Modeling and Analysis of Human Behavior Impacts on Energy Systems during Crisis Events

Abstract

Multiple examples have shown how vulnerable supply chains are to unexpected changes in consumer demand in recent history. Information on social media may be the cause of changes in demand by influencing consumers to panic buy certain goods. Until now, these events primarily involved physical goods. However, critical infrastructure that deals with non-physical goods such as the power grid could also be affected by sudden spikes of consumer demand. Critical infrastructure needs to be resilient towards various kinds of threats, hence it is important to analyze possible threats to the infrastructure before they happen to prepare countermeasures. In this thesis, the effects of demand changes based on information which is being spread are modelled and analyzed with different scenarios. A simulation framework is implemented, with a graph-based social network model and an epidemological model based on the susceptible-infectious-recovered model (SIR) to model information propagation processes. Furthermore, information propagation parameters are estimated with social media data. Then, four theoretical scenarios are used as examples to analyze sudden changes in demand for the power grid. The analysis shows that simultaneous shifts in consumer demand could pose risks to the stability of the power grid. Additionally, further adoption of energy-intensive technologies such as electric vehicles are shown to amplify the impact of synchronized changes in consumer demand on the energy infrastructure.