The increasingly complex residential microgrids (r-microgrid) consisting of renewable generation, energy storage systems, and residential buildings require a more intelligent scheduling method. Firstly, aiming at the radiant floor heating/cooling system widely utilized in residential buildings, the mathematical relationship between the operative temperature and heating/cooling demand is established based on the equivalent thermodynamic parameters (ETP) model, by which the thermal storage capacity is analyzed. Secondly, the radiant floor heating/cooling system is treated as virtual energy storage system (VESS), and an optimization model based on mixed-integer nonlinear programming (MINLP) for r-microgrid scheduling is established which takes thermal comfort level and economy as the optimization objectives. Finally, the optimal scheduling results of two typical r-microgrids are analyzed. Case studies demonstrate that the proposed scheduling method can effectively employ the thermal storage capacity of radiant floor heating/cooling system, thus lowering the operating cost of the r-microgrid effectively while ensuring the thermal comfort level of users.