Graduation Semester and Year




Document Type


Degree Name

Master of Science in Electrical Engineering


Electrical Engineering

First Advisor

Ioannis D Schizas


Coordinated charging of plug-in electric vehicles (PEVs) can effectively mitigate the negative effects imposed on the power distribution grid by uncoordinated charging. Simultaneously, coordinated charging algorithms can accommodate the PEV user’s needs in terms of desired state-of-charge and charging time. In this work, the problem of tracking an arbitrary power profile, by coordinated charging of PEVs, is formulated as a discrete scheduling process, while accounting for the heterogeneity in charging rates and restricting the charging to only the maximum rated power. Then, a novel distributed algorithm is proposed to coordinate the PEV charging and eliminate the need for a central aggregator. It is guaranteed to track, and not exceed, the power profile imposed by the utility, while maximizing the user convenience. A formal optimality analysis is provided to show that the algorithm is asymptotically optimal in the case of homogeneous charging, while it has a very small optimality gap for the heterogeneous case. The work also discusses techniques for achieving aggregate load profiles with minimum variance and peak in both centralized and decentralized settings. A theoretical analysis that proves that peak minimization is inherently achieved as part of an variance minimization process has also been presented. The impact of interrupted and uninterrupted electric vehicle charging on the aggregated load profile has been explored. The variance of the aggregate load profile is used as the metric for measuring valley filling capability of the scheduling scenarios. It is shown, that for low penetration levels (up to 30%), interrupted charging strategies result in considerably lower variance values on the aggregated load profile as compared to the uninterrupted case. It is also shown that the policy used for deciding the PEV priority for scheduling has almost no impact on these variance values. All the proposed algorithms and the related analysis are accompanied by numerical simulations under realistic charging scenarios.


Electric vehicle charging, Distributed algorithm, Valley filling, Tracking


Electrical and Computer Engineering | Engineering


Degree granted by The University of Texas at Arlington