Imperial College London
Alternate

DrIainStaffell

Faculty of Natural Sciences, Centre for Environmental Policy

Senior Lecturer in Sustainable Energy
 
 
 
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Contact

 

+44 (0)20 7594 9570i.staffell

 
 
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Location

 

202Weeks BuildingSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Ward:2023:10.1016/j.esr.2023.101235,
author = {Ward, KR and Bamisile, O and Staffell, I},
doi = {10.1016/j.esr.2023.101235},
journal = {Energy Strategy Reviews},
title = {Time-averaged wind power data hides variability critical to renewables integration},
url = {http://dx.doi.org/10.1016/j.esr.2023.101235},
volume = {50},
year = {2023}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - Most publicly available wind data are aggregated to a temporal resolution of 30 or 60 min. This is adequate for some purposes, such as large-scale wind integration studies. However, the consequent loss of high-frequency power fluctuations from the data can significantly impact analysis on local and regional scales. The importance of this missing variability to the accurate assessment of renewables integration is increasingly being recognised as wind power is considered for energy autarky and local energy systems. Here, we investigate the statistics of the lost variability using two high-temporal-resolution datasets from France and the US. In particular, we focus on the likelihood that minimum and maximum thresholds are exceeded and illustrate the importance of sub-half-hourly variability for assessing sector-coupling applications, such as wind farm – electrolyser systems. We find that using half-hourly averaged turbine data can underestimate the occurrence of zero power by a factor of two. This matters if a wind turbine is coupled to an electrolyser, either directly or with only short-duration storage, because of dynamic operating constraints. The lower output limit means that half-hourly wind output could overestimate the hydrogen production and energy storage capabilities of alkaline electrolysers by up to 70 %.
AU  - Ward,KR
AU  - Bamisile,O
AU  - Staffell,I
DO  - 10.1016/j.esr.2023.101235
PY  - 2023///
SN  - 2211-467X
TI  - Time-averaged wind power data hides variability critical to renewables integration
T2  - Energy Strategy Reviews
UR  - http://dx.doi.org/10.1016/j.esr.2023.101235
UR  - http://hdl.handle.net/10044/1/107360
VL  - 50
ER  -
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