@inproceedings{f3ddf9254e3c4e7cb4a805dc4767642d,
title = "Improved battery storage systems modeling for predictive energy management applications",
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 na{\"i}ve 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.",
keywords = "battery degradation, battery energy storage systems, linear model, microgrid, model predictive control",
author = "Ricardo Silva and Clara Gouveia and Leonel Carvalho and Jorge Pereira",
note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 2022 IEEE PES Innovative Smart Grid Technologies Conference Europe, ISGT-Europe 2022 ; Conference date: 10-10-2022 Through 12-10-2022",
year = "2022",
doi = "10.1109/ISGT-Europe54678.2022.9960620",
language = "English",
series = "IEEE PES Innovative Smart Grid Technologies Conference Europe",
publisher = "IEEE Computer Society",
booktitle = "Proceedings of 2022 IEEE PES Innovative Smart Grid Technologies Conference Europe, ISGT-Europe 2022",
address = "United States",
}