Ant colony system-based algorithm for constrained load flow problem

Vlachogiannis, JG; Hatziargyriou, ND; Lee, KY

dc.contributor.author	Vlachogiannis, JG	en
dc.contributor.author	Hatziargyriou, ND	en
dc.contributor.author	Lee, KY	en
dc.date.accessioned	2014-03-01T01:21:51Z
dc.date.available	2014-03-01T01:21:51Z
dc.date.issued	2005	en
dc.identifier.issn	0885-8950	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/16398
dc.subject	Ant colony system (ACS)	en
dc.subject	Combinatorial optimization	en
dc.subject	Constrained load flow (CLF)	en
dc.subject	Reinforcement learning (RL)	en
dc.subject.classification	Engineering, Electrical & Electronic	en
dc.subject.other	Algorithms	en
dc.subject.other	Electric losses	en
dc.subject.other	Heuristic methods	en
dc.subject.other	Optimization	en
dc.subject.other	Power control	en
dc.subject.other	Probability	en
dc.subject.other	Reactive power	en
dc.subject.other	Ant colony system (ACS)	en
dc.subject.other	Combinatorial optimization	en
dc.subject.other	Constrained load flow (CLF)	en
dc.subject.other	Reinforcement learning (RL)	en
dc.subject.other	Electric load flow	en
dc.title	Ant colony system-based algorithm for constrained load flow problem	en
heal.type	journalArticle	en
heal.identifier.primary	10.1109/TPWRS.2005.851969	en
heal.identifier.secondary	http://dx.doi.org/10.1109/TPWRS.2005.851969	en
heal.language	English	en
heal.publicationDate	2005	en
heal.abstract	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. © 2005 IEEE.	en
heal.publisher	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC	en
heal.journalName	IEEE Transactions on Power Systems	en
dc.identifier.doi	10.1109/TPWRS.2005.851969	en
dc.identifier.isi	ISI:000231001900006	en
dc.identifier.volume	20	en
dc.identifier.issue	3	en
dc.identifier.spage	1241	en
dc.identifier.epage	1249	en