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Technology Choices for New Entrants in Liberalised Markets: The Value of Operating Flexibility and Contractual Arrangements

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  • Roques, F.A.

Abstract

New entrants in liberalised electricity markets which are not vertically integrated and do not operate a large and diversified portfolio of generation technologies are likely to favour technologies which offer the best prospects to manage fuel and electricity price risks through contractual arrangements and operating flexibility. Monte Carlo simulations of a discounted cash flow model of investment in combined cycle gas turbine (CCGT), coal and nuclear power plant are run to compare the impact of fuel and electricity price risks on these different technologies, as well as the value of operating flexibility and contractual hedges. In the absence of long-term fixed-price power purchase contracts, CCGT is the least risky option as its cash flow is “self-hedged” given the high correlation between electricity and gas prices observed in most markets. Moreover, the value associated with operating flexibility and arbitrage between gas and power market is greater for CCGT plant. This makes CCGT particularly attractive to new entrants.

Suggested Citation

  • Roques, F.A., 2007. "Technology Choices for New Entrants in Liberalised Markets: The Value of Operating Flexibility and Contractual Arrangements," Cambridge Working Papers in Economics 0759, Faculty of Economics, University of Cambridge.
    • Handle: RePEc:cam:camdae:0759
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    Cited by:

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    2. Keyaerts, Nico & Hallack, Michelle & Glachant, Jean-Michel & D'haeseleer, William, 2011. "Gas market distorting effects of imbalanced gas balancing rules: Inefficient regulation of pipeline flexibility," Energy Policy, Elsevier, vol. 39(2), pages 865-876, February.
    3. Dominique Finon & Fabien Roques, 2010. "Contractual and Financing Arrangements for New Nuclear Investment in Liberalized Markets: Which Efficient Combination?," Chapters, in: François Lévêque & Jean-Michel Glachant & Julián Barquín & Christian von Hirschhausen & Franziska Ho (ed.), Security of Energy Supply in Europe, chapter 6, Edward Elgar Publishing.
    4. Locatelli, Giorgio & Mancini, Mauro & Lotti, Giovanni, 2020. "A simple-to-implement real options method for the energy sector," Energy, Elsevier, vol. 197(C).
    5. D. Finon & F. Roques, 2008. "Financing Arrangements and Industrial Organisation for New Nuclear Build in Electricity Markets," Competition and Regulation in Network Industries, Intersentia, vol. 9(3), pages 247-282, September.
    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. Hauteclocque, Adrien de & Glachant, Jean-Michel, 2009. "Long-term energy supply contracts in European competition policy: Fuzzy not crazy," Energy Policy, Elsevier, vol. 37(12), pages 5399-5407, December.
    8. Dominik Kryzia & Michał Kopacz & Katarzyna Kryzia, 2020. "The Valuation of the Operational Flexibility of the Energy Investment Project Based on a Gas-Fired Power Plant," Energies, MDPI, vol. 13(7), pages 1-16, March.
    9. Klaus Rennings & Peter Markewitz & Stefan Vögele, 2013. "How clean is clean? Incremental versus radical technological change in coal-fired power plants," Journal of Evolutionary Economics, Springer, vol. 23(2), pages 331-355, April.
    10. Drago Papler & Štefan Bojnec, 2015. "Competitiveness and Factors of Delivery of Electricity," Faculty of Management Koper Monograph Series, University of Primorska, Faculty of Management Koper, number 978-961-266-188-5, July.
    11. Rowan Adams & Tooraj Jamasb, 2016. "Optimal Power Generation Portfolios with Renewables: An Application to the UK," Working Papers EPRG 1620, Energy Policy Research Group, Cambridge Judge Business School, University of Cambridge.

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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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