抄録
Multi-energy complementary distributed energy systems (MCDESs) provide a promising framework for coordinating heterogeneous energy carriers and improving renewable energy integration; however, their development is constrained by unresolved challenges in multi-energy coupling representation, cross-scale optimization, and intelligent coordinated operation. This paper systematically reviews recent advances in the modeling, optimization, and operation of MCDESs and proposes a hierarchical analytical framework consisting of four interconnected dimensions: energy coupling mechanisms, system modeling, optimization and decision-making, and coordinated operation. Within this framework, existing approaches are classified from three perspectives, including modeling paradigms, optimization methodologies, and operational strategies. The review summarizes major modeling approaches, including physics-based multi-energy flow network models, energy hub models, and hybrid mechanism–data-driven models, and evaluates optimization techniques ranging from mathematical programming and uncertainty-aware optimization to intelligent algorithms. Furthermore, advances in planning–scheduling–control coordination, multi-timescale optimization, and source–grid–load–storage–hydrogen collaborative operation are critically analyzed. Key scientific challenges are identified, including the absence of unified theories for strongly coupled multi-energy flows, insufficient characterization of uncertainty propagation across heterogeneous energy networks, limited scalability of multi-timescale optimization, incomplete market coordination mechanisms, and inadequate integration of hydrogen-based flexibility resources. To address these gaps, future research directions are proposed toward digital twin-enabled intelligent modeling, physics-informed data-driven integration, renewable–storage–hydrogen coordinated regulation, multi-agent energy market interaction, and life-cycle low-carbon optimization. This review establishes a unified perspective linking physical coupling, computational intelligence, and market mechanisms, providing a theoretical foundation for advancing MCDESs from component-level coordination toward system-wide autonomous operation and scalable low-carbon energy transition.
