Optimized Scheduling of Electricity‐Hydrogen‐Ammonia‐Carbon Integrated Energy System Based on Distributionally Robust Optimization

To address the uncertainties of wind‐solar power outputs and multi‐energy collaborative optimization in integrated energy systems with high renewable penetration, this paper proposes a distributionally robust optimization (DRO) scheduling method for an electricity‐hydrogen‐ammonia‐carbon system. First, a Gaussian Mixture Model (GMM) is used to model the probability distribution of wind‐solar outputs, generating initial scenarios that cover diverse spatiotemporal fluctuation characteristics, which are then reduced to typical scenarios via improved K‐medoids clustering. Second, a hydrogen‐ammonia‐carbon multi‐energy coupling framework is designed, incorporating a collaborative decarbonization model for coal‐fired units via ammonia co‐firing and oxy‐fuel combustion, with a stepped carbon pricing mechanism and a power‐to‐hydrogen‐to‐ammonia conversion scheduling strategy embedded. A DRO approach is introduced to balance robustness and economy under extreme scenarios, constructing a wind‐solar output uncertainty set via combined 1‐norm/∞‐norm constraints and solving the model using the Column and Constraint Generation (C&CG) algorithm. Case studies validate the method's effectiveness in enhancing system economy, low‐carbon performance, and operational robustness.