A critical review of safe reinforcement learning strategies in power and energy systems
The high penetration of distributed energy resources (DERs) in modern smart power
systems introduces unforeseen uncertainties for the electricity sector, leading to increased …
systems introduces unforeseen uncertainties for the electricity sector, leading to increased …
Leveraging AI for enhanced power systems control: An introductory study of model-free DRL approaches
The power grids nowadays are facing increasing complexity and uncertainty due to the
continuously growing penetration of renewable energy sources, such as photovoltaic (PV) …
continuously growing penetration of renewable energy sources, such as photovoltaic (PV) …
AdapSafe2: Prior-Free Safe-Certified Reinforcement Learning for Multi-Area Frequency Control
Safe Reinforcement learning (RL) has been widely investigated to conduct power systems
frequency control under high renewable energy resources penetration. Nevertheless …
frequency control under high renewable energy resources penetration. Nevertheless …
Robustness verification of deep reinforcement learning based control systems using reward martingales
Deep Reinforcement Learning (DRL) has gained prominence as an effective approach for
control systems. However, its practical deployment is impeded by state perturbations that …
control systems. However, its practical deployment is impeded by state perturbations that …