The　proper　integration　of　wind　power-driven　grids　relies　heavily　on　a　reliable　balance　between　electricity　production　and　demand.　Therefore,　accurate　prediction　is　essential　for　planning　and　efficient　operation　of　wind　power　systems　to　ensure　their　continuous　supply.　However,　increasingly　severe　power　quality　disturbance　(PQD)　constantly　disturbs　this　equilibrium,　which　affects　the　accuracy　of　wind　power　prediction　to　a　large　extent.　For　this　purpose,　this　paper　developed　a　novel　optimization　methodology　to　improve　wind　power　prediction　accuracy　considering　micro　PQD　dimension　reduction　and　elimination　for　wind-storage　integrated　systems.　A　novel　idea　has　been　presented　in　this　optimization　methodology　to　eliminate　the　barrier　of　PQD　and　wind　power　prediction.　In　the　micro　aspect,　a　PQD　dimension　reduction　and　elimination　strategy　based　on　dynamic　mode　decomposition　(DMD)　and　Wiener　Filter　(WF)　is　proposed　to　eliminate　the　autonomy　of　PQD.　This　elimination　of　autonomy　allows　the　WF　to　get　a　higher　signal-to-noise　ratio　(SNR).　In　the　macro　aspect,　this　paper　takes　PQD　of　different　complexity　into　full　consideration,　and　compares　their　effects　on　improving　wind　power　prediction　accuracy　based　on　deep　learning-based　approaches.　Through　the　experimental　verification,　it　is　confirmed　that　the　proposed　DMD-WF　optimization　method　has　demonstrated　an　effective　dimension　reduction　and　elimination　of　PQD.　Moreover,　it　is　found　that　the　proposed　PQD　optimization　method　contributes　to　improve　the　deep　learning-based　prediction　accuracy　when　PQD　is　more　complex.　The　proposed　methodology　creates　a　novel　perspective　to　improve　the　short-term　wind　power　prediction　accuracy,　which　provides　a　theoretical　and　methodological　guidance　for　future　development　of　large-scale　integrated　wind-storage　systems.