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ABSTRACT
Power system failures due to extreme weather events can have devastating consequences. Although microgrids are increasingly adopted as a potential solution to main grid failure, they face similar disruption risks. Hence, it is crucial to limit the performance degradation from disruption events, so that restoration time is minimized. To achieve this goal, this paper introduces the notion of during-event resilience, and proposes a framework to generate resilient designs of distributed energy sources to maximize the resilience of micro-grids. The framework accounts for uncertainties from system failure scenarios, and renewable energy availability. A two-level optimization is proposed. The lower-level optimizes the power flow subject to power balance constraints, to maximize a cost-based resilience metric. The upper-level optimization maximizes the expected resilience while accounting for uncertainty. Using the IEEE 33 node test feeder power grid system, we show how the proposed framework can generate resilient designs in real-world settings.
Disclosure statement
No potential conflict of interest was reported by the authors.
Additional information
Notes on contributors
Xi Chen
Xi Chen is an assistant professor of Industrial and Manufacturing Systems Engineering at the University of Michigan-Dearborn. Her primary research interest concerns the management of operations in complex systems such as supply chains and transportation mobility systems. Her main research methodologies are optimization, game theory, discrete choice modeling, dynamic programming and data analytics. She received Ph.D. and M.S. degrees in Industrial and Systems Engineering from the University of Minnesota, and B.S. degree in Mathematics and Applied Mathematics from Fudan University.
Wencong Su
Wencong Su is an Associate Professor in the Department of Electrical and Computer Engineering at the University of Michigan-Dearborn, USA. Dr. Su received his B.S. degree (with distinction) from Clarkson University in 2008, his M.S. degree from Virginia Tech in 2009, and his Ph.D. degree from North Carolina State University in 2013, respectively. He is a registered Professional Engineer (P.E.) in the State of Michigan. His current research interests include power and energy systems, electrified transportation systems, and cyber-physical systems. He is a senior member of IEEE. He is an Editor of IEEE Transactions on Smart Grid and an Associate Editor of IEEE Access.
Abdollah Kavousi-Fard
Abdollah Kavousi-Fard (SM’19) received the B.Sc. degree in 2009; the M.Sc. degree in 2011; and the Ph.D. degree from the Shiraz University of Technology, Shiraz, Iran, in 2016, all in electrical engineering. He was a Postdoctoral Research Assistant with the University of Michigan, MI, USA from 2016–2018. He was a Researcher with the University of Denver, Denver, CO, USA from 2015 to 2016 conducting research on microgrids. He is currently an Assistant Professor with the Shiraz University of Technology. His current research interests include operation, man- agement, planning of power systems, cyber security analysis of smart grids, microgrid, smart city and electric vehicles, protection of power systems, reliability, artificial intelligence and deep learning in power systems. He authored more than 80 journal papers in top journals. He is an Associate Editor for Springer, ISTE Journal.
Annette G. Skowronska
Annette G. Skowronska is a Research Scientist at the U.S. Army Combat Capabilities Development Command (CCDC) Ground Vehicle Systems Center (GVSC). She is the technical lead for the Ground Vehicle Power and Mobility group in the area of Microgrids. Her current research interests are energy infrastructure for military ground vehicle, including microgrids, reliability-based design optimization, vehicle to grid technologies and autonomous vehicles. She is involved in technology assessments, industry partnerships, military research and development that enables CCDC to collaborate in complex and emerging new mission areas. Dr. Skowronska received her M.S. in Mechanical Engineering in 2009 and her B.S. in Mechanical Engineering from Oakland University in 2008. She holds a Ph.D. in reliability and optimal design of repairable systems from Oakland University.
Zissimos P. Mourelatos
Zissimos P. Mourelatos is a Professor of Mechanical Engineering at Oakland University where he holds the title of John F. Dodge Chair of Engineering and has served as the Chair of the Mechanical Engineering Department (2010-2014). Before joining Oakland University, he spent 18 years at the General Motors Research and Development Center. He received his PhD from the University of Michigan in 1985. Dr. Mourelatos conducts research in the areas of uncertainty quantification, design under uncertainty, reliability and warranty forecasting of repairable systems, Reliability-Based Design Optimization (RBDO), deterministic and random vibrations, and NVH (Noise, Vibration and Harshness). Dr. Mourelatos has published over 240 journal and conference publications and a book entitled, “Decision Making under Uncertainty using Limited Information.” He is the Editor-in-Chief of the International Journal of Reliability and Safety, an Associate Editor of the SAE International Journal of Materials and Manufacturing, and SAE International Journal of Commercial Vehicles. He has also served as an Associate Editor and Guest Co-Editor of the ASME Journal of Mechanical Design. Dr. Mourelatos is a Fellow of ASME and SAE.
Zhen Hu
Zhen Hu is an assistant professor in Department of Industrial and Manufacturing Systems Engineering (IMSE) at the University of Michigan-Dearborn. Prior to joining UM-Dearborn, he was a research assistant professor and postdoctoral research scholar at Vanderbilt University. Dr. Hu earned his Ph.D. degree from Missouri University of Science and Technology (formerly University of Missouri-Rolla) in 2014. He received his Master of Science degree in Mechatronics engineering from Huazhong University of Science and Technology, Wuhan, China in March 2011. He received his bachelor degree in Mechanical Engineering from Central South University, Changsha, China in 2008. His research areas of interest are uncertainty quantification, Bayesian data analytics, big data analytics, machine learning, optimization under uncertainty, and applications of data analytics and machine learning in aerospace, mechanical and manufacturing systems, and material science.