With large-scale integration of renewable generation and distributed energy resources
(DERs), modern power systems are confronted with new operational challenges, such as …

With large-scale integration of renewable generation and distributed energy resources,
modern power systems are confronted with new operational challenges, such as growing …

With the growing integration of distributed energy resources (DERs), flexible loads, and
other emerging technologies, there are increasing complexities and uncertainties for …

Deep reinforcement learning (RL) has been recognized as a promising tool to address the
challenges in real-time control of power systems. However, its deployment in real-world …

With the rise in electricity, gas and oil prices and the persistently high levels of carbon
emissions, there is an increasing demand for effective energy management in energy …

Due to increasing complexity, uncertainty and data dimensions in power systems,
conventional methods often meet bottlenecks when attempting to solve decision and control …

With the increasing penetration of renewable energy and flexible loads in smart grids, a
more complicated power system with high uncertainty is gradually formed, which brings …

In this paper, we review past (including very recent) research considerations in using
reinforcement learning (RL) to solve electric power system decision and control problems …

We propose a computationally efficient approach to safe reinforcement learning (RL) for
frequency regulation in power systems with high levels of variable renewable energy …

This paper presents the background material required for the Learning to Run Power
Networks Challenge. The challenge is focused on using Reinforcement Learning to train an …