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Tool Chain for Deriving Consistent Storage Model Parameters for Optimization Models

Author

Listed:
  • Kristin Wode

    (Fraunhofer Institute for Environmental, Safety and Energy Technology UMSICHT, Osterfelder Straße 3, 46047 Oberhausen, Germany)

  • Tom Strube

    (Fraunhofer Institute for Software and Systems Engineering ISST, Emil-Figge-Straße 91, 44227 Dortmund, Germany)

  • Eva Schischke

    (Fraunhofer Institute for Environmental, Safety and Energy Technology UMSICHT, Osterfelder Straße 3, 46047 Oberhausen, Germany)

  • Markus Hadam

    (Fraunhofer Institute for Environmental, Safety and Energy Technology UMSICHT, Osterfelder Straße 3, 46047 Oberhausen, Germany)

  • Sarah Pabst

    (Fraunhofer Institute for Environmental, Safety and Energy Technology UMSICHT, Osterfelder Straße 3, 46047 Oberhausen, Germany)

  • Annedore Mittreiter

    (Fraunhofer Institute for Environmental, Safety and Energy Technology UMSICHT, Osterfelder Straße 3, 46047 Oberhausen, Germany)

Abstract

Since existing energy system models often represent storage behavior in a simplified way, in this work, a tool chain for deriving consistent storage model parameters for optimization models is developed. The aim of our research work is to identify what are non-negligible influences on the the technical characteristics and dynamic behavior of the storage, to quantify the effect of these influences, and represent these effects in the model. This paper describes the developed tool chain and presents its application using an example. The tool chain consists of the steps “parameter screening”, “dynamic simulation”, “regression analysis” and “refining optimization model”. It is investigated which parameters have an influence on the storage system (here pumped hydroelectric energy storage (PHES)), how the storage behavior is modeled, which influencing factors have a measurable effect on the system, and how these findings can be integrated into optimization models. The main finding is that in the case of PHES, the dependency of the charging and discharging efficiency on the power is significant, but no further influencing factor has to be considered for accurate modeling (0.946 ≤ R 2 ≤ 0.988) of the efficiency. It is concluded that the presented toolchain is suitable for other storage technologies as well, including the analysis of aging behavior.

Suggested Citation

  • Kristin Wode & Tom Strube & Eva Schischke & Markus Hadam & Sarah Pabst & Annedore Mittreiter, 2023. "Tool Chain for Deriving Consistent Storage Model Parameters for Optimization Models," Energies, MDPI, vol. 16(3), pages 1-22, February.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:3:p:1525-:d:1057095
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    References listed on IDEAS

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    2. Francesco Buffa & Simon Kemble & Giampaolo Manfrida & Adriano Milazzo, 2013. "Exergy and Exergoeconomic Model of a Ground-Based CAES Plant for Peak-Load Energy Production," Energies, MDPI, vol. 6(2), pages 1-18, February.
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    4. Schlachtberger, D.P. & Brown, T. & Schramm, S. & Greiner, M., 2017. "The benefits of cooperation in a highly renewable European electricity network," Energy, Elsevier, vol. 134(C), pages 469-481.
    5. Dariusz Gołȩbiewski & Tomasz Barszcz & Wioletta Skrodzka & Igor Wojnicki & Andrzej Bielecki, 2022. "A New Approach to Risk Management in the Power Industry Based on Systems Theory," Energies, MDPI, vol. 15(23), pages 1-19, November.
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