Ant Colony Optimization-Based Approach to Optimal Reactive Power Dispatch: A Comparison of Various Ant Systems

The optimal reactive power dispatch (ORPD) problem is formulated as a combinatorial optimization problem involving nonlinear objective function with multiple local minima. In this paper, as a new approach, different ant colony optimization (ACO) algorithms are applied to the reactive power dispatch problem. Ant system (AS), the firstly introduced ant colony optimization algorithm, and its direct successors, elitist ant system (EAS), rank-based ant system (ASrank) and max-min ant system (MMAS), are employed to solve the reactive power dispatch problem. To analyze the efficiency and effectiveness of these modern search algorithms, the proposed methods are applied to the IEEE 30-bus system and the results are compared to those of conventional mathematical methods, genetic algorithm, evolutionary programming, and particle swarm optimization.

[1]  James A. Momoh,et al.  Improved interior point method for OPF problems , 1999 .

[2]  N. Grudinin.  Reactive power optimization using successive quadratic programming method , 1998 .

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

[4]  K. Lee,et al.  A United Approach to Optimal Real and Reactive Power Dispatch , 1985, IEEE Transactions on Power Apparatus and Systems.

[5]  Loi Lei Lai,et al.  Application of evolutionary programming to reactive power planning-comparison with nonlinear programming approach , 1997 .

[6]  A. J. Urdaneta,et al.  A hybrid genetic algorithm for optimal reactive power planning based upon successive linear programming , 1999 .

[7]  In-Keun Yu,et al.  Application of the ant colony search algorithm to short-term generation scheduling problem of thermal units , 1998, POWERCON '98. 1998 International Conference on Power System Technology. Proceedings (Cat. No.98EX151).

[8]  Marco Dorigo,et al.  Optimization, Learning and Natural Algorithms , 1992 .

[9]  S. Granville.  Optimal reactive dispatch through interior point methods , 1994 .

[10]  S. J. Huang,et al.  Enhancement of Hydroelectric Generation Scheduling Using Ant Colony System-Based Optimization Approaches , 2001, IEEE Power Engineering Review.

[11]  W. Tinney,et al.  Optimal Power Flow By Newton Approach , 1984, IEEE Transactions on Power Apparatus and Systems.

[12]  Daniel S. Kirschen,et al.  MW/voltage control in a linear programming based optimal power flow , 1988 .

[13]  Kenji Iba.  Reactive power optimization by genetic algorithm , 1993 .

[14]  Kenichi Aoki,et al.  Optimal VAr planning by approximation method for recursive mixed-integer linear programming , 1988 .

[15]  Li Ma,et al.  GA/SA/TS hybrid algorithms for reactive power optimization , 2000, 2000 Power Engineering Society Summer Meeting (Cat. No.00CH37134).

[16]  R. Adapa,et al.  The quadratic interior point method solving power system optimization problems , 1994 .

[17]  Marco Dorigo,et al.  Ant system: optimization by a colony of cooperating agents , 1996, IEEE Trans. Syst. Man Cybern. Part B.

[18]  Kwang Y. Lee,et al.  Optimal reactive power planning using evolutionary algorithms: a comparative study for evolutionary programming, evolutionary strategy, genetic algorithm, and linear programming , 1998 .

[19]  M. H. Rashid,et al.  Nonlinear load-adaptive MIMO controller for high performance DC motor field weakening , 2000, 2000 IEEE Power Engineering Society Winter Meeting. Conference Proceedings (Cat. No.00CH37077).