6 papers
Benchmarking State Space Models, Transformers, and Recurrent Networks for US Grid Forecasting
Sunki Hong, Jisoo Lee
Selecting the right deep learning model for power grid forecasting is challenging, as performance heavily depends on the data available to the operator. This paper presents a compr…
Efficient Policy Adaptation for Voltage Control Under Unknown Topology Changes
Jie Feng, Yuanyuan Shi, Deepjyoti Deka
Reinforcement learning (RL) has shown great potential for designing voltage control policies, but their performance often degrades under changing system conditions such as topology…
Stability Constrained Voltage Control in Distribution Grids with Arbitrary Communication Infrastructure
Zhenyi Yuan, Jie Feng, Yuanyuan Shi +1
We consider the problem of designing learning-based reactive power controllers that perform voltage regulation in distribution grids while ensuring closed-loop system stability. In…
Analytical Lyapunov Function Discovery: An RL-based Generative Approach
Haohan Zou, Jie Feng, Hao Zhao +1
Despite advances in learning-based methods, finding valid Lyapunov functions for nonlinear dynamical systems remains challenging. Current neural network approaches face two main is…
DiffOP: Reinforcement Learning of Optimization-Based Control Policies via Implicit Policy Gradients
Yuexin Bian, Jie Feng, Yuanyuan Shi
Real-world control systems require policies that are not only high-performing but also interpretable and robust. A promising direction toward this goal is model-based control, whic…
Online Event-Triggered Switching for Frequency Control in Power Grids with Variable Inertia
Jie Feng, Wenqi Cui, Jorge Cortés +1
The increasing integration of renewable energy resources into power grids has led to time-varying system inertia and consequent degradation in frequency dynamics. A promising solut…