My research interests lie in the general area of electrical power grid and networked systems. Currently we are interested in developing learning and inference algorithms by leveraging the increasing amount of real-time data in energy and related infrastructure systems. Please see a list of publications in Google Scholar.
- Shanny Lin and Hao Zhu, “Enhancing the Spatio-Temporal Observability of Residential Loads“
- Yuqi Zhou and Hao Zhu, “Bus Split Sensitivity Analysis for Enhanced Security in Power System Operations“
- K. Zhang, A. Koppel, H. Zhu, and T. M. Baser. “Global Convergence of Policy Gradient Methods to (Almost) Locally Optimal Policies “
- Yu Lan, Hao Zhu, and Xiaohong Guan, ” Fast Nonconvex SDP Solvers for Large-scale Power System State Estimation“
- Hao Zhu and Chen Chen, “A Sparse Representation Approach for Power System Anomaly Identication Using Synchrophasor Data“
- Jianhan Song, Emiliano Dall’Anese, Andrea Simonetto, and Hao Zhu, “Dynamic Distribution State Estimation Using Synchrophasor Data“
- A. Koppel, K. Zhang, H. Zhu, and T. M. Baser. “Projected Stochastic Primal-Dual Method for Constrained Online Learning with Kernels” in IEEE Trans. Signal Processing.
Current external funding:
- DOE SETO grant (co-PI, with Alex Huang and Surya Santoso): “SOLAr Critical Infrastructure Energization System (SOLACE)”
- NSF CAREER award (PI): “CAREER: Cyber-Physical Situational Awareness for the Power Grid Infrastructure”
- NSF ECCS award (PI): “Collaborative Research: Towards Communication-Cognizant Voltage Regulation and Energy Management for Power Distribution Systems”
- DOE CEDS grant (co-PI, with Sibin Mohan): “INGRESS: Integration of Green Renewable Energy Sources Securely with the
Buildings and Electric Power Grids”
- DOE ARPA-E grant (co-PI, with Tom Overbye, Anna Scaglione, Zhifang Wang, and Ray Zimmerman), “GridData: Synthetic Data for Power Grid R&D”