9 papers
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…
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…
JAX-Based Batched AC Power Flow for GPU Acceleration and AI Ecosystem Integration
Yihong Zhou, Dylan Cope, Jakob Foerster +1
Coordinating growing grid flexibility under uncertainty is becoming increasingly important for efficient and reliable power-system operation. A core computational requirement is th…
Strengthened and Faster Linear Approximation to Joint Chance Constraints with Wasserstein Ambiguity
Yihong Zhou, Yuxin Xia, Hanbin Yang +1
Many real-world decision-making problems have uncertain parameters in constraints. Wasserstein distributionally robust joint chance constraints (WDRJCC) offer a promising solution…
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…
FICA: Faster Inner Convex Approximation of Chance Constrained Grid Dispatch with Decision-Coupled Uncertainty
Yihong Zhou, Hanbin Yang, Thomas Morstyn
This paper proposes a Faster Inner Convex Approximation (FICA) method for solving power system dispatch problems with Wasserstein distributionally robust joint chance constraints (…