Temporal and spatial distribution of incoming vehicular charging demand is a significant challenge for the future planning of power systems. In this thesis the vehicular loading is-sue is categorized into two classes of stationary and mobile; they are then addressed in two phases.
The mobile vehicular load is investigated first; a location-based forecasting algorithm for the charging demand of plug-in electric vehicles at potential off-home charging stations is proposed and implemented for real-world case-studies. The result of this part of the re-search is essential to realize the scale of fortification required for a power grid to handle vehicular charging demand at public charging stations.
In the second phase of the thesis, a novel decentralized control strategy for scheduling vehicular charging demand at residential distribution networks is developed. The per-formance of the proposed algorithm is then evaluated on a sample test feeder employing real-world driving data. The proposed charging scheduling algorithm will significantly postpone the necessity for upgrading the assets of the network while effectively fulfilling customers’ transportation requirements and preferences. / October 2014
| Identifer | oai:union.ndltd.org:MANITOBA/oai:mspace.lib.umanitoba.ca:1993/23891 |
| Date | 03 1900 |
| Creators | Ghias Nezhad Omran, Nima |
| Contributors | Filizadeh, Shaahin (Electrical and Computer Engineering), Rajapakse, Atula (Electrical and Computer Engineering) Leblanc, Alex (Statistics) Crow, Mariesa L. (Electrical and Computer Engineering, Missouri University of Science and Technology) |
| Publisher | IEEE Transactions on Smart Grid |
| Source Sets | University of Manitoba Canada |
| Detected Language | English |
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