Distributionally robust planning method for expressway hydrogen refueling station powered by a wind-PV system

Hydrogen fuel has received widespread attention as one of the critical solutions to global energy issues. The popularization of hydrogen fuel cell vehicles (HFCV) requires the deep integration of transportation and energy fields. Considering the complementarity of wind and PV power in remote areas, this paper proposes a planning model of an expressway hydrogen refueling station, including a hydrogen production system (HPS) powered by a wind-PV system and several hydrogen refueling systems (HRSs). The interaction and operation constraints of hydrogen production, storage, delivery, and demand are employed to complete hydrogen supply chain integration in the deterministic planning model. The spatiotemporal distribution of refueling demand at each HRS is obtained by Monte Carlo Simulation. Furthermore, we consider the impact of uncertainty related to renewable energy generation and refueling demand on planning and depict them by ambiguity sets based on Kullback Leibler divergence. The planning problem is transformed into a data-driven distributionally robust model and solved by Value-at-Risk and sample average approximation method. Results indicate that 1) The proposed model can simultaneously collaborate with the segments of the hydrogen supply chain. 2) The proposed method balances the planning reliability and economic efficiency and enhances solution robustness.