历史数据驱动的综合能源系统分布式鲁棒低碳优化模型OA

To effectively improve the economic benefits of integrated energy systems(IES),reduce carbon emissions,promote the efficient utilization of hydrogen energy,and mitigate the uncertainty of renewable energy output,a distributed robust low-carbon optimization model for IES driven by historical data was proposed.A data-driven uncertainty quantification method was used to construct a probability distribution ambiguity set with joint constraints of 1-norm and∞-norm based on historical renewable energy output data.By identifying the worst-case probability distribution scenario,a min-max-min three-layer distributed robust optimization framework was constructed,ensuring robustness while reducing scheduling conservatism.An integrated tiered carbon trading mechanism and power-to-gas(P2G)technology were adopted to incentivize carbon reduction through phased carbon pricing,achieving closed-loop utilization of carbon elements within the system while balancing environmental benefits and economic costs.The distributed robust model was solved using the column-and-constraint generation(C&CG)algorithm.The results showed that the daily carbon trading cost obtained based on the hybrid-norm ambiguity set was approximately 1.6%lower than that derived from the single-norm ambiguity set,further reducing the conservatism of the renewable energy output model.Meanwhile,with a reasonable division of tiered intervals,carbon emissions and carbon trading costs were reduced by 16.35%and 22.35%,respectively,achieving a relative balance between carbon emissions and carbon trading costs.The experimental results verified the advantage of the model in reducing carbon trading costs.The proposed model provides theoretical support and practical reference for the planning and operation of IES.