Due to the proliferation of renewable energy and its intrinsic intermittency and stochasticity, current power systems face severe operational challenges.
Case studies performed on both voltage regulation and topology control tasks demonstrated the potential vulnerabilities of the standard reinforcement learning algorithms, and possible measures of machine learning robustness and security are discussed for power systems operation tasks.
Deep reinforcement learning (RL) has been recognized as a promising tool to address the challenges in real-time control of power systems.
This paper proposes a novel end-to-end deep learning framework that simultaneously identifies demand baselines and the incentive-based agent demand response model, from the net demand measurements and incentive signals.
In this work, we propose an efficient online control algorithm, COvariance Constrained Online Linear Quadratic (COCO-LQ) control, that guarantees input-to-state stability for a large class of LTV systems while also minimizing the control cost.
This is the first result (to the best of our knowledge) on the convergence property of learning algorithms with continuous action spaces that do not fall in the no-regret class.
This paper focuses on finding reinforcement learning policies for control systems with hard state and action constraints.
We provide a framework for incorporating robustness -- to perturbations in the transition dynamics which we refer to as model misspecification -- into continuous control Reinforcement Learning (RL) algorithms.
The main idea of our method is to leverage phrase properties to choose a subset of optimal phrases for generating the final summary.