An　increasing　number　of　enterprises　deploy　their　business　applications　in　green　data　centers　(GDCs)　to　address　irregular　and　drastic　natures　in　task　arrival　of　global　users.　GDCs　aim　to　schedule　tasks　in　the　most　cost-effective　way,　and　achieve　the　profit　maximization　by　increasing　green　energy　usage　and　reducing　brown　one.　However,　prices　of　power　grid,　revenue,　solar　and　wind　energy　vary　dynamically　within　tasks’　delay　constraints,　and　this　brings　a　high　challenge　to　maximize　the　profit　of　GDCs　such　that　their　delay　constraints　are　strictly　met.　Different　from　existing　studies,　a　Temporal-variation-aware　Profit-maximized　Task　Scheduling　(TPTS)　algorithm　is　proposed　to　consider　dynamic　differences,　and　intelligently　schedule　all　tasks　to　GDCs　within　their　delay　constraints.　In　each　interval,　TPTS　solves　a　constrained　profit　maximization　problem　by　a　novel　Simulated-annealing-based　Chaotic　Particle　swarm　optimization　(SCP).　Compared　to　several　state-of-the-art　scheduling　algorithms,　TPTS　significantly　increases　throughput　and　profit　while　strictly　meeting　tasks’　delay　constraints.　©　Springer　Nature　Switzerland　AG　2019.