Application and Parameter Sensitivities of a State-Space Cold Load Pickup Model for a Synthetic Restoration Test Case

Abstract

Following a partial or system-wide blackout, the demand needed to restore power is often significantly higher than normal conditions. This phenomenon is known as cold load and its restoration is described as a cold load pickup. Modeling cold load is highly complex and dependent on various factors such as load type, cause of outage, duration, and weather conditions. In this paper an end-use load model focusing on determining cold load demand after an outage is introduced. By using a state-space model, independent load characteristics present when interruption occurs can be captured for unpredictable outages based on system status and historical demand under normal conditions. Through the use of a load accumulation state variable, excess demand is determined based on outage duration where local limitations can be set by utilities to fine tune predictions to fit their areas serviced. Characteristic parameters were determined through sensitivity studies based on data from recorded blackstart events. The model is applied in this paper for integration into synthetic grids for blackout studies. Initial results remain consistent with prior work and available data, and show the effects of some of the factors affecting demand. With the ability to provide accurate load predictions, cold load data can be integrated into synthetic grids to simulate blackout restorations that reflect impacts of outages on the grid.