http://repository.iitr.ac.in/handle/123456789/22978
Title: | Critical Risk Indicators (CRIs) for the electric power grid: a survey and discussion of interconnected effects |
Authors: | Che-Castaldo J.P. Cousin R. Daryanto S. Deng G. Feng M.-L.E. Gupta R.K. Hong D. McGranaghan R.M. Owolabi O.O. Qu T. Ren W. Schafer T.L.J. Sharma, Ashutosh Shen C. Sherman M.G. Sunter D.A. Tao B. Wang L. Matteson D.S. |
Published in: | Environment Systems and Decisions |
Abstract: | The electric power grid is a critical societal resource connecting multiple infrastructural domains such as agriculture, transportation, and manufacturing. The electrical grid as an infrastructure is shaped by human activity and public policy in terms of demand and supply requirements. Further, the grid is subject to changes and stresses due to diverse factors including solar weather, climate, hydrology, and ecology. The emerging interconnected and complex network dependencies make such interactions increasingly dynamic, posing novel risks, and presenting new challenges to manage the coupled human–natural system. This paper provides a survey of models and methods that seek to explore the significant interconnected impact of the electric power grid and interdependent domains. We also provide relevant critical risk indicators (CRIs) across diverse domains that may be used to assess risks to electric grid reliability, including climate, ecology, hydrology, finance, space weather, and agriculture. We discuss the convergence of indicators from individual domains to explore possible systemic risk, i.e., holistic risk arising from cross-domain interconnections. Further, we propose a compositional approach to risk assessment that incorporates diverse domain expertise and information, data science, and computer science to identify domain-specific CRIs and their union in systemic risk indicators. Our study provides an important first step towards data-driven analysis and predictive modeling of risks in interconnected human–natural systems. © 2021, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature. |
Citation: | Environment Systems and Decisions, 41(4): 594-615 |
URI: | https://doi.org/10.1007/s10669-021-09822-2 http://repository.iitr.ac.in/handle/123456789/22978 |
Issue Date: | 2021 |
Publisher: | Springer |
Keywords: | Critical risk indicator Electric power grid Multi-disciplinary Risk Systemic risk Uncertainty |
ISSN: | 21945403 |
Author Scopus IDs: | 55523615900 54417011500 57226156346 44160919200 57222083452 57211565489 55204679600 55229795900 57202635984 57222089377 57226167543 57204897094 57204024490 16680028200 57173625900 35109937400 57226161527 57224004581 54904485700 |
Author Affiliations: | Che-Castaldo, J.P., Conservation & Science Department, Lincoln Park Zoo, 2001 N. Clark St. Chicago, Chicago, IL, United States Cousin, R., International Research Institute for Climate and Society, Earth Institute/Columbia University, New York, United States Daryanto, S., Department of Plant and Soil Sciences, College of Agriculture, Food and Environment, University of Kentucky, Lexington, United States Deng, G., Department of Statistics and Data Science, Cornell University, New York, United States Feng, M.-L.E., Conservation & Science Department, Lincoln Park Zoo, 2001 N. Clark St. Chicago, Chicago, IL, United States Gupta, R.K., Halicioglu Data Science Institute and Department of Computer Science & Engineering, University of California, San Diego, CA, United States Hong, D., Halicioglu Data Science Institute and Department of Computer Science & Engineering, University of California, San Diego, CA, United States McGranaghan, R.M., Atmospheric and Space Technology Research Associates, Louisville, CO, United States Owolabi, O.O., Department of Civil and Environmental Engineering, Tufts University, Medford, MA, United States Qu, T., Department of Finance, Isenberg School of Management, UMASS Amherst, Amherst, MA, United States Ren, W., Department of Plant and Soil Sciences, College of Agriculture, Food and Environment, University of Kentucky, Lexington, United States Schafer, T.L.J., Department of Statistics and Data Science, Cornell University, New York, United States Sharma, A., Civil and Environmental Engineering, Pennsylvania State University, State College, PA, United States, Department of Hydrology, Indian Institute of Technology Roorkee, Roorkee, India Shen, C., Civil and Environmental Engineering, Pennsylvania State University, State College, PA, United States Sherman, M.G., Department of Finance, Isenberg School of Management, UMASS Amherst, Amherst, MA, United States Sunter, D.A., Department of Civil and Environmental Engineering, Tufts University, Medford, MA, United States, Department of Mechanical Engineering, Tufts University, Medford, MA, United States, Tufts Institute of the Environment, Tufts University, Medford, MA, United States, Center for International Environment and Resource Policy at The Fletcher School, Tufts University, Medford, MA, United States Tao, B., Department of Plant and Soil Sciences, College of Agriculture, Food and Environment, University of Kentucky, Lexington, United States Wang, L., Department of Management Science, Miami Herbert Business School, University of Miami, Coral Gables, FL, United States Matteson, D.S., Department of Statistics and Data Science, Cornell University, New York, United States |
Funding Details: | Funding was provided by the NSF Harnessing the Data Revolution (HDR) program, “Collaborative Research: Predictive Risk Investigation SysteM (PRISM) for Multi-layer Dynamic Interconnection Analysis” (Awards #1940160, 2023755, 1940176, 1940190, 1940208, 1940223, 1940276, 1940291, and 1940696). R. McGranaghan was partially supported under the NSF Convergence Accelerator Award to the Convergence Hub for the Exploration of Space Science (CHESS) team (NSF Award Number: 1937152). We would like to thank Suoan Gao (UMASS Amherst) for research assistance. Funding was provided by the NSF Harnessing the Data Revolution (HDR) program, ?Collaborative Research: Predictive Risk Investigation SysteM (PRISM) for Multi-layer Dynamic Interconnection Analysis? (Awards #1940160, 2023755, 1940176, 1940190, 1940208, 1940223, 1940276, 1940291, and 1940696). R. McGranaghan was partially supported under the NSF Convergence Accelerator Award to the Convergence Hub for the Exploration of Space Science (CHESS) team (NSF Award Number: 1937152). We would like to thank Suoan Gao (UMASS Amherst) for research assistance. 1937152; National Science Foundation, NSF: 1940160, 1940176, 1940190, 1940208, 1940223, 1940276, 1940291, 1940696, 2023755 |
Corresponding Author: | Che-Castaldo, J.P.; Conservation & Science Department, 2001 N. Clark St. Chicago, United States; email: jchecastaldo@lpzoo.org |
Appears in Collections: | Journal Publications [HY] |
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