Moth flame optimizer-based solution approach for unit commitment and generation scheduling problem of electric power system

Abstract

This paper proposes a new approach based on the moth flame optimizer algorithm. Moth flame optimizer simulates the natural fervent navigation technique adopted by moths looking for a source of light. The proposed method is further improved by priority list-based ordering; the unit commitment problem (UCP) is a non-linear, non-convex, and combinatorial complex optimization problem. It contains both continuous and discrete variables. This further increases its complexity. Moth flame optimizer is very good at obtaining a commitment pattern: allocation of power on the committed units obtained by mixed-integer quadratic programming method. Heuristic search strategies are used to adopt for the repair of minimum up and downtime, and spinning reserve constraints. MFO effectiveness is tested on the standard UCP reference IEEE model buses 14 and 30, and 10 and 20 units. The efficiency of the projected algorithms is compared to classical PSO, PSOLR, HPSO, PSOSQP, hybrid MPSO, IBPSO, LCA-PSO, NPSO, PSO-GWO, and various other evolutionary algorithms. The comparison result shows that MFO can lead to all methods reported earlier in literature.