Prediction of Voluntary Load Reduction Based on Human Behaviors During Grid Emergencies

With the growing adoption of renewable energy resources and the increasing occurrence of high-impact, low-frequency events, maintaining grid reliability has become a significant concern for many countries. Currently, there is no standardized approach for transmission-level emergency load curtailment, as grid emergencies are typically addressed from a distribution system perspective. Consumers' energy conservation efforts, also known as voluntary load reduction, can help mitigate the risk of power outages during grid emergencies. However, the existing curtailment framework does not account for voluntary reductions, leading to unfairness for consumers who proactively reduce power usage to support the grid. This oversight also results in excessive load curtailment. Therefore, predicting voluntary reductions in advance is crucial for effective grid emergency management. This paper proposes a model to predict voluntary load reduction based on collective behaviors of residential consumers. The work is carried out at the transmission level. Micro-level consumer-behaviors are modeled using agent-based modeling with numerical analysis conducted on residential households in Wichita, Kansas. The results quantify voluntary reduction based on selected household attributes and temporal environmental factors, identifying the most crucial attributes that influence significant load reductions.