Jorge Cortés
Professor
Cymer Corporation Endowed Chair
Bridging transient and steady-state performance in voltage control: a reinforcement learning approach with safe gradient flow
J. Feng, W. Cui, J. Cortés, Y. Shi
Proceedings of the IEEE Conference on
Decision and Control, Singapore, 2023,
pp. 5556-5561
Abstract
Deep reinforcement learning approaches are becoming
appealing for the design of nonlinear controllers for voltage control
problems, but the lack of stability guarantees hinders their
deployment in real-world scenarios. This paper constructs a
decentralized RL-based controller featuring two components: a
transient control policy and a steady-state performance optimizer. The
transient policy is parameterized as a neural network, and the
steady-state optimizer represents the gradient of the long-term
operating cost function. The two parts are synthesized through a safe
gradient flow framework, which prevents the violation of reactive
power capacity constraints. We prove that if the output of the
transient controller is bounded and monotonically decreasing with
respect to its input, then the closed-loop system is asymptotically
stable and converges to the optimal steady-state solution. We
demonstrate the effectiveness of our method by conducting experiments
with IEEE 13-bus and 123-bus distribution system test feeders.
pdf
Mechanical and Aerospace Engineering,
University of California, San Diego
9500 Gilman Dr,
La Jolla, California, 92093-0411
Ph: 1-858-822-7930
Fax: 1-858-822-3107
cortes at ucsd.edu
Skype id:
jorgilliyo