Renewable energy integration and microgrid energy trading using multi-agent deep reinforcement learning
This work addresses energy cost reduction for microgrids through improved renewable integration and trading, representing an incremental advancement by applying existing multi-agent methods to this specific domain.
The paper tackled the problem of reducing energy costs in a microgrid by using multi-agent deep reinforcement learning to control hybrid energy storage systems and enable energy trading, finding that multi-agent methods significantly outperformed a single global agent and that trading with external microgrids greatly increased savings.
In this paper, multi-agent reinforcement learning is used to control a hybrid energy storage system working collaboratively to reduce the energy costs of a microgrid through maximising the value of renewable energy and trading. The agents must learn to control three different types of energy storage system suited 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 storage systems can better integrate renewable energy generation under dynamic pricing, and the second with how those same agents can be used alongside an aggregator agent to sell energy to self-interested external microgrids looking to reduce their own energy bills. This work found that the centralised learning with decentralised execution of the multi-agent deep deterministic policy gradient and its state-of-the-art variants allowed the multi-agent methods to perform significantly better than the control from a single global agent. It was also found that using separate reward functions in the multi-agent approach performed much better than using a single control agent. Being able to trade with the other microgrids, rather than just selling back to the utility grid, also was found to greatly increase the grid's savings.