Forecasting error processing techniques and frequency domain decomposition for forecasting error compensation and renewable energy firming in hybrid systems

Yuqing Yang, Stephen Bremner, Chris Menictas and Merlinde Kay

Applied Energy, 2022, vol. 313, issue C, No S0306261922002045

Abstract: Battery storage can provide a wide range of services in power systems. This paper focuses on battery storage co-locating with a hybrid wind and solar system to achieve forecasting error compensation and renewable energy firming. Multiple forecasting error processing techniques are presented, including the exclusion of seasonal frequency components, along with scaling and shifting methods to remove the effect from battery round-trip efficiency. The impacts of these error processing techniques on reducing the size of the battery system required have been extensively investigated. To include a variety of forecasting errors, both day-ahead and hour-ahead forecasting were performed utilising four different forecasting methods, including persistence, Elman neural network, wavelet neural network and autoregressive integrated moving average (ARIMA). Numerical simulations demonstrate that the exclusion of seasonal components combined with the scaling method can substantially reduce the size of battery systems required for forecasting error compensation, whilst the shifting method makes a significant contribution to reducing the required battery size for renewable energy firming.

Keywords: Battery energy storage system; Battery size determination; Fourier analysis; Forecasting bias; Renewable energy firming; Forecasting error compensation (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0306261922002045
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:appene:v:313:y:2022:i:c:s0306261922002045

Ordering information: This journal article can be ordered from
http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/bibliographic
http://www.elsevier. ... 405891/bibliographic

DOI: 10.1016/j.apenergy.2022.118748

Access Statistics for this article

Applied Energy is currently edited by J. Yan

More articles in Applied Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().