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Fast and Scalable Electrical Demand Scheduling for a Large Number of Residential Consumers

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thesis
posted on 2022-03-22, 04:31 authored by SHAN HE
This thesis addresses the questions of how to utilise a dynamic pricing scheme such as real-time pricing in demand response programs, and coordinate households to efficiently and effectively schedule demands for a large number of consumers, in order to reduce the overall peak demand and the electricity cost while maintaining consumers’ needs, satisfaction and privacy. It expands knowledge in the areas of modelling demand scheduling problems, solving large-scale demand scheduling problems, and distributed and iterative optimisation methods, as they relate to this topic.

History

Campus location

Australia

Principal supervisor

Campbell Wilson

Additional supervisor 1

Mark Wallace

Additional supervisor 2

Ariel Liebman

Year of Award

2022

Department, School or Centre

Data Science & Artificial Intelligence

Additional Institution or Organisation

Department of Data Science and Artificial Intelligent, Faculty of Information Technology

Course

Doctor of Philosophy

Degree Type

DOCTORATE

Faculty

Faculty of Information Technology