{'fraction_non_alphanumeric': 0.013059701492537313, 'fraction_numerical': 0.0018656716417910447, 'mean_word_length': 5.6234567901234565, '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}}
A computational method for optimizing storage placement to maximize power network reliability

The intermittent nature of renewable energy sources challenges the power network reliability. However, these challenges can be alleviated by incorporating energy storage devices into the network. We develop a computational technique which can find the optimal storage placement in the network with stochastic power injections, subject to minimizing a reliability index: the probability of a line current violation. We use the simulated annealing algorithm to minimize this probability under the variation of storage locations and capacities in the network, keeping the total storage capacity constant. In order to estimate the small probabilities of line current violations we use the splitting technique of rare-event simulation. We construct an appropriate importance function for splitting which enhances the efficiency of the probability estimator compared to the conventional Crude Monte Carlo estimator. As an illustration, we apply our method to the IEEE-14 bus network.