Air　source　heat　pump　(ASHP)　is　a　key　technology　for　the　electrification　of　building　heating,　and　its　participation　in　demand　response　(DR)　has　important　implications　for　reducing　the　grid　peak　load.　However,　current　DR　strategies　mainly　focus　on　changing　the　setpoint　of　indoor　temperature,　using　the　building＇s　thermal　mass　as　a　passive　thermal　storage,　while　the　impacts　of　water　temperature　on　the　performance　of　ASHP　units　are　often　overlooked.　Therefore,　this　study　proposes　an　optimal　water　temperature　scheduling　(OWTS)　method　to　respond　time–of–use　tariffs.　It　is　developed　based　on　a　room　thermal　dynamic　prediction　model　and　an　ASHP　power　consumption　prediction　model,　optimizing　supply　water　temperature　schedule　with　objectives　of　maintaining　indoor　thermal　comfort　and　reducing　heating　operating　costs.　The　effectiveness　of　the　OWTS　method　was　tested　on　a　field　ASHP　system　in　Beijing,　and　compared　with　two　benchmark　methods.　Results　demonstrate　that　the　application　of　the　OWTS　method　can　enhance　the　DR　capability　of　the　ASHP　heating　system,　with　the　average　value　of　Flexibility　Factor　increasing　from　0.134　to　0.728,　and　can　reduce　the　heating　operating　costs　by　20.8%–43.0%.　This　study　thereby　offers　a　promising　method　for　ASHP　heating　systems　to　effectively　participate　in　DR.　©　2024