{'fraction_non_alphanumeric': 0.02404965089216447, 'fraction_numerical': 0.0, 'mean_word_length': 5.231884057971015, '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}}
Traffic and grid-based parking lot allocation for PEVs considering driver behavioral model

In this study, optimal parking lots (PLs) for plug-in electric vehicles (PEVs) fleet are allocated on a feeder of a given electrical distribution network to minimize the total cost of the local distribution company (DISCO) considering driving patterns of the PEVs' drivers and their behavioral model in respect to the value of incentive (discount on charging fee) and the distance from the PL. The cost terms include investment cost for installing the PLs, maintenance cost of the PLs, the expense of incentive provided to the PEVs' drivers, the energy loss cost of the feeder, and the expected energy not supplied (EENS) cost over the operation period. In order to achieve realistic results, economic factors such as yearly inflation and interest rates and technical factors including hourly and daily variations of the load demand, yearly load growth of the feeder, and yearly growth rate of PEVs' application are considered in the PL allocation planning problem. The optimization problem is solved applying quantum-inspired simulated annealing algorithm (QSA). We demonstrate that the behavioral model of the drivers and their driving patterns can remarkably affect the outcomes of the problem.