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

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-the-art 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.