A binary differential evolution based memetic algorithm to solve profit based unit commitment problem

Abstract

Unit commitment problem (UCP) and profit based unit commitment problem (PBUCP) are two combinatorial optimization problems with several constraints in the power system optimization research. In nowadays, the PBUCP becomes more dominant over the UCP due to restructuring in power industry. In recent years, many researchers have used various evolutionary algorithms to solve the multi-constrained PBUCP but differential evolution (DE) algorithm has received less consideration. A binary differential evolution (BDE) algorithm is introduced in the present research for solving the PBUCP. Further, to enhance the performance of the proposed BDE algorithm, a synergy of the BDE algorithm and a hill climbing local search optimizer (HCLSO) is used to solve the PBUCP. In the proposed synergy, BDE algorithm explores global aspect and a HCLSO method exploits local aspect. A HCLSO is proposed in this paper which makes perturbations in unit status based on a priority of units. A new de-commitment procedure is adopted to de-commit the needlessly committed units. The competency of the proposed BDE algorithm and BDE-HCLSO algorithm has been investigated on 10-units power system for 24 hours scheduling period. Results are justified by performing a comparison with the earlier reported algorithms employed to solve the PBUCP. Further, the simulation results demonstrate that BDE-HCLSO algorithm gives superior results over BDE algorithm.