Ghias Nezhad Omran, Nima2014-08-262014-08-262014-03N. 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 2014http://hdl.handle.net/1993/23891Temporal 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.engvehicular charging demandLocation-based predictionoff-home charging stationfuzzy decision makingDecentralized controlMulti-agent systemResidential distribution networkFlexible loaddoctoral thesis