Orca Predation Algorithm as an Innovative Solution for IEEE 30 Bus

Abstract

The effective operation of the IEEE 30 Bus power system requires economic dispatch optimization to minimize production costs, align energy supply with demand, and ensure system stability. This economic dispatch problem is complex due to its non-linear characteristics, interdependence between generators, and the need to combine cost minimization with power loss reduction. Conventional optimization techniques often struggle to find global solutions, easily get stuck in local optima, and require significant computational time. This study introduces the Orca Predation Algorithm (OPA) as a new approach to address these challenges. Inspired by the hunting behavior of orcas, OPA balances exploration and exploitation through two distinct phases: pursuit and attack. Evaluated on the IEEE 30-Bus system using power loss computation with coefficient B, the algorithm ensures that generator output power allocation meets demand at the lowest cost. OPA's performance is comprehensively compared with Particle Swarm Optimization (PSO), Grey Wolf Optimization (GWO), Whale Optimization Algorithm (WOA), and Bat Algorithm. The results consistently show that OPA achieves the lowest total cost of $772,754 while maintaining superior system stability and effectively minimizing power losses among the evaluated algorithms. These findings highlight the significant potential of OPA to enhance energy management and advance power system optimization.