Bees Two-Hive Algorithm for Optimal Power Flow

Abstract:

This paper presents bees two-hive algorithm for solving the optimal power flow (OPF) problem with various constraints. The objective of the proposed technique is to improve the quality solution of the conventional bees algorithm that minimize the total fuel cost subject to operational and physical constraints i.e. energy balance, generation and transmission limits including security constraints. The proposed methodology is tested on the IEEE 30-bus test system. The results obtained using the proposed approach are compared to GA, PSO, BA and other conventional. The comparison of quality solution with other algorithms confirms performance of proposed technique. Simulation results demonstrate that bees two-hive algorithm provides better results than other heuristic techniques.

• Periodical: Applied Mechanics and Materials (Volumes 313-314)
• Pages: 870-875
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.313-314.870
• Online since: March 2013
• Authors: Nopbhorn Leeprechanon, Prakornchai Phonrattanasak
• Keywords: Bees Algorithm, Optimal Power Flow (OPF), Power System Network

References

[1] JA. Momoh, ME. El-Hawary and RA. Adapa, Review of selected optimal power flow literature to 1993 Part I: nonlinear and quadratic programming approaches. IEEE Trans Power Syst 1999;14(1):96–104.

DOI: 10.1109/59.744492

[2] Momoh JA, El-Hawary ME and RA. Adapa, Review of selected optimal power flow literature to 1993 Part II: Newton,linear programming and interior point methods. IEEE Trans Power Syst 1999;14(1):105–11.

DOI: 10.1109/59.744495

[3] LL. Lai, JT. Ma, R. Yokoma and M. Zhao Improved genetic algorithms for optimal power flow under both normal and contingent operation states, Electrical Power & Energy System, Vol. 19, No. 5, pp.287-292, 1997.

DOI: 10.1016/s0142-0615(96)00051-8

[4] J. Yuryich and KP.Wong, Evolutionary programming based optimal power flow algorithm. IEEE Trans Power Syst 1999;14(4):1245–50.

DOI: 10.1109/59.801880

[5] MA. Abido, Optimal power flow using particle swarm optimization. Electric Power Energy Syst 2002;24:563–71.

DOI: 10.1016/s0142-0615(01)00067-9

[6] Samir Sayah and Khaled Zehar. Modified differential evolution algorithm for optimal power flow with non-smooth cost function. Energy conversion and Management 2009:49:3036-3042

DOI: 10.1016/j.enconman.2008.06.014

[7] N. Leeprechanon and P. Polratanasak, Multiobjective Bees Algorithm with clustering technique for environmental/economic dispatch, Proc. of International Conference on Electrical Engineering/Electronics Computer Telecommunications and Information Technology (ECTI-CON), May 2010, pp.621-625.

DOI: 10.1109/ecticon.2018.8619978

[8] Serhat Duman, Uğur Güvenç, Yusuf Sönmez and Nuran Yörükeren, Optimal power flow using gravitational search algorithm, Energy Conversion and Management, Volume 59, July 2012, pp.86-95.

DOI: 10.1016/j.enconman.2012.02.024

[9] Pham, D.T., S.Otri, E.Koc, A.Ghanbarzadeh, S.Rahim, M.Zaidi Technical Note. The Bees Algorithm., Manufacuring Engineering Centre, Cardiff University: Cardiff, UK. (2005)

[10] Blackwell T., Branke J., "Multi-swarm Optimization in Dynamic Environments", Lecture Notes in Computer Science 2004,489-500,Volume 3005

DOI: 10.1007/978-3-540-24653-4_50