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


  • Lion Hirth

    (Potsdam-Institute for Climate Impact Research, Vattenfall GmbH)


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

  • Lion Hirth, 2013. "The Optimal Share of Variable Renewables. How the Variability of Wind and Solar Power Affects their Welfare-optimal Deployment," Working Papers 2013.90, Fondazione Eni Enrico Mattei.
  • Handle: RePEc:fem:femwpa:2013.90

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    References listed on IDEAS

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    Cited by:

    1. repec:eee:eneeco:v:65:y:2017:i:c:p:75-86 is not listed on IDEAS
    2. Nemet, Gregory F. & O’Shaughnessy, Eric & Wiser, Ryan & Darghouth, Naïm & Barbose, Galen & Gillingham, Ken & Rai, Varun, 2017. "Characteristics of low-priced solar PV systems in the U.S," Applied Energy, Elsevier, vol. 187(C), pages 501-513.
    3. repec:eee:renene:v:114:y:2017:i:pb:p:1333-1339 is not listed on IDEAS
    4. René Aïd & Matteo Basei & Huyên Pham, 2017. "The coordination of centralised and distributed generation," Working Papers hal-01517165, HAL.
    5. repec:eee:eecrev:v:99:y:2017:i:c:p:130-150 is not listed on IDEAS
    6. Schill, Wolf-Peter, 2014. "Residual Load, Renewable Surplus Generation and Storage Requirements in Germany," EconStor Open Access Articles, ZBW - German National Library of Economics, pages 65-79.
    7. Zerrahn, Alexander & Huppmann, Daniel, 2014. "Network Expansion to Mitigate Market Power: How Increased Integration Fosters Welfare," Annual Conference 2014 (Hamburg): Evidence-based Economic Policy 100459, Verein für Socialpolitik / German Economic Association.
    8. Zerrahn, Alexander & Krekel, Christian, 2015. "Sowing the Wind and Reaping the Whirlwind? The Effect of Wind Turbines on Residential Well-Being," Annual Conference 2015 (Muenster): Economic Development - Theory and Policy 112956, Verein für Socialpolitik / German Economic Association.
    9. Hansen, J.P. & Narbel, P.A. & Aksnes, D.L., 2017. "Limits to growth in the renewable energy sector," Renewable and Sustainable Energy Reviews, Elsevier, vol. 70(C), pages 769-774.
    10. 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.
    11. Sinn, Hans-Werner, 2017. "Buffering volatility: A study on the limits of Germany's energy revolution," European Economic Review, Elsevier, vol. 99(C), pages 130-150.
    12. Clemens Gerbaulet & Casimir Lorenz, 2017. "dynELMOD: A Dynamic Investment and Dispatch Model for the Future European Electricity Market," Data Documentation 88, DIW Berlin, German Institute for Economic Research.
    13. repec:eee:eneeco:v:64:y:2017:i:c:p:363-372 is not listed on IDEAS
    14. Ren'e Aid & Matteo Basei & Huy^en Pham, 2017. "The coordination of centralised and distributed generation," Papers 1705.01302,
    15. repec:eee:energy:v:128:y:2017:i:c:p:776-784 is not listed on IDEAS
    16. repec:eee:appene:v:207:y:2017:i:c:p:208-217 is not listed on IDEAS
    17. Tunç Durmaz & Aude Pommeret & Ian Ridley, 2017. "Willingness to Pay for Solar Panels and Smart Grids," Working Papers 2017.24, Fondazione Eni Enrico Mattei.

    More about this item


    Wind Power; Solar Power; Variable Renewables; Cost-Benefit Analysis; Numerical Optimization; Competitiveness;

    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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