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Email: gn2015729 AT gmail.com
I am currently a Postdoctoral Researcher at Purdue ECE, working with Prof. Junjie Qin. Before joining Purdue, I completed my Ph.D. in Computer Science at the Institute for Interdisciplinary Information Sciences at Tsinghua University, under the guidance of Prof. Chenye Wu. I was also a visiting student researcher at the University of Washington from January to June 2023, working with Prof. Daniel Kirschen. Before that, I received my bachelor's degree in Electrical Engineering with an additional major in Economics from Tsinghua University.
My research lies at the intersection of Computer Science and Power Systems, with a focus on intelligent solutions for power system modernization in the presence of energy-intensive infrastructures such as electrified transportation and distributed data centers, as well as the integration of renewable resources. My work spans planning, operations, and markets, leveraging tools from optimization, learning, and mechanism design.
Featured Work (See here for the complete list)
Accelerating Large Load Grid Interconnection via Flexible Connection
Our work addresses the challenge of integrating rapid load growth from AI data centers and transportation electrification when lengthy upgrade timelines constrain grid expansion. We develop network-aware optimization frameworks that characterize the trade-off between quality-of-service guarantees (such as limits on the frequency, duration, and magnitude of curtailment or delay interventions) and the additional hosting capacity unlocked by flexible connection programs.
Coupled Infrastructure System and Spatially Flexible Loads
Our work develops a generalized competitive equilibrium framework that captures the bidirectional influence between locational marginal prices and the spatial decisions of flexible loads, such as electric vehicles and datacenters, thereby characterizing cross-system interactions between power, transportation, and distributed datacenter systems.
Decision-Making under Uncertainty for Future Power Systems
This line of work combines optimization with learning to manage uncertainty in the integration of distributed energy resources, balancing robustness and cost-efficiency, and enabling sequential decision-making that adapts to real-time information.
Mechanism Design for Next-Generation Electricity Markets
This line of work studies electricity markets in which producers, consumers, and system operators act as interacting decision-makers, challenging the traditional top-down structure. We evaluate the economic value of coordination among market participants and design incentive-compatible and feasibility-aware market mechanisms.
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