This　article　proposes　a　novel　segmented　distributionally　robust　optimization　method　for　real-time　power　dispatch　with　correlated　wind　forecast　errors.　In　the　proposed　dispatch　model,　a　segmented　linear　decision　rule　incorporating　an　allowed　threshold　for　wind　forecast　error　is　developed.　The　excess　wind　fluctuation　beyond　the　optimized　threshold　will　be　first　discarded,　and　then　units　adopts　the　segmented　linear　decision　for　the　remaining　wind　forecast　error.　The　segmented　linear　decision　rule　upgrades　the　traditional　parameterized　ambiguous　set　into　a　variable-involved　segmented　ambiguous　set,　which　makes　the　dispatch　more　flexible　but　meanwhile　harder　to　solve.　Through　the　equivalent　conversion　of　uncertain　variables　and　dual　theory　of　semi-infinite　problems,　the　proposed　dispatch　model　is　recast　as　a　semi　definite　programming　with　nonconvex　bilinear　constraints.　To　solve　the　complex　problem,　a　difference-of-convex　optimization　(DCO)　addressing　bilinear　constraints　with　alternating　optimization　(AO)-based　initialization　is　developed.　AO　with　fast　computing　speed　accelerates　the　convergence　by　producing　a　good　enough　initial　feasible　solution,　while　DCO　with　stronger　search　ability　enhances　the　solution　quality　in　the　subsequent　optimization.　Finally,　numerical　simulations　in　three　cases　validate　the　economic　efficiency　of　the　proposed　model　and　the　superiority　of　the　convexified　solving　method.