Probabilistic Forecasting of Battery Energy Storage State-of-Charge under Primary Frequency Control
Mashlakov, Aleksei; Lensu, Lasse; Kaarna, Arto; Tikka, Ville; Honkapuro, Samuli (2019-11-07)
Post-print / Final draft
Mashlakov, Aleksei
Lensu, Lasse
Kaarna, Arto
Tikka, Ville
Honkapuro, Samuli
07.11.2019
IEEE Journal on Selected Areas in Communications
IEEE
School of Energy Systems
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© 2019 IEEE
© 2019 IEEE
Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi-fe2019111137612
https://urn.fi/URN:NBN:fi-fe2019111137612
Tiivistelmä
Multi-service market optimization of battery energy storage system (BESS) requires assessing the forecasting uncertainty arising from coupled resources and processes. For the primary frequency control (PFC), which is one of the highest-value applications of BESS, this uncertainty is linked to the changes of BESS state-of-charge (SOC) under stochastic frequency variations. In order to quantify this uncertainty, this paper aims to exploit one of the recent achievements in the field of deep learning, i.e. multi-attention recurrent neural network (MARNN), for BESS SOC forecasting under PFC. Furthermore, we extend the MARNN model for probabilistic forecasting with a hybrid approach combining Mixture Density Networks and Monte Carlo dropout that incorporate the uncertainties of the data noise and the model parameters in the form of prediction interval (PI). The performance of the model is studied on BESS SOC datasets that are simulated based on real frequency measurements from three European synchronous areas in Great Britain, Continental Europe, and Northern Europe and validated by three PI evaluation indexes. Compared with the state-of-theart quantile regression algorithms, the proposed hybrid model performed well with respect to the coverage probability of PIs for the different regulatory environments of the PFC.
Lähdeviite
Mashlakov, A., Lensu, L., Kaarna, A., Tikka, V., Honkapuro, S. (2019). Probabilistic Forecasting of Battery Energy Storage State-of-Charge under Primary Frequency Control. IEEE Journal on Selected Areas in Communications. DOI: 10.1109/JSAC.2019.2952195
Kokoelmat
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