Abstract:This study develops a stochastic-systems approach in modeling vehicle-grid integration (VGI), where load management strategies can be compared in terms of their economic value to plug-in electric vehicle (PEV) consumers and their local utility companies. The proposed methodology is demonstrated in an assessment of VGI for the Sacramento Municipal Utility District (SMUD) in California. Monte-Carlo simulations have been performed to randomly assign PEV charging characteristics of the households based on given statistical distributions. Consumer adoption of time-of-use (TOU) rates is modeled as an optimization problem where consumers seek the earliest PEV charge start time among the charge schedules resulting lowest cost and satisfying their transportation needs. The preliminary results show that, considering today’s grid system, the deployment of 60,000 PEVs in Sacramento Region will have significant but manageable impacts. These impacts included increasing annual peak demand by 86MWs (%5), and overloading up to 101 neighborhood transformers in the distribution system. On the other hand, adopting proper TOU rates presents a high potential for minimizing these negative impacts of widespread PEV deployment on the grid. The proposed methodology provided several improvements to the VGI modeling literature. These improvements included combining assessments for generation and distribution systems in the same model, and advancing uncertainty analysis for the PEV consumer behavior with considering realworld data sets.