Knowledge-based genetic algorithm for unit commitment

Author

Aldridge, C.J. ; McKee, S. ; McDonald, J.R. ; Galloway, S.J. ; Dahal, K.P. ; Bradley, M.E. ; Macqueen, J.F.

Author_Institution

Dept. of Math., Strathclyde Univ., Glasgow, UK

Volume

148

Issue

2

fYear

2001

fDate

3/1/2001 12:00:00 AM

Firstpage

146

Lastpage

152

Abstract

A genetic algorithm (GA) augmented with knowledge-based methods has been developed for solving the unit commitment economic dispatch problem. The GA evolves a population of binary strings which represent commitment schedules. The initial population of schedules is chosen using a method based on elicited scheduling knowledge. A fast rule-based dispatch method is then used to evaluate candidate solutions. The knowledge-based genetic algorithm is applied to a test system of ten thermal units over 24-hour time intervals, including minimum on/off times and ramp rates, and achieves lower cost solutions than Lagrangian relaxation in comparable computational time

Keywords

genetic algorithms; knowledge based systems; power generation dispatch; power generation economics; power generation planning; power generation scheduling; power system analysis computing; binary strings population; commitment schedules; computational time; computer simulation; economic dispatch problem; elicited scheduling knowledge; knowledge-based genetic algorithm; on/off times; ramp rates; thermal generating units; unit commitment;

fLanguage

English

Journal_Title

Generation, Transmission and Distribution, IEE Proceedings-

Publisher

iet

ISSN

1350-2360

Type

jour

DOI

10.1049/ip-gtd:20010022

Filename

920893