Improved battery storage systems modeling for predictive energy management applications
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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