Reinforcement Learning for Selective Key Applications in Power Systems: Recent Advances and Future Challenges

With large-scale integration of renewable generation and distributed energy resources, modern power systems are confronted with new operational challenges, such as growing complexity, increasing uncertainty, and aggravating volatility. Meanwhile, more and more data are becoming available owing to the widespread deployment of smart meters, smart sensors, and upgraded communication networks. As a result, data-driven control techniques, especially reinforcement learning (RL), have attracted surging attention in recent years. This paper provides a comprehensive review of various RL techniques and how they can be applied to decision-making and control in power systems. In particular, we select three key…

• NS
  National Science Foundation

  Awards: AitF-1637598, ECCS-1553407, ECCS-1931662, CNS-1518941
• RS
  Resnick Sustainability Institute for Science, Energy and Sustainability, California Institute of Technology