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In this paper, multi-agent reinforcement learning is used to control a hybridenergy storage system working collaboratively to reduce the energy costs of amicrogrid through maximising the value of renewable energy and trading. Theagents must learn to control three different types of energy storage systemsuited for short, medium, and long-term storage under fluctuating demand,dynamic wholesale energy prices, and unpredictable renewable energy generation.Two case studies are considered: the first looking at how the energy storagesystems can better integrate renewable energy generation under dynamic pricing,and the second with how those same agents can be used alongside an aggregatoragent to sell energy to self-interested external microgrids looking to reducetheir own energy bills. This work found that the centralised learning withdecentralised execution of the multi-agent deep deterministic policy gradientand its state-of-the-art variants allowed the multi-agent methods to performsignificantly better than the control from a single global agent. It was alsofound that using separate reward functions in the multi-agent approachperformed much better than using a single control agent. Being able to tradewith the other microgrids, rather than just selling back to the utility grid,also was found to greatly increase the grid's savings.

Renewable energy integration and microgrid energy trading using multi-agent deep reinforcement learning