Academic Profile : Faculty

Prof Zhao Yang Dong.jpg picture
Prof Z.Y. Dong
Professor, School of Electrical & Electronic Engineering
Journal Articles
(Not applicable to NIE
staff as info will be
pulled from PRDS)
ZY Dong, J Yang, L Yu, R Daiyan, R Amal, “A green hydrogen credit framework for international green hydrogen trading towards a carbon neutral future”, International Journal of Hydrogen Energy 47 (2), 728-734, 2022

J Yang, Z Dong, F Wen, Q Chen, B Liang, “Spot electricity market design for a power system characterized by high penetration of renewable energy generation”, Energy Conversion and Economics 2 (2), 67-78, 2021

C Li, Z Dong, G Chen, B Zhou, J Zhang, X Yu, “Data-driven Planning of Electric Vehicle Charging Infrastructure: A Case Study of Sydney, Australia”, IEEE Transactions on Smart Grid, V12, No. 4, pp. 3289-3304, 2021

C Zhang, ZY Dong, L Yang, “A Feasibility Pump Based Solution Algorithm for Two-Stage Robust Optimization With Integer Recourses of Energy Storage Systems”, IEEE Transactions on Sustainable Energy 12 (3), 1834-1837, 2021

ZY Dong, Y Zhang, C Yip, S Swift, K Beswick, “Smart campus: definition, framework, technologies, and services”, IET Smart Cities 2 (1), 43-54, 2020

AS Musleh, G Chen, ZY Dong, “A survey on the detection algorithms for false data injection attacks in smart grids”, IEEE Transactions on Smart Grid 11 (3), 2218-2234, 2019

J Yang, ZY Dong, F Wen, G Chen, Y Qiao, “A decentralized distribution market mechanism considering renewable generation units with zero marginal costs”, IEEE Transactions on Smart Grid 11 (2), 1724-1736, 2019

W Kong, ZY Dong, B Wang, J Zhao, J Huang, “A practical solution for non-intrusive type II load monitoring based on deep learning and post-processing”, IEEE Transactions on Smart Grid 11 (1), 148-160, 2019

C Zhang, Y Xu, ZY Dong, “Probability-weighted robust optimization for distributed generation planning in microgrids”, IEEE Transactions on Power Systems 33 (6), 7042-7051, 2018

W Kong, ZY Dong, Y Jia, DJ Hill, Y Xu, Y Zhang, “Short-term residential load forecasting based on LSTM recurrent neural network”, IEEE Transactions on Smart Grid 10 (1), 841-851, 2017