Since the load dynamics have substantial impacts on power system transient stability, load models are one critical factor that affects the power transfer limits.
However, a detailed WECC CLM model typically has a high degree of complexity, with over one hundred parameters, and no systematic approach to identifying and calibrating these parameters.
Short-term load forecasting (STLF) is essential for the reliable and economic operation of power systems.
In the first stage, all related features are utilized to train a point forecast model and also obtain the feature importance.
Traditional load analysis is facing challenges with the new electricity usage patterns due to demand response as well as increasing deployment of distributed generations, including photovoltaics (PV), electric vehicles (EV), and energy storage systems (ESS).
We demonstrate that the proposed method is able to generate realistic wind and photovoltaic power profiles with full diversity of behaviors.