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The Optimal Share of Variable Renewables. How the Variability of Wind and Solar Power Affects their Welfare-optimal Deployment

Author

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  • Hirth, Lion

Abstract

This paper estimates the welfare-optimal market share of wind and solar power, explicitly taking into account their output variability. We present a theoretical valuation framework that consistently accounts for output variability over time, forecast errors, and the location of generators in the power grid, and evaluate the impact of these three factors on the marginal value of electricity from renewables. Then we estimate the optimal share of wind and solar power in Northwestern Europe from a calibrated numerical power market model. The optimal long-term share of wind power of total electricity consumption is estimated to be 20% at cost levels of 50 €/MWh, about three times the current market share of wind; but this estimate is subject to significant parameter uncertainty. Variability significantly impacts results: if winds were constant, the optimal share would be 60%. In addition, the effect of technological change, price shocks, and policies on the optimal share is assessed. We present and explain several surprising findings, including a negative impact of CO2 prices on optimal wind deployment.

Suggested Citation

  • Hirth, Lion, 2013. "The Optimal Share of Variable Renewables. How the Variability of Wind and Solar Power Affects their Welfare-optimal Deployment," Energy: Resources and Markets 162373, Fondazione Eni Enrico Mattei (FEEM).
    • Handle: RePEc:ags:feemer:162373
    DOI: 10.22004/ag.econ.162373
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    Cited by:

    1. Huppmann, Daniel & Egging, Ruud, 2014. "Market power, fuel substitution and infrastructure – A large-scale equilibrium model of global energy markets," Energy, Elsevier, vol. 75(C), pages 483-500.
    2. Zerrahn, Alexander & Huppmann, Daniel, 2014. "Network Expansion to Mitigate Market Power: How Increased Integration Fosters Welfare," VfS Annual Conference 2014 (Hamburg): Evidence-based Economic Policy 100459, Verein für Socialpolitik / German Economic Association.
    3. Fogelberg, Sara & Lazarczyk, Ewa, 2015. "The Wind Power Volatility and the Impact on Failure Rates in the Nordic Electricity Market," Working Paper Series 1065, Research Institute of Industrial Economics.
    4. René Aïd & Matteo Basei & Huyên Pham, 2017. "The coordination of centralised and distributed generation," Working Papers hal-01517165, HAL.
    5. Schill, Wolf-Peter, 2014. "Residual Load, Renewable Surplus Generation and Storage Requirements in Germany," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 73, pages 65-79.
    6. Ren'e Aid & Matteo Basei & Huy^en Pham, 2017. "A McKean-Vlasov approach to distributed electricity generation development," Papers 1705.01302, arXiv.org, revised Nov 2019.

    More about this item

    Keywords

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    JEL classification:

    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • Q42 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Alternative Energy Sources
    • Q48 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Government Policy
    • D41 - Microeconomics - - Market Structure, Pricing, and Design - - - Perfect Competition

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