Date Approved

5-19-2023

Embargo Period

9-17-2023

Document Type

Dissertation

Degree Name

Doctor of Philosophy

Department

Electrical and Computer Engineering

College

Henry M. Rowan College of Engineering

Advisor

Jie Li, Ph.D.

Committee Member 1

Ying Tang, Ph.D.

Committee Member 2

Ben Wu, Ph.D.

Committee Member 3

Shen-Shyang Ho, Ph.D.

Committee Member 4

Tuyen Vu, Ph.D.

Keywords

Active Distribution Network, Microgrid, Operation and Control, Renewable Energy

Disciplines

Computer Engineering | Electrical and Computer Engineering | Engineering

Abstract

The growing integration of power-electronics converters enabled components causes low inertia in the evolving electric distribution networks, which also suffer from uncertainties due to renewable energy sources, electric demands, and anomalies caused by physical or cyber attacks, etc. These issues are addressed in this dissertation. First, a virtual synchronous generator (VSG) solution is provided for solar photovoltaics (PVs) to address the issues of low inertia and system uncertainties. Furthermore, for a campus AC microgrid, coordinated control of the PV-VSG and a combined heat and power (CHP) unit is proposed and validated. Second, for islanded AC microgrids composed of SGs and PVs, an improved three-layer predictive hierarchical power management framework is presented to provide economic operation and cyber-physical security while reducing uncertainties. This scheme providessuperior frequency regulation capability and maintains low system operating costs. Third, a decentralized strategy for coordinating adaptive controls of PVs and battery energy storage systems (BESSs) in islanded DC nanogrids is presented. Finally, for transient stability evaluation (TSE) of emerging electric distribution networks dominated by EV supercharging stations, a data-driven region of attraction (ROA) estimation approach is presented. The proposed data-driven method is more computationally efficient than traditional model-based methods, and it also allows for real-time ROA estimation for emerging electric distribution networks with complex dynamics.

Download

Share

COinS