Optimization of Fuel Cost in Electric Power Systems using Harmony Search Algorithm

Year 2021, Volume: 13 Issue: 2, 531 - 544, 18.06.2021

Ayşe Erdoğan Yıldırım

https://doi.org/10.29137/umagd.814025

Cited By: 1

Abstract

Fuel Cost Optimization emerges as an important issue in electrical load distribution systems. In this study, the performance of Harmony Search Algorithm, which has a significant place in the literature, has been observed for a sample problem in the field of fuel cost optimization. It is aimed to distribute the load provided by a three-unit power plant with minimum cost. Three restricted functions are defined in order to calculate the energy production cost per hour of three thermal power plants. For this reason, the problem has been solved by using equal weighted scalarization, one of the multi-objective optimization techniques and static penalty function with Harmony Search Algorithm. The obtained results are presented in comparison with the Lagrange solution and Artificial Bee Colony Algorithm, which were used to solve the same problem.

Keywords

Multi-objective optimization, Harmony search algorithm, Fuel cost optimization, Electric power systems, Power distribution systems, Economic load distribution

References

• Adaryani, M. R., & Karami, A. (2013). Artificial bee colony algorithm for solving multi-objective optimal power flow problem. International Journal of Electrical Power & Energy Systems, 53 (Dec 2013), 219-230.
• Ah King, T.F.R., & Rughooputh, C.S.H. (2003). Elitist multiobjective evolutionary algorithm for environmental/economic dispatch. 2003 Congress on Evolutionary Computation (CEC '03), 8-12 Dec 2003, Canberra, Australia, 1108-1114.
• Alia, O. M., & Mandava, R. (2011). The variants of the harmony search algorithm: An overview. Artificial Intelligence Review, 36(1), 49-68.
• Altun, H., & Yalcinoz, T. (2008). Implementing soft computing techniques to solve economic dispatch problem in power systems. Expert Systems with Applications, 35, 1668–1678.
• Brini, S., Abdallah, H.H., & Ouali, A. (2009). Economic dispatch for power system included wind and solar thermal energy. Leonardo Journal of Sciences, 14 (Jan-Jun 2009), 204-220.
• Ceylan, H., & Ceylan, H. (2013). Şehiriçi karayolu ağlarının sezgisel harmoni araştırması optimizasyon yöntemi ile ayrık tasarımı (in Turkish). IMO Teknik Dergi, 2013, 6211-6231.
• Duman, S., Döşoğlu, M. K., Öztürk, A., & Erdoğmuş, P. (2010). Türkiye’deki güç sisteminde tavlama benzetimi, genetik algoritma ve tabu araştırma algoritmaları kullanılarak ekonomik dağıtım (in Turkish). Engineering Sciences, 5(1), 64-78.
• Gao, X. Z., Govindasamy, V., Xu, H., Wang, X., & Zenger, K. (2015). Harmony search method: Theory and applications. Computational Intelligence and Neuroscience, 2015, 1-10.
• Geem, Z. W., Kim, J. H., & Loganathan, G. V. (2001). A new heuristic optimization algorithm: Harmony search, SIMULATION: Transactions of The Society for Modeling and Simulation, 76(2), 60-68.
• Geem Z. W. (2009). Music-inspired harmony search algorithm. Berlin, Germany, Springer.
• Kök, A., & Yalçınöz, T. (2005). Açık artırmalı ekonomik yük dağıtımı için yeni bir yaklaşım (in Turkish). I. Enerji Verimliliği ve Kalitesi Sempozyumu, 17-18 May 2005, Kocaeli, Turkey, 71-75.
• Lee, K. S., & Geem, Z. W. (2004). A new structural optimization method based on the harmony search algorithm. Computers and Structures, 82, 781-798.
• Mahdavi, M., Fesanghary, M., & Damangir, E. (2007). An improved harmony search algorithm for solving optimization problems. Applied Mathematics and Computation, 188, 1567-1579.
• Mohamed, A. A., Mohamed, Y. S., El-Gaafary, A. A. M., & Hemeida, A. M. (2017). Optimal power flow using moth swarm algorithm. Electric Power Systems Research, 142 (Jan 2017), 190-206.
• Öztürk, A., Çobanlı, S., Duman, S., Tosun, S., & Döşoğlu, K. (2011). Yapay arı kolonisi algoritması ile elektrik güç sistemi optimal yakıt maliyetinin belirlenmesi (in Turkish). 6th International Advanced Technologies Symposium (IATS’11), 16-18 May 2011, Elazığ, Turkey, 311-316.
• Rahman, T.K.A., Suliman, S.I., Musirin, I. (2006). Artificial immune-based optimization technique for solving economic dispatch in power system. In: Apolloni B., Marinaro M., Nicosia G., Tagliaferri R. (eds) Neural Nets. WIRN 2005, NAIS 2005. Lecture Notes in Computer Science, 3931, Berlin, Heidelberg, Springer.
• Smith, A. E., & Coit, D. W. (1997). Constraint-handling techniques - penalty functions. In Handbook of Evolutionary Computation - Chapter C5.2., Bristol, U.K., Institute of Physics Publishing and Oxford University Press.
• Song, Y. H., Chou, C. S. V., & Min, Y. (1999). Large-scale economic dispatch by artificial ant colony search algorithms. Electric Machines and Power Systems, 27(7), 679-690.
• Tosun, S., Öztürk, A., Erdoğmuş, P., Biçen, Y. & Hasırcı, U. (2009). Elektrik güç sistemlerinde optimal yakıt maliyetinin benzetim tavlama (BT) algoritması ile belirlenmesi (in Turkish). 5.Uluslararası İleri Teknolojiler Sempozyumu (IAT’09), 13-15 May 2009, Karabük, Turkey, 254-258.
• Yalcinoz, T., & Altun, H. (2001). Power economic dispatch using a hybrid genetic algorithm. IEEE Power Engineering Letters, IEEE Power Eng. Review, 21(3), 59-60.
• Yalçınöz, T., Yavuzer, T., & Altun, H. (2002). Tabu araştırması uygulanarak ekonomik yük dağıtımı probleminin çözümü (in Turkish). Elektrik-Elektronik-Bilgisayar Mühendisliği Sempozyumu (ELECO'2002), 18-22 Dec 2002, Bursa, Turkey, 30-34.