Robust Bi-Level Scheduling Framework for Coordinated Optimization of Networked Microgrids Considering Dynamic Communication Uncertainty

Networked microgrids (NMGs) enable coordinated scheduling of multiple microgrids, maximizing the flexibility of distributed energy resources (DERs) within each microgrid. However, communication failures can hinder the availability of DERs, disrupting the coordinated operation of NMGs. This paper proposes a robust bi-level programming model for coordinated optimization of NMGs under dynamic communication uncertainty. The upper-level objective of the model employs a robust method to construct a penalty term based on power imbalance, reflecting the impact of the unavailability of DERs on system operation due to communication reliability issues. The lower-level model minimizes the total operational cost of NMGs, aiming at economic efficiency. A dynamic communication reliability model is established using a Markov chain to fully consider the influence of time-varying communication states on communication reliability during different scheduling periods. A data-driven Wasserstein ambiguity set is employed to construct a deviation uncertainty set, addressing the uncertainty in communication reliability. The effectiveness of the proposed model is verified through simulations on a modified IEEE-118 test system. Simulation results show that, compared with the traditional OPF model, the proposed method increases scheduling costs by 11% but reduces communication-induced risk by 37%. Additionally, it achieves cost reductions of 9% and 23% and risk reductions of 31% and 26% compared to SP and DRO, respectively.