  We consider the problem of energy management in microgrid networks. A
microgrid is capable of generating a limited amount of energy from a renewable
resource and is responsible for handling the demands of its dedicated
customers. Owing to the variable nature of renewable generation and the demands
of the customers, it becomes imperative that each microgrid optimally manages
its energy. This involves intelligently scheduling the demands at the customer
side, selling (when there is a surplus) and buying (when there is a deficit)
the power from its neighboring microgrids depending on its current and future
needs. Typically, the transaction of power among the microgrids happens at a
pre-decided price by the central grid. In this work, we formulate the problems
of demand and battery scheduling, energy trading and dynamic pricing (where we
allow the microgrids to decide the price of the transaction depending on their
current configuration of demand and renewable energy) in the framework of
stochastic games. Subsequently, we propose a novel approach that makes use of
independent learners Deep Q-learning algorithm to solve this problem. Through
extensive empirical evaluation, we show that our proposed framework is more
beneficial to the majority of the microgrids and we provide a detailed analysis
of the results.
