A　metric　of　expected　regret　is　proposed　and　a　two-layer　optimization　model　with　the　goal　of　minimum　total　system　cost　and　minimum　penalty　cost　is　established,　and　the　multi-objective　quantum　particle　swarm　algorithm　is　improved　by　considering　the　operation　cost　of　the　system,　the　capacity　cost　and　energy　cost　of　spinning　reserve,　as　well　as　penalty　cost　for　loss　of　load　and　wind　curtailment　and　incorporating　time-of-use　electricity　prices.　In　the　outer　layer　of　the　model,　the　upward　and　downward　spinning　reserve　capacities　are　obtained　by　using　the　improved　multi-objective　quantum　particle　swarm　optimization　algorithm.　In　the　inner　layer　of　the　model,　the　unit　commitment　and　economic　distribution　of　the　spinning　reserve　capacities　are　acquired　by　the　priority　method.　Thus,　the　cost　expectation　regret-the　Pareto　fronts　that　punishes　the　expected　regret　are　obtained,　and　the　impact　of　the　time-of-use　electricity　price　and　weight　coefficient　on　the　total　cost　of　the　system　and　the　penalty　fee　are　analyzed.　Finally,　the　effectiveness　of　the　proposed　optimization　model　and　algorithm　is　verified　by　the　example.　©　2018,　Editorial　Board　of　Acta　Energiae　Solaris　Sinica.　All　right　reserved.