부산대학교 도서관

The remarkable increase in electric vehicles (EVs) has encouraged installing charging stations in parking lots (PLs). The aggregated energy of the EVs in the PLs can offer various benefits for the distribution systems. In this regard, the need to have an accurate model of the parking profiles of EVs is triggered to capture the exact available energy of EVs in PLs. This paper presents a probabilistic model for the main variables of a PL profile (i.e., arrival rates and parking durations) employing historical data. The data is categorized into different seasons and days to model their effect on the behavior of drivers. A probabilistic Markov Chain Monte Carlo simulation (MCMCS) is used to imitate the uncertainties in the EVs' arrival rates. Monte Carlo simulation (MCS) is applied to model the parking durations of EVs. The results show the ability of the model to generate an accurate synthetic parking profile to preserve the statistical properties of the historical data.