Considering　the　multiple　uncertainties　in　integrated　energy　system　(IES)　clusters　and　the　need　for　flexible　and　economically　efficient　coordinated　operation,　a　stochastic　robust　hybrid　optimization　(SRHO)　method　for　IES　clusters　based　on　different　contribution　factors　is　proposed.　Firstly,　a　multi-stage　SRHO　model　is　developed　to　handle　the　uncertainties　of　electricity　prices,　renewable　energy　(RE),　and　load.　The　flexible　robust　level　is　calculated　using　the　information　gap　decision　theory　(IGDT),　while　the　stochastic　scenarios　of　RE　and　load　are　generated　based　on　the　dynamic　Bayesian　network　(DBN).　Secondly,　a　cooperative　game　model　for　IES　cluster　is　constructed　using　asymmetric　Nash　bargaining　game　theory.　The　benefit　distribution　of　IES　is　carried　out　based　on　three　contribution　factors:　electricity　trading　contribution　rate,　marginal　contribution　rate,　and　deviation　rate　of　uncertainty　factors,　to　improve　the　enthusiasm　of　IES　to　participate　in　cooperation.　Finally,　the　stochastic　dichotomy　method　(SDM)　is　used　to　decouple　the　IGDT　and　SRHO　models,　and　the　IES　cluster　cooperative　game　model　is　solved　using　the　AOP-Looped　column-and-constraint　generation　(C&CG)　and　the　alternating　direction　method　of　multipliers　(ADMM)　algorithm.　Additionally,　the　progressive　hedging　(PH)　algorithm　is　employed　to　decompose　the　master　problem　into　scenario-based　subproblems　for　parallel　solving,　thereby　accelerating　the　overall　optimization　process.　The　simulation　results　verify　the　effectiveness　of　the　proposed　method　in　IES　cluster　optimization　scheduling.　©　2025　Elsevier　Ltd