To　improve　the　local　accommodation　of　distributed　renewable　energy　and　realize　hierarchical　optimal　scheduling　model　for　a　distribution　network,　this　paper　proposes　a　multi-objective　ant　colony　dynamic　partitioning　algorithm　based　on　MOEA/D　and　a　day-ahead　optimal　dispatching　model　based　on　dynamic　partitioning.　Using　a　power　flow　tracing　algorithm　and　bipartite　modularity　in　complex　network　theory,　an　energy　bipartite　modularity　index　that　quantifies　the　degree　of　energy　coupling　between　partitions　is　proposed.　Based　on　a　Jacobian　matrix　of　power　flow　calculation,　heuristic　information　in　the　ant　colony　algorithm　is　derived.　Combined　with　the　prediction　scenarios,　the　energy　bipartite　modularity　and　power　reserve　of　the　partitions　are　used　as　the　objective　function,　and　the　multi-objective　ant　colony　algorithm　is　used　to　generate　dynamic　partitions.　A　day-ahead　optimal　scheduling　model　based　on　dynamic　partitions　is　established　with　the　objectives　of　the　partitions＇　communication　line　power,　insufficient　flexibility　rate　and　lowest　cost.　The　Pareto　optimum　is　determined　based　on　the　NSGA-II　algorithm.　Finally,　based　on　the　IEEE33　bus　distribution　network,　the　proposed　model　and　method　are　verified.　The　results　show　that　the　dynamic　partitioning　and　day-ahead　scheduling　using　this　method　can　effectively　improve　the　system＇s　ability　to　deal　with　the　uncertainty　of　renewable　energy,　and　lay　the　foundation　for　suppressing　the　fluctuation　of　renewable　energy　locally.　©　2022　Power　System　Protection　and　Control　Press.　All　rights　reserved.