In　this　study,　in　order　to　improve　regional　energy　system　adjustment,　a　multistage　stochastic　inexact　robust　programming　(MSIRP)　is　proposed　for　electric-power　generation　planning　and　structure　adjustment　management　under　uncertainty.　Scenario-based　inexact　multistage　stochastic　programming　and　stochastic　robust　optimization　were　integrated　into　general　programming　to　reflect　uncertainties　that　were　expressed　as　interval　values　and　probability　distributions　in　the　objective　function　and　constraints.　An　MSIRP-based　energy　system　optimization　model　is　proposed　for　electric-power　structure　management　of　Zibo　City　in　Shandong　Province,　China.　Three　power　demand　scenarios　associated　with　electric-power　structure　adjustment,　imported　electricity,　and　emission　reduction　were　designed　to　obtain　multiple　decision　schemes　for　supporting　regional　sustainable　energy　system　development.　The　power　generation　schemes,　imported　electricity,　and　emissions　of　CO2　and　air　pollutants　were　analyzed.　The　results　indicated　that　the　model　can　effectively　not　only　provide　a　more　stable　energy　supply　strategies　and　electric-power　structure　adjustment　schemes,　but　also　improve　the　balanced　development　between　conventional　and　new　clear　power　generation　technologies　under　uncertainty.