Abstract:

The optimal scheduling of integrated energy systems （IES） is affected by fluctuations in uncertain factors such as renewable energy and load. Failure to accurately describe and process these uncertain parameters will constrain system reliability， and the lack of refined modeling and optimization methods makes uncertainty analysis more complex. To comprehensively and systematically analyze uncertainty modeling and optimization methods， the structure of IES， sources of uncertainty， and modeling approaches are reviewed. Monte Carlo simulation， information gap decision theory， interval methods， robust optimization， and data-driven methods are summarized， along with their applications and studies in uncertainty optimization. Research findings indicate that there is no single best optimization method. The complementarity of multiple methods can maximize the economic and environmental benefits of IES. Based on current research challenges and hotspots， future directions for uncertainty optimization are outlined.

Key words: integrated energy system, system structure, mathematical modeling, uncertainty optimization, Monte Carlo simulation, info-gap decision theory, robust optimization, energy hub