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

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  • Mavrotas, George
  • Florios, Kostas
  • Vlachou, Dimitra

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.

Suggested Citation

  • Mavrotas, George & Florios, Kostas & Vlachou, Dimitra, 2010. "Energy planning of a hospital using Mathematical Programming and Monte Carlo simulation for dealing with uncertainty in the economic parameters," MPRA Paper 105754, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:105754
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    Cited by:

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    4. Dominik Kryzia & Marta Kuta & Dominika Matuszewska & Piotr Olczak, 2020. "Analysis of the Potential for Gas Micro-Cogeneration Development in Poland Using the Monte Carlo Method," Energies, MDPI, vol. 13(12), pages 1-24, June.
    5. Caralis, George & Diakoulaki, Danae & Yang, Peijin & Gao, Zhiqiu & Zervos, Arthouros & Rados, Kostas, 2014. "Profitability of wind energy investments in China using a Monte Carlo approach for the treatment of uncertainties," Renewable and Sustainable Energy Reviews, Elsevier, vol. 40(C), pages 224-236.
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    7. Pablo Benalcazar & Przemysław Kaszyński & Jacek Kamiński, 2021. "Assessing the Effects of Uncertain Energy and Carbon Prices on the Operational Patterns and Economic Results of CHP Systems," Energies, MDPI, vol. 14(24), pages 1-19, December.
    8. Capozzoli, Alfonso & Piscitelli, Marco Savino & Neri, Francesco & Grassi, Daniele & Serale, Gianluca, 2016. "A novel methodology for energy performance benchmarking of buildings by means of Linear Mixed Effect Model: The case of space and DHW heating of out-patient Healthcare Centres," Applied Energy, Elsevier, vol. 171(C), pages 592-607.
    9. Zhigang Duan & Yamin Yan & Xiaohan Yan & Qi Liao & Wan Zhang & Yongtu Liang & Tianqi Xia, 2017. "An MILP Method for Design of Distributed Energy Resource System Considering Stochastic Energy Supply and Demand," Energies, MDPI, vol. 11(1), pages 1-23, December.
    10. Vaziri, Shabnam Mahmoudzadeh & Rezaee, Babak & Monirian, Masoud Amel, 2020. "Utilizing renewable energy sources efficiently in hospitals using demand dispatch," Renewable Energy, Elsevier, vol. 151(C), pages 551-562.
    11. Mohseni, Soheil & Brent, Alan C. & Burmester, Daniel, 2020. "A comparison of metaheuristics for the optimal capacity planning of an isolated, battery-less, hydrogen-based micro-grid," Applied Energy, Elsevier, vol. 259(C).
    12. Mavromatidis, Georgios & Orehounig, Kristina & Carmeliet, Jan, 2018. "Uncertainty and global sensitivity analysis for the optimal design of distributed energy systems," Applied Energy, Elsevier, vol. 214(C), pages 219-238.
    13. Karmellos, M. & Georgiou, P.N. & Mavrotas, G., 2019. "A comparison of methods for the optimal design of Distributed Energy Systems under uncertainty," Energy, Elsevier, vol. 178(C), pages 318-333.
    14. Song, Tangnyu & Huang, Guohe & Zhou, Xiong & Wang, Xiuquan, 2018. "An inexact two-stage fractional energy systems planning model," Energy, Elsevier, vol. 160(C), pages 275-289.
    15. Dong, Cong & Huang, Guohe & Cai, Yanpeng & Cheng, Guanhui & Tan, Qian, 2016. "Bayesian interval robust optimization for sustainable energy system planning in Qiqihar City, China," Energy Economics, Elsevier, vol. 60(C), pages 357-376.
    16. Mavromatidis, Georgios & Orehounig, Kristina & Carmeliet, Jan, 2018. "A review of uncertainty characterisation approaches for the optimal design of distributed energy systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 88(C), pages 258-277.
    17. Zhou, Zhe & Zhang, Jianyun & Liu, Pei & Li, Zheng & Georgiadis, Michael C. & Pistikopoulos, Efstratios N., 2013. "A two-stage stochastic programming model for the optimal design of distributed energy systems," Applied Energy, Elsevier, vol. 103(C), pages 135-144.
    18. Zheng, Donglin & Yu, Lijun & Wang, Lizhen, 2019. "A techno-economic-risk decision-making methodology for large-scale building energy efficiency retrofit using Monte Carlo simulation," Energy, Elsevier, vol. 189(C).
    19. Tao Zhang & Minli Wang & Peihong Wang & Junyu Liang, 2020. "Optimal Design of a Combined Cooling, Heating, and Power System and Its Ability to Adapt to Uncertainty," Energies, MDPI, vol. 13(14), pages 1-17, July.
    20. Wei, F. & Wu, Q.H. & Jing, Z.X. & Chen, J.J. & Zhou, X.X., 2016. "Optimal unit sizing for small-scale integrated energy systems using multi-objective interval optimization and evidential reasoning approach," Energy, Elsevier, vol. 111(C), pages 933-946.
    21. Abokersh, Mohamed Hany & Vallès, Manel & Cabeza, Luisa F. & Boer, Dieter, 2020. "A framework for the optimal integration of solar assisted district heating in different urban sized communities: A robust machine learning approach incorporating global sensitivity analysis," Applied Energy, Elsevier, vol. 267(C).
    22. Pablo Benalcazar & Jacek Kamiński & Karol Stós, 2022. "An Integrated Approach to Long-Term Fuel Supply Planning in Combined Heat and Power Systems," Energies, MDPI, vol. 15(22), pages 1-22, November.
    23. Zhu, H. & Huang, W.W. & Huang, G.H., 2014. "Planning of regional energy systems: An inexact mixed-integer fractional programming model," Applied Energy, Elsevier, vol. 113(C), pages 500-514.
    24. Afzali, Sayyed Faridoddin & Cotton, James S. & Mahalec, Vladimir, 2020. "Urban community energy systems design under uncertainty for specified levels of carbon dioxide emissions," Applied Energy, Elsevier, vol. 259(C).
    25. Petkov, Ivalin & Gabrielli, Paolo, 2020. "Power-to-hydrogen as seasonal energy storage: an uncertainty analysis for optimal design of low-carbon multi-energy systems," Applied Energy, Elsevier, vol. 274(C).

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    More about this item

    Keywords

    Energy Planning; Mathematical Programming; MILP; Uncertainty; Monte Carlo;
    All these keywords.

    JEL classification:

    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • Q49 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Other

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