Enhancing Electricity-System Resilience with Adaptive Robust Optimization and Conformal Uncertainty Characterization
Professor Ramteen Sioshansi (Homepage | Google Scholar | Bio), IEEE Fellow, one of the leading scholars in power system operation and electricity markets from Carnegie Mellon University, will visit DTU Wind and Energy Systems on October 29 and give a seminar. IEEE PES Denmark has the pleasure to co-host this event.
Abstract:
Extreme weather is straining electricity systems, exposing the limitations of reactive responses, and prompting the need for proactive resilience planning. Most existing approaches to enhance electricity-system resilience employ simplified uncertainty models and decouple proactive and reactive decisions. We propose a novel tri-level optimization model that integrates proactive actions, adversarial disruptions, and reactive responses. Conformal prediction is used to construct distribution-free system-disruption uncertainty sets with coverage guarantees. The tri-level problem is solved by using duality theory to derive a bi-level reformulation and employing Benders’s decomposition. Numerical experiments demonstrate that our approach outperforms conventional methods.
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- Co-sponsored by DTU Wind and Energy Systems
Speakers
Prof. Ramteen Sioshansi of Carnegie Mellon University
Enhancing Electricity-System Resilience with Adaptive Robust Optimization and Conformal Uncertainty Characterization
Extreme weather is straining electricity systems, exposing the limitations of reactive responses, and prompting the need for proactive resilience planning. Most existing approaches to enhance electricity-system resilience employ simplified uncertainty models and decouple proactive and reactive decisions. We propose a novel tri-level optimization model that integrates proactive actions, adversarial disruptions, and reactive responses. Conformal prediction is used to construct distribution-free system-disruption uncertainty sets with coverage guarantees. The tri-level problem is solved by using duality theory to derive a bi-level reformulation and employing Benders’s decomposition. Numerical experiments demonstrate that our approach outperforms conventional methods.
Biography:
Ramteen Sioshansi is a professor in the Department of Engineering and Public Policy, Department of Electrical and Computer Engineering, and Heinz College of Information Systems and Public Policy at Carnegie Mellon University. He also serves as the director of Carnegie Mellon Electricity Industry CenterOpens in new window and is a faculty affiliate of Carnegie Mellon Scott Institute for Energy InnovationOpens in new window.
Prior to joining CMU, Sioshansi was a professor in the Department of Integrated Systems Engineering and Department of Electrical and Computer Engineering, founding director of the EmPOWERment National Science Foundation (NSF) Research Traineeship (NRT) Program, and an associate fellow in the Center for Automotive Research at The Ohio State University.
His research focuses on the integration of advanced energy technologies, including renewable energy, energy storage, and electric transportation, into energy systems. He works also in energy policy and electricity-market design, especially as they pertain to advanced energy technologies. He is an IEEE Fellow and served three two-year terms on the Electricity Advisory Committee, a federal advisory committee to the U.S. Secretary of Energy, and chaired its Energy Storage (Technologies) Subcommittee.
Address:Denmark
Agenda
- Welcome by Prof. Jalal Kazempour, Head of Section, Energy Markets & Analytics, DTU Wind and Energy Systems
- Professor Ramteen Sioshansi: Enhancing Electricity-System Resilience with Adaptive Robust Optimization and Conformal Uncertainty Characterization
- Conclusion