Distributed Model Predictive Control-based Collaborative Scheduling for Multi-microgrids Industrial Park

Multi-microgrids collaboration can offer great potential benefits to industrial park since they possess considerable flexibility in local consumption of renewable energy and reduction of operation cost. However, the uncertainties and fluctuations arising from photovoltaic and load pose great challenges for revenue maximization of industrial park. In light of this, a distributed model predictive control (DMPC) with consistency theory and synchronous alternating direction multiplier method (SADMM) is proposed for collaborative scheduling of multi-microgrids industrial park, which only requires the information exchanging among interconnected units. Firstly, the robust equivalence associated with uncertain photovoltaic and load is formed by polyhedron uncertainty set, and a robust revenue maximization model for industrial park is proposed in consideration of time-of-use price, demand price, and on-grid price under two-part tariff. After that, a DMPC with consistency theory based strategy considering incremental operation cost of various controllable resources is presented to address the fluctuations in real-time power regulation. Finally, we provide a SADMM to optimize the proposed control strategy to achieve completely distributed computing. Simulation results conducted on a multi-microgrids industrial park illustrate that the proposed collaborative scheduling can efficiently manage flexible resources to lessen the influence of uncertainties and fluctuations on the revenue of industrial park.