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Using Probabilistic Analysis to Value Power Generation Investments Under Uncertainty

Author

Listed:
  • Fabien A. Roques

    (Electricity Policy Research Group, University of Cambridge)

  • William J. Nuttall

    (Electricity Policy Research Group, University of Cambridge)

  • David M. Newbery

    (Electricity Policy Research Group, University of Cambridge)

Abstract

This paper reviews the limits of the traditional ‘levelised cost’ approach to properly take into account risks and uncertainties when valuing different power generation technologies. We introduce a probabilistic valuation model of investment in three base-load technologies (combined cycle gas turbine, coal plant, and nuclear power plant), and demonstrate using three case studies how such a probabilistic approach provides investors with a much richer analytical framework to assess power investments in liberalised markets. We successively analyse the combined impact of multiple uncertainties on the value of alternative technologies, the value of the operating flexibility of power plant managers to mothball and de-mothball plants, and the value of mixed portfolios of different production technologies that present complementary risk-return profiles.
(This abstract was borrowed from another version of this item.)

Suggested Citation

  • Fabien A. Roques & William J. Nuttall & David M. Newbery, 2006. "Using Probabilistic Analysis to Value Power Generation Investments Under Uncertainty," Working Papers EPRG 0619, Energy Policy Research Group, Cambridge Judge Business School, University of Cambridge.
    • Handle: RePEc:enp:wpaper:eprg0619
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    Cited by:

    1. Vithayasrichareon, Peerapat & MacGill, Iain F., 2013. "Assessing the value of wind generation in future carbon constrained electricity industries," Energy Policy, Elsevier, vol. 53(C), pages 400-412.
    2. Foley, A.M. & Ó Gallachóir, B.P. & Hur, J. & Baldick, R. & McKeogh, E.J., 2010. "A strategic review of electricity systems models," Energy, Elsevier, vol. 35(12), pages 4522-4530.
    3. Santos, Lúcia & Soares, Isabel & Mendes, Carla & Ferreira, Paula, 2014. "Real Options versus Traditional Methods to assess Renewable Energy Projects," Renewable Energy, Elsevier, vol. 68(C), pages 588-594.
    4. Chignell, Simon & Gross, Robert J.K., 2013. "Not locked-in? The overlooked impact of new gas-fired generation investment on long-term decarbonisation in the UK," Energy Policy, Elsevier, vol. 52(C), pages 699-705.
    5. Locatelli, Giorgio & Mancini, Mauro & Lotti, Giovanni, 2020. "A simple-to-implement real options method for the energy sector," Energy, Elsevier, vol. 197(C).
    6. Brouwer, Anne Sjoerd & van den Broek, Machteld & Özdemir, Özge & Koutstaal, Paul & Faaij, André, 2016. "Business case uncertainty of power plants in future energy systems with wind power," Energy Policy, Elsevier, vol. 89(C), pages 237-256.
    7. Sunderkötter, Malte & Weber, Christoph, 2012. "Valuing fuel diversification in power generation capacity planning," Energy Economics, Elsevier, vol. 34(5), pages 1664-1674.
    8. Geissmann, Thomas, 2017. "A probabilistic approach to the computation of the levelized cost of electricity," Energy, Elsevier, vol. 124(C), pages 372-381.
    9. Muñoz, José Ignacio & Sánchez de la Nieta, Agustín A. & Contreras, Javier & Bernal-Agustín, José L., 2009. "Optimal investment portfolio in renewable energy: The Spanish case," Energy Policy, Elsevier, vol. 37(12), pages 5273-5284, December.
    10. Jamasb, Tooraj & Nuttall, William J. & Pollitt, Michael, 2008. "The case for a new energy research, development and promotion policy for the UK," Energy Policy, Elsevier, vol. 36(12), pages 4610-4614, December.
    11. Thiam, Djiby-Racine, 2010. "Renewable decentralized in developing countries: Appraisal from microgrids project in Senegal," Renewable Energy, Elsevier, vol. 35(8), pages 1615-1623.
    12. Konsta Värri & Sanna Syri, 2019. "The Possible Role of Modular Nuclear Reactors in District Heating: Case Helsinki Region," Energies, MDPI, vol. 12(11), pages 1-24, June.
    13. Roques, Fabien A., 2008. "Technology choices for new entrants in liberalized markets: The value of operating flexibility and contractual arrangements," Utilities Policy, Elsevier, vol. 16(4), pages 245-253, December.
    14. Şakir Sakarya & Hasan Hüseyin Yıldırım, 2017. "Usıng Monte Carlo Sımulatıon for Wınd Power Generatıon Investment’s Assessment," Journal of Finance Letters (Maliye ve Finans Yazıları), Maliye ve Finans Yazıları Yayıncılık Ltd. Şti., vol. 32(108), pages 49-70, October.
    15. Reinhard Madlener & Christioph Wenk, 2008. "Efficient Investment Portfolios for the Swiss Electricity Supply Sector," FCN Working Papers 2/2008, E.ON Energy Research Center, Future Energy Consumer Needs and Behavior (FCN).
    16. Vithayasrichareon, Peerapat & Riesz, Jenny & MacGill, Iain F., 2015. "Using renewables to hedge against future electricity industry uncertainties—An Australian case study," Energy Policy, Elsevier, vol. 76(C), pages 43-56.
    17. Locatelli, Giorgio & Mancini, Mauro, 2010. "Small-medium sized nuclear coal and gas power plant: A probabilistic analysis of their financial performances and influence of CO2 cost," Energy Policy, Elsevier, vol. 38(10), pages 6360-6374, October.
    18. Nicola Comincioli & Mattia Guerini & Sergio Vergalli, 2024. "Carbon Taxation and Electricity Price Dynamics: Empirical Evidence from the Australian Market," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 87(12), pages 3131-3161, December.
    19. Vithayasrichareon, Peerapat & MacGill, Iain F., 2012. "A Monte Carlo based decision-support tool for assessing generation portfolios in future carbon constrained electricity industries," Energy Policy, Elsevier, vol. 41(C), pages 374-392.
    20. Daniel Ziegler & Katrin Schmitz & Christoph Weber, 2012. "Optimal electricity generation portfolios," Computational Management Science, Springer, vol. 9(3), pages 381-399, August.

    More about this item

    Keywords

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    JEL classification:

    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty
    • L94 - Industrial Organization - - Industry Studies: Transportation and Utilities - - - Electric Utilities

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