A1 Journal article (refereed), original research

Probabilistic Forecasting of Battery Energy Storage State-of-Charge under Primary Frequency Control


Open Access publication

Publication Details
Authors: Mashlakov Aleksei, Lensu Lasse, Kaarna Arto, Tikka Ville, Honkapuro Samuli
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Publication year: 2019
Language: English
Related Journal or Series Information: IEEE Journal on Selected Areas in Communications
ISSN: 0733-8716
JUFO-Level of this publication: 3
Open Access: Open Access publication
Location of the parallel saved publication: http://urn.fi/URN:NBN:fi-fe2019111137612

Abstract

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.


Last updated on 2020-20-03 at 10:03