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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.

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Paper provided by Fondazione Eni Enrico Mattei in its series Working Papers with number 2013.90.

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Date of creation: Oct 2013
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Handle: RePEc:fem:femwpa:2013.90
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  1. Hirth, Lion & Ueckerdt, Falko, 2013. "Redistribution effects of energy and climate policy: The electricity market," Energy Policy, Elsevier, vol. 62(C), pages 934-947.
  2. William D. Nordhaus, 2009. "The Perils of the Learning Model For Modeling Endogenous Technological Change," NBER Working Papers 14638, National Bureau of Economic Research, Inc.
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  5. Neuhoff, K. & Ehrenmann, A. & Butler, L. & Cust, J. & Hoexter, H. & Keats, K. & Kreczko,A. & Sinden, G., 2006. "Space and Time: Wind in an Investment Planning Model," Cambridge Working Papers in Economics 0620, Faculty of Economics, University of Cambridge.
  6. Martin, Brian & Diesendorf, Mark, 1983. "The economics of large-scale wind power in the UK A model of an optimally mixed CEGB electricity grid," Energy Policy, Elsevier, vol. 11(3), pages 259-266, September.
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  9. 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.
  10. Lion Hirth, 2012. "The Market Value of Variable Renewables," Working Papers 2012.15, Fondazione Eni Enrico Mattei.
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  14. Nagl, Stephan & Fürsch, Michaela & Lindenberger, Dietmar, 2012. "The costs of electricity systems with a high share of fluctuating renewables - a stochastic investment and dispatch optimization model for Europe," EWI Working Papers 2012-1, Energiewirtschaftliches Institut an der Universitaet zu Koeln.
  15. Möst, Dominik & Fichtner, Wolf, 2010. "Renewable energy sources in European energy supply and interactions with emission trading," Energy Policy, Elsevier, vol. 38(6), pages 2898-2910, June.
  16. Francis Bessière, 1970. "The "Investment '85" Model of Electricite de France," Management Science, INFORMS, vol. 17(4), pages B192-B211, December.
  17. Lise, Wietze & Kruseman, Gideon, 2008. "Long-term price and environmental effects in a liberalised electricity market," Energy Economics, Elsevier, vol. 30(2), pages 230-248, March.
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