抄録
Distributed collaborative scheduling of multi-region integrated energy systems (MR-IESs) has emerged as a key research paradigm for enhancing multi-energy coupling efficiency, facilitating large-scale renewable energy integration, and supporting the transition toward the Energy Internet under carbon neutrality objectives. However, the deep coupling of heterogeneous energy networks—including electricity, heating, gas, and energy storage—together with the increasing uncertainty of renewable energy generation, poses significant challenges to conventional centralized scheduling approaches in terms of computational scalability, communication efficiency, privacy preservation, and cross-regional coordination. This review presents a comprehensive synthesis of recent advances in distributed collaborative scheduling for MR-IESs and proposes a unified analytical framework encompassing system architecture, multi-energy coupling modeling, collaborative scheduling mechanisms, distributed optimization algorithms, uncertainty management, and engineering implementation. Classical distributed optimization methods, including Lagrangian dual decomposition, the alternating direction method of multipliers (ADMM), and analytical target cascading (ATC), are systematically reviewed alongside emerging artificial intelligence-driven scheduling paradigms based on multi-agent reinforcement learning, federated learning, and generative artificial intelligence. Furthermore, stochastic optimization, robust optimization, distributionally robust optimization, and scenario generation techniques are comparatively analyzed with respect to modeling capability, convergence characteristics, communication overhead, privacy preservation, and scalability under renewable energy uncertainty. Building upon this synthesis, the review identifies several critical research challenges, including dynamic multi-energy coupling, cross-regional coordination mechanisms, multi-timescale distributed optimization, and communication–computation co-design. Finally, future research directions are outlined, highlighting physics-informed intelligent optimization, coordinated source–grid–load–storage–carbon scheduling, autonomous multi-timescale collaborative control, and industrial-scale distributed scheduling platforms. This review provides a systematic research roadmap and methodological foundation for the development of next-generation distributed collaborative scheduling strategies for MR-IESs and the sustainable evolution of the Energy Internet.
