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cs.LG2026
Decision-Focused Scenario Generation and Selection for Efficient and Robust Grid Dispatch
Yangze Zhou, Yihong Zhou, Thomas Morstyn +1
The increasing uncertainty from flexible demand and renewable generation has made distributionally robust optimization (DRO) an important tool for robust power system dispatch. DRO…
cs.LG2026
Supervised Reinforcement Learning for the Coordination of Distributed Energy Resources
Haoyuan Deng, Yihong Zhou, Thomas Morstyn +1
The increasing integration of distributed energy resources (DERs) is crucial for power system decarbonization, yet unlocking DERs' flexibility is challenged by their inherent uncer…
cs.LG2026
GradMAP: Gradient-Based Multi-Agent Proximal Learning for Grid-Edge Flexibility
Yihong Zhou, Hongtai Zeng, Thomas Morstyn
Coordinating large populations of grid-edge devices requires learning methods that remain fully decentralised in deployment while still respecting three-phase AC distribution-netwo…