{'fraction_non_alphanumeric': 0.021181001283697046, 'fraction_numerical': 0.0025673940949935813, 'mean_word_length': 5.042635658914729, '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}}
Selecting the Optimal Charging Strategy of Electric Vehicles Using Simulation Based on Users’ Behavior Pattern Data

Low-carbon-emitting vehicles, such as plug-in hybrid electric vehicles (PHEVs) and electric vehicles (EVs), are becoming increasingly common as an emerging solution to environmental problems. As the number of EVs increases in a market, EV charging requires significantly more electricity use and charging strategies could have a significant impact on the peak load and electricity demand in the power system, as well as the electricity bill for each household. The purpose of this paper is to develop a model to find a user-specific optimal EV charging strategy using actual data of users’ behavior patterns in daily life. Toward this end, we deduce the time when each user utilizes electrical appliances and drives an EV based on actual user behavior data. The daily electricity consumption of a household by time is then calculated according to three different charging strategies. Comparing these results, we can obtain the optimal charging strategy for users to minimize their electricity bills. As a result of a simulation, it is found that, when each user follows her optimal charging strategy, the total electricity bill can be decreased by 35% and the peak-to-average ratio by 12% compared to when every user’s charging is uncontrolled. The results show that the charging EVs according to the optimal strategy obtained by our model would significantly affect not only the user’s electricity bill but also the whole power grid system.