{'fraction_non_alphanumeric': 0.010510510510510511, 'fraction_numerical': 0.0, 'mean_word_length': 5.942708333333333, '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}}
Probabilistic Impact Analysis of Residential Electric Vehicle Charging on Distribution Transformers

Transportation electrification by means of increasing plug-in electric vehicle (PEV) adoption presents both opportunities and challenges to power system operators. Although the electrification of transportation offers power utilities the opportunity to increase total load served in an era of flat year-over-year electricity sales, the sustainable integration of PEVs over the long term requires improved understanding of overload risks on existing power distribution systems. In this context, we propose a probabilistic model to study the impacts of residential PEV charging load on distribution systems. More specifically, the proposed model captures the uncertainty of PEV charging load and the uncertainty of residential customer load through Poisson and Gaussian distribution models respectively. These models are then utilized in a Monte Carlo simulation approach to calculate the statistical significance of resulting total load profiles and ultimately quantify the overloading risk levels on distribution transformers. The proposed model is utilized to study multiple PEV integration scenarios using residential customer load profile data of Salt Lake City and PEV charging load data available by Idaho National Laboratory.