Exploring the Orca Predation Algorithm for Economic Dispatch Optimization in Power Systems

The Economic Dispatch problem is essential for minimizing generation costs while satisfying power demand in electrical systems. This research looks into the Orca Predation Algorithm, an optimization method based on biology that can solve the Economic Dispatch problem for systems with 6, 13, or 15 producing units. The idea behind Orca Predation Algorithm came from the way orcas hunt for food. It solves problems that other optimization methods and bio-inspired algorithms have, like too much population diversity and too early convergence. This research shows that Orca Predation Algorithm consistently does better than other bio-inspired algorithms like Particle Swarm Optimization, Whale Optimization Algorithm, Grey Wolf Optimizer, the Bat Algorithm, Genetic Algorithm and Ant Colony Optimization in terms of minimum cost, average cost, and solution stability. The sensitivity analysis of the parameters regulating the exploration-exploitation balance in Orca Predation Algorithm demonstrated substantial performance enhancements. By changing these parameters, the best prices came in at $15,275.93 for the 6-unit system, $17,932.49 for the 13-unit system, and $32,256.97 for the 15-unit system. These prices are lower than those in the previous parameter setting. Although Orca Predation Algorithm demonstrates greater performance, it necessitates extended computing time, which future research could mitigate by exploring parallelization or hybrid methodologies. This paper shows that Orca Predation Algorithm is a reliable tool for optimizing Economic Dispatch problems. It gives useful information to power system engineers who are looking for effective and scalable optimization methods for modern power systems.