Due　to　the　inherent　uncertainty　and　the　mismatch　between　renewable　energy　output　and　load　demand,　renewable　energy-based　system　optimization　is　increasingly　important　in　China.　Therefore,　electricity　demand　response　(EDR)　is　critical　to　the　stability　and　efficiency　of　an　integrated　renewable　energy　system　(IRES).　The　customers＇　demand　response　under　the　time-of-use　(TOU)　mechanism　is　the　key　to　the　entire　process.　However,　how　to　optimize　integrated　renewable　energy　system　based　on　accurately　identifying　the　electricity　demand　response　participation　of　customers　is　one　of　the　main　issues　at　this　stage.　To　solve　the　problem,　this　paper　presents　a　novel　approach　for　integrated　renewable　energy　system　optimization　considering　electricity　demand　response　management　and　multistage　energy　storage　systems　from　the　perspective　of　Fujian　Province,　China.　To　enable　large-scale　renewable　energy　integration,　the　original　output　is　processed　using　the　Hampel　identifier　(HI)-Savitzky-Golay　filter　(SGF)　technology.　A　machine　learning　(ML)-based　identification　model　is　developed　for　EDR　to　determine　whether　the　customers　are　participating　or　not.　Driven　by　the　time-of-use　mechanism,　customers　can　make　dynamic　adjustments　based　on　the　Logistics　function.　After　that,　an　integrated　renewable　energy　system　model　optimization　strategy　is　proposed　based　on　satisfying　customers＇　electricity　demand　response.　Throughout　the　optimization　process,　the　multistage　energy　storage　system　plays　a　vital　role　in　the　residual　fluctuation　absorption　for　renewable　energy　filtering,　the　dynamic　adjustment　process　of　the　demand　response,　and　the　integrated　renewable　energy　system　optimization.　Through　extensive　case　studies　and　discussions,　it　is　demonstrated　that　random　forest　(RF)　and　support　vector　machine　(SVM)　reach　80　%　identification　accuracy　with　a　20　%　testing　ratio.　According　to　the　optimization　approach　proposed　in　this　paper,　the　minimum　operation　cost　can　be　calculated　to　be　9.336*106　CNY.　The　methodology　presented　in　this　paper　establishes　a　special　relationship　between　electricity　demand　response　identification　and　dynamic　adjustments,　and　provides　an　optimization　model　for　Fujian　Province,　China,　which　is　a　reliable　reference　for　future　work.