{'fraction_non_alphanumeric': 0.012987012987012988, 'fraction_numerical': 0.0, 'mean_word_length': 5.027932960893855, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Partition-Combine Uncertainty Set for Robust Unit Commitment

The construction of the uncertainty set is a key aspect of robust unit commitment (RUC) for the power systems with wind power integrated. The existing uncertainty sets based on the convex hull of historical data are limited to the specific shape of the historical data set or with high computational burden. In this letter, we propose the partition-combine method to build the minimal uncertainty set with the irregularly-distributed historical data. The partition of the box set is used to reduce the size of the uncertainty so that the set can properly adapt the data provided and is no longer limited to a specific shape. The combination of non-empty subsets is used to formulate the model of uncertainty set instead of the extreme points. The scale of uncertainty variables is reduced with quick identification of inner subsets which contain no extreme points. The simulation results validated the enhanced performances of the proposed method from both the conservativeness and computational burden perspectives.