Characterising wind power extremes over Kenya using an enhanced process-based reanalysis-driven model

Ang'u, C., Bloomfield, H. C., Hirons, L. C. ORCID: https://orcid.org/0000-0002-1189-7576, Woolnough, S. J. ORCID: https://orcid.org/0000-0003-0500-8514, Brayshaw, D. J. ORCID: https://orcid.org/0000-0002-3927-4362, Gitau, W., Masukwedza, G. I. T., Mutemi, J., Ochieng, W., Olago, D., Oludhe, C. and Wainwright, C. M. ORCID: https://orcid.org/0000-0002-7311-7846 (2026) Characterising wind power extremes over Kenya using an enhanced process-based reanalysis-driven model. Renewable Energy, 273. 126048. ISSN 1879-0682 doi: 10.1016/j.renene.2026.126048

Abstract/Summary

This study presents a robust framework for addressing systematic biases in the ERA5 wind speeds to model long-term, high-resolution wind energy and characterise wind power extremes in data-sparse regions. By integrating Weibull Quantile Mapping, hub-height extrapolation, and dynamic efficiency, the study models hourly output for three Kenyan wind farms: Lake Turkana Wind Power, Kipeto, and Ngong Hills. The model significantly reduced Mean Bias Error and Root Mean Square Error in the reanalysis while preserving temporal rank correlations. The reanalysis-driven model captures the fundamental variability of wind power generation. Persistence and ramp diagnostics using Threshold-Duration Frequency analysis reveal that: LTWP exhibits low variability, with high-output events (>80% Capacity Factor) sustained for durations exceeding 14 days, contrasting with the frequent multi-day droughts and pronounced ramping typical of mid-latitude wind turbine fleets. Kipeto and Ngong Hills sites exhibit strong diurnal cycling, necessitating short-term storage rather than seasonal balancing. While LTWP frequently undergoes large shifts (>60% Δ Capacity Factor) over diurnal cycles, extreme volatility at shorter timescales (3-hours) is heavily damped. This framework demonstrates a transferable process for realistic wind power modelling in data-sparse environments, supporting regional energy planning and integration of renewables into developing power systems.

Item Type	Article
URI	https://centaur.reading.ac.uk/id/eprint/130789
Identification Number/DOI	10.1016/j.renene.2026.126048
Refereed	Yes
Divisions	Science > School of Mathematical, Physical and Computational Sciences > NCAS Science > School of Mathematical, Physical and Computational Sciences > Department of Meteorology
Publisher	Elsevier
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