Multi-objective Optimal Power Flow Using Biogeography-based Optimization

Abstract This article presents a novel biogeography-based optimization algorithm for solving constrained optimal power flow problems in power systems, considering valve point non-linearities of generators. In this article, the feasibility of the proposed algorithm is demonstrated for 9-bus, 26-bus, and IEEE 118-bus systems with three different objective functions, and it is compared to other well-established population-based optimization techniques. A comparison of simulation results reveals better solution quality and computational efficiency of the proposed algorithm over evolutionary programming, genetic algorithm, and mixed-integer particle swarm optimization for the global optimization of multi-objective constrained optimal power flow problems.

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

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

[3]  E. Prempain,et al.  An improved particle swarm optimization for optimal power flow , 2004, 2004 International Conference on Power System Technology, 2004. PowerCon 2004..

[4]  Zwe-Lee Gaing,et al.  Real-coded mixed-integer genetic algorithm for constrained optimal power flow , 2004, 2004 IEEE Region 10 Conference TENCON 2004..

[5]  Kit Po Wong,et al.  Evolutionary programming based optimal power flow algorithm , 1999, 1999 IEEE Power Engineering Society Summer Meeting. Conference Proceedings (Cat. No.99CH36364).

[6]  S. R. Paranjothi,et al.  Optimal Power Flow Using Refined Genetic Algorithm , 2002 .

[7]  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.

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

[9]  B. Zhao,et al.  Improved particle swam optimization algorithm for OPF problems , 2004, IEEE PES Power Systems Conference and Exposition, 2004..

[10]  Sakti Prasad Ghoshal,et al.  A novel crazy swarm optimized economic load dispatch for various types of cost functions , 2008 .

[11]  Dan Simon,et al.  Biogeography-Based Optimization , 2022 .

[12]  R. Yokoyama,et al.  Improved genetic algorithms for optimal power flow under both normal and contingent operation states , 1997 .

[13]  Akshya Swain,et al.  A novel hybrid evolutionary programming method for function optimization , 2000, Proceedings of the 2000 Congress on Evolutionary Computation. CEC00 (Cat. No.00TH8512).

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

[15]  T. J. Stonham,et al.  Combined heat and power economic dispatch by improved ant colony search algorithm , 1999 .

[16]  Z. Gaing.  Constrained optimal power flow by mixed-integer particle swarm optimization , 2005, IEEE Power Engineering Society General Meeting, 2005.

[17]  A. Chatterjee,et al.  Bio-inspired fuzzy logic based tuning of power system stabilizer , 2009, Expert Syst. Appl..

[18]  C.A. Roa-Sepulveda,et al.  A solution to the optimal power flow using simulated annealing , 2001, 2001 IEEE Porto Power Tech Proceedings (Cat. No.01EX502).