A Data-driven Probabilistic Calculation Method for Optimal Electricity-Hydrogen-Traffic Flows

Aiming at an efficient distributed hydrogen supply network (DHSNs), this paper proposes a data-driven probabilistic calculation method for optimal electricity-hydrogen-traffic flows. Firstly, an optimal electricity-hydrogen-traffic flow model is established considering the couplings of mixed traffic flows on hydrogen and charging demand, power couplings, as well as inter-station hydrogen couplings by hydrogen tube trailers. Then, a data-driven probabilistic solving method based on polynomial chaos expansion is proposed, to improve the solving efficiency. The proposed method is established on the raw data of uncertainties, requiring no probability density function (PDF) information and effectively reducing the computation burdens and assuring accuracy stability by analytical computation based on the sparse grid-based Gaussian integration. Finally, case studies are carried out and verify a 43.25% improvement in overall profit and high accuracy of the probabilistic solving method.