Due to increasing complexity

uncertainty and data dimensions in power systems

conventional methods often meet bottlenecks when attempting to solve decision and control problems. Therefore

data-driven methods toward solving such problems are being extensively studied. Deep reinforcement learning (DRL) is one of these data-driven methods and is regarded as real artificial intelligence (AI). DRL is a combination of deep learning (DL) and reinforcement learning (RL). This field of research has been applied to solve a wide range of complex sequential decision-making problems

including those in power systems. This paper firstly reviews the basic ideas

models

algorithms and techniques of DRL. Applications in power systems such as energy management

demand response

electricity market

operational control

and others are then considered. In addition

recent advances in DRL including the combination of RL with other classical methods

and the prospect and challenges of applications in power systems are also discussed.