Hierarchical Distributed Model Predictive Control of Interconnected Microgrids

In this work, we propose a hierarchical distributed model predictive control strategy to operate interconnected microgrids (MGs) with the goal of increasing the overall infeed of renewable energy sources. In particular, we investigate how renewable infeed of MGs can be increased by using a transmission network allowing the exchange of energy. To obtain an model predictive control scheme which is scalable with respect to the number of MGs and preserves their independent structure, we make use of the alternating direction method of multipliers leading to local controllers communicating through a central entity. This entity is in charge of the power lines and ensures that the constraints on the transmission capacities are met. The results are illustrated in a numerical case study.