  Microgrid (MG) energy management is an important part of MG operation.
Various entities are generally involved in the energy management of an MG,
e.g., energy storage system (ESS), renewable energy resources (RER) and the
load of users, and it is crucial to coordinate these entities. Considering the
significant potential of machine learning techniques, this paper proposes a
correlated deep Q-learning (CDQN) based technique for the MG energy management.
Each electrical entity is modeled as an agent which has a neural network to
predict its own Q-values, after which the correlated Q-equilibrium is used to
coordinate the operation among agents. In this paper, the Long Short Term
Memory networks (LSTM) based deep Q-learning algorithm is introduced and the
correlated equilibrium is proposed to coordinate agents. The simulation result
shows 40.9% and 9.62% higher profit for ESS agent and photovoltaic (PV) agent,
respectively.
