Transmission expansion planning by enhanced differential evolution

Orfanos, GA; Georgilakis, PS; Korres, GN; Hatziargyriou, ND

dc.contributor.author	Orfanos, GA	en
dc.contributor.author	Georgilakis, PS	en
dc.contributor.author	Korres, GN	en
dc.contributor.author	Hatziargyriou, ND	en
dc.date.accessioned	2014-03-01T02:53:31Z
dc.date.available	2014-03-01T02:53:31Z
dc.date.issued	2011	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/36374
dc.subject	Differential evolution	en
dc.subject	electricity markets	en
dc.subject	evolutionary optimization algorithms	en
dc.subject	power systems	en
dc.subject	reference network	en
dc.subject	transmission expansion planning	en
dc.subject.other	Differential Evolution	en
dc.subject.other	electricity markets	en
dc.subject.other	Evolutionary optimization algorithm	en
dc.subject.other	Reference network	en
dc.subject.other	transmission expansion planning	en
dc.subject.other	Commerce	en
dc.subject.other	Deregulation	en
dc.subject.other	Evolutionary algorithms	en
dc.subject.other	Expansion	en
dc.subject.other	Intelligent systems	en
dc.subject.other	Power transmission	en
dc.subject.other	Standby power systems	en
dc.subject.other	Thermoelectric power	en
dc.subject.other	Electric power transmission	en
dc.title	Transmission expansion planning by enhanced differential evolution	en
heal.type	conferenceItem	en
heal.identifier.primary	10.1109/ISAP.2011.6082249	en
heal.identifier.secondary	http://dx.doi.org/10.1109/ISAP.2011.6082249	en
heal.identifier.secondary	6082249	en
heal.publicationDate	2011	en
heal.abstract	The restructuring and deregulation has exposed the transmission planner to new objectives and uncertainties. As a result, new criteria and approaches are needed for transmission expansion planning (TEP) in deregulated electricity markets. This paper proposes a new market-based approach for TEP. An enhanced differential evolution (EDE) model is proposed for the solution of this new market-based TEP problem. The modifications of EDE in comparison to the simple differential evolution method are: 1) the scaling factor F is varied randomly within some range, 2) an auxiliary set is employed to enhance the diversity of the population, 3) the newly generated trial vector is compared with the nearest parent, and 4) the simple feasibility rule is used to treat the constraints. Results from the application of the proposed method on the IEEE 30 bus test system demonstrate the feasibility and practicality of the proposed EDE for the solution of TEP problem. © 2011 IEEE.	en
heal.journalName	2011 16th International Conference on Intelligent System Applications to Power Systems, ISAP 2011	en
dc.identifier.doi	10.1109/ISAP.2011.6082249	en