The　stochastic　volatility　of　renewable　energy　generation　and　the　time　series　coupling　characteristics　of　energy　storage　operation　control　bring　many　challenges　to　the　energy　management　and　optimal　operation　of　microgrid,　which　becomes　a　hot　topic　for　academic　research.　This　paper　proposes　an　energy　management　and　optimization　strategy　for　microgrid　based　on　improved　dueling　deep　Q　network　(DQN)　algorithm.　The　strategy　adopts　a　multi-parameter　operation　exploration　mechanism　and　an　optimally　designed　neural　network　structure　to　learn　the　environmental　information　such　as　the　power　output　of　distributed　renewable　energy,　the　electricity　price　of　energy　trading　market　and　the　state　of　electric　load,　and　applies　the　learned　strategy　to　microgrid　energy　management　and　optimization.　The　simulation　results　show　that　the　performance　of　the　energy　management　and　optimization　strategy　for　microgrid　based　on　the　improved　dueling　DQN　algorithm　is　better　than　the　scenario-based　stochastic　programming　algorithm,　the　DQN　algorithm　and　the　dueling　DQN　algorithm.　©　2022　Automation　of　Electric　Power　Systems　Press.