【目的】 为了保证虚拟电厂调度策略的低碳经济性，提出了考虑新型分布式资源与电碳交易的虚拟电厂分布鲁棒低碳调度模型。【方法】 首先，建立了虚拟电厂电碳交易框架。其次，在虚拟电厂中针对电制氢系统与碳捕集系统这2种新型分布式资源进行建模，并考虑储能、风电、光伏等传统分布式资源。接着，以成本最小化为目标，建立虚拟电厂低碳调度模型。鉴于风光出力与电氢负荷的精确概率分布难以获得，利用1范数和无穷范数构建其概率分布不确定集合，为避免传统多离散场景分布鲁棒方法的复杂迭代过程，将分布鲁棒调度模型进行对偶转化求解。最后通过算例验证了所提分布鲁棒调度模型在处理源荷不确定性、提升虚拟电厂调度经济性与低碳性方面的有效性。【结果】 考虑电碳交易比不考虑电碳交易降低了约24.7%，过剩的清洁能源可全部在电力市场售出，使运行调度实现获利；同时考虑碳捕集系统与电制氢系统后，弃电量与运行成本比仅考虑碳捕集系统分别进一步降低了约34.7%和28.1%，比仅考虑电制氢系统分别进一步降低了约2.6%和1.8%；所提分布鲁棒方法的总收益误差约为1.7%，求解速度提升了约40%。【结论】 考虑电碳交易或同时考虑电制氢系统与碳捕集系统这2种新型分布式资源能够降低调度成本、弃电量与碳排放，且所提分布鲁棒方法决策结果精确性良好，求解速度得到大幅提升。

[Objective] To improve the low-carbon economic performance of scheduling strategies for virtual power plants， this study proposes a distributionally robust low-carbon scheduling model that incorporates emerging distributed resources and electricity-carbon trading. [Methods] First， this study established an electricity-carbon trading framework for a virtual power plant. Second， two emerging distributed resources （e.g.， electric hydrogen production system and carbon capture system） were modeled within virtual power plants， along with traditional distributed resources （e.g.， energy storage， wind power， and photovoltaics）. Next， to minimize costs and consider the impact of electricity carbon trading， a low-carbon scheduling model for virtual power plants was established. Owing to the difficulty in obtaining accurate probability distributions of wind and solar power outputs and electric hydrogen loads， an uncertainty set of probability distributions was constructed using the 1-norm and infinite-norm. To avoid the complex iterations required in traditional multiple discrete-scenario distributionally robust optimization methods， this study solves the proposed model using a strong duality. Finally， the effectiveness of the proposed model in addressing source-load uncertainty and improving economic and low-carbon performance was verified based on numerical examples.[Results] Electricity-carbon trading reduced costs by approximately 24.7% compared to no electricity-carbon trading. Excess renewable energy could be sold entirely to the electricity market to obtain profitable operational results. Considering both the carbon capture system and the electric hydrogen production system， both abandoned electricity and operating costs are further respectively reduced by about 34.7% and 28.1% when only considering the carbon capture system， and respectively by about 2.6% and 1.8% when only considering the electric hydrogen production system. The total profit error of the proposed distributionally robust optimization method was approximately 1.7%， and the solving speed improved by approximately 40%.[Conclusions] Electricity-carbon trading and the integration of electric hydrogen production system and carbon capture system can jointly reduce scheduling costs， abandoned electricity， and carbon emissions. Moreover， the proposed distributionally robust optimization method showed high accuracy in decision-making results and significantly improved the solving speed.