Application of Differential Evolution for Congestion Management in Power System

In the emerging restructured power system, the congestion management (CM) has become extremely important in order to ensure the security and reliability of the system. This paper proposes an algorithm for congestion management in a pool based electricity market based on differential evolution (DE). The aim of the proposed work is to minimize deviations from preferred transaction schedules and hence the congestion cost. Numerical results on test system namely IEEE 30 Bus System is presented for illustration purpose and the results are compared with Particle swarm optimization (PSO) in terms of solution quality. The comprehensive experimental results prove that the DE is one among the challenging optimization methods which is indeed capable of obtaining higher quality solutions for the proposed problem.

[1]  Rainer Storn,et al.  Differential Evolution – A Simple and Efficient Heuristic for global Optimization over Continuous Spaces , 1997, J. Glob. Optim..

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

[3]  A.J. Conejo,et al.  Congestion management ensuring voltage stability , 2008, IEEE Transactions on Power Systems.

[4]  T. Meena,et al.  Cluster Based Congestion Management in Deregulated Electricity Market Using PSO , 2005, 2005 Annual IEEE India Conference - Indicon.

[5]  M.M.A. Salama,et al.  Opposition-Based Differential Evolution , 2008, IEEE Transactions on Evolutionary Computation.

[6]  Kit Po Wong,et al.  Differential Evolution, an Alternative Approach to Evolutionary Algorithm , 2006, Proceedings of the 13th International Conference on, Intelligent Systems Application to Power Systems.

[7]  D. Ernst,et al.  Transient stability-constrained optimal power flow , 1999, PowerTech Budapest 99. Abstract Records. (Cat. No.99EX376).

[8]  J. Hazra,et al.  Congestion management using multiobjective particle swarm optimization , 2007, 2008 IEEE Power and Energy Society General Meeting - Conversion and Delivery of Electrical Energy in the 21st Century.

[9]  S. Dutta,et al.  Optimal Rescheduling of Generators for Congestion Management Based on Particle Swarm Optimization , 2008, IEEE Transactions on Power Systems.

[10]  A. Kumar,et al.  A zonal congestion management approach using real and reactive power rescheduling , 2004, IEEE Transactions on Power Systems.

[11]  L. Coelho,et al.  Combining of chaotic differential evolution and quadratic programming for economic dispatch optimization with valve-point effect , 2006, IEEE Transactions on Power Systems.

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

[13]  Chyi Hwang,et al.  Optimal approximation of linear systems by a differential evolution algorithm , 2001, IEEE Trans. Syst. Man Cybern. Part A.

[14]  A.J. Conejo,et al.  Congestion management ensuring voltage stability , 2006, 2008 IEEE Power and Energy Society General Meeting - Conversion and Delivery of Electrical Energy in the 21st Century.

[15]  Yoshikazu Fukuyama,et al.  A hybrid particle swarm optimization for distribution state estimation , 2003, 2003 IEEE Power Engineering Society General Meeting (IEEE Cat. No.03CH37491).

[16]  D. Thukaram,et al.  Congestion management in open access based on relative electrical distances using voltage stability criteria , 2007 .

[17]  A. Kumar,et al.  Call for Papers ESMO 2003 , 2002 .

[18]  K.P. Wong,et al.  Application of Differential Evolution Algorithm for Transient Stability Constrained Optimal Power Flow , 2008, IEEE Transactions on Power Systems.

[19]  Federico Milano,et al.  Multiobjective optimization for pricing system security in electricity markets , 2003 .

[20]  Ka Wing Chan,et al.  Transient stability constrained optimal power flow using particle swarm optimisation , 2007 .

[21]  Anjan Bose,et al.  A computationally simple method for cost-efficient generation rescheduling and load shedding for congestion management , 2005 .