| dc.description.abstract | The electric grid is the backbone of modern society, providing an essential service that powers our homes, businesses, and industries. These intricate networks of power generation, transmission, and distribution work tirelessly to deliver reliable and uninterrupted electrical energy to meet the ever-growing demands of our technologically advanced world. Over the past three decades, extreme weather events such as hurricanes, floods, and droughts have historically increased in frequency. In power systems, these events can lead to service disruptions due to damage to equipment or failures in the network, resulting in outages or blackouts. While it is impossible to prepare for every scenario, understanding and modeling the characteristics of the grid under the influence of these events can help reduce downtime. High impact events, such as natural disasters and cyberattacks, are known to cause outages lasting anywhere from minutes to several days, affecting the electric grid’s infrastructure and delaying restoration efforts. It takes power to generate power; this is particularly important after a blackout. For a restoration to be successful following partial or total system outages, a restoration plan must be organized, put into action, and tested as required by reliability organizations. Restoring load after a prolonged interruption—generally referred to as cold load pickup—requires additional power which can exceed equipment’s rating and restricts grid operators from simultaneously re-energizing the affected area. Demand after an outage is typically leads to cold load conditions. The additional power needed is caused by thermostatically controlled loads (TCL) which commonly have a temperature set by the end-user and is referred to as cold load; the naming is due to the equipment being "cold" or offline for a prolonged period. When energizing cold load takes place, this procedure is generally described as cold load pickup (CLPU). Modeling cold load is highly complex and dependent on various factors such as load type, cause of outage, duration, and weather conditions. This thesis focuses on applying an end-use load model to investigate cold load demand after a blackout. 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 actions to optimize restoration. Characteristic parameters were determined through sensitivity studies based on data from recorded blackstart events. By applying the proposed model to a restoration test case, we can determine the effects energizing cold load have on the system to assess grid operator’s flexibility during blackstart restorations. A synthetic case is constructed with restoration performed for a baseline study as well as the study where cold load is considered. The model proposed is applied to demonstrate the considerations needed when formulating restoration procedures that meet federal regulatory organization requirements. 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. | |
