Improved battery storage systems modeling for predictive energy management applications

dc.contributor.author Ricardo Silva en
dc.contributor.author Gouveia C. en
dc.contributor.author Carvalho L. en
dc.contributor.author Jorge Correia Pereira en
dc.contributor.other 1809 en
dc.contributor.other 7343 en
dc.date.accessioned 2023-05-04T08:53:22Z
dc.date.available 2023-05-04T08:53:22Z
dc.date.issued 2022 en
dc.description.abstract This paper presents a model predictive control (MPC) framework for battery energy storage systems (BESS) management considering models for battery degradation, system efficiency and V-I characteristics. The optimization framework has been tested for microgrids with different renewable generation and load mix considering several operation strategies. A comparison for one-year simulations between the proposed model and a naïve BESS model, show an increase in computation times that still allows the application of the framework for real-time control. Furthermore, a trade-off between financial revenue and reduced BESS degradation was evaluated for the yearly simulation, considering the degradation model proposed. Results show that a conservative BESS usage strategy can have a high impact on the asset's lifetime and on the expected system revenues, depending on factors such as the objective function and the degradation threshold considered. © 2022 IEEE. en
dc.identifier P-00X-KG2 en
dc.identifier.uri http://dx.doi.org/10.1109/isgt-europe54678.2022.9960620 en
dc.identifier.uri https://repositorio.inesctec.pt/handle/123456789/13707
dc.language eng en
dc.rights info:eu-repo/semantics/openAccess en
dc.title Improved battery storage systems modeling for predictive energy management applications en
dc.type en
dc.type Publication en
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