The Use of Genetic Algorithm Optimization Approach in Comparison with Lambda Iteration Technique to Solve Economic Load Dispatch Problem

Sabo Aliyu (Department of Electrical Engineering, Faculty of Engineering and Engineering Technology, Nigerian Defence Academy (NDA), Kaduna, Nigeria)
Sadiq N. Buba (Department of electrical and electronics engineering Nigerian defense academy)
Olutosin Ogunleye (Department of Electrical and Electronics Engineering, Faculty of Engineering, Post graduate school, Nigerian Defense Academy, Nigeria)
Kabir Mohammed (Department of Electrical and Electronics Engineering, Faculty of Engineering, Post graduate school, Nigerian Defense Academy, Nigeria)
Samuel Ephraim Kalau (Department of Electrical and Electronics Engineering, Faculty of Engineering, Post graduate school, Nigerian Defense Academy, Nigeria)
Daramdla P. Olaniyi (Department of Electrical and Electronics Engineering, Faculty of Engineering, Post graduate school, Nigerian Defense Academy, Nigeria)

Vokasi UNESA Bulletin of Engineering, Technology and Applied Science
Vol. 2 No. 2 (2025)

The increasing demand for efficient and reliable power generation systems has amplified the importance of solving Economic Load Dispatch (ELD) problems. This study compares the performance of two optimization techniques—Genetic Algorithm (GA), a robust metaheuristic approach, and Lambda Iteration, a traditional iterative method—on the IEEE 39-bus 10-generator test system. The analysis focuses on fuel cost minimization and computational efficiency. GA achieves a significant reduction in total fuel cost to $1390.29, outperforming Lambda Iteration's $2324.22. However, Lambda Iteration demonstrates faster convergence at 0.2 seconds compared to GA's 1.2 seconds. The results underscore the trade-offs between cost efficiency and computational speed, providing valuable insights into the suitability of advanced optimization methods like GA for complex ELD problems and the practicality of Lambda Iteration for simpler systems.