Mavrotas, George and Florios, Kostas and Vlachou, Dimitra (2010): Energy planning of a hospital using Mathematical Programming and Monte Carlo simulation for dealing with uncertainty in the economic parameters. Published in: Energy Conversion and Management , Vol. 51, No. 4 (1 April 2010): pp. 722731.

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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.
Item Type:  MPRA Paper 

Original Title:  Energy planning of a hospital using Mathematical Programming and Monte Carlo simulation for dealing with uncertainty in the economic parameters 
Language:  English 
Keywords:  Energy Planning, Mathematical Programming, MILP, Uncertainty, Monte Carlo 
Subjects:  C  Mathematical and Quantitative Methods > C6  Mathematical Methods ; Programming Models ; Mathematical and Simulation Modeling > C61  Optimization Techniques ; Programming Models ; Dynamic Analysis C  Mathematical and Quantitative Methods > C6  Mathematical Methods ; Programming Models ; Mathematical and Simulation Modeling > C63  Computational Techniques ; Simulation Modeling Q  Agricultural and Natural Resource Economics ; Environmental and Ecological Economics > Q4  Energy > Q49  Other 
Item ID:  105754 
Depositing User:  Kostas Florios 
Date Deposited:  08 Feb 2021 11:08 
Last Modified:  08 Feb 2021 11:08 
References:  Antunes CH, Martins AG (Eds.). OR Models for Energy Policy, Planning and Management. Annals of Operational Research (Special issue), 120 (2003) 1517. Diakoulaki D, Antunes CH, Martins AG, MCDA and Energy Planning, in: Figueira J, Greco S, Ehrgott M (Eds.) Multiple Criteria Decision Analysis: State of the Art Surveys. Springer: Berlin; 2005. pp. 859898. Chang CT, Hwang JR. A multiobjective programming approach to waste minimization in the utility systems of chemical processes. , Chemical Engineering Science 51 (1997) 39513965. Burer M, Tanaka K, Favrat D, Yamada K, Multicriteria optimization of a district cogeneration plant integrating a solid oxide fuel cellgas turbine combined cycle, heat pumps and chillers. Energy 28 (2003) 497518. Mavrotas G, Demertzis H, Meintani A, Diakoulaki D. Energy Planning in Buildings under uncertainty in fuel costs: The case of a hotel unit in Greece. Energy Conversion & Management 44 (2003) 13031321. RathNagel S, Voss A. Energy models for planning and policy assessment. European Journal of Operational Research 8 (1981) 99114. Meier P, Mubayi V. Modelling energyeconomic interactions in developing countries: A linear programming approach. European Journal of Operational Research 13 (1983) 4159. Kavrakoglu I. Models for National Energy Policy. Automatica 16 (1980) 379392. Kavrakoglu I. Energy models. European Journal of Operational Research 28 (1987) 121131. Groscurth HM, Schweiker A. Contribution of computer models to solving the energy problem. Energy Sources 17 (1995) 161177. Piacentino, A., Cardona, F. EABOTEnergetic analysis as a basis for robust optimization of trigeneration systems by linear programming. Energy Conversion & Management 49 (2008) 30003016. Murtagh BA. Advanced Linear Programming: Computation and Practice. McGrawHill. 1981. Iyer RR, Grossmann IE. Synthesis and operational planning of utility systems for multiperiod operation. Computers & Chemical Engineering 22 (1998) 979993. Yokoyama, R., Hasegawa, Y. Ito, K. A MILP decomposition approach to large scale optimization in structural design of energy suplí systems. Energy Conversion & Management 43 (2002) 771790. Vose, D. (2006). Risk Analysis: A Quantitative Guide. 2nd edition. John Willey and sons. Diwekar, U.M., Rubin, E., Frey, C. Optimal Design of Advanced Power Systems Under Uncertainty. Energy Conversion & Management 38 (1997) 17251735. Infager, G. (1999). GAMS/DECIS User Guide, Vienna Technologies, Stanford. Lamiri, M., Xie, X., Dolgui, A. Grimaud, F. A stochastic model for operating room planning with elective and emergency demand for surgery. European Journal of Operational Research 185, (2008) 10261037. European Commission, SAVE II project. Assessment of CHP implementation possibilities in the tourist sector. Final report. June 2001. Regulatory Authority of Energy (RAE), www.rae.gr (accessed November 2008). Brooke A, Kendrick D, Meeraus A, Raman R. GAMS. A user’s guide. GAMS development corporation: Washington; 1998 (available also in www.gams.com). 
URI:  https://mpra.ub.unimuenchen.de/id/eprint/105754 