Restricted　by　conventional　energy　resources　and　environmental　space,　the　sustainable　development　of　urban　power　sector　faces　enormous　challenges.　Renewable　energy　generation　and　carbon　capture　and　storage　(CCS)　are　attractive　technologies　for　reducing　conventional　energy　resource　consumption　and　improving　CO2　emission　mitigation.　Considering　the　limitation　of　expensive　investment　cost　on　their　wide　application,　a　stochastic　optimization　model　for　the　optimal　design　and　operation　strategy　of　regional　electric　power　system　is　proposed　to　achieve　conventional　resource-consumption　reduction　and　CO2　emission　mitigation　under　cost-risk　control.　The　hybrid　method　integrats　interval　two-stage　stochastic　programming　with　downside　risk　theory.　It　can　not　only　effecttively　deal　with　the　complex　uncertainties　expressed　as　discrete　intervals　and　probability　distribution,　but　also　help　decision-makers　make　cost-risk　tradeoff　under　predetermined　budget.　The　proposed　model　is　applied　in　the　electric　power　system　planning　of　Zhejiang　Province,　an　economically　developed　area　with　limited　fossil　energy　resources.　The　influences　of　different　resource　and　environmental　policies　on　the　investment　portfolio　and　power　system　operation　are　analyzed　and　discussed　under　various　scenarios.　The　results　indicated　that　different　policies　would　lead　to　different　generation　technology　portfolios.　The　aggressive　CO2　emission　reduction　policy　could　stimulate　the　development　of　CCS　technology,　and　the　electric　power　system　would　still　heavily　rely　on　coal　resource,　while　the　tough　coal-consumption　control　policy　could　directly　promote　regional　renewable　energy　development　and　electric　power　structure　adjustment.