  The increasing penetration of renewable generations, along with the
deregulation and marketization of power industry, promotes the transformation
of power market operation paradigms. The optimal bidding strategy and
dispatching methodology under these new paradigms are prioritized concerns for
both market participants and power system operators, with obstacles of
uncertain characteristics, computational efficiency, as well as requirements of
hyperopic decision-making. To tackle these problems, the Reinforcement Learning
(RL), as an emerging machine learning technique with advantages compared with
conventional optimization tools, is playing an increasingly significant role in
both academia and industry. This paper presents a comprehensive review of RL
applications in deregulated power market operation including bidding and
dispatching strategy optimization, based on more than 150 carefully selected
literatures. For each application, apart from a paradigmatic summary of
generalized methodology, in-depth discussions of applicability and obstacles
while deploying RL techniques are also provided. Finally, some RL techniques
that have great potentiality to be deployed in bidding and dispatching problems
are recommended and discussed.
