Power grid planning for vehicular demand: forecasting and decentralized control

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Ghias Nezhad Omran, Nima
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IEEE Transactions on Smart Grid
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.
vehicular charging demand, Location-based prediction, off-home charging station, fuzzy decision making, Decentralized control, Multi-agent system, Residential distribution network, Flexible load
N. Ghiasnezhad, and S. Filizadeh, “Location-based forecasting of vehicular charging load on the distribution system,” IEEE Trans. Smart Grid, vol. 5, no. 2, pp. 632-641, March 2014