Title :
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
fDate :
3/1/2001 12:00:00 AM
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;
Journal_Title :
Generation, Transmission and Distribution, IEE Proceedings-
DOI :
10.1049/ip-gtd:20010022
