Multi-objective Optimization of Combined Economic Emission Dispatch Problem in Solar PV Energy Using Hybrid Bat-Crow Search Algorithm

The current paper deals with multi-objective fuel cost optimization of a conventional power plant (CPP) and emission minimization in CPPs and Solar PV Power Plants (SPVPPs) using hybrid Bat-Crow search algorithm. To resolve this complicated, non-convex, and excessively nonlinear problem, a variety of meta-heuristic optimization algorithms are developed and effectively employed. To handle the evolutionary multi-objective algorithms’ inadequacies like early convergences, slowly meeting Pareto-optimal front, and narrow trapping, generally applying a combination of different algorithms is unusual. This paper presents the hybrid evolutionary multi-objective optimization process, which is based on combining the Crow search optimization with the Bat algorithm for dealing with the combined economic emission dispatch (CEED) through SPVPP. A hybrid technique combined with the proposed constriction handling method can balance out exploitation and exploration tasks. Different IEEE standard bus systems were tested with the proposed hybrid method using the quadratic cost function and monitoring the transmission losses. The result of proposed algorithm is compared with Bat, PSO and Crow Search Algorithm. The proposed method can be effective with the simulation results.