With　the　extensive　utilization　of　fossil　fuels,　environmental　concerns　have　been　growing.　Hydrogen-powered　vehicles　(HPVs)　as　well　as　hydrogen　refueling　stations　(HRSs)　are　expected　to　proliferate　to　alleviate　the　pollution.　In　order　to　promote　the　economic　valibity　of　HRSs,　this　paper　proposes　an　optimal　scheduling　model　for　distributed　HRSs　to　maximize　HRSs’　revenue　during　a　day.　HRSs　serves　both　the　mobility　sector　with　fuel　individually　and　the　power　system　with　reserve　demand　response　collaboratively.　The　elaborated　operating　of　electrolysers　and　relaxed　control　of　hydrogen　storage　are　involved　to　enhance　practicability　and　flexibility　of　the　scheduling.　Considering　that　this　model　has　multiple　variables,　intractable　high-dimension　nonlinear　constraints,　a　transfer　multi-agent　reinforcement　learning　algorithm　is　developed.　The　algorithm　consists　of　two　parts:　the　one　is　the　multi-agent　optimization　method　by　transforming　multi-decision　optimization　into　a　multi-agent　optimization;　the　other　is　to　transfer　the　empirical　knowledge　of　the　source　task　to　reinforcement　learning　according　to　the　similarity　between　the　source　task　and　the　target　task,　making　it　cut　down　the　blindness　of　exploration　and　accelerate　convergence.　Numerical　studies　reveal　that　participating　in　the　reserve　services　market　collaboratively　increases　overall　revenues　of　HRSs　by　up　to　32.90%,　and　the　computation　time　is　sharply　shortened　by　approximately　34　times　compared　to　the　basic　reinforcement　learning.　©　2023　Hydrogen　Energy　Publications　LLC