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Cost Uncertainties in Energy System Optimization Models: A Quadratic Programming Approach for Avoiding Penny Switching Effects

Author

Listed:
  • Peter Lopion

    (Institute of Electrochemical Process Engineering (IEK-3), Forschungszentrum Jülich GmbH, Wilhelm-Johnen-Str., 52428 Jülich, Germany)

  • Peter Markewitz

    (Institute of Electrochemical Process Engineering (IEK-3), Forschungszentrum Jülich GmbH, Wilhelm-Johnen-Str., 52428 Jülich, Germany)

  • Detlef Stolten

    (Institute of Electrochemical Process Engineering (IEK-3), Forschungszentrum Jülich GmbH, Wilhelm-Johnen-Str., 52428 Jülich, Germany
    Chair of fuel cells, RWTH Aachen University, c/o Institute of Electrochemical Process Engineering (IEK-3), Forschungszentrum Jülich GmbH, Wilhelm-Johnen-Str., 52428 Jülich, Germany)

  • Martin Robinius

    (Institute of Electrochemical Process Engineering (IEK-3), Forschungszentrum Jülich GmbH, Wilhelm-Johnen-Str., 52428 Jülich, Germany)

Abstract
Designing the future energy supply in accordance with ambitious climate change mitigation goals is a challenging issue. Common tools for planning and calculating future investments in renewable and sustainable technologies are often linear energy system models based on cost optimization. However, input data and the underlying assumptions of future developments are subject to uncertainties that negatively affect the robustness of results. This paper introduces a quadratic programming approach to modifying linear, bottom-up energy system optimization models to take cost uncertainties into account. This is accomplished by implementing specific investment costs as a function of the installed capacity of each technology. In contrast to established approaches such as stochastic programming or Monte Carlo simulation, the computation time of the quadratic programming approach is only slightly higher than that of linear programming. The model’s outcomes were found to show a wider range as well as a more robust allocation of the considered technologies than the linear model equivalent.

Suggested Citation

  • Peter Lopion & Peter Markewitz & Detlef Stolten & Martin Robinius, 2019. "Cost Uncertainties in Energy System Optimization Models: A Quadratic Programming Approach for Avoiding Penny Switching Effects," Energies, MDPI, vol. 12(20), pages 1-12, October.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:20:p:4006-:d:278843
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    References listed on IDEAS

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    Cited by:

    1. 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).
    2. Maximilian Hoffmann & Leander Kotzur & Detlef Stolten & Martin Robinius, 2020. "A Review on Time Series Aggregation Methods for Energy System Models," Energies, MDPI, vol. 13(3), pages 1-61, February.
    3. Leonard Goke & Jens Weibezahn & Christian von Hirschhausen, 2021. "A collective blueprint, not a crystal ball: How expectations and participation shape long-term energy scenarios," Papers 2112.04821, arXiv.org, revised Dec 2022.
    4. Lohr, C. & Schlemminger, M. & Peterssen, F. & Bensmann, A. & Niepelt, R. & Brendel, R. & Hanke-Rauschenbach, R., 2022. "Spatial concentration of renewables in energy system optimization models," Renewable Energy, Elsevier, vol. 198(C), pages 144-154.

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