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dc.contributor.advisorMao, Shiwenen_US
dc.contributor.authorWang, Yuen_US
dc.date.accessioned2015-07-02T13:45:19Z
dc.date.available2015-07-02T13:45:19Z
dc.date.issued2015-07-02
dc.identifier.urihttp://hdl.handle.net/10415/4674
dc.description.abstractSmart grid (SG) is regarded as the next generation power grid, which implements an innovative idea for a highly automated and integrated power system. The two-way energy and information flows in the SG, together with the smart devices, bring new perspectives to energy management and demand response. Meanwhile, innovative grid components, such as microgrid (MG) and electric vehicle, are emerging as new applications which bring many benefits as well as more chanllegens in SG. Therefore, we explore possible solutions to these chanllegening but interesting problems. In this dissertation, we first present an introduction of the SG, and the research involved in different areas of SG. We then investigate an online algorithm for energy distribution in a SG environment. The proposed online algorithm are quite general, suitable for a wide range of utility, cost and pricing functions. And it is asymptotically optimal without any future information. Following this, we then propose a distributed online algorithm. Comparing to the previous one, it solves the online problem in a distributed manner and mitigates the user privacy issue by not sharing user utility functions. Both algorithms are evaluated with trace-driven simulations and shown to outperform a benchmark scheme. We then propose a hierarchical power scheduling approach to optimally manage power trading, storage and distribution in a smart power grid with a Macrogrid and cooperative MGs. We develop online algorithms both for cooperative MGs and the Macrogrid. The proposed hierarchical power scheduling algorithms are evaluated with trace-driven simulations and are shown to outperform several existing schemes with considerable gains. Also, we also introduce the simultaneous inference for power generation forecasting from renewable energy resources. We then apply it for solar intensity prediction using a real trace of weather data, where the performance is demonstrated over existing approaches.en_US
dc.subjectElectrical Engineeringen_US
dc.titleAlgorithms for Optimal Energy Management in the Smart Griden_US
dc.typeDissertationen_US
dc.embargo.statusNOT_EMBARGOEDen_US
dc.contributor.committeeTugnait, Jitendraen_US
dc.contributor.committeeNelms, Roberten_US
dc.contributor.committeeLiao, Mingen_US


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