An enhanced approach for solvingmulti‐objectivecogeneration based unit commitment problem

Abstract

AbstractEnvironmental concerns motivate the researchers to consider the pollutants emission as an important objective in the cogeneration‐based unit commitment problem (CBUCP). Hence, single objective CBUCP has been extended as a multi‐objective optimization problem (MOP). In the MO CBUC problem (MO‐CBUCP), in which operating costs are optimized along with pollutants. The CBUCP is a nonlinear, mixed‐integer, and highly constrained optimization problem. The nature‐inspired binary and continuous particle swarm optimization (BPSO‐PSO) approach have been applied to deal with binary (ON/OFF) and real variables of the CBUCP, respectively. To attain a global best solution with the minimum CPU burden, an enhanced optimization approach (EOA) based on the combination of conventional and global optimization techniques is also applied. The enhanced successive approximation approach (SAA) is applied to deal with binary variables of the UC problem and global search is employed to search continuous variables. In the MOP, a fuzzy membership approach unifies the different objectives of MO‐CBUCP. The decision‐making method decides the best‐satisfied non‐dominated solution among Pareto optimal solutions. The MO‐CBUCP test system has been undertaken and results reveal that the ‘EOA’ technique is superior as compared to the BPSO‐PSO approach. It has been examined from results that cogeneration unit has a significant impact on operating cost and pollutants emission of the cogeneration system.