Ant colony optimization for active/reactive operational planning

Abstract This paper proposes the application of Ant Colony Optimization (ACO) for active/reactive operational planning of power systems. The ACO is a newly developed method belonging to the class of evolutionary computation methods inspired from real ants life. Specifically, ACO algorithm aims to determine the optimal settings of control variables, such as generator outputs, generator voltages, transformer taps and shunt VAR compensation devices, considered as nodes of an Ant-System (AS) graph. Results are compared to those given by Simulated Annealing for the IEEE 30-bus test system, exhibiting superior performance.

[1]  A. El-Keib,et al.  Calculating short-run marginal costs of active and reactive power production , 1997 .

[2]  N. H. Dandachi,et al.  OPF for reactive pricing studies on the NGC system , 1995 .

[3]  G. Chicco,et al.  Unbundled Reactive Support Service: Key Characteristics and Dominant Cost Component , 2002, IEEE Power Engineering Review.

[4]  David J. Hill,et al.  Designing ancillary services markets for power system security , 2000 .

[5]  K. Bhattacharya,et al.  Reactive Power as an Ancillary Service , 2001, IEEE Power Engineering Review.

[6]  Hong-Tzer Yang,et al.  Evolutionary programming based economic dispatch for units with non-smooth fuel cost functions , 1996 .

[7]  G. Di Caro,et al.  Ant colony optimization: a new meta-heuristic , 1999, Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406).

[8]  Kit Po Wong,et al.  Combined genetic algorithm/simulated annealing/fuzzy set approach to short-term generation scheduling with take-or-pay fuel contract , 1996 .

[9]  Luca Maria Gambardella,et al.  Ant colony system: a cooperative learning approach to the traveling salesman problem , 1997, IEEE Trans. Evol. Comput..

[10]  Kwang Y. Lee,et al.  Optimal real and reactive power dispatch , 1984 .

[11]  Ji-Pyng Chiou,et al.  Ant direction hybrid differential evolution for solving large capacitor placement problems , 2004 .

[12]  Chun-Chang Liu,et al.  Multi-objective VAR Planning Using An Interactive Satisfying Method , 1995 .

[13]  V.M. Dona,et al.  Reactive power pricing in competitive electric markets using the transmission losses function , 2001, 2001 IEEE Porto Power Tech Proceedings (Cat. No.01EX502).

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

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

[16]  Kwang Y. Lee,et al.  Optimization method for reactive power planning by using a modified simple genetic algorithm , 1995 .

[17]  Ying-Tung Hsiao,et al.  A computer package for optimal multi-objective VAr planning in large scale power systems , 1993 .

[18]  Hong-Tzer Yang,et al.  A new thermal unit commitment approach using constraint logic programming , 1997 .

[19]  Marco Dorigo,et al.  The ant colony optimization meta-heuristic , 1999 .

[20]  Shangyou Hao,et al.  Reactive power pricing and management , 1997 .

[21]  E.L. da Silva,et al.  Practical cost-based approach for the voltage ancillary service , 2002, 2002 IEEE Power Engineering Society Winter Meeting. Conference Proceedings (Cat. No.02CH37309).

[22]  Yuan-Lin Chen,et al.  Weak bus-oriented optimal multi-objective VAr planning , 1996 .

[23]  Kwang Y. Lee,et al.  Optimal operation of large-scale power systems , 1988 .