Large-scale　wind　power　penetration　impacts　the　electricity　market　in　many　aspects,　especially　the　day-ahead　clearing　schedule,　due　to　the　uncertainty　and　low　predictability　of　wind　power.　Based　on　forecast　intervals　of　wind　power　and　absorption　of　wind　power　with　a　preference,　the　day-ahead　clearing　model　of　energy　and　reserve　is　built　and　contains　2　objective　functions:　the　minimum　clearing　cost　and　real-time　imbalance　power.　The　constraints　for　the　clearing　schedule　and　for　meeting　the　uncertainty　of　wind　power　in　forecast　intervals　are　included.　To　supply　market　operators　with　upper　and　lower　boundary　information,　the　interval　optimization　model　is　converted　into　double-layer　nonlinear　pessimistic　and　optimistic　models.　The　improved　multiobjective　quantum　particle　swarm　optimization　(MOQPSO)　with　a　straight　Pareto　front　is　developed　to　approximate　the　real,　smooth,　and　uniform　Pareto　front.　The　day-ahead　clearing　schedule　and　corresponding　reserve　capacity　are　analyzed　by　testing　IEEE30-bus　system.　The　computational　times　of　the　improved　MOQPSO　and　general　MOQPSO　are　nearly　the　same,　but　the　Pareto　front　is　remarkably　reformative.　Furthermore,　the　upward/downward　energy　price　in　the　real-time　market　can　considerably　impact　the　day-ahead　clearing　schedule.