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Risk–return incentives in liberalised electricity markets

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  • Lynch, Muireann Á.
  • Shortt, Aonghus
  • Tol, Richard S.J.
  • O'Malley, Mark J.

Abstract

We employ Monte Carlo analysis to determine the distribution of returns for various electricity generation technologies. Costs and revenues for each technology are calculated by means of a unit commitment and economic dispatch algorithm at hourly resolution. This represents a considerable contribution to the literature as costs and revenues are determined endogenously, which in turn allows the returns of midmerit and peaking plant to be examined. Market entry is determined on the basis of a heuristic while market exit is according to a predetermined retirement schedule. The results show that CCGT is the investment technology of choice for baseload-only portfolios, while OCGT proves optimal when all technologies are considered. The high capital costs of baseload generation reduce incentives to invest. The methodology can be expanded to consider random outages, revenues from scarcity prices, capacity markets and ancillary service payments.

Suggested Citation

  • Lynch, Muireann Á. & Shortt, Aonghus & Tol, Richard S.J. & O'Malley, Mark J., 2013. "Risk–return incentives in liberalised electricity markets," Energy Economics, Elsevier, vol. 40(C), pages 598-608.
    • Handle: RePEc:eee:eneeco:v:40:y:2013:i:c:p:598-608
    DOI: 10.1016/j.eneco.2013.08.015
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    Cited by:

    1. Paulino Martinez-Fernandez & Fernando deLlano-Paz & Anxo Calvo-Silvosa & Isabel Soares, 2019. "Assessing Renewable Energy Sources for Electricity (RES-E) Potential Using a CAPM-Analogous Multi-Stage Model," Energies, MDPI, vol. 12(19), pages 1-20, September.
    2. Chen, H. & Chyong CK. & Kang, J-N. & Wei Y-M., 2018. "Economic dispatch in the electricity sector in China: potential benefits and challenges ahead," Cambridge Working Papers in Economics 1836, Faculty of Economics, University of Cambridge.
    3. Lynch, Muireann Á & Devine, Mel & Bertsch, Valentin, 2018. "The role of power-to-gas in the future energy system: how much is needed and who wants to invest?," Papers WP590, Economic and Social Research Institute (ESRI).
    4. Curtis, John & Lynch, Muireann Á. & Zubiate, Laura, 2016. "The impact of the North Atlantic Oscillation on electricity markets: A case study on Ireland," Energy Economics, Elsevier, vol. 58(C), pages 186-198.
    5. Lynch & John Curtis, 2016. "The effects of wind generation capacity on electricity prices and generation costs: a Monte Carlo analysis," Applied Economics, Taylor & Francis Journals, vol. 48(2), pages 133-151, January.
    6. Paulino Martinez-Fernandez & Fernando deLlano-Paz & Anxo Calvo-Silvosa & Isabel Soares, 2018. "Pollutant versus non-pollutant generation technologies: a CML-analogous analysis," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 20(1), pages 199-212, December.
    7. Jano-Ito, Marco A. & Crawford-Brown, Douglas, 2017. "Investment decisions considering economic, environmental and social factors: An actors' perspective for the electricity sector of Mexico," Energy, Elsevier, vol. 121(C), pages 92-106.
    8. deLlano-Paz, Fernando & Calvo-Silvosa, Anxo & Antelo, Susana Iglesias & Soares, Isabel, 2017. "Energy planning and modern portfolio theory: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 77(C), pages 636-651.
    9. Zeng, Ximei & Zhou, Zhongbao & Gong, Yeming & Liu, Wenbin, 2022. "A data envelopment analysis model integrated with portfolio theory for energy mix adjustment: Evidence in the power industry," Socio-Economic Planning Sciences, Elsevier, vol. 83(C).
    10. Janko, Samantha A. & Johnson, Nathan G., 2018. "Scalable multi-agent microgrid negotiations for a transactive energy market," Applied Energy, Elsevier, vol. 229(C), pages 715-727.
    11. Muireann Á. Lynch & Richard Tol & Mark J. O’Malley, 2014. "Minimising costs and variability of electricity generation by means of optimal electricity interconnection utilisation," Working Paper Series 6814, Department of Economics, University of Sussex Business School.
    12. Koltsaklis, Nikolaos E. & Dagoumas, Athanasios S., 2018. "State-of-the-art generation expansion planning: A review," Applied Energy, Elsevier, vol. 230(C), pages 563-589.
    13. Russo, Marianna & Bertsch, Valentin, 2020. "A looming revolution: Implications of self-generation for the risk exposure of retailers," Energy Economics, Elsevier, vol. 92(C).
    14. Lynch, Muireann & Devine, Mel T. & Bertsch, Valentin, 2019. "The role of power-to-gas in the future energy system: Market and portfolio effects," Energy, Elsevier, vol. 185(C), pages 1197-1209.
    15. Curtis, John & Lynch, Muireann Á. & Zubiate, Laura, 2016. "Carbon dioxide (CO2) emissions from electricity: The influence of the North Atlantic Oscillation," Applied Energy, Elsevier, vol. 161(C), pages 487-496.
    16. Tietjen, Oliver & Pahle, Michael & Fuss, Sabine, 2016. "Investment risks in power generation: A comparison of fossil fuel and renewable energy dominated markets," Energy Economics, Elsevier, vol. 58(C), pages 174-185.
    17. Wei, Yi-Ming & Chen, Hao & Chyong, Chi Kong & Kang, Jia-Ning & Liao, Hua & Tang, Bao-Jun, 2018. "Economic dispatch savings in the coal-fired power sector: An empirical study of China," Energy Economics, Elsevier, vol. 74(C), pages 330-342.

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    More about this item

    Keywords

    Monte Carlo simulation; Mean–variance portfolio theory; Electricity generation investment;
    All these keywords.

    JEL classification:

    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • L94 - Industrial Organization - - Industry Studies: Transportation and Utilities - - - Electric Utilities

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