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Industrial peak shaving with battery storage using a probabilistic forecasting approach: Economic evaluation of risk attitude

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  • Henni, Sarah
  • Becker, Jonas
  • Staudt, Philipp
  • vom Scheidt, Frederik
  • Weinhardt, Christof

Abstract

Industrial peak shaving is a regularly discussed application of battery storage. We introduce the notion of risk attitude in the context of joint industrial peak shaving and frequency containment reserve provision with battery storage. To this end, we combine a probabilistic quantile forecast with a rolling-horizon battery control mechanism. Probabilistic forecasts incorporate prediction uncertainty by generating a distribution of future load. An industrial consumer has an incentive to plan conservatively when reserving battery capacities for peak shaving, as a single missed peak can drive up annual electricity costs steeply in the presence of peak-load charges. However, this limits the potential use of battery storage capacity for other financially attractive applications. We find that extremely risk averse planning behavior can lead to a decrease of up to 10% in economic performance of a battery investment. This loss might be tolerated in exchange for the significantly reduced risk of missing a critical peak. Moreover, moderately risk averse planning behavior does not lead to financial losses in most cases and can even improves economic performance by up to 3% in certain of the evaluated cases.

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  • Henni, Sarah & Becker, Jonas & Staudt, Philipp & vom Scheidt, Frederik & Weinhardt, Christof, 2022. "Industrial peak shaving with battery storage using a probabilistic forecasting approach: Economic evaluation of risk attitude," Applied Energy, Elsevier, vol. 327(C).
    • Handle: RePEc:eee:appene:v:327:y:2022:i:c:s0306261922013459
    DOI: 10.1016/j.apenergy.2022.120088
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    2. Castillejo-Cuberos, A. & Cardemil, J.M. & Escobar, R., 2023. "Techno-economic assessment of photovoltaic plants considering high temporal resolution and non-linear dynamics of battery storage," Applied Energy, Elsevier, vol. 334(C).
    3. Roman V. Klyuev & Irbek D. Morgoev & Angelika D. Morgoeva & Oksana A. Gavrina & Nikita V. Martyushev & Egor A. Efremenkov & Qi Mengxu, 2022. "Methods of Forecasting Electric Energy Consumption: A Literature Review," Energies, MDPI, vol. 15(23), pages 1-33, November.

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