A mathematical programming framework for energy planning in services' sector buildings under uncertainty in load demand: The case of a hospital in Athens

Mavrotas, G; Diakoulaki, D; Florios, K; Georgiou, P

dc.contributor.author	Mavrotas, G	en
dc.contributor.author	Diakoulaki, D	en
dc.contributor.author	Florios, K	en
dc.contributor.author	Georgiou, P	en
dc.date.accessioned	2014-03-01T01:27:41Z
dc.date.available	2014-03-01T01:27:41Z
dc.date.issued	2008	en
dc.identifier.issn	0301-4215	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/18544
dc.subject	Energy planning	en
dc.subject	Minimax regret	en
dc.subject	Multi-objective programming	en
dc.subject.classification	Energy & Fuels	en
dc.subject.classification	Environmental Sciences	en
dc.subject.classification	Environmental Studies	en
dc.subject.other	Computer simulation	en
dc.subject.other	Fuzzy sets	en
dc.subject.other	Mathematical models	en
dc.subject.other	Mathematical programming	en
dc.subject.other	Optimization	en
dc.subject.other	Pareto principle	en
dc.subject.other	Minimax regret	en
dc.subject.other	Multi-objective programming	en
dc.subject.other	Energy policy	en
dc.subject.other	energy planning	en
dc.subject.other	fuzzy mathematics	en
dc.subject.other	modeling	en
dc.subject.other	multiobjective programming	en
dc.subject.other	optimization	en
dc.subject.other	planning method	en
dc.subject.other	Athens [Attica]	en
dc.subject.other	Attica	en
dc.subject.other	Eurasia	en
dc.subject.other	Europe	en
dc.subject.other	Greece	en
dc.subject.other	Southern Europe	en
dc.title	A mathematical programming framework for energy planning in services' sector buildings under uncertainty in load demand: The case of a hospital in Athens	en
heal.type	journalArticle	en
heal.identifier.primary	10.1016/j.enpol.2008.01.011	en
heal.identifier.secondary	http://dx.doi.org/10.1016/j.enpol.2008.01.011	en
heal.language	English	en
heal.publicationDate	2008	en
heal.abstract	The aim of this paper is to provide an integrated modeling and optimization framework for energy planning in large consumers of the services' sector based on mathematical programming. The power demand is vaguely known and the underlying uncertainty is modeled using elements from fuzzy set theory. The defined fuzzy programming model is subsequently transformed to an equivalent multi-objective problem, where the minimization of cost and the maximization of demand satisfaction are the objective functions. The Pareto optimal solutions of this problem are obtained using a novel version of the e-constraint method and represent the possibly optimal solutions of the original problem under uncertainty. In the present case, in order to select the most preferred Pareto optimal solution, the minimax regret criterion is properly used to indicate the preferred configuration of the system (i.e. the size of the installed units) given the load uncertainty. Furthermore, the paper proposes a model reduction technique that can be used in similar cases and further examines its effect in the final results. The above methodology is applied to the energy rehabilitation of a hospital in the Athens area. The technologies under consideration include a combined heat and power unit for providing power and heat, an absorption unit and/or a compression unit for providing cooling load. The obtained results demonstrate that, increasing the degree of demand satisfaction, the total annual cost increases almost linearly. Although data compression allows obtaining realistic results, the size of the proposed units might be slightly changed. (C) 2008 Elsevier Ltd. All rights reserved.	en
heal.publisher	ELSEVIER SCI LTD	en
heal.journalName	Energy Policy	en
dc.identifier.doi	10.1016/j.enpol.2008.01.011	en
dc.identifier.isi	ISI:000257725900010	en
dc.identifier.volume	36	en
dc.identifier.issue	7	en
dc.identifier.spage	2415	en
dc.identifier.epage	2429	en