Distributed decision-making for energy trading between agent-based dual microgrids

This paper presents a novel decision-making scheme for facilitating energy trading between two agent-based microgrids (MGs) in an autonomous way. The interactions during the energy trading process are appropriately analyzed using Stackelberg game theory, where a unique Stackelberg equilibrium (SE) is proven to exist in the game. To protect the private information of each MG, a fully distributed algorithm is proposed to find the SE through message exchanges, which yields the optimal trading price as well as the optimal trading quantity for participating MGs. Simulation results show that the proposed approach is effective for obtaining the optimal energy trading price and quantity, and also helps each MG arrive at maximal utility or minimal cost.