Multi-Timescale Forecasting of Battery Energy Storage State-of-Charge under Frequency Containment Reserve for Normal Operation

Abstract

Forecasting the state-of-charge changes of battery energy storage, anticipated from a provision of different services, can facilitate planning of its market participation strategy and leverage the maximum potential of its energy capacity. This paper provides a performance comparison study of multiple decision-tree and data-driven machine learning methods for point forecasts of the state-of-charge of battery energy storage under frequency containment reserve for normal operation on day-, hour-, and 15-minute-ahead basis. The battery state-of-charge data for the performance evaluation were simulated with a droop curve battery model based on the historical frequency data in the northern Europe synchronous area. The results show that the data-driven methods outperform the decision-tree based methods on the 15-minuteand day-ahead time scales while demonstrating a comparable performance for the hour-ahead time scale.