Stochastic PSO-based heat and power dispatch under environmental constraints incorporating CHP and wind power units

Piperagkas, GS; Anastasiadis, AG; Hatziargyriou, ND

dc.contributor.author	Piperagkas, GS	en
dc.contributor.author	Anastasiadis, AG	en
dc.contributor.author	Hatziargyriou, ND	en
dc.date.accessioned	2014-03-01T01:37:08Z
dc.date.available	2014-03-01T01:37:08Z
dc.date.issued	2011	en
dc.identifier.issn	0378-7796	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/21457
dc.subject	Cogeneration	en
dc.subject	Economic dispatch	en
dc.subject	Environmental dispatch	en
dc.subject	Multi-objective particle swarm optimization	en
dc.subject	Wind power estimation	en
dc.subject.classification	Engineering, Electrical & Electronic	en
dc.subject.other	Cogeneration	en
dc.subject.other	Economic dispatch	en
dc.subject.other	Environmental dispatch	en
dc.subject.other	Multi objective particle swarm optimization	en
dc.subject.other	Wind power estimation	en
dc.subject.other	Cogeneration plants	en
dc.subject.other	Computer simulation	en
dc.subject.other	Constraint theory	en
dc.subject.other	Emission control	en
dc.subject.other	Particle swarm optimization (PSO)	en
dc.subject.other	Random variables	en
dc.subject.other	Scheduling	en
dc.subject.other	Stochastic models	en
dc.subject.other	Stochastic systems	en
dc.subject.other	Wind power	en
dc.subject.other	Multiobjective optimization	en
dc.title	Stochastic PSO-based heat and power dispatch under environmental constraints incorporating CHP and wind power units	en
heal.type	journalArticle	en
heal.identifier.primary	10.1016/j.epsr.2010.08.009	en
heal.identifier.secondary	http://dx.doi.org/10.1016/j.epsr.2010.08.009	en
heal.language	English	en
heal.publicationDate	2011	en
heal.abstract	In this paper an extended stochastic multi-objective model for economic dispatch (ED) is proposed, that incorporates in the optimization process heat and power from CHP units and expected wind power. Stochastic restrictions for the CO2, SO2 and NO emissions are used as inequality constraints. The ED problem is solved using a multi-objective particle swarm optimization technique. The available wind power is estimated from a transformation of the wind speed considered as a random variable to wind power. Simulations are performed on the modified IEEE 30 bus network with 2 cogeneration units and actual wind data. Results concerning minimum cost and emissions reduction options are finally drawn. (c) 2010 Elsevier B.V. All rights reserved.	en
heal.publisher	ELSEVIER SCIENCE SA	en
heal.journalName	Electric Power Systems Research	en
dc.identifier.doi	10.1016/j.epsr.2010.08.009	en
dc.identifier.isi	ISI:000284523800026	en
dc.identifier.volume	81	en
dc.identifier.issue	1	en
dc.identifier.spage	209	en
dc.identifier.epage	218	en