2003Power System TechnologyRequires access

UNIT COMMITMENT BASED ON GENETIC ALGORITHMS

Chu Zhuang

Open publisher page 4 citations

Abstract

How to solve unit commitment (UC) and load dispatch of power system by genetic algorithms (GAs) is researched. Using binary coding GAs to solve UC the amount of calculation and employed ram will be greatly increased and the classical GAs does not possess the ability of asymptotic convergence. For these problems a coding scheme is used in which the binary encoding and floating numbers are combined and according to this feature the corresponding genetic operators are designed. For the randomness of classical GA which appears in calculation process, the contraction mapping GA is applied to make the calculation asymptotically convergent. The results of calculation examples show that the proposed GAs is symptotically convergent, comparing with the algorithm of binary coding this algorithm needs less calculation time and less ram to be employed and the relevant information of UC can be led into more easily.

About this research paper

What this paper is about

How to solve unit commitment (UC) and load dispatch of power system by genetic algorithms (GAs) is researched. Using binary coding GAs to solve UC the amount of calculation and employed ram will be greatly increased and the classical GAs does not possess the ability of asymptotic convergence. For these problems a coding scheme is used in which the binary encoding and floating numbers are combined and according to this feature the corresponding genetic operators are designed. For the randomness of classical GA which appears in calculation process, the contraction mapping GA is applied to make the calculation asymptotically convergent. The results of calculation examples show that the proposed GAs is symptotically convergent, comparing with the algorithm of binary coding this algorithm needs less calculation time and less ram to be employed and the relevant information of UC can be led into more easily.

Why it matters

OpenAlex reports 4 citations for this work. Citation counts describe recorded attention and do not establish research quality.

Key contribution

A contribution statement is not available in the OpenAlex record.

Method / approach

Method details are not available in the OpenAlex metadata.

Main findings

Findings are not separately available in the OpenAlex metadata.

Limitations

Limitations are not available in the OpenAlex metadata.

Applications

Application details are not available in the OpenAlex metadata.

Available abstract

How to solve unit commitment (UC) and load dispatch of power system by genetic algorithms (GAs) is researched. Using binary coding GAs to solve UC the amount of calculation and employed ram will be greatly increased and the classical GAs does not possess the ability of asymptotic convergence. For these problems a coding scheme is used in which the binary encoding and floating numbers are combined and according to this feature the corresponding genetic operators are designed. For the randomness of classical GA which appears in calculation process, the contraction mapping GA is applied to make the calculation asymptotically convergent. The results of calculation examples show that the proposed GAs is symptotically convergent, comparing with the algorithm of binary coding this algorithm needs less calculation time and less ram to be employed and the relevant information of UC can be led into more easily.

Key concepts: Randomness, Binary number, Coding (social sciences), Algorithm, Binary code, Power system simulation, Convergence (economics), Computer science

Related papers

Back to paper searchBrowse research topicsOriginal source
UNIT COMMITMENT BASED ON GENETIC ALGORITHMS — Research Paper | ScholarLens