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Combined Heat and Power in Commercial Buildings: Investment and Risk Analysis

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  • Karl Magnus Maribu
  • Stein-Erik Fleten

Abstract

Combined heat and power (CHP) systems can generate electricity locally while they recover heat to satisfy heating loads in buildings, which means they provide efficient energy. On-site generators may reduce both the expected energy costs and cost risk exposure for developers. With volatile energy prices, a deterministic modeling framework will not yield a fair value of CHP systems because flexibility in the operational response to price changes is not taken into account. In this paper, we present a Monte Carlo simulation model that is used to find the CHP value under uncertain future wholesale electricity and natural gas prices. When considering investing in a CHP system on should consider both return and risk. Clearly, both investment return and risk depend on local energy tariffs and energy loads. We highlight an example where CHP is marginally profitable and the investment decision is not straightforward. Interestingly, CHP systems were found particularly attractive with volatile electricity prices because their ability to respond to high prices provides efficient hedges to energy cost risk. Therefore, developers should not be discouraged but rather embrace on-site generation in markets with volatile prices. From the analysis, it can also be concluded that sizing of CHP systems can be related to the energy tariff structure and cost risk preferences as well as to energy loads.

Suggested Citation

  • Karl Magnus Maribu & Stein-Erik Fleten, 2008. "Combined Heat and Power in Commercial Buildings: Investment and Risk Analysis," The Energy Journal, International Association for Energy Economics, vol. 0(Number 2), pages 123-150.
    • Handle: RePEc:aen:journl:2008v29-02-a07
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    Citations

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    Cited by:

    1. Xiaolong Yang & Yan Li & Dongxiao Niu & Lijie Sun, 2019. "Research on the Economic Benefit Evaluation of Combined Heat and Power (CHP) Technical Renovation Projects Based on the Improved Factor Analysis and Incremental Method in China," Sustainability, MDPI, vol. 11(19), pages 1-23, September.
    2. Østergaard, Poul Alberg & Andersen, Anders N. & Sorknæs, Peter, 2022. "The business-economic energy system modelling tool energyPRO," Energy, Elsevier, vol. 257(C).
    3. Athawale, Rasika & Felder, Frank A. & Goldman, Leo A., 2016. "Do Combined Heat and Power plants perform? Case study of publicly funded projects in New York," Energy Policy, Elsevier, vol. 97(C), pages 618-627.
    4. Tolis, Athanasios I. & Rentizelas, Athanasios A. & Tatsiopoulos, Ilias P., 2010. "Optimisation of electricity energy markets and assessment of CO2 trading on their structure: A stochastic analysis of the Greek Power Sector," Renewable and Sustainable Energy Reviews, Elsevier, vol. 14(9), pages 2529-2546, December.
    5. Rocha, Paula & Kaut, Michal & Siddiqui, Afzal S., 2016. "Energy-efficient building retrofits: An assessment of regulatory proposals under uncertainty," Energy, Elsevier, vol. 101(C), pages 278-287.
    6. Hanna, Ryan & Ghonima, Mohamed & Kleissl, Jan & Tynan, George & Victor, David G., 2017. "Evaluating business models for microgrids: Interactions of technology and policy," Energy Policy, Elsevier, vol. 103(C), pages 47-61.
    7. Joanna Rzempała & Daniel Borkowski & Artur Piotr Rzempała, 2021. "Risk Identification in Cogeneration (Combined Heat and Power) Projects: A Polish Case Study," Energies, MDPI, vol. 15(1), pages 1-16, December.
    8. Botterud, Audun & Yildiz, Bilge & Conzelmann, Guenter & Petri, Mark C., 2008. "Nuclear hydrogen: An assessment of product flexibility and market viability," Energy Policy, Elsevier, vol. 36(10), pages 3961-3973, October.

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    JEL classification:

    • F0 - International Economics - - General

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