  The introduction of more renewable energy sources into the energy system
increases the variability and weather dependence of electricity generation.
Power system simulations are used to assess the adequacy and reliability of the
electricity grid over decades, but often become computational intractable for
such long simulation periods with high technical detail. To alleviate this
computational burden, we investigate the use of outlier detection algorithms to
find periods of extreme renewable energy generation which enables detailed
modelling of the performance of power systems under these circumstances.
Specifically, we apply the Maximum Divergent Intervals (MDI) algorithm to power
generation time series that have been derived from ERA5 historical climate
reanalysis covering the period from 1950 through 2019. By applying the MDI
algorithm on these time series, we identified intervals of extreme low and high
energy production. To determine the outlierness of an interval different
divergence measures can be used. Where the cross-entropy measure results in
shorter and strongly peaking outliers, the unbiased Kullback-Leibler divergence
tends to detect longer and more persistent intervals. These intervals are
regarded as potential risks for the electricity grid by domain experts,
showcasing the capability of the MDI algorithm to detect critical events in
these time series. For the historical period analysed, we found no trend in
outlier intensity, or shift and lengthening of the outliers that could be
attributed to climate change. By applying MDI on climate model output, power
system modellers can investigate the adequacy and possible changes of risk for
the current and future electricity grid under a wider range of scenarios.
