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dc.contributor.advisorAravinthan, Visvakumar
dc.contributor.authorZhao, Long
dc.date.accessioned2015-03-02T21:44:43Z
dc.date.available2015-03-02T21:44:43Z
dc.date.issued2014-07
dc.identifier.othert14061
dc.identifier.urihttp://hdl.handle.net/10057/11131
dc.descriptionThesis (M.S.)--Wichita State University, College of Engineering, Dept. of Electrical Engineering and Computer Science
dc.description.abstractA new residential-level load management system in the presence of real-time pricing for future power systems with smart grid technologies and concepts was developed in this work. In the first part of this research, a new strategy combining demand response (DR) and vehicle-to-grid (V2G) application was designed. Since DR will inevitably affect a customer's comfort level and the V2G application will significantly degrade the life of a vehicle battery, it was hypothesized that the two together could compensate for their individual disadvantages. By reducing peak loading, a customer's comfort level and electric vehicle (EV) battery degradation are considered. Based on the first part of this work, a new control model was developed to reduce load-forecasting errors so that real-time demand would be as close as possible to the forecasting load. Currently, making a forecasted demand equal to a real-time demand is a very challenging task in power systems. Sometimes the unexpected load causes serious problems in power systems. From a technical perspective, overloading could affect the lifespan of infrastructures significantly at the distribution level. From an economical perspective, unexpected load demand could increase the market risks for utility companies, and eventually, the electricity bill of customers could be increased. If utility companies could offer an incentive model that customers could directly control, then this solution could help both utilities and customers reduce costs.
dc.format.extentx, 34 p.
dc.language.isoen_US
dc.publisherWichita State University
dc.rightsCopyright 2014 Long Zhao
dc.subject.lcshElectronic dissertations
dc.titleResidential-level smart distribution systems with integration of demand response and electric vehicles using aggregators
dc.typeThesis


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