Power Loss Minimization in Microgrids Using Bayesian Reinforcement Learning with Coalition Formation

Energy trading among microgrids has been emerging as a promising solution to implement community microgrids, also known as energy sharing communities. The key idea behind these communities is to share the surplus energy in one microgrid with another microgrid that has higher demand than its generation. The objective of these transactions can be monetary as well as optimizing a system parameter. In this paper, we focus on energy trading for the purpose of power loss minimization. We assume microgrids form coalitions to avoid exporting energy from the utility grid or a distant microgrid which might cause higher line losses due to increased distance. We propose a novel Bayesian Reinforcement Learning (BRL) based algorithm, which allows the microgrids to reduce the overall power loss. We compare this scheme with a coalitional game theory-based approach, Q-learning based approach, random coalition formation approach, as well as with a case that has no coalitions. Our results show that more than 50% reduction in power loss compared to no coalitions and less power loss than the other approaches is achieved. We also show power loss can be further reduced by proper sizing of the storage unit.