Title :
Optimal standing reserve utilisation using genetic algorithms
Author :
Li, F. ; Zhang, X. ; Dunn, R.W.
Author_Institution :
Dept. of Electron. & Electr. Eng., Bath Univ., UK
Abstract :
This paper proposes a genetic algorithm (GA) based economic contracting strategy for standing reserve in a typical standing reserve market. The aim of the contracting procedure is to identify the standing reserve tenders to be contracted and the correct-contract order among the tender options received and scheduled reserve alternatives, to meet the reserve requirement at the lowest possible cost. The contract ordering is a simple problem when all options are rendering for a fixed period of time, however, it becomes troublesome with the presence of flexible contracts, i.e. with standing reserve only available for a partial service window. The proposed GA contracting strategy aims to address the complexity caused by flexible contracts. The results for a system with 16 fixed and flexible tender options are presented and compared with those of mixed integer linear programming (MILP) methods
Keywords :
contracts; electricity supply industry; genetic algorithms; power system economics; contract ordering; economic contracting strategy; flexible contracts; genetic algorithms; optimal standing reserve utilisation; partial service window; tender options; Consumer electronics; Contracts; Costs; Economic forecasting; Frequency; Genetic algorithms; Load forecasting; Mixed integer linear programming; Power generation economics; Power system security;
Conference_Titel :
Power Engineering Society Winter Meeting, 2001. IEEE
Conference_Location :
Columbus, OH
Print_ISBN :
0-7803-6672-7
DOI :
10.1109/PESW.2001.916907
