An enhanced bacterial foraging algorithm approach for optimal power flow problem including FACTS devices considering system loadability.

Obtaining optimal power flow solution is a strenuous task for any power system engineer. The inclusion of FACTS devices in the power system network adds to its complexity. The dual objective of OPF with fuel cost minimization along with FACTS device location for IEEE 30 bus is considered and solved using proposed Enhanced Bacterial Foraging algorithm (EBFA). The conventional Bacterial Foraging Algorithm (BFA) has the difficulty of optimal parameter selection. Hence, in this paper, BFA is enhanced by including Nelder-Mead (NM) algorithm for better performance. A MATLAB code for EBFA is developed and the problem of optimal power flow with inclusion of FACTS devices is solved. After several run with different initial values, it is found that the inclusion of FACTS devices such as SVC and TCSC in the network reduces the generation cost along with increased voltage stability limits. It is also observed that, the proposed algorithm requires lesser computational time compared to earlier proposed algorithms.

[1]  Boon-Teck Ooi,et al.  Assessment and control of the impact of FACTS devices on power system performance , 1996 .

[2]  Laszlo Gyugyi,et al.  Understanding FACTS: Concepts and Technology of Flexible AC Transmission Systems , 1999 .

[3]  Kevin M. Passino,et al.  Biomimicry of bacterial foraging for distributed optimization and control , 2002 .

[4]  Q. Henry Wu,et al.  Bacterial Foraging Algorithm for Optimal Power Flow in Dynamic Environments , 2008, IEEE Transactions on Circuits and Systems I: Regular Papers.

[5]  George Lindfield,et al.  Numerical Methods Using MATLAB , 1998 .

[6]  Yog Raj Sood,et al.  Optimal location of FACTS devices in power system using Genetic Algorithm , 2009, 2009 World Congress on Nature & Biologically Inspired Computing (NaBIC).

[7]  S. R. Spea,et al.  Optimal power flow using differential evolution algorithm , 2010 .

[8]  Edson C Bortoni,et al.  Optimal load distribution between units in a power plant. , 2007, ISA transactions.

[9]  M. Saravanan,et al.  Application of particle swarm optimization technique for optimal location of FACTS devices considering cost of installation and system loadability , 2007 .

[10]  Goran Andersson,et al.  Power flow control by use of controllable series components , 1993 .

[11]  Malabika Basu,et al.  Optimal power flow with FACTS devices using differential evolution , 2008 .

[12]  John A. Nelder,et al.  A Simplex Method for Function Minimization , 1965, Comput. J..

[13]  Claudio A. Roa-Sepulveda,et al.  A solution to the optimal power flow using simulated annealing , 2003 .

[14]  M. Todorovski,et al.  An initialization procedure in solving optimal power flow by genetic algorithm , 2006, IEEE Transactions on Power Systems.

[15]  T. S. Chung,et al.  A hybrid GA approach for OPF with consideration of FACTS devices , 2000 .

[16]  Bijaya Ketan Panigrahi,et al.  Bacterial foraging optimisation: Nelder-Mead hybrid algorithm for economic load dispatch , 2008 .

[17]  K. S. Swarup,et al.  Multi Objective Harmony Search Algorithm For Optimal Power Flow , 2010 .

[18]  Weerakorn Ongsakul,et al.  Optimal power flow with FACTS devices by hybrid TS/SA approach , 2002 .

[19]  C. N. Bhende,et al.  Bacterial Foraging Technique-Based Optimized Active Power Filter for Load Compensation , 2007, IEEE Transactions on Power Delivery.

[20]  M. A. Abido,et al.  Optimal power flow using particle swarm optimization , 2002 .

[21]  Heidar Ali Shayanfar,et al.  Reliability improvement of distribution systems using SSVR. , 2009, ISA transactions.

[22]  Ronald L. Rardin,et al.  Optimization in operations research , 1997 .

[23]  Francisco D. Galiana,et al.  A survey of the optimal power flow literature , 1991 .