Abstract: Hydrogen energy is one of the important carriers for the low-carbon transition of China’s energy industry.Mobile hydrogen energy systems （MHESs） utilize surplus renewable energy to produce hydrogen,which is injected into hydrogen storage tanks for storage and then transported to hydrogen refueling stations （HRSs） via hydrogen tube trailers （HTs）.This paper proposes a collaborative optimization method for hydrogen generation,storage,and transportation in MHESs based on distributionally robust chance constraint （DRCC）.First,a collaborative optimization model for renewable energy to hydrogen,hydrogen storage,and hydrogen transportation is established,where a“dummy node insertion”method is adopted to equivalently simplify the route planning model of HTs.Then,a data-driven DRCC approach is introduced to deal with the uncertainty of hydrogen demand of each HRS.A Wasserstein distance-based distributionally robust model is constructed for MHESs,which is further transformed into a mixed-integer linear programming based conditional value-at-risk approximation.Finally,case studies are conducted on a typical MHES based on traffic network and simulation results of Xi’an in China,which verifies the validity of the proposed model and method.