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Heating System for a Group of Condominiums

ESD.71 Application Portfolio. Heating System for a Group of Condominiums. Rory Clune Dept. of Civil & Environmental Engineering, Massachusetts Institute of Technology. 1. Introduction 2. Uncertainties 3. Modelling & Design Levers 4. Decision Tree 5. Binomial Lattice 6. Conclusion.

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Heating System for a Group of Condominiums

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  1. ESD.71 Application Portfolio Heating System for a Group of Condominiums Rory Clune Dept. of Civil & Environmental Engineering, Massachusetts Institute of Technology

  2. 1. Introduction2. Uncertainties3. Modelling & Design Levers4. Decision Tree 5. Binomial Lattice 6. Conclusion

  3. -What is the system? 1. Introduction -Heating plant – natural gas boiler heats water & pipes it to holiday homes Gas (fuel) in Cold water returned Hot water to homes

  4. -Two major uncertainties (a) Price of natural gas – an input cost 2. Uncertainties ν = 0.90% σ = 14.00% Source: Energy Information Administration (http://tonto.eia.doe.gov/dnav/ng/hist )

  5. (b) Demand for heat 2. Uncertainties Assumption: Demand for heat ~ number of tourists visiting condos ~ number of tourists visiting Ireland (for which data available) ν = 3.63% σ = 6.74% Source: Central Statistics Office of Ireland (http://www.cso.ie )

  6. -Excel – Based on thermodynamic/HVAC theory 3. Modelling & Design Levers -NPV over 10 years. Monthly resolution -Option to ‘expand medium’, ‘expand big’ or do nothing at year 5

  7. 1. Introduction2. Uncertainties3. Modelling & Design Levers4. Decision Tree 5. Binomial Lattice 6. Conclusion

  8. Fixed Design Capacity to meet forecast demand level at year 5 (my decision point) Bigger now means less efficient initially – boiler theory 4. Decision Tree Flexible Design Capacity to meet today’s demand only Capability to expand to meet anything up to maximum possible demand Two uncertainties (gas price & demand level) 3 levels over each period – favourable, forecast & unfavourable

  9. 2 decision nodes (shown in yellow) 4. Decision Tree 2 uncertainties, each with 3 possible outcomes

  10. -Analysis over 6 years 5. Binomial Lattice -Just one uncertainty – natural gas price Monthly σ, νparameters from regression converted to annual ($ per kWh of gas)

  11. Annual undiscounted cash flows (Year 0 includes Capex) 5. Binomial Lattice -Option: close at any time, without penalty. Can be exercised only once Double-checked using standard NPV of annual cash flows method

  12. -Decision Tree: Flexibility increases E(NPV) & shifts most of VARG to the right Noticeably reduced downside 5. Conclusion Easy to adapt to situation, but laborious -Binomial Lattice: Flexibility to expand increases E(NPV) Effect on VARG curve not remarkable (short time period) More awkward to apply – assumptions had to be made -number of uncertainties = 1 -path independence

  13. End

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