An　inexact　two-stage　stochastic　risk-aversion　model　was　developed　in　this　study　for　supporting　regional　energy　system　planning　and　management.　It　can　tackle　uncertainties　described　in　terms　of　probability　distributions　and　permits　the　in-depth　analysis　of　various　policy　scenarios　when　the　promised　policy　targets　are　violated.　Moreover,　it　can　help　local　decision-makers　evaluate　trade-offs　between　energy　system　economy　and　stability　associated　with　different　robust　criteria　(risk-aversion　levels).　An　actual　case　study　in　the　Beijing-Tianjin-Hebei　(BTH)　region,　China,　was　provided　for　demonstrating　the　applicability　of　the　developed　model.　The　results　indicated　that　electricity　generated　by　coal-burning　(coal-fired　power　and　coal-fired　cogeneration)　and　gas-fired　heating　would　be　the　main　power　forms　for　power　and　heat　supply　to　get　the　maximum　security.　Furthermore,　some　policy　implications　can　be　concluded　as　follows:　(a)　more　attention　should　be　focused　on　increasing　the　local　power　generation　capacity　in　the　BTH　region　rather　than　importing　electricity　from　other　regions;　(b)　the　introduction　of　risk　aversion　into　the　optimization　model　is　conducive　to　the　development　of　solar　power,　biomass　power,　and　gas-fired　cogeneration;　and　(c)　renewable　energy　(e.g.,　biomass　energy　and　geothermal　energy)　development　in　the　BTH　region　still　has　a　long　way　to　go,　because　it　is　constrained　by　resource　endowment　and　geographical　location.　Generally,　the　proposed　model　not　only　can　help　decision-makers　identify　the　desired　energy　system　management　policies　under　risk　considerations,　but　also　could　be　viewed　as　a　prime　example　for　energy　system　planning　and　management　at　different　regional　levels　(e.g.,　city,　province,　and　multiprovince)　and　used　as　a　reference　for　energy　structure　adjustment　in　other　metropolitan　groups　such　as　the　Yangtze　River　Delta　and　Pearl　River　Delta　urban　agglomerations.　Published　by　AIP　Publishing.