A synergy of binary differential evolution and binary local search optimizer to solve multi-objective profit based unit commitment problem

Abstract

Environmentally constrained profit based unit commitment problem (PBUCP) is a combinatorial multi-objective optimization problem experienced in deregulated power systems, which optimizes ON/OFF status of generating units to maximize economic benefits with less environmental impacts. The PBUCP has gained less attention in the multi-objective framework. The Kyoto Protocol and various incentive policies have forced generation companies to consider emission minimization as an objective function while solving the PBUCP. In most of the recent studies in the literature, emissions are considered as a constraint, not as an objective function. A few studies have considered the emissions as an objective and suggested the compromised solutions of the multi-objective PBUCP. A synergy of the binary differential evolution (BDE) algorithm and a binary local search optimizer (BLSO) is proposed in the present research to solve the multi-objective PBUCP. A novel BLSO is proposed which deals with binary variables while exploiting the local search and is implemented on the commitment part of the multi-objective PBUCP. The proposed BLSO makes perturbations in the unit status based on the priority of units to refine the optimal solution searched by the BDE algorithm. The efficacy of proposed algorithms has been investigated on the small, medium and large power systems. A comparison with previously known best solutions is performed for the validation of obtained results. Computed results of proposed algorithms are dominant than the already published algorithms applied to solve the multi-objective PBUCP.