A Decision-Dependent Hydrogen Supply Infrastructure Planning Approach Considering Causality Between Vehicles and Stations

In the early commercialization stage of hydrogen fuel cell vehicles (HFCVs), reasonable hydrogen supply infrastructure (HSI) planning decisions is a premise for promoting the popularization of HFCVs. However, there is a strong causality between HFCVs and hydrogen refueling stations (HRSs): the planning decisions of HRSs could affect the hydrogen refueling demand of HFCVs, and the growth of demand would in turn stimulate the further investment in HRSs, which is prompted by the chicken-egg conundrum. Meanwhile, there is a cost contradiction between energy planning and hydrogen refueling convenience of HFCVs in infrastructure planning issues caused by HRSs siting planning. These pose great challenges to solving the optimization problem. To this end, this work establishes a multi-network HSI planning model coordinating hydrogen, power, and transportation networks. Then, to reflect the causal relation between HFCVs and HRSs effectively in the early stage of hydrogen infrastructure investment planning without sufficient historical data, hydrogen demand decision-dependent uncertainty (DDU) and a distributionally robust optimization framework are developed. The uncertainty of hydrogen demand is modeled as a Wasserstein ambiguity set with a decision-dependent empirical probability distribution. Subsequently, to reduce the computational complexity caused by the introduction of a large number of scenarios and high-dimensional nonlinear constraints, we developed an improved distribution shaping method and techniques of scenario and variable reduction to derive the solvable form with less computing burden. Finally, the simulation results demonstrate that this method can reduce costs by at least 7.7% compared with traditional methods and will be more effective in large-scale HSI planning issues. Further, we put forward effective suggestions for the policymakers and investors to formulate relevant policies and decisions.