A4 Conference proceedings

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


Open Access publication

Publication Details
Authors: Mashlakov Aleksei, Honkapuro Samuli, Tikka Ville, Kaarna Arto, Lensu Lasse
Publication year: 2019
Language: English
Related Journal or Series Information: International Conference On The European Energy Market
Title of parent publication: 2019 16th International Conference on the European Energy Market (EEM)
Journal acronym: EEM
ISBN: 978-1-7281-1258-9
eISBN: 978-1-7281-1257-2
ISSN: 2165-4077
eISSN: 2165-4093
JUFO-Level of this publication: 1
Open Access: Open Access publication
Location of the parallel saved publication: http://urn.fi/URN:NBN:fi-fe2019120245120

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-minute- and day-ahead time scales while demonstrating a comparable performance for the hour-ahead time scale.


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