Abstract: The Power Economic Dispatch (PED) problem plays a crucial role in the optimal allocation of generating resources to meet electricity demand while minimizing operational costs in power systems. This project presents the application of the Firefly Algorithm (FA), a bio-inspired optimization technique, to solve the PED problem with an emphasis on cost minimization. The Firefly Algorithm, known for its efficiency in exploring complex solution spaces, was adapted to optimize the dispatch of power generation units considering various constraints, such as generation limits and demand requirements. The objective is to minimize the total fuel cost of power generation, which can significantly enhance the operational efficiency of power systems. The model was implemented and tested on a set of benchmark test systems, demonstrating the superiority of Firefly Algorithm in terms of solution quality and computational efficiency compared to traditional optimization methods. The results confirm that the Firefly Algorithm-based cost optimization model provides a promising approach for solving the PED problem, with potential applications in real time energy management and grid optimization. Additionally, the paper highlights the potential of the Firefly Algorithm to be adapted and scaled for other power plants, demonstrating its flexibility and adaptability to different system sizes and operational conditions. This makes it a promising tool for optimizing generation costs in power plants seeking to minimize their operational costs while maintaining the reliability and stability of the grid.
Keywords: Economic Dispatch, Firefly Algorithm, Cost Optimization Model, Power Distribution for Stations, Particle Swarm Optimization (PSO), Power Allocation.
Title: SOLVING POWER ECONOMIC DISPATCH PROBLEM USING FIREFLY ALGORITHM-BASED COST OPTIMIZATION MODEL
Author: Prof. S.A Oluwadare, Dr (Mrs) O. Owolafe, Ojeyinka James, Dr. (Mrs) Adegun IyanuOluwa, Dr. Adisa Solagbade
International Journal of Computer Science and Information Technology Research
ISSN 2348-1196 (print), ISSN 2348-120X (online)
Vol. 13, Issue 3, July 2025 - September 2025
Page No: 92-99
Research Publish Journals
Website: www.researchpublish.com
Published Date: 14-July-2025
