{'fraction_non_alphanumeric': 0.022818791946308724, 'fraction_numerical': 0.0, 'mean_word_length': 5.601769911504425, '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}}
Variance-Aware Optimal Power Flow: Addressing the Tradeoff Between Cost, Security, and Variability

Recent work has studied modifications to DC optimal power flow (DC-OPF) computations so as to better account for risk arising as a result of stochastic variation in the output of renewable sources. Typically such modifications rely on mathematical constructs, such as chance-constraints, that can still yield convex formulations. However, numerical simulations show that the computed policies can translate into power flow patterns with high variance. We introduce a number of convex variants of OPF that tradeoff variance and cost minimization, describe practical algorithms for the solution of such problems, and present numerical experiments.