A new decision-dependent DRO framework considering indirect network effects for hydrogen supply infrastructure expansion planning

At the early commercialization stage of hydrogen fuel cell vehicle (HFCV), there is a ubiquitous interaction relationship between the sales of HFCVs and the deployment of hydrogen refueling stations (HRSs), which poses difficulties in the investment planning of HRSs. This work introduces a new hydrogen supply infrastructure (HSI) expansion planning method that takes into account indirect network effects. First, to characterize the effects of HRS planning decisions on HFCVs, we propose a traffic flow redistribution model and a hydrogen demand differentiated calculation method. Second, to characterize the effects of HFCV parc on HRS planning decisions, we propose a decision-dependent ambiguity set in which the endogenous and exogenous factors affecting uncertainty are portrayed. Combining the concept of out-of-sample disappointment, we identify a meta-optimization problem and further derive a tighter upper bound on the out-of-sample risk by applying the probability scaling theory and measure concentration theory. On this basis, we present a novel approach to quantify the actual investment risk. Then, we establish a decision-dependent distributionally robust optimization (DRO) problem considering potential investment attitudes, to resolve the contradiction that lower decision conservatism and lower out-of-sample disappointment cannot be achieved simultaneously. To solve this optimization problem, we propose a logic-based iterative solution method. It can effectively avoid uncontrolled infrastructure planning due to positive effects between vehicles and stations. Simulation results show that the proposed method can reduce the out-of-sample costs by 5.2% and 4.1% relatively while lowering the investment cost.