Dartmouth Autonomy Seminar Series

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Password: 876687

"Mean-Field Learning for Storage Aggregation"

Distributed energy storage devices can be pooled and coordinated by aggregators to participate in power system operations and market clearing. This requires representing a massive device population as a single, tractable surrogate that is computationally efficient, accurate, and compatible with market participation requirements. However, surrogate identification is challenging due to heterogeneity, nonconvexity, and the high dimensionality of storage devices. To address these challenges, we develop a mean-field learning framework for storage aggregation. We interpret aggregation as the average behavior of a large storage population and show that, as the population grows, aggregate performance converges to a unique, convex mean-field limit, enabling tractable population-level modeling. This convexity further yields a price-responsive characterization of aggregate storage behavior and allows us to bound the mean-field approximation error. Leveraging these results, we construct a convex surrogate model that approximates the aggregate behavior of large storage populations and can be embedded directly into power system operations and market clearing. Surrogate parameter identification is formulated as an optimization problem using historical market price-response data, and we adopt a gradient-based algorithm for efficient learning. Case studies validate the theoretical findings and demonstrate the effectiveness of the proposed framework in approximation accuracy, data efficiency, and profit outcomes.

Light refreshments will be served.
Sponsored by Thayer School of Engineering and the Neukom Institute.

The Dartmouth Autonomy Seminar Series explores how common principles of autonomy link fields such as robotics, economics, and cognition, and brings together academia and industry to discuss autonomous systems.

About the Speaker(s)

Jingguan Liu
PhD Candidate, Dartmouth

Jingguan Liu received the bachelor's degree in electrical engineering from Huazhong University of Science and Technology, Wuhan, China, in 2022, where he is currently pursuing the PhD degree under the supervision of Professor Xiaomeng Ai and Prof. Jiakun Fang. Since September 2025, he has been a visiting student at Dartmouth under the supervision of Professor Cong Chen. His research focuses on demand-side aggregation, energy storage operation, and optimization under uncertainty.

Contact

For more information, contact Ada Yildirim at ada.yildirim.th@dartmouth.edu .