Two-Stage Distributionally Robust Optimization Scheduling for Industrial Park Integrated Energy Systems Considering Tiered Carbon Trading and Renewable Energy Uncertainty

Industrial parks serve as the core carriers for low-carbon transition in the industrial sector, while multi-energy complementary integrated energy systems (IESs) represent the key technological pathway for achieving efficient decarbonization in such parks. However, the output volatility of high-penetration renewable energy sources, coupled with the scheduling complexity under carbon emission constraints, poses severe challenges to day-ahead decision-making for park-level integrated energy systems. To address this issue, this paper developed a two-stage distributionally robust optimization (DRO) scheduling model based on the Wasserstein distance. First, an electricity–heat–gas–hydrogen IES architecture is established, and a day-ahead scheduling optimization model is formulated with the objective of minimizing the sum of system operation and maintenance costs, energy purchase costs, tiered carbon trading costs, and renewable curtailment penalty costs. Second, to cope with the uncertainty of wind and solar power outputs, the recourse decisions are parameterized after uncertainty achievement, transforming the original problem into a single-level program that can be directly solved by commercial solvers. Finally, simulation verification is conducted using an aluminum-based new material industrial park as a case study. The results demonstrate that the hydrogen energy storage system can effectively achieve cross-period transfer and efficient accommodation of renewable electricity. Compared with the conventional box-uncertainty robust optimization method, the proposed approach achieves a superior balance between scheduling economy and operational robustness, validating the effectiveness and engineering practicality of the model.