In　this　paper,　we　investigate　a　novel　real-time　pricing　scheme,　which　considers　both　renewable　energy　resources　and　traditional　power　resources　and　could　effectively　guide　the　participants　to　achieve　individual　welfare　maximization　in　the　system.　To　be　specific,　we　develop　a　Lagrangian-based　approach　to　transform　the　global　optimization　conducted　by　the　power　company　into　distributed　optimization　problems　to　obtain　explicit　energy　consumption,　supply,　and　price　decisions　for　individual　participants.　Also,　we　show　that　these　distributed　problems　derived　from　the　global　optimization　by　the　power　company　are　consistent　with　individual　welfare　maximization　problems　for　end-users　and　traditional　power　plants.　We　also　investigate　and　formalize　the　vulnerabilities　of　the　real-time　pricing　scheme　by　considering　two　types　of　data　integrity　attacks:　Ex-ante　attacks　and　Ex-post　attacks,　which　are　launched　by　the　adversary　before　or　after　the　decision-making　process.　We　systematically　analyze　the　welfare　impacts　of　these　attacks　on　the　real-time　pricing　scheme.　Through　a　combination　of　theoretical　analysis　and　performance　evaluation,　our　data　shows　that　the　real-time　pricing　scheme　could　effectively　guide　the　participants　to　achieve　welfare　maximization,　while　cyber-attacks　could　significantly　disrupt　the　results　of　real-time　pricing　decisions,　imposing　welfare　reduction　on　the　participants.