules by multi-objective particle swarm optimization (MOPSO), wherein the calculation process by the multi-objective particle swarm optimization (MOPSO) comprises the steps of performing initialization, creating an external Archive and creating a copy in the Archive according to position information of non-dominated particles in popVar, and iterating. According to the method disclosed by the invention, the particle swarm optimization is adopted for replacing linear regression. The algorithm has the advantages of simple flow process, easiness in implementation, few algorithm parameters, fast convergence speed and the like and is further widely adopted. By applying the method to the generation of a reservoir dispatching function, the established dispatching function can be utilized for optimizing optimal parameters and the reservoir optimal dispatching function can be further obtained.

Description

technical field [0001] The invention relates to a scheduling function optimization method based on a multi-objective genetic algorithm, which is a cascade reservoir group optimization scheduling method, and a cascade reservoir group scheduling function optimization model construction and solution method considering multi-objective optimization. Background technique [0002] At present, most of the optimal dispatching of cascade reservoirs studied at home and abroad is to establish an optimization model with a single objective, which can reduce the difficulty of cascade optimal dispatching, but at the same time ignores the comprehensive benefits that can be developed by hydropower stations, resulting in extreme and single research results. In the optimal dispatching of reservoirs, only considering improving the power generation efficiency of hydropower stations includes research goals such as maximum power generation, minimum water consumption, and maximum energy storage. Thes...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

Application Information

Patent Timeline
no application Login to view more
IPC IPC(8): G06F17/50G06N3/12
Inventor 胡宇丰李献新雷晓辉张云辉王旭于茜高力书林旭郭易苏景辉
Owner STATE GRID HUBEI ELECTRIC POWER COMPANY
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap