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

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

Applied Energy, 2022, vol. 327, issue C, No S0306261922013459

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.

Keywords: Battery storage; Industrial peak shaving; Probabilistic forecast; Risk (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0306261922013459
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:appene:v:327:y:2022:i:c:s0306261922013459

Ordering information: This journal article can be ordered from
http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/bibliographic
http://www.elsevier. ... 405891/bibliographic

DOI: 10.1016/j.apenergy.2022.120088

Access Statistics for this article

Applied Energy is currently edited by J. Yan

More articles in Applied Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().