With the rapid penetration of distributed renewable generation

energy storage and heterogeneous loads

the scheduling of distributed energy systems (DESs) exhibits complex operational characteristics featured by deep coupling of multi-energy flows

multi-timescale dynamic coordination

and multi-agent interactions. Multi-source uncertainties propagate and disturb across energy carriers and time horizons

reducing system safety margins and increasing the difficulty of maintaining feasible system operation. As a result

DES scheduling is driven to evolve from deterministic cost-oriented optimization toward risk-aware and feasibility-guaranteed robust decision-making. Firstly

this paper summarizes the scheduling modeling framework for DESs. Combined with the propagation characteristics of uncertain disturbances in multi-energy conversion chains and network constraints

the main sources of uncertainties as well as their cross-carrier and cross-time-horizon propagation features are systematically analyzed. Secondly

modeling paradigms including scenario sets

uncertainty sets and distributional ambiguity sets are summarized

and their different impacts on scheduling feasibility and risk control are compared. Special emphasis is placed on the model formulation

solution methodologies and applicable scenarios of robust optimization

multi-stage adaptive robust optimization and distributionally robust optimization for DES scheduling. Key issues including non-anticipativity

full-scenario feasibility

conservativeness control and out-of-sample risk management are elaborated

together with their application boundaries concerning safety

economy and data dependence in DESs. Finally

future research directions for robust scheduling of DESs under uncertainties are discussed.