We propose and analyze the application of statistical functional depth metrics for the selection of extreme scenarios in day-ahead grid planning. Our primary motivation is screening of probabilistic scenarios for realized load and renewable generation, in order to identify scenarios most relevant for operational risk mitigation. To handle the high-dimensionality of the scenarios across asset classes and intra-day periods, we employ functional measures of depth to sub-select outlying scenarios that are most likely to be the riskiest for the grid operation. We investigate a range of functional depth measures, as well as a range of operational risks, including load shedding, operational costs, reserves shortfall and variable renewable energy curtailment. The effectiveness of the proposed screening approach is demonstrated through a case study on the realistic Texas-7k grid.

翻译：我们提出并分析了在日前电网规划中应用统计函数深度指标筛选极端场景的方法。主要动机是通过对实际负荷和可再生能源出力概率场景进行筛选，以识别对缓解运行风险最关键的场景。为处理跨资产类别和日内时段的场景高维特性，我们采用函数深度度量方法，筛选出对电网运行最具潜在风险的离群场景。我们研究了多种函数深度指标及多种运行风险类型，包括甩负荷、运行成本、备用缺额和可再生能源弃电。通过德克萨斯-7k实际电网案例研究，验证了所提出筛选方法的有效性。