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This paper presents a new distributed smart charging strategy for grid integration of plug-in electric vehicles (PEVs). The main goal is to smooth the daily grid load profile while ensuring that each PEV has a desired state of charge level at the time of departure. Communication and computational overhead, and PEV user privacy are also considered during the development of the proposed strategy. It consists of two stages: 1) an offline process to estimate a reference operating power level based on the forecasted mobility energy demand and base loading profile, and 2) a real-time process to determine the charging power for each PEV so that the aggregated load tracks the reference loading level. Tests are carried out both on primary and secondary distribution networks for different heuristic charging scenarios and PEV penetration levels. Results are compared to that of the optimal solution and other state-of-the-art techniques in terms of variance and peak values, and shown to be competitive. Finally, a real vehicle test implementation is done using a commercial-ofthe-shelf charging station and an electric vehicle.

This study focuses on the potential role of plugin electric vehicles (PEVs) as a distributed energy storage unit to provide peak demand minimization in power distribution systems. Vehicle-to-grid (V2G) power and currently available information transfer technology enables utility companies to use this stored energy. The V2G process is first formulated as an optimal control problem. Then, a two-stage V2G discharging control scheme is proposed. In the first stage, a desired level for peak shaving and duration for V2G service are determined off-line based on forecasted loading profile and PEV mobility model. In the second stage, the discharging rates of PEVs are dynamically adjusted in real time by considering the actual grid load and the characteristics of PEVs connected to the grid. The optimal and proposed V2G algorithms are tested using a real residential distribution transformer and PEV mobility data collected from field with different battery and charger ratings for heuristic user case scenarios. The peak shaving performance is assessed in terms of peak shaving index and peak load reduction. Proposed solution is shown to be competitive with the optimal solution while avoiding high computational loads. The impact of the V2G management strategy on the system loading at night is also analyzed by implementing an off-line charging scheduling algorithm.

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