The　risk　management　and　decision　making　for　power　systems　have　been　evolving　from　the　qualitative　methods　to　the　quantitative　methods.　The　traditional　decision-making　model　based　on　the　system　average　risk　and　the　average　cost　has　commensurated　events　of　low-probability/high-damage　and　events　of　high-probability/low-damage,　which　ignores　the　relative　importance　of　these　events　as　they　are　viewed　by　the　decision　makers.　In　this　paper,　we　propose　the　concepts　of　conditional　risk　indices　and　conditional　costs　of　power　systems,　and　establish　a　novel　risk　decision-making　model　based　on　these　indices.　The　conditional　indices　will　be　helpful　for　the　risk　decision　makers　to　thoroughly　tradeoff　between　different　risk　domains　according　to　their　preferences.　Further,　the　system　risk　is　monetarily　represented　as　the　reliability　cost　by　the　customer　composite　damage　function,　and　the　decision-making　model　is　solved　by　sequential　Monte　Carlo　simulation.　Finally,　the　modified　IEEE-RTS　incorporating　wind　energy　is　investigated　by　the　proposed　model　and　method.　The　effects　of　decision　preferences,　the　wind　power　capacity　and　cost　on　the　system　decision　making　are　discussed.