This　paper　is　aimed　at　proposing　a　short-term　hybrid　energy　system　robust　optimization　model　for　regional　energy　system　planning　and　air　pollution　mitigation　based　on　the　inexact　multi-stage　stochastic　integer　programming　and　conditional　value-at-risk　method　through　a　case　study　in　Shandong　Province,　China.　Six　power　conversion　technologies　(i.e.,　coal-fired　power,　hydropower,　photovoltaic　power,　wind　power,　biomass　power,　and　nuclear　power)　and　power　demand　sectors　(agriculture,　industry,　building　industry,　transportation,　business,　and　residential　department)　were　considered　in　the　proposed　model.　The　optimized　electricity　generation,　capacity　expansion　schemes,　and　economic　risks　were　selected　to　analyze　nine　defined　scenarios.　Results　revealed　that　electricity　generations　of　clean　and　new　power　had　obvious　increasing　risks　and　were　key　considerations　of　establishing　additional　capacities　to　meet　the　rising　social　demands.　Moreover,　the　levels　of　pollutants　mitigation　and　risk-aversion　had　a　significant　influence　on　different　power　generation　schemes　and　on　the　total　system　cost.　In　addition,　the　optimization　method　developed　in　this　paper　could　effectively　address　uncertainties　expressed　as　probability　distributions　and　interval　values,　and　could　avoid　the　system　risk　in　energy　system　planning　problems.　The　proposed　optimization　model　could　be　valuable　for　supporting　the　adjustment　or　justification　of　air　pollution　mitigation　management　and　electric　power　planning　schemes　in　Shandong,　as　well　as　in　other　regions　of　China.