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.

翻译：将更多可再生能源引入能源系统增加了发电的变率和对天气的依赖性。电力系统仿真被用于评估电网在数十年内的充裕性和可靠性，但对于这种具有高技术细节的长期仿真周期，其计算往往变得难以处理。为减轻这一计算负担，我们研究了使用异常值检测算法来寻找极端可再生能源发电时段，从而能够详细建模电力系统在这些情况下的性能。具体而言，我们将最大发散区间（MDI）算法应用于基于ERA5历史气候再分析资料（覆盖1950年至2019年期间）得到的发电时间序列。通过在这些时间序列上应用MDI算法，我们识别出了极低和极高能源生产区间。为确定一个区间的异常程度，可使用不同的散度度量。其中交叉熵度量会导致较短且尖锐的异常峰值，而无偏Kullback-Leibler散度倾向于检测更长且更持久的区间。这些区间被领域专家视为电网的潜在风险，展示了MDI算法在这些时间序列中检测关键事件的能力。对于所分析的历史时期，我们未发现可归因于气候变化的异常强度趋势，也未见异常的偏移或拉长。通过将MDI应用于气候模型输出，电力系统建模者可以在更广泛的场景下研究当前和未来电网的充裕性及风险的潜在变化。