• Energy Research

AbstractThe growing reliance on intermittent sources of renewable energy poses challenges for developing reliable electricity networks. This study has analysed the spatial and temporal characteristics of the UK's offshore wind energy production based on reanalysis of offshore wind speeds between 2000–2017, considering both the 2019 and future distribution of offshore wind farm capacity in UK waters. The extent and frequency of low wind power events are assessed, which are shown to vary seasonally. The correlation in output from offshore wind farm pairs reduces with increasing distance between them, albeit at a slower rate than has been observed for onshore wind farms. The conditional probability between regions of low energy production is also evaluated, which reveals that regions such as the English Channel are much more susceptible to simultaneous periods of low wind energy production if there is limited wind energy production in south‐west England or in the North Sea. Nevertheless, aggregating energy production from all offshore wind farms has a positive effect in reducing the likelihood of prolonged periods of country‐wide very low energy production. Comments are made on the possibilities for regional distribution of installed capacity to reduce the overall variability of wind energy and the relevance of these findings to electricity networks with increasing penetration of wind‐generated electricity.

Due to the growing proportion of wind energy in Great Britain’s energy mix, prolonged periods of low wind power generation have become a significant challenge for decarbonising the electricity system. As such, characterising drought severity and duration is important for ensuring the reliability of the electricity system. Employing concepts derived from hydrology, an extreme value analysis was carried out on wind drought events in Great Britain based on 72 years of ERA5 reanalysis data. The application of pooling procedures was found to be beneficial in robustly identifying wind droughts in cases where the capacity factor is not constantly below an arbitrary threshold. The sequent peak algorithm pooling was found to have particular relevance for electricity systems where energy storage technologies are used to compensate for low wind power generation. The Pearson-III distribution was identified as a suitable model to represent extreme wind droughts, while the Lognormal and Generalised Pareto distributions are also viable alternatives. Sustained periods of low wind power generation with a duration of 14 days were estimated to have a return period of five years and the longest event on record of approximately 26 days is expected to occur once every 100 years. The investigation of these wind droughts from a hydrological perspective has thus shown that they may not be particularly rare occurrences.

Low wind speed events represent one of the biggest challenges in fully de-carbonising the electricity system due to the growing proportion of wind energy in the UK energy mix. While reanalysis products are a useful tool to study the spatio-temporal characteristics of these occurrences, their performance and limitations should be understood prior to usage. In this study, hourly 10 m ERA5 reanalysis wind speed data were evaluated against in-situ wind speed measurements from 205 onshore and offshore observation stations around the UK. It was found that ERA5 has biases in mean wind speed of 0.166 m/s and −0.136 m/s for onshore and offshore domains respectively, and biases in hourly wind speed standard deviation of −0.354 m/s and −0.425 m/s for onshore and offshore domains respectively. Both errors are more pronounced in autumn and winter. These errors lead to an underestimation of the percentage frequency of short-duration low wind speed events. Furthermore, the findings suggest that the largest errors are from sites which are situated in coastal and mountainous regions where short-range topographical variability and local wind effects may not be resolved by ERA5. Despite these shortcomings, ERA5 nevertheless outperforms its global reanalysis counterparts in the UK domain and therefore, can provide valuable information in the context of low wind speed events prediction.