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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

Author

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  • 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)

Abstract

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.

Suggested Citation

  • 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 (EWI).
    • Handle: RePEc:ris:ewikln:2012_001
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Nagl, Stephan, 2013. "The Effect of Weather Uncertainty on the Financial Risk of Green Electricity Producers under Various Renewable Policies," EWI Working Papers 2013-15, Energiewirtschaftliches Institut an der Universitaet zu Koeln (EWI).
    2. Fürsch, Michaela & Hagspiel, Simeon & Jägemann, Cosima & Nagl, Stephan & Lindenberger, Dietmar & Tröster, Eckehard, 2013. "The role of grid extensions in a cost-efficient transformation of the European electricity system until 2050," Applied Energy, Elsevier, vol. 104(C), pages 642-652.
    3. Zerrahn, Alexander & Schill, Wolf-Peter, 2017. "Long-run power storage requirements for high shares of renewables: review and a new model," Renewable and Sustainable Energy Reviews, Elsevier, vol. 79(C), pages 1518-1534.
    4. Stephan Nagl, Michaela Fursch, and Dietmar Lindenberger, 2013. "The Costs of Electricity Systems with a High Share of Fluctuating Renewables: A Stochastic Investment and Dispatch Optimization Model for Europe," The Energy Journal, International Association for Energy Economics, vol. 0(Number 4).
    5. Maulén, Lucas & Castro, Margarita & Lorca, Álvaro & Negrete-Pincetic, Matías, 2023. "Optimization-based expansion planning for power and hydrogen systems with feedback from a unit commitment model," Applied Energy, Elsevier, vol. 343(C).
    6. Lion Hirth, 2015. "The Optimal Share of Variable Renewables: How the Variability of Wind and Solar Power affects their Welfare-optimal Deployment," The Energy Journal, International Association for Energy Economics, vol. 0(Number 1).
    7. Lion Hirth & Falko Ueckerdt & Ottmar Edenhofer, 2014. "Why Wind Is Not Coal: On the Economics of Electricity," Working Papers 2014.39, Fondazione Eni Enrico Mattei.
    8. Jägemann, Cosima, 2012. "Decarbonizing Europe’s power sector by 2050 - Analyzing the implications of alternative decarbonization pathways," EWI Working Papers 2012-13, Energiewirtschaftliches Institut an der Universitaet zu Koeln (EWI).
    9. N. Gülpınar & F. Oliveira, 2014. "Analysis of relationship between forward and spot markets in oligopolies under demand and cost uncertainties," Computational Management Science, Springer, vol. 11(3), pages 267-283, July.
    10. Nagl, Stephan, 2013. "Prices vs. Quantities: Incentives for Renewable Power Generation - Numerical Analysis for the European Power Market," EWI Working Papers 2013-4, Energiewirtschaftliches Institut an der Universitaet zu Koeln (EWI).
    11. repec:dui:wpaper:1305 is not listed on IDEAS
    12. Jonas Egerer and Wolf-Peter Schill, 2014. "Power System Transformation toward Renewables: Investment Scenarios for Germany," Economics of Energy & Environmental Policy, International Association for Energy Economics, vol. 0(Number 2).
    13. Unteutsch, Michaela, 2014. "Who Benefits from Cooperation? - A Numerical Analysis of Redistribution Effects Resulting from Cooperation in European RES-E Support," EWI Working Papers 2014-2, Energiewirtschaftliches Institut an der Universitaet zu Koeln (EWI).
    14. Spiecker, Stephan & Weber, Christoph, 2014. "The future of the European electricity system and the impact of fluctuating renewable energy – A scenario analysis," Energy Policy, Elsevier, vol. 65(C), pages 185-197.
    15. Fürsch, Michaela & Malischek, Raimund & Lindenberger, Dietmar, 2012. "Der Merit-Order-Effekt der erneuerbaren Energien - Analyse der kurzen und langen Frist," EWI Working Papers 2012-14, Energiewirtschaftliches Institut an der Universitaet zu Koeln (EWI).
    16. Seljom, Pernille & Tomasgard, Asgeir, 2015. "Short-term uncertainty in long-term energy system models — A case study of wind power in Denmark," Energy Economics, Elsevier, vol. 49(C), pages 157-167.
    17. Seljom, Pernille & Tomasgard, Asgeir, 2017. "The impact of policy actions and future energy prices on the cost-optimal development of the energy system in Norway and Sweden," Energy Policy, Elsevier, vol. 106(C), pages 85-102.
    18. Malischek, Raimund & Trüby, Johannes, 2016. "The future of nuclear power in France: an analysis of the costs of phasing-out," Energy, Elsevier, vol. 116(P1), pages 908-921.
    19. Jägemann, Cosima, 2014. "A note on the inefficiency of technology- and region-specific renewable energy support - The German case," EWI Working Papers 2014-5, Energiewirtschaftliches Institut an der Universitaet zu Koeln (EWI).
    20. Andreas Schröder & Maximilian Bracke, 2012. "Integrated Electricity Generation Expansion and Transmission Capacity Planning: An Application to the Central European Region," Discussion Papers of DIW Berlin 1250, DIW Berlin, German Institute for Economic Research.

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    More about this item

    Keywords

    Stochastic programming; electricity; renewable energy;
    All these keywords.

    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
    • Q40 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - General

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