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Stochastic NPV Based vs Stochastic LCOE Based Power Portfolio Selection Under Uncertainty

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  • Carlo Mari

    (Department of Economics, University of Chieti-Pescara, 65127 Pescara (PE), Italy)

Abstract

This paper investigates the problem of power portfolio selection under uncertainty using two different metrics, namely the stochastic Net Present Value (NPV) and the stochastic Levelized Cost of Electricity (LCOE). In the first metric, stochastic revenues, as well as stochastic costs incurred during the whole lifetime of power plants, are taken into account. The second metric is based on stochastic costs only. This means that revenues deriving from selling electricity in power markets over long-term horizons play an important role in determining optimal portfolios under the stochastic NPV metric, but they have no impact on optimal portfolios under the stochastic LCOE metric. Uncertainty arising from unpredictable movements of electricity market prices, fossil fuels, and nuclear fuel prices is considered. Moreover, stochastic CO 2 costs are included into the analysis. The aim of this study was to examine in what circumstances efficient NPV-based portfolios differ in a significant way from efficient LCOE-based portfolios. The portfolio selection is performed using two different risk measures, namely the standard deviation and the Conditional Value at Risk Deviation (CVaR) deviation. The proposed methodology can be used as a powerful tool of analysis for planning profitable investments in new generating technologies paying attention to risk reducing strategies through power sources diversification.

Suggested Citation

  • Carlo Mari, 2020. "Stochastic NPV Based vs Stochastic LCOE Based Power Portfolio Selection Under Uncertainty," Energies, MDPI, vol. 13(14), pages 1-18, July.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:14:p:3677-:d:385557
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    1. Rafal Weron & Florian Ziel, 2018. "Electricity price forecasting," HSC Research Reports HSC/18/08, Hugo Steinhaus Center, Wroclaw University of Technology.
    2. Feng, Zhen-Hua & Zou, Le-Le & Wei, Yi-Ming, 2011. "Carbon price volatility: Evidence from EU ETS," Applied Energy, Elsevier, vol. 88(3), pages 590-598, March.
    3. Roques, Fabien A. & Newbery, David M. & Nuttall, William J., 2008. "Fuel mix diversification incentives in liberalized electricity markets: A Mean-Variance Portfolio theory approach," Energy Economics, Elsevier, vol. 30(4), pages 1831-1849, July.
    4. Nowotarski, Jakub & Weron, Rafał, 2018. "Recent advances in electricity price forecasting: A review of probabilistic forecasting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 1548-1568.
    5. Ueckerdt, Falko & Hirth, Lion & Luderer, Gunnar & Edenhofer, Ottmar, 2013. "System LCOE: What are the costs of variable renewables?," Energy, Elsevier, vol. 63(C), pages 61-75.
    6. Mjelde, James W. & Bessler, David A., 2009. "Market integration among electricity markets and their major fuel source markets," Energy Economics, Elsevier, vol. 31(3), pages 482-491, May.
    7. Hanson, Donald & Schmalzer, David & Nichols, Christopher & Balash, Peter, 2016. "The impacts of meeting a tight CO2 performance standard on the electric power sector," Energy Economics, Elsevier, vol. 60(C), pages 476-485.
    8. Balietti, Anca Claudia, 2016. "Trader types and volatility of emission allowance prices. Evidence from EU ETS Phase I," Energy Policy, Elsevier, vol. 98(C), pages 607-620.
    9. Hamm, Gregory & Borison, Adam, 2006. "Forecasting Long-Run Electricity Prices," The Electricity Journal, Elsevier, vol. 19(7), pages 47-57.
    10. Lucheroni, Carlo & Mari, Carlo, 2017. "CO2 volatility impact on energy portfolio choice: A fully stochastic LCOE theory analysis," Applied Energy, Elsevier, vol. 190(C), pages 278-290.
    11. Rockafellar, R. Tyrrell & Uryasev, Stan & Zabarankin, Michael, 2006. "Master funds in portfolio analysis with general deviation measures," Journal of Banking & Finance, Elsevier, vol. 30(2), pages 743-778, February.
    12. Carlo Lucheroni & Carlo Mari, 2018. "Optimal Integration of Intermittent Renewables: A System LCOE Stochastic Approach," Energies, MDPI, vol. 11(3), pages 1-21, March.
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