Hybrid Knowledge and Data-Driven Hydrogen Trading for Renewable-Dominated Hydrogen Refueling Stations

Abstract

This paper proposes a hybrid knowledge and data-driven predict-then-optimize paradigm for green hydrogen (H₂) trading among renewable-dominated hydrogen refueling stations (HRSs). Firstly, a data-driven H₂ load forecasting method is formulated where the key influencing features are captured by XGBoost and the Informer algorithm with encoder and decoder processes is utilized to generate the predicted time series of hydrogen load. Then, a bi-level hybrid knowledge and data-driven H₂ trading model with rolling horizon optimization is proposed to determine the optimal trading quantity of H₂ and dynamically optimize the transportation routes for the traded H₂ based on the cell transmission model and traffic state. Moreover, a fully distributed solution algorithm is developed to decompose the complex multi-period H₂ trading problem into local electricity and hydrogen dispatch subproblems of HRSs for efficiently obtaining the optimal H₂ trading amount. Comparative studies have demonstrated the superior performance of the proposed methodology on the improvement of the distributed renewable energy accommodation and economic benefits for HRSs.