Power grid planning for vehicular demand: forecasting and decentralized control

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dc.contributor.authorGhias Nezhad Omran, Nima
dc.contributor.examiningcommitteeRajapakse, Atula (Electrical and Computer Engineering) Leblanc, Alex (Statistics) Crow, Mariesa L. (Electrical and Computer Engineering, Missouri University of Science and Technology)en_US
dc.contributor.supervisorFilizadeh, Shaahin (Electrical and Computer Engineering)en_US
dc.date.accessioned2014-08-26T21:22:46Z
dc.date.available2014-08-26T21:22:46Z
dc.date.issued2014-03en_US
dc.degree.disciplineElectrical and Computer Engineeringen_US
dc.degree.levelDoctor of Philosophy (Ph.D.)en_US
dc.description.abstractTemporal 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.en_US
dc.description.noteOctober 2014en_US
dc.identifier.citationN. 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 2014en_US
dc.identifier.urihttp://hdl.handle.net/1993/23891
dc.language.isoengen_US
dc.publisherIEEE Transactions on Smart Griden_US
dc.rightsopen accessen_US
dc.subjectvehicular charging demanden_US
dc.subjectLocation-based predictionen_US
dc.subjectoff-home charging stationen_US
dc.subjectfuzzy decision makingen_US
dc.subjectDecentralized controlen_US
dc.subjectMulti-agent systemen_US
dc.subjectResidential distribution networken_US
dc.subjectFlexible loaden_US
dc.titlePower grid planning for vehicular demand: forecasting and decentralized controlen_US
dc.typedoctoral thesisen_US
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