Thesis Examination Committee

Prof Ophelia K. C. TSUI, PHYS/HKUST (Chairperson)
Prof Danny Hin Kwok TSANG, ECE/HKUST (Thesis Supervisor)
Prof Alberto LEON-GARCIA, Department of Electrical and Computer Engineering, University of Toronto (External Examiner)
Prof Wai Ho MOW, ECE/HKUST
Prof Ling SHI, ECE/HKUST
Prof Xiangtong QI, IELM/HKUST

Abstract

Distributed energy storage (DES) has gained profound importance in modern power grid due to the ever-growing penetration of ubiquitous distributed energy resources. Electric vehicles (EVs), driven by carbon emissions control and oil supply risks, are universally projected to be the future of transportation. Therefore, recent years have witnessed an urgent demand of establishing advanced energy refueling infrastructure networks for supporting EVs.  The energy management of DES and the energy refueling of EVs share a common bond through their dedication to the charging/discharging operation of batteries. Towards this end, this thesis focuses on developing new models and algorithms that lead to computational-efficient and optimal solutions for the operation of batteries, and then apply them to the energy management of DES and energy refueling of EVs in smart grid. 
 
Due to battery's high capital cost and uncertain lifetime, one of the central challenges confronting DES operators is that the calendar aging of batteries is a very complicated process, making it extremely difficult to quantify the exact relationship between lifetime and charging/discharging trajectories. The first part of this thesis focuses on the design of novel stochastic models and algorithms that can efficiently quantify the economic value of a DES system over its entire lifetime, and further characterize the trade-off between achieving better economic value and extending longer lifetime. After understanding the optimal operation of DES in the first part, the second part of this thesis focuses on optimal planning and operation of energy refueling infrastructure networks of EVs. The key contribution of the second part is a mixed queueing network model for battery swapping and charging stations, of which both the steady-state and asymptotic performance are analytically derived. Based on this queueing model, we further investigate the optimal operational strategies of a centralized battery charging station that serves EVs based on battery swapping.