An　advanced　metro　system　becomes　imperative　towards　efficient　and　sustainable　operations　with　the　rapid　growth　of　urban　construction.　As　the　essential　factors　to　implement　transport　sustainability,　a　referred　energy-efficient　trajectory　and　an　energy-saving　infrastructure　for　train　operations　could　guide　trains　running　through　with　conquering　complicated　environmental　impacts　and　help　to　control　the　energy　flows　in　traction　processes.　Thus,　we　attempt　to　discover　the　optimal　way　of　adapting　to　the　track　changes　and　resistances　in　train　running　and　evaluate　the　energy　exchanges　with　Energy　Storage　System　(ESS)　in　an　extended　power　supply　network.　In　this　study,　we　firstly　optimize　a　train　trajectory　with　consideration　of　air　and　track　resistances　by　using　a　modified　Q-Learning　(QL)　method.　Secondly,　for　coordinated　application　between　ESS　and　trains,　an　integration　module　is　designed　by　implanting　train　trajectories.　Specifically,　based　on　the　working　properties　of　ESS,　a　deceleration-acceleration　time　separation　is　matched　for　supplementing　the　charging/discharging　times　of　the　devices.　The　space-time　presentation　is　introduced　to　portray　train　running　phases　for　connections　with　control　processes　of　ESS.　Then,　a　Mixed　Integer　Linear　Programming　(MILP)　model　is　proposed　for　minimizing　deceleration-acceleration　overlaps.　A　Tabu　Search　(TS)　algorithm　is　introduced　to　solve　such　complex　problems.　Finally,　a　numerical　test　is　conducted　to　demonstrate　the　feasibility　of　the　model　in　Beijing,　China.