Ali AKDAĞLI, Kadir ABACI, Volkan YAMAÇLI

Optimal power flow with SVC devices by using the artificial bee colony algorithm

In this paper a simple and efficient heuristic search method based on the artificial bee colony (ABC) algorithm is presented and used for the optimal power flow (OPF) problem in power systems with static VAR compensator (SVC) devices. The total generation cost of a power system with SVC devices (which improve the voltage stability at load buses) is optimally minimized with the use of ABC. The ABC, which is based on the foraging behavior of honey bees searching for the best food source, is a recently proposed optimization algorithm. The performance of the presented ABC algorithm was tested and verified on the IEEE 11-bus and IEEE 30-bus power systems by comparing it with several other optimization methods. Furthermore, ABC is used not only for optimizing the total generation cost and active power loss, but also for improving the voltage stability of the 22-bus power system in Turkey. Our results illustrate that ABC can successfully be used to solve nonlinear problems related to power systems.

PDF

___

[1] Haifeng L, Licheng J, McCalley J, Kumar R, Ajjarapu V. Planning minimum reactive compensation to mitigate voltage instability. Power Engineering Society General Meeting; 1822 June 2006; Montreal, Canada: IEEE. pp.102109.
[2] Hingorani NG. High power electronics and flexible AC transmission system. IEEE Power Engineering Review 1988; 8: 34.
[3] Wood A, Wollenberg B. Power generation, operation, and control. 2nd ed. New York, NY, USA: Wiley, 1996.
[4] Kalyani RP, Crow ML, Tauritz DR. Optimal placement and control of unified power flow control devices using evolutionary computing and sequential quadratic programming. Power Systems Conference and Exposition; 2931 October 2006; Atlanta, GA, USA: IEEE. pp. 959964.
[5] Ching-Tzong S, Guor-Rurng L. Reliability optimization design of distribution systems via multi-level hierarchical procedures and generalized reduced gradient method. Energy Management and Power Delivery Conference; 2123 November 1995; Singapore: IEEE. pp. 180185.
[6] Rong-Mow J, Nanming C. Application of the fast Newton-Raphson economic dispatch and reactive power/voltage dispatch by sensitivity factors to optimal power flow. IEEE Transactions on Energy Conversion 1995; 10: 293301.
[7] Kuzle I, Zdrilic M, Pandzic H. Virtual power plant dispatch optimization using linear programming. Environment and Electrical Engineering Conference; 811 May 2011; Zagreb, Croatia: IEEE. pp. 14.
[8] Shoults RR, Sun DT. Optimal power flow based upon p-q decomposition. IEEE Transactions on Power Apparatus and Systems 1982; 101: 397405.
[9] Xie K, Song YH. Power market oriented optimal power flow via an interior point method. IEE Proceedings: Generation, Transmission and Distribution 2001; 148: 594556.
[10] El Ela About AA, Abido MA, Spea SR. Optimal power flow using differential evolution algorithm. Electric Power Systems Research 2010; 80: 878885.
[11] Zhao B, Guo CX, Cao YJ. A multiagent-based particle swarm optimization approach for optimal reactive power dispatch. IEEE Transactions on Power Systems 2005; 20: 10701078.
[12] Metwally MME, El Emary AA, El Bendary FM, Mosaad MI. Optimal allocation of FACTS devices in power system using genetic algorithms. International Middle-East Power System Conference; 1215 March 2008; Aswan, Egypt:IEEE. pp. 14.
[13] Acha E, Claudio R, Ambriz-Perez H, Angeles-Camacho C. FACTS modelling and simulation in power networks.New York, NY, USA: Wiley, 2004.
[14] El-Dib AA, Youssef H, El-Metwally MM, Osman Z. Maximum loadability of power systems using hybrid particle swarm optimization. IEEE Transactions on Power Systems 2005; 20: 10701078.
[15] Mahdad B, Bouktir T, Srairi K. Strategy of location and control of FACTS devices for enhancing power quality. Mediterranean Electrotechnical Conference; 1619 May 2006; Malaga, Spain: IEEE. pp. 10681072.
[16] Karaboga D. An idea based on honey bee swarm for numerical optimization. Erciyes University Technical Report 2005; TR06: Kayseri, Turkey.
[17] Karaboga D, Basturk B. A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. Journal of Global Optimization 2007; 39: 459971.
[18] Karaboga D, Basturk B. Artificial bee colony (ABC) optimization algorithm for solving constrained optimization problems. International Fuzzy System Association World Congress 1821 June 2007; Cancun, Mexico: LNAI. pp.789798.
[19] Karaboga D, Basturk B. On the performance of artificial bee colony (ABC) algorithm. Applied Soft Computing 2008; 8: 687697.
[20] Akay B, Karaboga D. Parameter tuning for the artificial bee colony algorithm. International Conference on Computational Collective Intelligence - Technologies and Applications; 57 October 2009; Wroclaw, Poland: Springer.pp. 608619.
[21] Bala JL, Thanikachalam A. An improved second order method for optimal load flow. IEEE Transactions on Power Apparatus and Systems 1978; 97: 12391244.
[22] Storn R. Price K. Minimizing the real functions of the ICEC96 contest by differential evolution. Proceedings of IEEE International Conference on Evolutionary Computation; 2022 May 1996; Munich, Germany: IEEE. pp.842884.
[23] Ba¸saran UA. Various power flow and economic dispatch analyses on 380 kV- interconnected power system in Turkey. ¨ MSc, Anadolu University, Eski¸sehir, Turkey, 2004.
[24] Ba¸saran UA. Economic dispatch analysis using two different methods for 22-bus 380-kV power system in Türkey. Sakarya University Journal of Natural Sciences 2007; 11: 8995.