Optimal standing reserve utilisation using genetic algorithms

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