Optimization Operation of Park CCHP Systems Under Uncertainty Considering Ladder Carbon Trading Mechanism

With the growing emphasis on carbon neutrality, the low-carbon optimal scheduling of energy systems has become a key research focus. This paper proposes an uncertainty-aware optimization model for Combined Cooling, Heating, and Power (CCHP) systems in industrial parks under the constraints of a Ladder Carbon Trading Mechanism (LCTM). To address the risks caused by renewable energy fluctuations and load forecasting errors, a Distributionally Robust Optimization (DRO) approach is adopted. An ambiguity set based on the Wasserstein distance is constructed to enhance the model's adaptability and robustness under uncertainty. The model integrates tiered carbon pricing to dynamically adjust carbon costs and incentivize emission reductions. A case study based on a high-tech industrial park in central China demonstrates that the proposed model effectively balances electricity, heating, and cooling loads while minimizing operational costs and carbon emissions. Compared with deterministic, stochastic, and traditional robust optimization methods, the DRO model shows superior economic performance and robustness across multiple scenarios. Additionally, the introduction of energy storage further improves system flexibility and renewable energy utilization, significantly reducing carbon trading costs. This study provides theoretical insights and practical solutions for the optimal scheduling of park-level integrated energy systems under carbon market constraints.