Gene

Quantum mechanics

Engineering

Control engineering

Computer security

Robustness (evolution)

Smart grid

Operating system

Power (physics)

Scalability

Biochemistry

Computer science

Risk analysis (engineering)

Distributed computing

Physics

Reinforcement learning

Database

Renewable energy

Electric power system

Artificial intelligence

Medicine

Chemistry

Software deployment

Electrical engineering

Key (lock)

IEEE Transactions on Smart Grid

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 applications, i.e., frequency regulation, voltage control, and energy management, as examples to illustrate RL-based models and solutions. We then present the critical issues in the application of RL, i.e., safety, robustness, scalability, and data. Several potential future directions are discussed as well.

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