Solving Optimal Power Flow Using Modified Bacterial Foraging Algorithm Considering FACTS Devices

Abstract

In this paper, a new Modified Bacterial Foraging Algorithm (MBFA) method is developed to incorporate FACTS devices in optimal power flow (OPF) problem. This method can provide an enhanced economic solution with the use of controllable FACTS devices. Two types of FACTS devices, thyristor controlled series compensators (TCSC) and Static VAR Compensator (SVC) are considered in this method. The basic bacterial foraging algorithm (BFA) is an evolutionary optimization technique inspired by the foraging behavior of the E. coli bacteria. The strategy of the OPF problem is decomposed in two sub-problems, the first sub-problem related to active power planning to minimize the fuel cost function, and the second sub-problem designed to make corrections to the voltage deviation and reactive power violation based in an efficient reactive power planning of multi Static VAR Compensator (SVC). The specified power flow control constraints due to the use of FACTS devices are included in the OPF problem. The proposed method decomposes the solution of such modified OPF problem into two sub problems’ iteration. The first sub problem is a power flow control problem and the second sub problem is a modified Bacterial foraging algorithm (MBFA) OPF problem. The two sub problems are solved iteratively until convergence. Case studies are presented to show the effectiveness of the proposed method.

Share and Cite:

Ravi, K. , Shilaja, C. , Babu, B. and Kothari, D. (2014) Solving Optimal Power Flow Using Modified Bacterial Foraging Algorithm Considering FACTS Devices. Journal of Power and Energy Engineering, 2, 639-646. doi: 10.4236/jpee.2014.24086.

Conflicts of Interest

The authors declare no conflicts of interest.

References

[1] Huneault and Galiana, F.D. (1991) A Survey of the Optimal Power Flow Literature. IEEE Transactions on Power, Systems, PWRS-6, 762-770.
[2] Hingorani, N.G. (1993) Flexible ac Transmission. IEEE Spectrum, 40-45. http://dx.doi.org/10.1109/6.206621
[3] Taranto, G.N., Pinto, L.M.V.G. and Pereira, M.V.F. (1992) Representation of FACTS Devices in Power System Economic Dispatch. IEEE Transactions on Power Systems, 7, 572-576.
[4] Noroozian, M. and Andersson, G. (1993) Power Flow Control by Use of Controllable Series. IEEE Transactions on Power Deliver, 8, 1420-1429. http://dx.doi.org/10.1109/61.252669
[5] Stott, B. and Marinho, J.L. (1979) Linear Programming for Power System Network Security Application. IEEE Transactions on Power Apparatus and Systems, PAS-98, 837-848.
[6] Gotham, D.J. and Heydt, G.T. (1998) Power Flow Control and Power Flow Studies for Systems with FACTS Devices. IEEE Transactions on Power and Systems, 13, 60-65. http://dx.doi.org/10.1109/59.651614
[7] Sttot, B. and Marinho, J.L. (1979) Linear Programming for Power System Network Security Applications. IEEE Transactions on Power Apparatus and Systems, PAS-98, 837-848.
[8] Alsac, O. and Stott, B. (1974) Optimal Load Flow with Steady State Security. IEEE Transactions on Power Apparatus and Systems, 745-751.
[9] Sivanandam, S.N. and Deepa, S.N. (2008) Introduction to Genetic Algorithm. Springer-Verlag, Berlin, Heidelberg.
[10] Pothiya, S., Nagamroo, I. and Kongprawechnon, W. (2008) Application of Multiple Tabu Search Algorithm to Solve Dynamic Economic Dispatch Considering Generator Constraints. Journal of Energy Conversion Manage, 49, 506-516. http://dx.doi.org/10.1016/j.enconman.2007.08.012
[11] Baskar, G. and Mohan, M.R. (2008) Security Constrained Economic Load Dispatch Using Improved Particle Swarm Optimization Suitable for Utility System. Electric Power and Energy Systems, 30, 609-613. http://dx.doi.org/10.1016/j.ijepes.2008.09.001
[12] Bouktir, T., Slimani, L. and Mahdad, B. (2008) Optimal Power Dispatch for Large-Scale Power System Using Stochastic Search Algorithms. International Journal of Electrical Power & Energy Systems, 28, 1-10.
[13] Saini, A., Chaturvedi, D.K. and Saxena, A.K. (2006) Optimal Power Flow Solution: A GA-Fuzzy System Approach. International Journal of Electrical Power & Energy Systems, 5, 1-21. http://dx.doi.org/10.2202/1553-779X.1091
[14] Gaing, Z.L. (2003) Particle Swarm Optimization to Solving the Economic Dispatch Considering the Generator Constraints. IEEE Transactions on Power and Systems, 18, 1187-1195. http://dx.doi.org/10.1109/TPWRS.2003.814889
[15] Chien Kuo, C. (2008) A Novel String Structure for Economic Dispatch Problems with Practical Constraint’s. Energy Conversion Manage, 49, 3571-3577. http://dx.doi.org/10.1016/j.enconman.2008.07.007
[16] Bouktir, T., Slimani, L. and Mahdad, B. (2008) Optimal Power Dispatch for Large-Scale Power System Using Stochastic Search Algorithms. International Journal of Electrical Power & Energy Systems, 28, 1-10.
[17] Alsac, O., Bright, J., Prais, M. and Stott, B. (1990) Further Developments in LP-Based Optimal Power Flow. IEEE Transactions on Power System, 5, 697-710.
[18] Passino, K.M. (2002) Biomimicry of Bacterial Foraging Algorithm for Distributed Optimization and Control. IEEE System Magazine, 22, 52-67.
[19] Bakistzis, A.G., Biskas, P.N., Zoumas, C.E. and Petridis, V. (2002) Optimal Power Flow by Enhanced Genetic Algorithm. IEEE Transactions on Power Systems, 17, 229-236. http://dx.doi.org/10.1109/TPWRS.2002.1007886
[20] Slimani, L. and Bouktir, T. (2007) Economic Power Dispatch of Power System with Pollution Control Using Multi Objective ant Colony Optimization. International Journal of Computational Intelligence Research, 3, 145-153.
[21] Mahdad, B., et al. (2009) Optimal Power Flow for Large Scale Power System with Shunt FACTS Devices Using Efficient Parallel GA. International Journal of Electrical Power & Energy Systems, 1-11.
[22] Mahdad, B., Bouktir, T. and Srairi, K. (2007) Methodology Based in Practical Fuzzy Rules Coordinated with Asymmetric Dynamic Compensation Applied to the Unbalanced Distribution Network. International Review of Electrical Engineering (IREE), 3, 145-153.

Copyright © 2024 by authors and Scientific Research Publishing Inc.

Creative Commons License

This work and the related PDF file are licensed under a Creative Commons Attribution 4.0 International License.