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Zhao Yang Dong

Researcher at University of New South Wales

Publications -  930
Citations -  33916

Zhao Yang Dong is an academic researcher from University of New South Wales. The author has contributed to research in topics: Electric power system & Electricity market. The author has an hindex of 77, co-authored 872 publications receiving 23835 citations. Previous affiliations of Zhao Yang Dong include University of Newcastle & University of Queensland.

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Proceedings ArticleDOI

Comprehensive solution of networked microgrid towards enhanced overload resiliency

TL;DR: This paper presents a comprehensive solution of overloading resiliency enhancement in a networked microgrid, where ac-dc inverter and dc-dc converter control is proposed with internal information exchange system in a distributed way to achieve the overload resiliencies.
Proceedings ArticleDOI

A composite sensitivity factor based method for networked distributed generation planning

TL;DR: A novel composite sensitivity factor based method (CSFBM) is proposed for optimizing locations and sizes of network owned DG units to decrease the losses and to improve the voltage stability simultaneously in a distribution network.
Journal ArticleDOI

Interactive Grid Synchronization-Based Virtual Synchronous Generator Control Scheme on Weak Grid Integration

TL;DR: In this paper , the authors proposed a novel interactive control scheme of voltage source converter (VSC) connected to weak grid, where a grid synchronization approach is performed using VSC side voltage transformed on rotating reference frame with respect to grid voltage, which is carried out based on lowest short circuit ratio (SCR) as approved by utilities such as Australian Energy Market Operator's system strength report.
Journal ArticleDOI

Efficient Robust Scheduling of Integrated Electricity and Heat Systems: A Direct Constraint Tightening Approach

TL;DR: A novel efficient robust dispatch model of combined heat and power systems based on extensions of disturbance invariant sets that has high computational efficiency and provides flexible and robust strategies with an adjustable level of conservativeness is proposed.