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Both slow residential and public fast charging (PFC) of electric vehicles (EVs) degrade the performance of power distribution networks (PDNs) in terms of distortion of the daily demand profile, deterioration of the bus voltage magnitude profile, and increased PDN losses. Optimal allocation of renewable-based distributed generation (RBDG) resources can alleviate the detrimental effects of EV charging on the PDN performance. To address this problem, in this article, RBDG units are optimally allocated in a 240-bus PDN using a metaheuristic, human-based driving training-based optimization (DTBO) technique. This is accomplished by generating the stochastic RBDG outputs and the PDN demand profiles with EV charging demand (EVCD) for different seasons, using appropriate probabilistic models. The EVCD, due to slow residential and Tesla Supercharger-based PFC Stations, is generated from the U.S.-based 2017 National Household Travel Survey and Fastned, a charging infrastructure provider, respectively. The windspeed and solar irradiance data are taken from the NASA website, while the wind turbine and solar PV module data are taken directly from the manufacturers’ datasheets. The results demonstrate that with optimal RBDG allocation using DTBO, the maximum reductions in the PDN losses and network voltage deviation are 54% and 73%, respectively.
Painuli et al. (Tue,) studied this question.