Title :
An evolutionary generation scheduling in an open electricity market
Author :
Dahal, Keshav P. ; Siewierski, T.A. ; Galloway, S.J. ; Burt, G.M. ; McDonald, J.R.
Author_Institution :
Sch. of Informatics, Bradford Univ., UK
Abstract :
The classical generation scheduling problem defines on/off decisions (commitment) and dispatch level of all available generators in a power system for each scheduling period. In recent years researchers have focused on developing new approaches to solve nonclassical generation scheduling problems in the newly deregulated and decentralized electricity market place. In this paper a GA-based approach has been developed for a system operator to schedule generation in a market akin to that operating in England and Wales. A generation scheduling problem has been formulated and solved using available trading information at the time of dispatch. The solution is updated after information is obtained in a rolling fashion. The approach is tested for two IEEE network-based problems, and achieves comparable results with a branch and bound technique in reasonable CPU time.
Keywords :
genetic algorithms; power engineering computing; power generation scheduling; tree searching; IEEE network-based problems; branch-and-bound technique; electricity market; evolutionary generation scheduling; genetic algorithm; power system; Costs; Electrical equipment industry; Electricity supply industry; Job shop scheduling; Power generation; Power generation economics; Power system economics; Power system reliability; Power system security; Power systems;
Conference_Titel :
Evolutionary Computation, 2004. CEC2004. Congress on
Print_ISBN :
0-7803-8515-2
DOI :
10.1109/CEC.2004.1330989