Combined economic and emission power dispatch problems through multi-objective squirrel search algorithm

Over the years, due to the increasing deterioration of environmental problems, combined economic and environmental power dispatch has become one of the active research areas in the power system operation and control. This paper proposes a new multi-objective squirrel search algorithm to solve the combined economic and environmental power dispatch problem. The proposed multi-objective squirrel search algorithm integrates squirrel search algorithm along with Pareto-dominance principle to generate non-dominated solutions. It uses an external elitist depository mechanism with crowding distance sorting to maintain the distribution diversity of Pareto-optimal solutions as the evolution proceeds. In addition, a fuzzy decision-making strategy is used to select the best compromised solution from the obtained Pareto frontiers. The combined economic and environmental power dispatch problem is also solved by squirrel search algorithm based weighted sum approach, which transfers both the fuel cost and emission objectives into a single-objective function. The performances of multi-objective squirrel search algorithm are tested on three test systems with diverse complexities. The simulation results and comparisons with the other state-of-the-art heuristic algorithms confirm that the proposed multi-objective squirrel search algorithm for combined economic and environmental power dispatching can obtain a better trade-off between fuel cost and emission objectives. [Display omitted] •Multi-objective squirrel search algorithm (MOSSA) is bestowed in this paper.•The approach is employed to solve the economic and emission power dispatch problem.•MOSSA employs the Pareto dominance and crowding distance concepts.•MOSSA is robust as compared to other state-of-the-art approaches in the literature.