Grid Resilience to Natural Disasters: Challenges and Opportunities Lie Ahead

 By Yezhou Wang, Jianhui Wang and Dan Ton

In recent years, many blackouts have occurred as a result of natural disasters, including such well-known disasters as 2005’s Hurricane Katrina in the United States, Japan’s 2011 earthquake, and 2012’s Hurricane Sandy (also in the United States). Between 2003 and 2012, 679 U.S.-based power outages resulted from weather events that each affected more than 50,000 customers. Therefore, research on the power grid’s resilience to natural disasters is of major societal significance.

Research on the power grid’s resilience to natural disasters can be traced to the 1938 New England Hurricane that struck the Boston area. After nearly a century of research and development, however, mass power outages still occur after natural disasters, seriously affecting human activities, recovery efforts, and even causing loss of life. Reasons for the lack of effective research in this domain include the issue’s complexity and its interdisciplinary nature, which means that comprehensive understanding across different related subjects is relatively sparse. Thorough review and a clear vision of the challenges and opportunities surrounding this issue are lacking.

Forecast Models, Hardening and Resilience Optimization

Forecast models are used to estimate the power grid’s resilience-related performance under natural disaster conditions. These performance levels could include the duration and capacity of power outages, asset damages, etc. Because of the issue’s complex physical nature, most current forecast models are statistical models, which utilize two types of input data — power system data and environmental data — to feed into various statistical models (e.g., generalized linear models, generalized additive models, tree-based data mining models). The statistical methods rely heavily on information that is case dependent and geographically restricted. In addition, the statistical methods generally do not consider the mechanisms of the blackouts/damage. Therefore, sophisticated and detailed simulation-based models may be needed to provide more insights. The challenges to developing such models include modelers’ access to detailed transmission and distribution (T&D) data, as well as the model’s ability to handle uncertainties in disaster dynamics.

Without effective short- and long-term disaster forecast models, it is very hard to design appropriate corrective actions, such as grid islanding plans, hardening, and resilience activities, which may include investments in substation elevations and the upgrading of T&D structures. Although the utilities have set aside millions (if not billions) of dollars in their storm-hardening programs, many lack rigorous and convincing cost and benefit studies, necessitating more thorough understanding and research advances on optimizing grid resiliency activities. Major advances are envisioned in the more effective, easy-to-implement, and reliable statistical and simulation-based models.

Power System Restoration Issues

Extensive research has been performed on power system restoration methods. In general, the restoration process is planned in three stages: preparation, system restoration, and load restoration. Table 1 summarizes differences between natural disaster–related outages and other typical outages, and indicates that there are multiple unique characteristics in natural disaster–related outages, including unique spatiotemporal correlations with the faults, interdependence with other infrastructure assets, and other factors. Such uniqueness may help or restrict the applicability of conventional restoration methods. Therefore, planners need to exercise care in selecting appropriate plans and methods to deal with restoration issues associated with potential natural disasters.

Distributed generation, storage systems (e.g., flywheels, batteries, photovoltaic systems, fuel cells), and microgrids can be utilized to enhance grid resilience dramatically by improving generation availability. There are many ways that microgrids can be deployed to alleviate outages in the absence of a reliable grid. For example, microgrids can be used to provide resources for bulk system restoration, in which applications for black-start capabilities could be improved. Microgrids in the distribution systems can also aid the conventional load restoration by maximizing the restored load and minimizing the number of switching operations. Last but not least, specific implementations of microgrids can act as an island to sustain critical loads like hospitals and data centers. The challenges of integrating these newly developed concepts and technologies lie in understanding interactions with existing infrastructure and issues of investment optimization and deployment. During a disaster, these resilience-related assets may also experience full or partial damage, so operation and control under stringent conditions need to be carefully studied.

Interdependence Among Different Infrastructure Assets

One of the unique characteristics of natural disasters is the complexity of dynamic responses among multiple infrastructure assets during the event. Unlike during conventional component failures or even some terrorist attacks where only electrical assets go out of service, natural disasters may cause damage to numerous infrastructure components, including roads and bridges, communication infrastructure, and so on, creating extremely challenging and yet interesting topics for research. For example, damaged roads and bridges may delay utilities’ efforts to send restoration personnel, whereas the lack of electric-powered machinery and traffic lights may prevent rapid road repair. In such a case, having a clear understanding of infrastructure interdependence is critical to supporting the coordinated efforts of emergency response plans.

Conclusion

Grid resilience under natural disaster conditions has been an important topic and key foundation in the development of modern infrastructure. In our view, advanced smart grid technologies can be utilized to enhance grid resilience. Nevertheless, the issue’s complexity means that state-of-the-art interdisciplinary techniques, such as statistics, meteorology, power engineering, optimization, communication, and control, are involved, as well as policies and regulations. Several challenges and opportunities for future research exist. Continued innovation and advancement in these areas will be extremely beneficial to academia as well as industry.

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Contributors 

 

wang yezhou

Yezhou Wang is currently with Castleton Commodities International. He was a power market analyst at Direct Energy, Houston, Texas. Dr. Wang received B.Eng degrees from the University of Birmingham, UK and Huazhong University of Science and Technology, China, both in electrical engineering. He received M.S. and Ph.D. degrees in electrical engineering from the University of Texas at Austin.

 

wang jianhui

Jianhui Wang is the Section Lead for Advanced Power Grid Modeling at the Energy Systems Division at Argonne National Laboratory, Argonne, IL, USA. Dr. Wang is the secretary of the IEEE Power & Energy Society (PES) Power System Operations Committee. He is an associate editor of Journal of Energy Engineering and an editorial board member of Applied Energy. He is also an affiliate professor at Auburn University and an adjunct professor at University of Notre Dame. He has held visiting positions in Europe, Australia and Hong Kong including a VELUX Visiting Professorship at the Technical University of Denmark (DTU). Dr. Wang is the Editor-in-Chief of the IEEE Transactions on Smart Grid and an IEEE PES Distinguished Lecturer. He is also the recipient of the IEEE PES Power System Operation Committee Prize Paper Award in 2015.

 

ton dan

Dan Ton is Program Manager of Smart Grid R&D within the U.S. Department of Energy (DOE) Office of Electricity Delivery and Energy Reliability (OE). He is responsible for developing and implementing a multi-year R&D program plan for next-generation smart grid technologies to transform the electric grid in the United States, through public/private partnerships. He has served as Acting Deputy Assistant Secretary of Power Systems Engineering Division within the U.S. Department of Energy (DOE) Office of Electricity Delivery and Energy Reliability (OE). Previously, Dan managed the Renewable Systems Integration program within the DOE Solar Energy Technologies Program. Dan holds a B.S. in Electrical Engineering and an M.S. in Business Management, both from the University of Maryland..


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