Metamodel-assisted evolutionary algorithms for the unit commitment problem with probabilistic outages

Georgopoulou, CA; Giannakoglou, KC

dc.contributor.author	Georgopoulou, CA	en
dc.contributor.author	Giannakoglou, KC	en
dc.date.accessioned	2014-03-01T01:33:43Z
dc.date.available	2014-03-01T01:33:43Z
dc.date.issued	2010	en
dc.identifier.issn	0306-2619	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/20561
dc.subject	Unit commitment	en
dc.subject	Probabilistic outages	en
dc.subject	Metamodels	en
dc.subject	Two-level evolutionary algorithms	en
dc.subject.classification	Energy & Fuels	en
dc.subject.classification	Engineering, Chemical	en
dc.subject.other	STOCHASTIC OPTIMIZATION	en
dc.subject.other	CONSTRAINTS	en
dc.subject.other	SYSTEM	en
dc.title	Metamodel-assisted evolutionary algorithms for the unit commitment problem with probabilistic outages	en
heal.type	journalArticle	en
heal.identifier.primary	10.1016/j.apenergy.2009.10.013	en
heal.identifier.secondary	http://dx.doi.org/10.1016/j.apenergy.2009.10.013	en
heal.language	English	en
heal.publicationDate	2010	en
heal.abstract	An efficient method for solving power generating unit commitment (UC) problems with probabilistic unit outages is proposed. It is based on a two-level evolutionary algorithm (EA) minimizing the expected total operating cost (TOC) of a system of power generating units over a scheduling period, with known failure and repair rates of each unit. To compute the cost function value of each EA population member, namely a candidate UC schedule, a Monte Carlo simulation must be carried out. Some thousands of replicates are generated according to the units' outage and repair rates and the corresponding probabilities. Each replicate is represented by a series of randomly generated availability and unavailability periods of time for each unit and the UC schedule under consideration accordingly. The expected TOC is the average of the TOCs of all Monte Carlo replicates. Therefore, the CPU cost per Monte Carlo evaluation increases noticeably and so does the CPU cost of running the EA. To reduce it, the use of a meta model-assisted EA (MAEA) with on-line trained surrogate evaluation models or metamodels (namely, radial-basis function networks) is proposed. A novelty of this method is that the metamodels are trained on a few "representative" unit outage scenarios selected among the Monte Carlo replicates generated once during the optimization and, then, used to predict the expected TOC Based on this low cost, approximate pre-evaluation, only a few top individuals within each generation undergo Monte Carlo simulations. The proposed MAEA is demonstrated on test problems and shown to drastically reduce the CPU cost, compared to EAs which are exclusively based on Monte Carlo simulations. (C) 2009 Elsevier Ltd. All rights reserved.	en
heal.publisher	ELSEVIER SCI LTD	en
heal.journalName	APPLIED ENERGY	en
dc.identifier.doi	10.1016/j.apenergy.2009.10.013	en
dc.identifier.isi	ISI:000274943400034	en
dc.identifier.volume	87	en
dc.identifier.issue	5	en
dc.identifier.spage	1782	en
dc.identifier.epage	1792	en