Abstract
Driven by the accelerated advancement of microgrid technologies and the surging demand for regional power supply assurance, multi-microgrid (MMG) systems confront significant operational challenges pertaining to economic efficiency and power supply reliability. Based on the assumption that the microgrid adopts the grid-connected mode, this study proposes a bi-level robust optimization framework for interconnected system coordination to address the inherent stochasticity of renewable energy generation in MMG collaborative operations. Primarily, a microgrid-level robust optimization model is formulated to identify adverse boundary conditions through operating cost maximization under internal uncertainty scenarios, thereby yielding a day-ahead scheduling strategy that achieves optimal balance between conservativeness and economic viability. Subsequently, a cooperative optimization paradigm for MMG systems is developed, which enables spatio-temporal optimization of cross-microgrid resource allocation through rigorous analysis of energy exchange constraints and distribution network interconnection dynamics. The solution methodology employs a column-and-constraint generation (C&CG) algorithm integrated with strong duality theory, implementing a master-subproblem alternating iteration mechanism for model decomposition. The simulation results demonstrate that the established multi-microgrid interconnected system can reduce the overall operational cost of the microgrid cluster by up to 28.9%, validating its significant economic and robustness advantages under conditions of electricity price fluctuations.