Optimasi Pembangkitan Ekonomis Berbasis Whale Optimization Algorithm Pada Sistem Multimesin

Abstract

This study addresses the problem of generation cost optimization for thermal power plants in the Sulbagsel multimachine power system. An advanced swarm intelligence approach, the Whale Optimization Algorithm (WOA), is employed as the primary optimization technique. WOA, inspired by the bubble-net hunting strategy of humpback whales, has emerged as a promising metaheuristic with strong capabilities in exploration and exploitation. The main objective of this study is to minimize thermal generation costs while ensuring effective performance under real system operating conditions. To provide a comparative benchmark, Particle Swarm Optimization (PSO) is also applied to the same problem. Statistical evaluation is conducted to assess convergence behavior, accuracy, and consistency of both methods. The results indicate that WOA demonstrates superior balance between exploration and exploitation, leading to stable convergence and reliable solutions. Under peak daytime load conditions, PSO achieves a cost reduction of 23.02%, whereas the proposed WOA-based method achieves a comparable reduction of 23.78%. Although PSO yields a slightly higher cost saving, WOA demonstrates stronger robustness and statistical reliability across multiple trials. These findings confirm that WOA is a competitive alternative for generation cost optimization in complex multimachine systems, offering significant potential for future applications in economic dispatch problems with larger-scale renewable energy integration.