  This paper presents a Reinforcement Learning (RL) based energy market for a
prosumer dominated microgrid. The proposed market model facilitates a real-time
and demanddependent dynamic pricing environment, which reduces grid costs and
improves the economic benefits for prosumers. Furthermore, this market model
enables the grid operator to leverage prosumers storage capacity as a
dispatchable asset for grid support applications. Simulation results based on
the Deep QNetwork (DQN) framework demonstrate significant improvements of the
24-hour accumulative profit for both prosumers and the grid operator, as well
as major reductions in grid reserve power utilization.
