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

This paper deals with the multi-objective fuel cost optimization of a conventional power plant (CPP) and emission minimization in CPPs and solar PV power plants (SPVPPs) using a 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 evolutionary multi-objective algorithms’ inadequacies, such as early convergences, slowly meeting the Pareto-optimal front, and narrow trapping, applying a combination of different algorithms is unusual. This paper offers a hybrid evolutionary multi-objective optimization process based on combining the crow search optimization with the bat algorithm for dealing with the combined economic emission dispatch problem for SPVPPs. A hybrid technique combined with the proposed constriction handling method can balance 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 results of the proposed algorithm have also been compared with those of the bat, PSO, and crow search algorithms. The proposed method can be said to be effective considering the simulation results.