Optimization of Power Systems based on Ant Colony System Algorithms: An Overview

This study presents the ant colony system (ACS) algorithms for optimization of power systems planning. The developed ACS algorithms formulate complex problems as combinatorial optimization problems. They are distributed algorithms composed by a set of cooperating artificial agents, called ants, which cooperate to find an optimum solution of the combinatorial problems. A pheromone matrix that plays the role of global memory provides the cooperation between ants. The study consists of mapping the solution space, expressed by an objective function of the combinatorial problems on the space of control variables, ant system (AS)-graph, where ants walk. In this study an ACS algorithm is applied to the constrained load flow (CLF) problem on IEEE 14-bus test system and 136 bus system. The results are compared with those given by the probabilistic CLF and the reinforcement learning (RL) methods, demonstrating the superiority and flexibility of the ACS algorithm. Moreover, ACS algorithm is applied to the reactive power control problem on the IEEE 14-bus test system in order to minimize the real power losses subject to operating constraints over the whole planning period. Finally, the application of ACS algorithm for active/reactive operational planning of power systems on IEEE 30-bus test system is presented and results are compared to those given by simulated annealing (SA), exhibiting superior performance

[1]  Cyril Fonlupt,et al.  Parallel Ant Colonies for the quadratic assignment problem , 2001, Future Gener. Comput. Syst..

[2]  Anil Pahwa,et al.  Multi-level ant colony algorithm for optimal placement of capacitors in distribution systems , 2004, Proceedings of the 2004 Congress on Evolutionary Computation (IEEE Cat. No.04TH8753).

[3]  Yonghua Song,et al.  Modern optimisation techniques in power systems , 1999 .

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

[5]  Peter Dayan,et al.  Q-learning , 1992, Machine Learning.

[6]  J. Teng,et al.  A Novel ACS-Based Optimum Switch Relocation Method , 2002, IEEE Power Engineering Review.

[7]  N.D. Hatziargyriou,et al.  Ant colony system-based algorithm for constrained load flow problem , 2005, IEEE Transactions on Power Systems.

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

[9]  M. H. Wang,et al.  A Novel Extension Method for Transformer Fault Diagnosis , 2002, IEEE Power Engineering Review.

[10]  Eleonora Riva Sanseverino,et al.  Multiobjective ant colony search algorithm optimal electrical distribution system planning , 2004, Proceedings of the 2004 Congress on Evolutionary Computation (IEEE Cat. No.04TH8753).

[11]  Jun Ouyang,et al.  A multi-group ant colony system algorithm for TSP , 2004, Proceedings of 2004 International Conference on Machine Learning and Cybernetics (IEEE Cat. No.04EX826).

[12]  P. Moran,et al.  Mathematics of Statistics , 1948, Nature.

[13]  Marcel Bergerman,et al.  Cultural algorithms: concepts and experiments , 2000, Proceedings of the 2000 Congress on Evolutionary Computation. CEC00 (Cat. No.00TH8512).

[14]  Nazanin Mansouri,et al.  Enhancing scheduling solutions through ant colony optimization , 2004, 2004 IEEE International Symposium on Circuits and Systems (IEEE Cat. No.04CH37512).

[15]  Ric,et al.  Adaptive Memories for the Quadratic Assignment Problem , 1997 .

[16]  Sang-Yun Yun,et al.  Application of Ant Colony Algorithm for Network Reconfiguration in Distribution Systems , 2003 .

[17]  Y. H. Song,et al.  Large-Scale Economic Dispatch by Artificial Ant Colony Search Algorithms , 1999 .

[18]  Nikos D. Hatziargyriou,et al.  Probabilistic constrained load flow based on sensitivity analysis , 1994 .

[19]  É. Taillard,et al.  Adaptive memories for the Quadratic Assignment Problems , 1997 .

[20]  Weiqing Xiong,et al.  An improved ant colony algorithm for TSP , 2004, Fifth World Congress on Intelligent Control and Automation (IEEE Cat. No.04EX788).

[21]  Yonghua Song,et al.  Optimisation techniques for electrical power systems. Part 1: Mathematical optimisation methods , 2000 .

[22]  In-Keun Yu,et al.  A novel short-term generation scheduling technique of thermal units using ant colony search algorithms , 2001 .

[23]  Ajith Abraham,et al.  Web usage mining using artificial ant colony clustering and linear genetic programming , 2003, The 2003 Congress on Evolutionary Computation, 2003. CEC '03..

[24]  Yunhe Hou,et al.  Generalized ant colony optimization for economic dispatch of power systems , 2002, Proceedings. International Conference on Power System Technology.

[25]  Luca Maria Gambardella,et al.  Ant-Q: A Reinforcement Learning Approach to the Traveling Salesman Problem , 1995, ICML.

[26]  Silvano Martello,et al.  Meta-Heuristics: Advances and Trends in Local Search Paradigms for Optimization , 2012 .

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

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

[29]  Andrew W. Moore,et al.  Reinforcement Learning: A Survey , 1996, J. Artif. Intell. Res..

[30]  W. Ongsakul,et al.  Ant colony search algorithm for unit commitment , 2003, IEEE International Conference on Industrial Technology, 2003.

[31]  Ronald N. Allan,et al.  Probabilistic techniques in AC load flow analysis , 1977 .

[32]  Corso Elvezia.  Ant Colonies for the QAP , 1997 .

[33]  Sadiq M. Sait,et al.  Fuzzified ant colony optimization algorithm for efficient combinational circuits synthesis , 2004, Proceedings of the 2004 Congress on Evolutionary Computation (IEEE Cat. No.04TH8753).

[34]  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).

[35]  G. Chicco,et al.  Ant-colony search-based minimum losses reconfiguration of distribution systems , 2004, Proceedings of the 12th IEEE Mediterranean Electrotechnical Conference (IEEE Cat. No.04CH37521).

[36]  Jin Hao,et al.  Short-term generation scheduling with reliability constraint using ant colony optimization algorithm , 2004, Fifth World Congress on Intelligent Control and Automation (IEEE Cat. No.04EX788).

[37]  John G. Vlachogiannis.  Control adjustments in fast decoupled load flow , 1994 .

[38]  Yonghua Song,et al.  Optimisation techniques for electrical power systems. II. Heuristic optimisation methods , 2001 .

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

[40]  Thomas Stützle,et al.  The MAX–MIN Ant System and Local Search for Combinatorial Optimization Problems: Towards Adaptive Tools for Global Optimization , 1997 .

[41]  Kwang Y. Lee,et al.  Ant colony optimization for active/reactive operational planning , 2005 .

[42]  N. Sisworahardjo,et al.  Unit commitment using the ant colony search algorithm , 2002, LESCOPE'02. 2002 Large Engineering Systems Conference on Power Engineering. Conference Proceedings.

[43]  N.D. Hatziargyriou,et al.  Reinforcement learning for reactive power control , 2004, IEEE Transactions on Power Systems.