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

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...

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Vydáno v:Energy conversion and management Ročník 51; číslo 4; s. 722 - 731
Hlavní autoři: Mavrotas, George, Florios, Kostas, Vlachou, Dimitra
Médium: Journal Article
Jazyk:angličtina
Vydáno: Kidlington Elsevier Ltd 01.04.2010
Elsevier
Témata:
Applied sciences
Computer simulation
Economic data
Electric energy
Energy
Energy consumption
Energy economics
Energy planning
Energy policy
Exact sciences and technology
General, economic and professional studies
Marketing
Mathematical models
Mathematical Programming
Methodology. Modelling
MILP
Monte Carlo
Monte Carlo methods
Uncertainty
Greece
Europe
Uncertainty
MILP
Monte Carlo
Mathematical Programming
Energy planning
Cost minimization
Monte Carlo method
Energy cost
Cooling
Energetic system
Methodology
Cogeneration
Power production
Tertiary activities
Economic optimization
Heat production
Restructuration
Constrained optimization
Case study
Production cost
Hospital
Planning
Electric power production
Energy management
Mathematical programming
ISSN:0196-8904, 1879-2227
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Popis
Shrnutí: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.
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ISSN:0196-8904
1879-2227
DOI:10.1016/j.enconman.2009.10.029