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Strategies against shocks in power systems – An analysis for the case of Europe

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  • Nahmmacher, Paul
  • Schmid, Eva
  • Pahle, Michael
  • Knopf, Brigitte

Abstract

Electricity systems are constantly exposed to geopolitical, techno-economic and natural uncertainties that may endanger security of supply. Therefore, it is crucial that policy makers concerned about it consider a variety of possible futures — and not only the one that is perceived as the most likely. In particular, they should account for the possibility of sudden shocks in their decisions with the goal of making the system more “robust”. However, long-term power system models which are an important pillar of policy decision making are typically designed to determine the cost-minimal power system for a specific expected future; such a system is not necessarily the most robust one. By combining the classic investment optimization approach with the tools of Robust Decision Making we analyze the viability of different strategies that may potentially increase the robustness of a power system. For the case of the European power system we pursue a dedicated analysis with the European power system model LIMES-EU. Based on a total of more than 40,000 model runs, we find that strategies promoting the ability of countries to always produce at least 95% of their electricity demand domestically significantly help to reduce the loss of load in case of shocks. Such a strategy is not cost-optimal for the expected future without shocks; but the additional costs (about 0.1% of total system costs) are low compared to the benefits of significantly increasing the power system's robustness.

Suggested Citation

  • Nahmmacher, Paul & Schmid, Eva & Pahle, Michael & Knopf, Brigitte, 2016. "Strategies against shocks in power systems – An analysis for the case of Europe," Energy Economics, Elsevier, vol. 59(C), pages 455-465.
    • Handle: RePEc:eee:eneeco:v:59:y:2016:i:c:p:455-465
    DOI: 10.1016/j.eneco.2016.09.002
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    Cited by:

    1. Peter, Jakob, 2019. "How does climate change affect electricity system planning and optimal allocation of variable renewable energy?," Applied Energy, Elsevier, vol. 252(C), pages 1-1.
    2. Quentin Perrier, 2017. "The French Nuclear Bet," Working Papers 2017.18, Fondazione Eni Enrico Mattei.
    3. Sirin, Selahattin Murat & Erten, Ibrahim, 2022. "Price spikes, temporary price caps, and welfare effects of regulatory interventions on wholesale electricity markets," Energy Policy, Elsevier, vol. 163(C).
    4. Plaga, Leonie Sara & Bertsch, Valentin, 2023. "Methods for assessing climate uncertainty in energy system models — A systematic literature review," Applied Energy, Elsevier, vol. 331(C).
    5. Steinhäuser, J. Micha & Eisenack, Klaus, 2020. "How market design shapes the spatial distribution of power plant curtailment costs," Energy Policy, Elsevier, vol. 144(C).
    6. Perrier, Quentin, 2018. "The second French nuclear bet," Energy Economics, Elsevier, vol. 74(C), pages 858-877.
    7. Pilpola, Sannamari & Lund, Peter D., 2020. "Analyzing the effects of uncertainties on the modelling of low-carbon energy system pathways," Energy, Elsevier, vol. 201(C).
    8. Kosai, Shoki & Unesaki, Hironobu, 2020. "Short-term vs long-term reliance: Development of a novel approach for diversity of fuels for electricity in energy security," Applied Energy, Elsevier, vol. 262(C).
    9. Thomas Mobius & Mira Watermeyer & Oliver Grothe & Felix Musgens, 2023. "Enhancing Energy System Models Using Better Load Forecasts," Papers 2302.11017, arXiv.org.
    10. Abadie, Luis Ma & Chamorro, José M., 2019. "Physical adequacy of a power generation system: The case of Spain in the long term," Energy, Elsevier, vol. 166(C), pages 637-652.
    11. Quentin Perrier, 2017. "The French nuclear bet," CIRED Working Papers halshs-01487296, HAL.

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    More about this item

    Keywords

    European power system; Energy security; Investment optimization; Robust Decision Making;
    All these keywords.

    JEL classification:

    • Q41 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Demand and Supply; Prices
    • Q48 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Government Policy
    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty

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