Method to Embed Behavioral Battery Model in Predictive Energy Management Systems

dc.contributor.authorSutter, Axel
dc.contributor.authorGorecki, Tomasz T.
dc.contributor.authorBhoir, Shubham S.
dc.date.accessioned2023-11-23T12:08:55Z
dc.date.available2023-11-23T12:08:55Z
dc.date.issued2023-10
dc.descriptionCopyright is with IEEE
dc.description.abstractIn the context of battery use in an energy system, accurate degradation models are crucial to improve economic efficiency, because these models help to ensure that the cost of degradation is lower than the benefits of using the battery. This paper proposes a novel method to integrate a complex empirical degradation model of a lithium-ion battery into the optimization of an Energy Management System (EMS). The model uses relaxed nonlinear equations of a multi-factor degradation model that are based on empirical test data. This approach allows the degradation model to be included in an optimization framework and to monitor the State-of-Health (SoH) of the battery. The paper presents a case study where the proposed degra dation model is applied in an arbitrage scenario to evaluate the benefits of the model in the optimization process. The results show that even a simple degradation model yields a positive benefit of 6,250 EUR/MWh/year, while a more complex model can achieve up to 10,000 EUR/MWh/year. These findings demonstrate the efficacy of the proposed model in achieving efficient battery use in an energy system.
dc.description.sponsorshipThis work has received funding from the Swiss Federal Office of Energy under project BATMAESTRO
dc.identifier.citationISGT Europe 2023, Grenoble, France
dc.identifier.urihttps://hdl.handle.net/20.500.12839/1286
dc.language.isoen
dc.titleMethod to Embed Behavioral Battery Model in Predictive Energy Management Systems
dc.typeProceedings Article
dc.type.csemdivisionsBU-V
dc.type.csemresearchareasDigital Energy
dc.type.csemresearchareasBatteries
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