Data-driven Power Systems Control with Stability Guarantee
Power systems are experiencing a period of rapid changes due to the proliferation of renewable generation and distributed energy resources including rooftop solar, electric vehicles, and batteries. Many of these new technologies are interfaced with the grid through power electronic interfaces (i.e., inverters) that can be controlled at a much faster timescale compared to conventional machines. However, how to leverage such flexibility is nontrivial due to the nonlinearity, complexity, and uncertainty in the grids.
To overcome these challenges, our research develops a new data-driven control framework for power system voltage and frequency control with formal stability guarantees. The main tool we use is the Lyapunov stability theory, which is a rich topic studied in the control community and a wealth of results on Lyapunov and energy functions for power system control. However, these techniques were not studied with reinforcement learning (RL) and neural networks in mind. We propose a stability-constrained RL framework that bridges the flexibility of neural networks, the ease of optimization provided by RL, and the stability guarantees certified by Lyapunov functions.
See below for some recent publications related to this project.
Stability Constrained Reinforcement Learning for Real-Time Voltage Control
NSF EPCN 2200692