Multi-Objective Squirrel Search Algorithm for Multi-Area Economic Environmental Dispatch With Multiple Fuels and Valve Point Effects

The essential goal of multi-area economic environmental dispatch (MAEED) is to determine the optimum power generation schedule of each unit and power transfer between the areas in order to minimize fuel costs and pollutant emissions, when the generation, power balance and tie-line limits are satisfied. This paper focuses on developing multi-objective squirrel search algorithm (MOSSA) to solve the MAEED problem, of which the goal is to simultaneously minimize the total fuel cost and emission considering valve point effects and multi-fuel options. The proposed MOSSA combines squirrel search algorithm along with Pareto-dominance theory to generate non-dominated solutions. It uses an external elitist depository mechanism with crowding distance sorting to preserve the distribution diversity of Pareto-optimal solutions as the evolution continues. In addition, a fuzzy decision maker is used to select the best compromised solution from the obtained Pareto frontiers. Furthermore, the MAEED problem is unraveled by squirrel search algorithm based weighted sum approach with price penalty factors, artificial bee colony and exchange market algorithm. Different case studies are performed on 10-unit with three-area system, 40-unit with four-area system and 140-unit real Korean power system considering valve point effects and multi-fuel options which testify the supremacy of the suggested approach. The comparisons with state-of-the-art approaches suggest that MOSSA can generate more competitive trade-off solutions for solving the MAEED problems.

Automated summary

This paper focuses on the multi-area economic environmental dispatch (MAEED) problem and proposes a multi-objective squirrel search algorithm (MOSSA) to solve it. The MOSSA combines squirrel search algorithm with Pareto-dominance theory, uses a fuzzy decision maker to select the best solution, and generates competitive trade-off solutions compared to existing methods.