Decentralized-distributed robust electric power scheduling for multi-microgrid systems

This paper proposes a novel decentralized-distributed (DD) adaptive robust optimization (ARO) method to address the efficient distributed scheduling of multi-microgrid systems involving various entities, uncertainties and accidental communication failures. To avoid an unavailable coordination center of the whole network, a DD-ARO model is firstly proposed based on the parallelizing-distributed (PD) framework, which allocates the task of tie-line power coordination in the virtual center to the neighboring stakeholders. According to the properties of nonlinear ARO, a modified analytical target cascading method is further developed to formulate the consistency constraints on shared tie-lines, thus guaranteeing the optimality and enhancing the solution quality of the DD-ARO model. Finally, an iterative solution algorithm is proposed to efficiently solve the resulting multi-level nonconvex DD-ARO model. Case studies and computational results illustrate the effectiveness of the DD-ARO approach for an AC/DC hybrid multi-microgrid system under normal and faulty communications.