An Agent-based Platform for Demand Response Implementation in Smart Buildings

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dc.contributor.authorKhamphanchai, Warodomen
dc.contributor.committeechairRahman, Saifuren
dc.contributor.committeememberBroadwater, Robert P.en
dc.contributor.committeememberHaghighat, Alirezaen
dc.contributor.committeememberPipattanasomporn, Manisaen
dc.contributor.committeememberClancy, Thomas Charles IIIen
dc.contributor.committeememberAl-Durra, Ahmed Abaden
dc.contributor.departmentElectrical and Computer Engineeringen
dc.date.accessioned2016-04-29T08:00:22Zen
dc.date.available2016-04-29T08:00:22Zen
dc.date.issued2016-04-28en
dc.description.abstractThe efficiency, security and resiliency are very important factors for the operation of a distribution power system. Taking into account customer demand and energy resource constraints, electric utilities not only need to provide reliable services but also need to operate a power grid as efficiently as possible. The objective of this dissertation is to design, develop and deploy the Multi-Agent Systems (MAS) - together with control algorithms - that enable demand response (DR) implementation at the customer level, focusing on both residential and commercial customers. For residential applications, the main objective is to propose an approach for a smart distribution transformer management. The DR objective at a distribution transformer is to ensure that the instantaneous power demand at a distribution transformer is kept below a certain demand limit while impacts of demand restrike are minimized. The DR objectives at residential homes are to secure critical loads, mitigate occupant comfort violation, and minimize appliance run-time after a DR event. For commercial applications, the goal is to propose a MAS architecture and platform that help facilitate the implementation of a Critical Peak Pricing (CPP) program. Main objectives of the proposed DR algorithm are to minimize power demand and energy consumption during a period that a CPP event is called out, to minimize occupant comfort violation, to minimize impacts of demand restrike after a CPP event, as well as to control the device operation to avoid restrikes. Overall, this study provides an insight into the design and implementation of MAS, together with associated control algorithms for DR implementation in smart buildings. The proposed approaches can serve as alternative solutions to the current practices of electric utilities to engage end-use customers to participate in DR programs where occupancy level, tenant comfort condition and preference, as well as controllable devices and sensors are taken into account in both simulated and real-world environments. Research findings show that the proposed DR algorithms can perform effectively and efficiently during a DR event in residential homes and during the CPP event in commercial buildings.en
dc.description.degreePh. D.en
dc.format.mediumETDen
dc.identifier.othervt_gsexam:7444en
dc.identifier.urihttp://hdl.handle.net/10919/70869en
dc.publisherVirginia Techen
dc.rightsIn Copyrighten
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.subjectDemand Responseen
dc.subjectHome Energy Management Systemen
dc.subjectBuilding Energy Management Systemen
dc.subjectInternet of Thingsen
dc.subjectMulti-Agent Systemsen
dc.titleAn Agent-based Platform for Demand Response Implementation in Smart Buildingsen
dc.typeDissertationen
thesis.degree.disciplineElectrical Engineeringen
thesis.degree.grantorVirginia Polytechnic Institute and State Universityen
thesis.degree.leveldoctoralen
thesis.degree.namePh. D.en

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