Ant colony system-based algorithm for constrained load flow problem

• Published in: IEEE transactions on power systems Vol. 20; no. 3; pp. 1241 - 1249
• Main Authors: Vlachogiannis, J.G., Hatziargyriou, N.D., Lee, K.Y.
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.08.2005; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Ant colony optimization; Ant colony system (ACS); Ants; Colonies; Combinatorial analysis; combinatorial optimization; constrained load flow (CLF); Constraint optimization; Constraints; Control systems; Distributed algorithms; Flexibility; Learning; Load flow; Optimal control; Optimization; Power system planning; Reactive power control; reinforcement learning (RL); Studies; Switches
• ISSN: 0885-8950; 1558-0679
• DOI: 10.1109/TPWRS.2005.851969

This paper presents the ant colony system (ACS) method for network-constrained optimization problems. The developed ACS algorithm formulates the constrained load flow (CLF) problem as a combinatorial optimization problem. It is a distributed algorithm composed of a set of cooperating artificial agents, called ants, that cooperate among them to find an optimum solution of the CLF problem. 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 CLF on the space of control variables [ant system (AS)-graph], that ants walk. The ACS algorithm is applied to the IEEE 14-bus system and the IEEE 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, the ACS algorithm is applied to the reactive power control problem for the IEEE 14-bus system in order to minimize real power losses subject to operating constraints over the whole planning period.