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Dr. ZHU, Ziqing
Dr. ZHU, Ziqing
朱子晴博士
BEng, MSc, PhD
Research Assistant Professor, Department of Computer Science
 

About

Dr. Ziqing Zhu received his Ph.D at The Hong Kong Polytechnic University, in Jan. 2023, and M.Sc at The University of Manchester, UK, in Dec. 2019, and B.Eng in Jun. 2018, all in Electrical (Power Systems) Engineering. He served as the postdoctoral research fellow at The Hong Kong Polytechnic University, from Mar. 2023 to Jul. 2023.

With a focus on the AI-based electrical power system operations and electricity market simulations, Dr. Zhu has published 10 high-quality journal articles and 2 conference papers. Dr. Zhu’s doctoral thesis was selected as a finalist for the IEEE PES Dissertation Challenge at IEEE PES Grid Edge Technologies Conference, San Diego, CA, 2023. Furthermore, he was honored with the Best Student Paper award at the IET APSCOM conference, HK, 2022. Dr. Zhu is currently served as the reviewer for several top-journals, including IEEE TPWRS, TSG, TSTE, etc.


Research Interests

  • AI applications on power (energy) system operation and control
  • AI-enabled power (energy) market simulation and market design
  • Data-driven energy policy analysis

Selected Publications

  • Z. Zhu, K. W. Chan, S. Bu, B. Zhou, S. Xia, “Real Time Interaction of Active Distribution Network and Virtual Microgrids: Market Paradigm and Data-Driven Stakeholder Behavior Analysis,” in Applied Energy, vol. 297, p. 117107, 2021.
  • Z. Zhu, K. W. Chan, S. Bu, S. W. Or, X. Gao and S. Xia, “Analysis of Evolutionary Dynamics for Bidding Strategy Driven by Multi-Agent Reinforcement Learning,” in IEEE Transactions on Power Systems, doi: 10.1109/TPWRS.2021.3099693. 2021.
  • Z. Zhu, K. W. Chan, S. Xia, S. Bu, “Optimal Bi-Level Bidding and Dispatching Strategy between Active Distribution Network and Virtual Alliances Using Distributed Robust Multi-Agent Deep Reinforcement Learning,” in IEEE Transactions on Smart Grid, 2021.
  • Q. Hu, Z. Zhu, S. Bu, K. W. Chan, and F. Li, “A multi-market nanogrid P2P energy and ancillary service trading paradigm: Mechanisms and implementations,” in Applied Energy, vol. 293, p. 116938, 2021.
  • Z. Zhu, Z. Hu, K. W. Chan, S. Bu, B. Zhou, S. Xia, “Reinforcement Learning on Deregulated Power Systems with Electricity Market: A Comprehensive Review”, Applied Energy, in press.
  • Z. Zhu, K. W. Chan, S. Bu, S. Xia, “Nash Equilibrium Estimation and Analysis in Joint Peer-to-Peer Electricity and Carbon Emission Auction Market with Microgrid Prosumers,” IEEE Transactions on Power Systems, in press.
  • Z. Zhu, K. W. Chan, S. Bu, S. Xia, “Analysis of Strategic Interactions among Distributed Virtual Alliances in Electricity and Carbon Emission Markets Using Multi-Agent Reinforcement Learning,” Renewable & Sustainable Energy Reviews, in press.
  • M. Ban, W. Bai, W. Song, L. Zhu, S. Xia, Z. Zhu, T. Wu, “Optimal Scheduling for Integrated Energy-Mobility Systems Based on Renewable-to-Hydrogen Stations and Tank Truck Fleets,” IEEE transactions on industry applications, vol. 58, no. 2, pp. 2666–2676, 2022.
  • G. Sun, G. Li, P. Li, S. Xia, Z. Zhu, and M. Shahidehpour, “Coordinated Operation of Hydrogen-Integrated Urban Transportation and Power Distribution Networks Considering Fuel Cell Electric Vehicles,” IEEE transactions on industry applications, vol. 58, no. 2, pp. 2652–2665, 2022.