THESIS
2018
xiv, 143 pages : illustrations ; 30 cm
Abstract
Recently, distributed battery energy storage systems (BESSs) has gained profound importance
due to the ever-growing penetration of distributed energy resources (e.g., rooftop
solar, wind turbines, etc.) in power grid. Electric vehicles (EVs), driven by carbon
emissions control and oil supply risks, are universally believed to be the future of transportation.
Hence, recent years have witnessed an urgent demand of establishing advanced
EV networks for supporting transportation electrification. The planning and operation
of distributed BESSs and EV networks, though possess different technical and economic
constraints, share a common bond through their dedication to the charging/discharging
operation of batteries. It is therefore an important and urgent research task to investigate...[
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Recently, distributed battery energy storage systems (BESSs) has gained profound importance
due to the ever-growing penetration of distributed energy resources (e.g., rooftop
solar, wind turbines, etc.) in power grid. Electric vehicles (EVs), driven by carbon
emissions control and oil supply risks, are universally believed to be the future of transportation.
Hence, recent years have witnessed an urgent demand of establishing advanced
EV networks for supporting transportation electrification. The planning and operation
of distributed BESSs and EV networks, though possess different technical and economic
constraints, share a common bond through their dedication to the charging/discharging
operation of batteries. It is therefore an important and urgent research task to investigate
new models and algorithms for the operation of batteries, and further apply them to the
planning and operation of distributed BESSs and EV networks in smart grid.
One of the central challenges confronting BESS investors/operators is that batteries
have high capital cost and the degradation of batteries is a very complicated process,
making it extremely difficult to estimate the economic value of a distributed BESS over
its entire lifetime. The first part of this thesis focuses on the design of a novel stochastic
model and algorithm that can efficiently quantify the exact relationship between batteries’
lifetime and specific operational trajectories, and further investigates the operational policy
that can characterize the optimal trade-off between achieving better economic value
and extending longer lifetime. The second part of this thesis focuses on queueing network
modeling, quality-of-service analysis and optimal scheduling of EV networks. Our
key contribution in the second part is the establishment of a mixed queueing network
model for EV battery swapping and charging stations, of which both the steady-state
and asymptotic performance are analytically derived. Based on this queueing model,
we further propose a computationally-efficient optimal charging strategy for scheduling a
centralized battery charging station that serves EVs based on battery swapping.
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