为了满足日益增长的能源需求并减少对环境的破坏, 节能成为全球经济和社会发展的一项长远
战略方针, 加强能源管理能够提高能源利用效率, 促进节能减排. 然而, 可再生能源和柔性负载的 …

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

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 …

With the widespread use of reinforcement learning (RL) in the energy system, the damage to
the system caused by the agents' stochastic exploration is beginning to be appreciated. We …

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

Reinforcement learning (RL) techniques have emerged as powerful tools for optimizing
energy systems, offering the potential to enhance efficiency, reliability, and sustainability …

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

To mitigate global climate change and ensure a sustainable energy future, China has
launched a new energy policy of achieving carbon peaking by 2030 and carbon neutrality …

The dynamic nature of sustainable energy and electric systems can vary significantly along
with the environment and load change, and they represent the features of multivariate, high …