Sequential quadratic programming based differential evolution algorithm for optimal power flow problem

This study proposes a hybrid algorithm combining sequential quadratic programming (SQP) and differential evolution (DE) algorithm for solving the optimal power flow (OPF) problem. In this hybrid method, SQP is used to generate an individual, which is a member of an initial population, for DE algorithm. Having generated an individual by SQP, which will be nearer to the optimal solution, DE algorithm can reach the optimal solution more effectively than the classical evolutionary algorithms can. The proposed method has been used to solve the OPF problem on the standard IEEE 30- and IEEE 118-bus test systems to validate the effectiveness. Two different objectives, namely fuel cost considering valve-point effects and the transmission line losses, have been considered. The simulation results obtained from the proposed hybrid method reveal that this algorithm gives better solution for the problem having more non-convexity.

[1]  William F. Tinney,et al.  Optimal Power Flow Solutions , 1968 .

[2]  W. F. Tinney,et al.  Some deficiencies in optimal power flow , 1988 .

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

[4]  Jinyu Wen,et al.  Optimal reactive power dispatch using an adaptive genetic algorithm , 1997 .

[5]  R. Adapa,et al.  A review of selected optimal power flow literature to 1993. I. Nonlinear and quadratic programming approaches , 1999 .

[6]  Kit Po Wong,et al.  Evolutionary programming based optimal power flow algorithm , 1999 .

[7]  R. Adapa,et al.  A review of selected optimal power flow literature to 1993. II. Newton, linear programming and interior point methods , 1999 .

[8]  Vassilios Petridis,et al.  Optimal power flow by enhanced genetic algorithm , 2002 .

[9]  P. Attaviriyanupap,et al.  A Hybrid EP and SQP for Dynamic Economic Dispatch with Nonsmooth Fuel Cost Function , 2002, IEEE Power Engineering Review.

[10]  D.C. Yu,et al.  A novel optimal reactive power dispatch method based on an improved hybrid evolutionary programming technique , 2004, IEEE Transactions on Power Systems.

[11]  A. Ebenezer Jeyakumar,et al.  Hybrid PSO–SQP for economic dispatch with valve-point effect , 2004 .

[12]  T.A.A. Victoire,et al.  Reserve constrained dynamic dispatch of units with valve-point effects , 2005, IEEE Transactions on Power Systems.

[13]  Chuangxin Guo,et al.  A multiagent-based particle swarm optimization approach for optimal reactive power dispatch , 2005 .

[14]  J.G. Vlachogiannis,et al.  A Comparative Study on Particle Swarm Optimization for Optimal Steady-State Performance of Power Systems , 2006, IEEE Transactions on Power Systems.

[15]  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.

[16]  Fang Liu,et al.  A hybrid genetic algorithm-interior point method for optimal reactive power flow , 2006, IEEE Transactions on Power Systems.

[17]  M. El-Hawary,et al.  Hybrid Particle Swarm Optimization Approach for Solving the Discrete OPF Problem Considering the Valve Loading Effects , 2007, IEEE Transactions on Power Systems.

[18]  Xianzhong Duan,et al.  Study of differential evolution for optimal reactive power flow , 2007 .

[19]  D. Lowther,et al.  Differential Evolution Strategy for Constrained Global Optimization and Application to Practical Engineering Problems , 2006, IEEE Transactions on Magnetics.

[20]  K. S. Swarup,et al.  Differential evolutionary algorithm for optimal reactive power dispatch , 2008 .

[21]  M. E. El-Hawary,et al.  Applications of computational intelligence techniques for solving the revived optimal power flow problem , 2009 .