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The costs of electricity systems with a high share of fluctuating renewables - a stochastic investment and dispatch optimization model for Europe

  • Nagl, Stephan

    ()

    (Energiewirtschaftliches Institut an der Universitaet zu Koeln)

  • Fürsch, Michaela

    ()

    (Energiewirtschaftliches Institut an der Universitaet zu Koeln)

  • Lindenberger, Dietmar

    ()

    (Energiewirtschaftliches Institut an der Universitaet zu Koeln)

Renewable energies are meant to produce a large share of the future electricity demand. However, the availability of wind and solar power depends on local weather conditions and therefore weather characteristics must be considered when optimizing the future electricity mix. In this article we analyze the impact of the stochastic availability of wind and solar energy on the cost-minimal power plant mix and the related total system costs. To determine optimal conventional, renewable and storage capacities for di fferent shares of renewables, we apply a stochastic investment and dispatch optimization model to the European electricity market. The model considers stochastic feed-in structures and full load hours of wind and solar technologies and different correlations between regions and technologies. Key findings include the overestimation of fluctuating renewables and underestimation of total system costs compared to deterministic investment and dispatch models. Furthermore, solar technologies are - relative to wind turbines - underestimated when neglecting negative correlations between wind speeds and solar radiation.

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Paper provided by Energiewirtschaftliches Institut an der Universitaet zu Koeln in its series EWI Working Papers with number 2012-1.

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Length: 35 pages
Date of creation: 09 Jan 2012
Date of revision:
Handle: RePEc:ris:ewikln:2012_001
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  1. George B. Dantzig, 1955. "Linear Programming under Uncertainty," Management Science, INFORMS, vol. 1(3-4), pages 197-206, 04-07.
  2. Neuhoff, Karsten & Ehrenmann, Andreas & Butler, Lucy & Cust, Jim & Hoexter, Harriet & Keats, Kim & Kreczko, Adam & Sinden, Graham, 2008. "Space and time: Wind in an investment planning model," Energy Economics, Elsevier, vol. 30(4), pages 1990-2008, July.
  3. Beenstock, Michael, 1995. "The stochastic economics of windpower," Energy Economics, Elsevier, vol. 17(1), pages 27-37, January.
  4. Möst, Dominik & Keles, Dogan, 2010. "A survey of stochastic modelling approaches for liberalised electricity markets," European Journal of Operational Research, Elsevier, vol. 207(2), pages 543-556, December.
  5. Richter, Jan, 2011. "DIMENSION - A Dispatch and Investment Model for European Electricity Markets," EWI Working Papers 2011-3, Energiewirtschaftliches Institut an der Universitaet zu Koeln.
  6. DeCarolis, Joseph F. & Keith, David W., 2006. "The economics of large-scale wind power in a carbon constrained world," Energy Policy, Elsevier, vol. 34(4), pages 395-410, March.
  7. Heide, Dominik & von Bremen, Lueder & Greiner, Martin & Hoffmann, Clemens & Speckmann, Markus & Bofinger, Stefan, 2010. "Seasonal optimal mix of wind and solar power in a future, highly renewable Europe," Renewable Energy, Elsevier, vol. 35(11), pages 2483-2489.
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