Reinforcement learning (RL) is a promising optimal control technique for multi-energy
management systems. It does not require a model a priori-reducing the upfront and ongoing …

Safe reinforcement learning (RL) with hard constraint guarantees is a promising optimal
control direction for multi-energy management systems. It only requires the environment …

Energy systems undergo major transitions to facilitate the large-scale penetration of
renewable energy technologies and improve efficiencies, leading to the integration of many …

Abstract Model predictive control (MPC) offers an optimal control technique to establish and
ensure that the total operation cost of multi-energy systems remains at a minimum while …

The increasingly complex energy systems are turning the attention towards model-free
control approaches such as reinforcement learning (RL). This work proposes novel RL …

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 …

The transfer of reinforcement learning (RL) techniques into real-world applications is
challenged by safety requirements in the presence of physical limitations. Most RL methods …

Despite recent advances in Reinforcement Learning (RL), its applications in real-world
engineering systems are still rare. The primary reason is that RL algorithms involve …

While reinforcement learning (RL) is gaining popularity in energy systems control, its real-
world applications are limited due to the fact that the actions from learned policies may not …

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