Uncertain power sources are increasingly integrated into distribution networks and causing more challenges for the traditional load modeling. A variety of distributed load components present dynamic characteristics with time-varying parameters. Toward this end, this paper proposes a robust time-varying parameter identification (TVPI) method for synthesis load modeling (SLM) in distribution networks, including time-varying ZIP, induction motor, and equivalent impedance models. The nonlinear optimization model is developed and solved by the nonlinear least square (NLS) to find the minimum error between estimated outputs and measurements. To cope with TVPI deteriorated by voltage anomalies, dynamic programming is first used to detect anomalies. Then, a robust TVPI engine is designed to constrain the estimated time-varying parameters within a stable range. Furthermore, advanced tolerance thresholds are also required during iterations of NLS. Numerical simulations on the 9- and 129-bus distribution systems verify the effectiveness and robustness of the proposed TVPI method. Also, this method can be robust to the ambient noise of measurements.