Energy planning of a hospital using Mathematical Programming and Monte Carlo simulation for dealing with uncertainty in the economic parameters

Mavrotas, G; Florios, K; Vlachou, D

dc.contributor.author	Mavrotas, G	en
dc.contributor.author	Florios, K	en
dc.contributor.author	Vlachou, D	en
dc.date.accessioned	2014-03-01T01:33:22Z
dc.date.available	2014-03-01T01:33:22Z
dc.date.issued	2010	en
dc.identifier.issn	0196-8904	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/20396
dc.subject	Energy planning	en
dc.subject	Mathematical Programming	en
dc.subject	MILP	en
dc.subject	Monte Carlo	en
dc.subject	Uncertainty	en
dc.subject.classification	Thermodynamics	en
dc.subject.classification	Energy & Fuels	en
dc.subject.classification	Mechanics	en
dc.subject.classification	Physics, Nuclear	en
dc.subject.other	Complementary energy	en
dc.subject.other	Cooling load	en
dc.subject.other	Cost minimization	en
dc.subject.other	Decision makers	en
dc.subject.other	Economic parameters	en
dc.subject.other	Energy consumer	en
dc.subject.other	Energy markets	en
dc.subject.other	Energy model	en
dc.subject.other	Energy planning	en
dc.subject.other	Fuel cost	en
dc.subject.other	Interest rates	en
dc.subject.other	Load data	en
dc.subject.other	Mixed integer linear programming	en
dc.subject.other	Model reduction	en
dc.subject.other	Modelling framework	en
dc.subject.other	MONTE CARLO	en
dc.subject.other	Monte Carlo Simulation	en
dc.subject.other	Objective functions	en
dc.subject.other	Regional levels	en
dc.subject.other	Technical progress	en
dc.subject.other	Uncertainty modelling	en
dc.subject.other	Computer simulation	en
dc.subject.other	Functions	en
dc.subject.other	Hospitals	en
dc.subject.other	Integer programming	en
dc.subject.other	Linearization	en
dc.subject.other	Monte Carlo methods	en
dc.subject.other	Probability distributions	en
dc.subject.other	Risk analysis	en
dc.subject.other	Risk management	en
dc.subject.other	Wireless telecommunication systems	en
dc.subject.other	Data compression	en
dc.title	Energy planning of a hospital using Mathematical Programming and Monte Carlo simulation for dealing with uncertainty in the economic parameters	en
heal.type	journalArticle	en
heal.identifier.primary	10.1016/j.enconman.2009.10.029	en
heal.identifier.secondary	http://dx.doi.org/10.1016/j.enconman.2009.10.029	en
heal.language	English	en
heal.publicationDate	2010	en
heal.abstract	For more than 40 years, Mathematical Programming is the traditional tool for energy planning at the national or regional level aiming at cost minimization subject to specific technological, political and demand satisfaction constraints. The liberalization of the energy market along with the ongoing technical progress increased the level of competition and forced energy consumers, even at the unit level, to make their choices among a large number of alternative or complementary energy technologies, fuels and/or suppliers. In the present work we develop a modelling framework for energy planning in units of the tertiary sector giving special emphasis to model reduction and to the uncertainty of the economic parameters. In the given case study, the energy rehabilitation of a hospital in Athens is examined and the installation of a cogeneration, absorption and compression unit is examined for the supply of the electricity, heating and cooling load. The basic innovation of the given energy model lies in the uncertainty modelling through the combined use of Mathematical Programming (namely, Mixed Integer Linear Programming, MILP) and Monte Carlo simulation that permits the risk management for the most volatile parameters of the objective function such as the fuel costs and the interest rate. The results come in the form of probability distributions that provide fruitful information to the decision maker. The effect of model reduction through appropriate data compression of the load data is also addressed. © 2009 Elsevier Ltd. All rights reserved.	en
heal.publisher	PERGAMON-ELSEVIER SCIENCE LTD	en
heal.journalName	Energy Conversion and Management	en
dc.identifier.doi	10.1016/j.enconman.2009.10.029	en
dc.identifier.isi	ISI:000275095800013	en
dc.identifier.volume	51	en
dc.identifier.issue	4	en
dc.identifier.spage	722	en
dc.identifier.epage	731	en