Optimal location of unified power flow controller by differential evolution algorithm considering transmission loss reduction

This paper presents a Differential Evolutionary (DE) algorithm for finding the optimal location and the best parameter setting of Unified Power Flow Controller (UPFC) for minimizing the active and reactive power losses in the power system. UPFC is one of the most important Flexible Alternating Current Transmission Systems (FACTS) devices that can simultaneously control the voltage magnitude at the sending end and the active and reactive power flows at the receiving end bus. By re-dispatching the power flows in power systems, the minimization of power losses can be obtained through optimal allocation of UPFC. However, the cost of installing UPFC in the power system is too high. Therefore the objective function developed in this paper in such a way to find a compromise solution to this problem. Simulations have been implemented in MATLAB and the IEEE 14-bus and IEEE 30-bus systems have been used as a case study. Also for the purpose of comparison the proposed technique was compared with another optimization technique namely Particle Swarm Optimization (PSO). The results we have obtained indicate that DE is an easy to use, robust, and powerful optimization technique compared with particle swarm optimization (PSO). Installing UPFC in the optimal location determined by DE can significantly minimize the active and reactive power loss in the network.

[1]  Xiao-Ping Zhang,et al.  Flexible AC Transmission Systems: Modelling and Control , 2006 .

[2]  Laszlo Gyugyi,et al.  Unified power-flow control concept for flexible AC transmission systems , 1992 .

[3]  R. Storn,et al.  Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series) , 2005 .

[4]  J. Baskaran,et al.  Optimal location of FACTS devices in a power system solved by a hybrid approach , 2006 .

[5]  S.J. Cheng,et al.  Optimal Location and Parameter Setting of TCSC by Both Genetic Algorithm and Particle Swarm Optimization , 2007, 2007 2nd IEEE Conference on Industrial Electronics and Applications.

[6]  C. Fuerte-Esquivel,et al.  Unified power flow controller: a critical comparison of Newton-Raphson UPFC algorithms in power flow studies , 1997 .

[7]  M. A. Abido,et al.  Optimal placement of FACTS devices for multi-objective voltage stability problem , 2009, 2009 IEEE/PES Power Systems Conference and Exposition.

[8]  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).

[9]  K.P. Wong,et al.  Parallel Optimal Reactive Power Flow Based on Cooperative Co-Evolutionary Differential Evolution and Power System Decomposition , 2007, IEEE Transactions on Power Systems.

[10]  Kenneth V. Price,et al.  An introduction to differential evolution , 1999 .

[11]  R. Storn,et al.  Differential Evolution - A simple and efficient adaptive scheme for global optimization over continuous spaces , 2004 .

[12]  Seyed Abbas Taher,et al.  Optimal placement of UPFC in power systems using immune algorithm , 2011, Simul. Model. Pract. Theory.

[13]  K. Sundareswaran,et al.  Optimal placement of FACTS devices using probabilistic Particle Swarm Optimization , 2011, ISGT2011-India.

[14]  N.G. Hingorani,et al.  Flexible AC transmission , 1993, IEEE Spectrum.

[15]  I. Marouani,et al.  Optimal location of multi type FACTS devices for multiple contingencies using genetic algorithms , 2011, Eighth International Multi-Conference on Systems, Signals & Devices.

[16]  Mohammad Tavakoli Bina,et al.  Looking for optimal number and placement of FACTS devices to manage the transmission congestion , 2011 .

[17]  G. I. Rashed,et al.  Optimal location and parameter setting of UPFC for enhancing power system security based on Differential Evolution algorithm , 2011 .