J
Junhua Zhao
Researcher at The Chinese University of Hong Kong
Publications - 210
Citations - 8635
Junhua Zhao is an academic researcher from The Chinese University of Hong Kong. The author has contributed to research in topics: Computer science & Electricity market. The author has an hindex of 41, co-authored 163 publications receiving 6103 citations. Previous affiliations of Junhua Zhao include Zhejiang University & University of Newcastle.
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Cloud-Based Information Infrastructure for Next-Generation Power Grid: Conception, Architecture, and Applications
TL;DR: How different categories of the power applications can benefit from the cloud-based information infrastructure is discussed, including how to develop practical compute-intensive and data-intensive power applications by utilizing different layers provided by the state-of-the-art public cloud computing platforms.
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Generalized FDIA-Based Cyber Topology Attack With Application to the Australian Electricity Market Trading Mechanism
TL;DR: A generalized false data injection attack-based cyber topology attack capable of disturbing conventional transactions in the electricity market by making small, undetected price deviations during each attack interval over an extended period is proposed.
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Integrated Electricity and Hydrogen Energy Sharing in Coupled Energy Systems
TL;DR: With the integrated energy sharing of hydrogen and electricity, the total system cost is the lowest and the largest total social welfare can be reached and the distributed energy storage can be more effective to improve the system stability.
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Power system fault diagnosis based on history driven differential evolution and stochastic time domain simulation
TL;DR: A novel optimization algorithm, namely history driven differential evolution (HDDE) is proposed to solve the formulated optimization problem of fault diagnosis and is tested using comprehensive case studies to demonstrate its effectiveness.
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Extreme learning machine based genetic algorithm and its application in power system economic dispatch
TL;DR: A novel optimization algorithm, which utilizes the key ideas of both genetic algorithm (GA) and extreme learning machine (ELM), is proposed, which is applied to the power system economic dispatch problem and can ensure the faster convergence and provide more economical dispatch plans.