Abstract
Computer models are very important to planning, operation, and control of power system. Although elements such as generators and transmission lines have been relatively well understood, developing a comprehensive power system model is a daunting task because challenges associated with loads modeling (they change all the time and utilities have very little control on). Unfortunately, inaccurate load models have serious implications such as unsafe operating conditions, power outages, under-utilization of system capacity, or inappropriate capital investment. This paper presents the use of state-of-the art Bayesian calibration framework for simultaneous load model selection and calibration. The approach aims at identifying configuration and reducing parameters uncertainty of the Western Electricity Coordinating Council’s (WECC) composite load model in the presence of measured field data. The success of the approach is illustrated with synthetic field data and a simplified model.
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