Electric vehicle aggregator (EVAGG) is an independent entity that facilitates exchanging electricity between electric vehicles (EVs) and the grid. Energy hub (EH) is another independent entity playing a remarkable role in enhancing the efficiency, flexibility, and reliability of multi-energy systems. Although interacting between various agents is beneficial to enhance their capability, it is challenging to schedule such interconnected entities. In this paper, EVAGG and EH, as independent entities, are scheduled independently and only exchange the information of electrical energy. The EVAGG scheduling is a function of EV owners’ driving patterns, including EVs’ arrival and departure times and the initial state of charge. Besides, both the EVAGG and EH operations are affected by the uncertainty of the locational marginal prices. Hence, this paper proposes a hybrid decentralized robust optimization-stochastic programming (DRO-SP) model based on the alternating direction method of multipliers to coordinate the management of entities. Stochastic programming is used to model the uncertainties of the EVs patterns, while the uncertainties of the locational marginal prices are modeled via robust optimization to grasp the worst-case realization. Simulation results demonstrate the effectiveness of the proposed hybrid DRO-SP in terms of economic scheduling the entities while guaranteeing information privacy between entities.

电动汽车聚合商 (EVAGG) 是一个独立实体，可促进电动汽车 (EV) 与电网之间的电力交换。能源中心 (EH) 是另一个独立实体，在提高多能源系统的效率、灵活性和可靠性方面发挥着显着作用。尽管各种代理之间的交互有利于增强它们的能力，但调度这种相互关联的实体具有挑战性。在本文中，EVAGG和EH作为独立的实体，独立调度，仅交换电能信息。EVAGG 调度是电动汽车车主驾驶模式的函数，包括电动汽车的到达和离开时间以及初始充电状态。此外，EVAGG 和 EH 业务都受到区域边际价格不确定性的影响。因此，本文提出了一种基于乘法器交替方向法的混合分散鲁棒优化-随机规划（DRO-SP）模型来协调实体的管理。随机规划用于对电动汽车模式的不确定性进行建模，而位置边际价格的不确定性则通过稳健优化进行建模，以掌握最坏情况的实现。仿真结果证明了所提出的混合 DRO-SP 在实体经济调度方面的有效性，同时保证了实体之间的信息隐私。而位置边际价格的不确定性则通过稳健优化建模，以掌握最坏情况的实现。仿真结果证明了所提出的混合 DRO-SP 在实体经济调度方面的有效性，同时保证了实体之间的信息隐私。而位置边际价格的不确定性则通过稳健优化建模，以掌握最坏情况的实现。仿真结果证明了所提出的混合 DRO-SP 在实体经济调度方面的有效性，同时保证了实体之间的信息隐私。