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

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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 http://hdl.handle.net/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


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