In recent years, with the rapid development of the energy Internet and the deepening of the complementary coupling of various energy sources, the concept of multi-energy virtual power plant comes into being. At the same time, insufficient research on optimal scheduling of multi-energy virtual power plants under multiple uncertainties. Here we propose a robust stochastic optimal dispatching method to solve the scheduling problem under multiple uncertainties. For the source side uncertainties, the uncertain set of cardinalities with a robust adjustable coefficient is adopted to describe the output of wind turbines and photovoltaics. For the load side uncertainties, the Wasserstein generative adversarial network with gradient penalty is used to generate electric, thermal, cooling, and natural gas load scenarios, and the K-medoids clustering is used to get typical scenes. A two-stage robust stochastic optimal model of the min-max-min structure was established. Based on the dual transformation theory and the column constraint generation algorithm, the original model was solved alternately. Finally, the effectiveness of the proposed model and algorithm is verified by simulation analysis. The proposed method can get the scheduling scheme with the lowest operating cost in the worst scenario and is conducive to reducing the overall scheduling cost of the system.

近年来，随着能源互联网的飞速发展和各种能源互补耦合的加深，多能源虚拟电厂的概念应运而生。同时，关于多不确定性下的多能源虚拟电厂优化调度的研究不足。在此，我们提出了一种鲁棒的随机最优调度方法来解决多种不确定性情况下的调度问题。对于源端不确定性，采用具有鲁棒可调系数的不确定基数集来描述风力涡轮机和光伏发电设备的输出。对于负载方面的不确定性，使用具有梯度惩罚的Wasserstein生成对抗网络生成电，热，冷和天然气负载场景，K-medoids聚类用于获取典型场景。建立了最小-最大-最小结构的两阶段鲁棒随机最优模型。基于对偶变换理论和列约束生成算法，交替求解原始模型。最后，通过仿真分析验证了所提模型和算法的有效性。所提出的方法可以在最坏的情况下获得具有最低运行成本的调度方案，有利于降低系统的整体调度成本。仿真分析验证了所提模型和算法的有效性。所提出的方法可以在最坏的情况下获得具有最低运行成本的调度方案，有利于降低系统的整体调度成本。仿真分析验证了所提模型和算法的有效性。所提出的方法可以在最坏的情况下获得具有最低运行成本的调度方案，有利于降低系统的整体调度成本。