{'fraction_non_alphanumeric': 0.014084507042253521, 'fraction_numerical': 0.004225352112676056, 'mean_word_length': 5.459090909090909, '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}}
Parallel Computing and Multicore Platform to Assess Electric Vehicle Hosting Capacity

The impact generated by the massive connection of electric vehicles (EVs) in the distribution networks needs to be evaluated through methodologies that consider the stochastic behavior of the EV connections. These methodologies must allow obtaining representative results of annual simulations, considering different charging scenarios, with high resolution of time steps. This kind of analysis implies a combinatorial explosion of case studies with a high computational cost. This article presents a methodology aimed at reducing the simulation time required to determine the EV hosting capacity (HC), understood as the maximum EV penetration level that a distribution network could host before affecting its operating limits, through the co-simulation of hardware and software. Monte Carlo methods and quasi-static time series simulation concepts are used to model the connection features of EVs. The simulation and implementation are developed using parallel computing, and MATLAB and OpenDSS software. The methodology is tested on the IEEE 123 nodes system which shows the dependence of the HC indicator with the location and type EV chargers. Finally, the benefits of parallel computing in the proposed methodology are exposed to get a significant reduction in the execution time to evaluate the HC in the IEEE 123 nodes system.