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Reinforcement Learning Based Symbolic Regression for Load Modeling
With the increasing penetration of renewable energy sources, growing demand variability, and evolving grid control strategies, accurate and efficient load modeling has become a critical yet challenging task.
Mitigating Parameter Degeneracy using Joint Conditional Diffusion Model for WECC Composite Load Model in Power Systems
Data-driven modeling for dynamic systems has gained widespread attention in recent years.
On Approximating the Dynamic Response of Synchronous Generators via Operator Learning: A Step Towards Building Deep Operator-based Power Grid Simulators
This paper designs an Operator Learning framework to approximate the dynamic response of synchronous generators.
Decentralized Coordinated State Estimation in Integrated Transmission and Distribution Systems
Current transmission and distribution system states are mostly unobservable to each other, and state estimation is separately conducted in the two systems owing to the differences in network structures and analytical models.
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