Comparative Performance Analysis of Sharpbelly Fish and Equilibrium Optimizers for Green Economic Load Dispatch Considering Prohibited Operating Zones and Multiple Fuel Constraints

This study investigates the Green Economic Load Dispatch (GELD) problem, focusing on the integration of renewable energy into traditional thermal grids. We evaluate and compare the performance of two meta-heuristic algorithms: the Sharpbelly Fish Optimizer (SFO) and the Equilibrium Optimizer (EO), under realistic non-convex constraints. The methodology is validated on two distinct scenarios: a 6-unit thermal system incorporating prohibited operational zones, and a 10-unit system considering multi-fuel constraints alongside 100 MW of wind generation. Comparative results indicate a scale-dependent performance divergence. While EO shows high reliability in smaller configurations, SFO exhibits superior exploration and faster convergence as system dimensionality increases, resulting in more significant fuel cost reductions. These findings offer a practical framework for optimizing hybrid energy portfolios and highlight the efficacy of swarm intelligence in achieving low-carbon power dispatch.

Keywords: Economic Load Dispatch; Sharpbelly Fish Optimizer; Equilibrium Optimizer; Non-Convex Optimization; Metaheuristic Algorithms; Power System Optimization.