Economic Load Dispatch Optimization for Thermal Power Plant Using Particle Swarm Optimization and Lambda Iteration
Downloads
Nigeria’s socioeconomic growth cannot improve effectively if the rate of electricity generation and dispatch continues at its current status which is due to several challenges facing the country’s power industry. This study investigates the optimization of Economic Load Dispatch for six thermal power generators using Particle Swarm Optimization (PSO) and Lambda Iteration methods. The primary objective is to minimize fuel costs while meeting a total power demand of 600MW. The study employs quadratic cost functions obtained from historical datas derived from input-output characteristics of the generators and simulates both methods using MATLAB. Results indicate that PSO achieves superior cost efficiency with a total generation cost of N507,103,545 compared to N507,232,335 using Lambda Iteration. PSO with a 0.0254% improvement in cost over the Lambda iteration method, and saves about N128,790.0, which is minor but could be significant for large-scale dispatches Furthermore, PSO demonstrates faster convergence and greater flexibility, making it a more effective tool for modern power systems. The findings underscore the potential of advanced optimization techniques like PSO in enhancing operational efficiency and cost-effectiveness in power generation.
Abdul Wahab N., Mohamed A. and Aini H. (2011) “Fast Transient Stability Assessment of Large Power System using Probabilistic Neural Network with feature reduction techniques”, Int. J. Expert Systems with Applications 38, pp. 11112–11119.
Al. Mahfazur Rahman & Md. F Islam,(2021) “Analysis of Economic Load Dispatch for a Power System Network”. The SEU Journal of Electrical and Electronics Engineering (SEUJEE), Volume 01, pp 2710-2130, 2021.
Alayande A.S, J.T. Olowolaju, & I. K. Okakwu (2019). Solving Optimal Generation Dispatch Problem in Power Networks through PSO and Lambda Iteration Techniques”, Nigerian Journal of Technology (NIJOTECH), Vol. 38, No. 1, pp. 165-176.
Alrashidi M.R., El-Hawary M. E. (2009). Application of computational intelligence Techniques for solving Optimal Power Flow problem. Electric power System. Res., Vol. 79, No. 4, pp. 694-702.
Athe, O. D. C. Idoniboyeobu, & S. L. Braide(2020), Optimization of Generation Cost for Economic Operation of Sapele Thermal Power Plant using Particle Swarm Optimization Method; American Journal of Engineering Research (AJER) Volume-9, pp. 167-179.
Buraimoh, E., Ejidokun, T. O. and Ayamolowo, O. J. (2018) “Optimization of an Expanded Nigeria Electricity Grid System using Economic Load Dispatch”, ABUAD Journal of Engineering Research and Development, Vol. 1 No. 1, pp.61-66, Oct. 2018.
Gabir S., and M. Agieb, (1998) “Constrained Economic Dispatch Problem and applications” Proceedings of IEEE Int. Conference on Optimal power flowvol. 67, no.8, pp.184–202.
Haruna, Y, S., Idris S., G. A. Bakare & A. U. Abu, (2018). Comparative Analysis of the Economic Load Dispatch Problem of the Nigerian Thermal Power Stations. GSJ: Vol.6, Issue 4, April 2018.
Haruna Y.S., Yisah Y.A., Bakare G.A, Haruna M.S., & Ooda S.O, (2017) "Optimal Economic Load Dispatch of the Nigerian Thermal Power Stations Using Particle Swarm Optimization (PSO)," The International Journal Of Engineering And Science (IJES), 2017, vol. 6, No.1, pp. 17-23.
Kennedy and R. C. Eberhart, (1995) “Particle swarm optimization”, Proceedings of IEEE International Conference on Neural Networks (ICNN’95), Vol. iv, pp. 1942-1948, Perth, Australia.
Kumar, V. J. Saraswat, (2016) “A Review on Optimization Techniques for Economic Load Dispatch Problem,” International Research Journal of Engineering & Technology, vol. 3, No.7, pp. 375-379.
Lazarus O. K, Uzoechi, T. J. Ibechem & Ernest Ezugwu (2021); Evaluation of Optimal Power Dispatch of the Nigerian Power System Using PSO Technique. American Journal of Engineering Research (AJER), Volume-10, Issue-03, pp-01-18. 2021.
Narasimha L., V Rao (2014) “PSO Technique for Solving the Economic Dispatch Problem Considering the Generator Constraints” International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering Vol. 3.
Nguyen, H. D., Truong K. H., Le N.A. (2023). The Application of Equilibrium Optimizer for solving modern economic load dispatch problem considering renewable energies and multiple fuel thermal units. Department of Power Delivery, Ho Chi Minh City University of Technology (HCMUT) IJRED 2023.
Okozi S. O., Ogbonna G. C., Olubiwe M. & Ezugwu E. O. (2019)‘Solution to the Economic Dispatch Problem of the Nigerian Power System using Genetic Algorithm’, Nigerian Journal of Technology (NIJOTECH) Vol. 38, No. 4, October 2019, pp. 1036 – 1047.
Soleymani S., M. Hayatdavudi (2014),”Solving Economic Load Dispatch Using A Novel Method Based on PSO Algorithm & GAMS Software” International Journal Of Scientific &Technology Research Volume 3.
Tijanni M. A., Adepoju G. A., Okelola M O., (2022) Optimization Approaches to Generation Dispatch problems: Review of Nigerian Power System. Nigerian Journal of Technological Development , vol 19, No 2.
Xin J., Yu L., Li N. (2022). A diversity-based parallel particle swarm optimization for non convex economic dispatch problem. IEEE 9th Power India International conference (PIICON), Vol 25, issue 3 pp.393-399.
Zaineb N. J. (2021); Economic Load Dispatch using Lambda Iteration, Particle Swarm Optimization & Genetic Algorithm International Journal for Research in Applied Science and Engineering Technology (IJRASET), Vol 9.
Zajmi, L. A, Jaharadak, A. A. (2018). Concepts, methods and Performance of Particle Swarm Optimization, Back propagation, and Neural networks. Applied Computational Intelligence and Soft computing, vol. 2018, pp. 1-7.
Zhang, Y., Wang, S., & Ji, G. (2010). A comprehensive survey on Particle Swarm Optimization Algorithm & its Applications. Mathematical Problems in Engineering, vol. 2015, pp.137.
