Hydrogen fueling stations (HFSs) will proliferate in the near future as they are prerequisites for the fast developing hydrogen-powered vehicles (HVs). The HFSs can utilize cheap renewable energy from, e.g., wind turbines (WTs), to generate and store hydrogen on site locally. Conventional studies usually ignore the independence of the WT and HFSs and perform joint operation optimization to them. This article, however, proposes a cooperative operation model for the WT and HFSs considering individual benefit. Nash bargaining theory is employed to deal with the energy trading and benefit sharing problems during the cooperation. In particular, the conditional value-at-risk (CVaR) is used to characterize the risk-preference degree of HFSs against the uncertainties of electricity price. Moreover, an improved benders decomposition (BD) algorithm is proposed to solve the energy trading problem in a distributed manner for privacy concern; while an analytical method is developed to solve the payment bargaining problem. Numerical experiments based on two case studies indicate that the cooperation can greatly reduce the hydrogen production cost for HFSs. In addition, the proposed algorithm outperforms conventional alternating direction method of multipliers (ADMM) algorithm in the case studies.

中文翻译：

---

风力涡轮机和加氢站的现场氢气协同运行

氢燃料站（HFS）将在不久的将来激增，因为它们是快速发展的氢动力汽车（HV）的先决条件。HFS可以利用来自例如风力涡轮机（WT）的廉价可再生能源在当地就地产生和存储氢。常规研究通常会忽略WT和HFS的独立性，并对其进行联合操作优化。但是，本文提出了考虑个人利益的WT和HFS的合作操作模型。纳什讨价还价理论被用来解决合作过程中的能源交易和利益共享问题。特别是，使用条件风险值（CVaR）来表征HFS针对电价不确定性的风险偏好程度。此外，针对隐私问题，提出了一种改进的Benders分解算法，以解决分布式能源交易问题。同时开发了一种解决支付谈判问题的分析方法。基于两个案例研究的数值实验表明，这种合作可以大大降低HFS的制氢成本。此外，在案例研究中，该算法优于传统的乘数交替方向法（ADMM）。