Yan Ziming (EEE)

Lab Position: Ph.D. Students (2018, Spring)

Email: yanz0007@e.ntu.edu.sg

Office: S2-B7c-05, Clean Energy Research Lab

Research Topic: Data-driven methods for power system control and operation

Brief Biography:

Yan Ziming is a Ph.D candidate in School of Electrical and Electronic Engineering (EEE) under Asst. Prof Xu Yan. His current research interest includes power systems control&operation with constraints-aware, single/multi-agent deep reinforcement learning.

Education:
2013 - 2017: BSc student, School of Electrical Engineering, Chongqing University, China
2018 - NOW: PhD, School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore

Publications:

[1] Z. Yan and Y. Xu, "Data-Driven Load Frequency Control for Stochastic Power Systems: A Deep Reinforcement Learning Method With Continuous Action Search," in IEEE Transactions on Power Systems, vol. 34, no. 2, pp. 1653-1656, March 2019.

[2] Z. Yan, Y. Xu, "A Multi-Agent Deep Reinforcement Learning Method for Cooperative Load Frequency Control of Multi-Area Power Systems", IEEE Transactions on Power Systems, 2020.

[3] Z. Yan and Y. Xu, "Real-Time Optimal Power Flow: A Lagrangian based Deep Reinforcement Learning Approach," in IEEE Transactions on Power Systems, 2020.

[4] Z. Yan and Y. Xu, " A Hybrid Data-driven Method for Fast Solution of Security-Constrained Optimal Power Flow," in IEEE Transactions on Power Systems, 2022.

[5] Z. Yan, Y. Xu, Y. Wang and X. Feng, "Data-driven Learning-based Economic Control of Battery Energy Storage System Considering Battery Degradation", IET Generation Transmission & Distribution, 2020.

[6] Z. Yan, T. Zhao, Y. Xu, et al. Data-driven robust planning of electric vehicle charging infrastructure for urban residential car parks. IET Generation, Transmission & Distribution, 2021, 14(26): 6545-6554.

[7] Z. Yan, Y. Xu,Y. Wang and X. Feng, "Data-driven Economic Control of Battery Energy Storage System considering Battery Degradation", presented at 9th ICPES, 2019.

[8] Z. Yan and Y. Xu, "Combining deep reinforcement learning and domain knowledge for topology optimization of power systems", Automation of Electric Power Systems, accepted, 2022.

[9] Y. Zheng, Z. Yan, K. Chen, J. Sun, Y. Xu and Y. Liu, "Vulnerability Assessment of Deep Reinforcement Learning Models for Power System Topology Optimization," IEEE Transactions on Smart Grid, 2021.

[10] B. Wang, Y. Xu, B. Soong, Z. Yan, "A Multi-Agent Deep Reinforcement Learning Based Multi-Timescale Voltage Control for Distribution System", 5th IEEE Conference on Energy Internet and Energy System Integration, 2021.

[11] Z. Du, Z. Yan, Y. Xu, R. Zhang, " A Probabilistic Forecasting Method for Residential Load Demand based on Statistical Error Analysis", the 2022 IEEE Power & Energy Society (PES) General Meeting (GM), 2022.