Statistical Synthesis of Physically Based Load Models with Applications to Cold Load Pickup

Abstract

Physically based load models are useful in the planning, operation and control of power systems, especially when it is necessary to predict the behavior of the system load due to changes in the system. This paper presents a methodology for predicting the load at a point in the system starting from elementary component load models. The methodology consists of three key steps: modeling of elementary component loads; classification into homogeneous (similar) groups; and aggregation of the load models in each homogeneous group using statistical techniques. The methodology is illustrated with the cold load pickup problem, where a mathematical model for the evolution of the fraction of "on" space heaters is developed. The resulting model consists of a system of coupled ordinary and partial differential equations for each homogeneous group of component loads. Numerical solutions of the equations are used to predict the fraction of "on" loads. The methodology is also applicable to direct load management.