Investigation of impacts of plug-in hybrid electric vehicles’ stochastic characteristics modeling on smart grid reliability under different charging scenarios

Abstract

Although it is essential to investigate the impacts of the analytical modeling of plug-in hybrid electric vehicles (PHEVs) for reliability and adequacy evaluation of smart grids, this issue has received less attention. This article tries to develop a new analytical reliability and adequacy model of smart grids, considering the precise model of PHEVs. Until now, no solution has been reported to determine the appropriate number of PHEVs’ states in the analytical reliability evaluation methods. In this paper, a novel framework is developed to determine the suitable number of PHEVs’ states that simultaneously guarantees the speed and accuracy of the reliability calculations. The Monte Carlo simulation (MCS) is used to validate the proposed reliability evaluation method of smart grids using the developed analytical model of PHEVs. In addition, the impacts of different stochastic parameters on the adequacy evaluation of PHEVs, such as distance driven, departure time, and arrival time are studied by performing a variety of sensitivity analyses under different charging scenarios. The proposed method is applied to an actual electric distribution network of Kashan, which is located in the Isfahan province distribution company. The comparative test results illustrate the advantages of the introduced analytical reliability evaluation framework. One point that claims attention is the importance of distance driven’s impact and its state reduction in comparison to the impact of arrival time or departure time. Test results imply that reliability calculations of smart grids could be accelerated significantly by investigating the suitable number of discretized states of PHEVs’ characteristics.