Wind　power　is　affected　by　natural　conditions　and　has　high　random　fluctuation　and　intermittency.　With　the　continuous　increase　of　wind　power　penetration,　the　large-scale　fluctuation　of　wind　power　are　increasingly　affecting　the　reliability　and　stability　of　power　system.　Due　to　the　flexible　operation　of　charging/discharging,　battery　energy　storage　system(BESS)　is　widely　used　in　power　system　to　absorb　the　fluctuation　of　renewable　energy　power.　This　paper　presents　a　robust　model　predictive　control(RMPC)　based　strategy,　through　which　BESS　can　be　effectively　scheduled　to　reduce　the　wind　power　fluctuation.　In　the　first　place,　the　structure　of　the　proposed　RMPC　strategy　is　introduced,　and　the　uncertainty　set　of　wind　power　is　constructed.　In　addition,　combining　the　advantages　of　robust　optimization　and　model　predictive　control(MPC),　the　min-max　optimization　problem　is　constructed,　which　is　afterwards　transformed　into　a　deterministic　optimization　problem　according　to　the　dual　transformation　and　solved　by　rolling　optimization.　The　proposed　method　considers　the　maximum　prediction　error　of　wind　power,　thereby　the　effectiveness　of　BESS　in　mitigating　wind　power　fluctuation　can　be　improved.　The　simulation　is　conducted　on　the　real　data　of　wind　power,　and　the　simulation　results　show　the　superiorities　of　RMPC　over　traditional　MPC.　©　2023　IEEE.