Quantifying Power System Resilience Improvement Through Network Reconfiguration In Cases Of Extreme Emergencies
Electricity grid complexity, together with diversity of critical infrastructures (CIs) such as generating units, transportation network, etc. are increasingly driving a complicated network that is vulnerable to unpredictable hazards. Disruptive events, whether they are natural catastrophes like floods, hurricanes, thunderstorms, or malicious cyber-physical attacks or even human-caused faults may have significant impacts on such real-time complex power networks composed of numerous interconnected structural and functional components. Because in the electric power grid, the electricity generated by large-scale power plants is transferred to a variety of commercial, industrial and residential customers via distribution and transmission networks, it can be disrupted over a vast geographical area when an unpredictable disaster occurs. From the 1980’s to 2014, both the frequency and intensity of weather-caused grid outages have been trending higher. For instance, due to a hurricane in 2008, more than 2.8 million residential/industrial customers in the Greater Houston area were affected by power outages, which lasted from a few days to several weeks, resulting in losses estimated at $24.9 billion to the U.S. government. With such drastic changes of weather conditions, the risk associated with transmission line insulation breakdowns may increase and power transformers (and other components) may be stressed and overloaded. Such faults may impose a risk to electric safety due to high fault currents, exposed faulted conductors, or other unsafe conditions. Therefore, having a proper and predictive resilience-based strategy and corrective plans for dealing with the aftermath of such fatal phenomena is of great concern for electric utilities nationwide. The task of improving resiliency of the electricity grid in the face of emergencies is challenging. While the term “resilience” is increasingly used in research articles, government documents, and the media, specific research focuses are still needed on quantifying the concept of resilience and making it usable in practice. Planning for enhanced system resilience has not been well explored, especially in the context of power transmission systems, and thus attention needs to focus on allocation of tangible resources, tradeoffs among various dimensions of system resilience, the relationship between community resilience and that of the built environment, and data-driven standards ensuring resilience. There is a national push to model the electric grid in a smarter way as well as to introduce advanced technologies and control mechanisms into grid operations. One aspect of the smart grid aims at making better use of the current infrastructure. System operators may have the opportunity to harness the flexibility of the transmission system topology by temporarily removing transmission lines out of the system under authorized power system topology control, often called transmission line switching (TLS). By changing the way that electricity flows through the system, TLS can be employed either in emergency scenarios -to alleviate voltage violations, congestions and overloading conditions, and even load shed recovery-, or during normal operating conditions for higher economic benefits or loss improvements. This research proposes a resilience-based smart grid application of harnessing the full control of transmission assets in the case of emergency scenarios, and the associated practical considerations aiming at improving preparedness and mitigation of the electric safety risks. Using resilience options concluded in this study, plans could be developed ahead of disruption time to provide operators with the opportunity to make the right decision at the time of disturbances.
Power system, Resilience, Electricity loss, Emergency, Grid reconfiguration, Decision making
Dehghanian, P. (2017). <i>Quantifying power system resilience improvement through network reconfiguration in cases of extreme emergencies</i> (Unpublished thesis). Texas State University, San Marcos, Texas.