Abstract:
This article addresses the challenge of efficiently recovering exact solutions to the optimal power flow problem in real-time electricity markets. The proposed solution, ...Show MoreMetadata
Abstract:
This article addresses the challenge of efficiently recovering exact solutions to the optimal power flow problem in real-time electricity markets. The proposed solution, named Physics-Informed Market-Aware Active Set learning OPF (PIMA-AS-OPF), leverages physical constraints and market properties to ensure physical and economic feasibility of market-clearing outcomes. Specifically, PIMA-AS-OPF employs the active set learning technique and expands its capabilities to account for curtailment in load or renewable power generation, which is a common challenge in real-world power systems. The core of PIMA-AS-OPF is a fully-connected neural network that takes the net load and the system topology as input. The outputs of this neural network include active constraints such as saturated generators and transmission lines, as well as non-zero load shedding and wind curtailments. These outputs allow for reducing the original market-clearing optimization to a system of linear equations, which can be solved efficiently and yield both the dispatch decisions and the locational marginal prices (LMPs). The dispatch decisions and LMPs are then tested for their feasibility with respect to the requirements for efficient market- clearing results. The accuracy and scalability of the proposed method is tested on a realistic 1814-bus NYISO system with current and future renewable energy penetration levels.
Published in: IEEE Transactions on Energy Markets, Policy and Regulation ( Volume: 2, Issue: 1, March 2024)
Funding Agency:
Program in Applied Mathematics and Department of Mathematics, University of Arizona, Tucson, AZ, USA
Robert Ferrando received the B.S. degree in mathematics from the Macaulay Honors College at the College of Staten Island, City University of New York, New York City, NY, USA, and the M.S. degree in applied mathematics from The University of Arizona, Tucson, AZ, USA, where he is currently working toward the Ph.D. degree with the Graduate Interdisciplinary Program in applied mathematics. His research focuses on using physic...Show More
Robert Ferrando received the B.S. degree in mathematics from the Macaulay Honors College at the College of Staten Island, City University of New York, New York City, NY, USA, and the M.S. degree in applied mathematics from The University of Arizona, Tucson, AZ, USA, where he is currently working toward the Ph.D. degree with the Graduate Interdisciplinary Program in applied mathematics. His research focuses on using physic...View more
Program in Applied Mathematics and Department of Mathematics, University of Arizona, Tucson, AZ, USA
Laurent Pagnier (Member, IEEE) received the M.S. and Ph.D. degrees in theoretical physics from École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland, in 2014 and 2019, respectively. He is currently a Visiting Assistant Professor with The University of Arizona, Tucson, AZ, USA. His research interests include developing new modeling and monitoring methods for power systems. He is also aiming at applying Machine Le...Show More
Laurent Pagnier (Member, IEEE) received the M.S. and Ph.D. degrees in theoretical physics from École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland, in 2014 and 2019, respectively. He is currently a Visiting Assistant Professor with The University of Arizona, Tucson, AZ, USA. His research interests include developing new modeling and monitoring methods for power systems. He is also aiming at applying Machine Le...View more
Department of Industrial and Systems Engineering at Rutgers University, Piscataway, NJ, USA
Robert Mieth (Member, IEEE) received the Doctorate degree in engineering from the Technical University of Berlin, Berlin, Germany, in 2021. He is currently an Assistant Professor with the Department of Industrial and Systems Engineering, Rutgers University, New Brunswick, NJ, USA. Prior to joining Rutgers, he was a Postdoctoral Fellow with the Department of Electrical and Computer Engineering, Princeton University, Prince...Show More
Robert Mieth (Member, IEEE) received the Doctorate degree in engineering from the Technical University of Berlin, Berlin, Germany, in 2021. He is currently an Assistant Professor with the Department of Industrial and Systems Engineering, Rutgers University, New Brunswick, NJ, USA. Prior to joining Rutgers, he was a Postdoctoral Fellow with the Department of Electrical and Computer Engineering, Princeton University, Prince...View more
Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD, USA
Zhirui Liang received the B.E. degree from North China Electric Power University, Beijing, China, in 2017, and the M.Sc. degree from Xi'an Jiaotong University, Xi'an, China, in 2020. She is currently working toward the Ph.D. degree in electrical and computer engineering with Johns Hopkins University, Baltimore, MD, USA. She began her Ph.D. study with the Tandon School of Engineering, New York University, New York City, NY...Show More
Zhirui Liang received the B.E. degree from North China Electric Power University, Beijing, China, in 2017, and the M.Sc. degree from Xi'an Jiaotong University, Xi'an, China, in 2020. She is currently working toward the Ph.D. degree in electrical and computer engineering with Johns Hopkins University, Baltimore, MD, USA. She began her Ph.D. study with the Tandon School of Engineering, New York University, New York City, NY...View more
Department of Civil and Systems Engineering, Electrical and Computer Engineering and Ralph O'Connor Sustainable Energy Institute, Johns Hopkins University, Baltimore, MD, USA
Yury Dvorkin (Member, IEEE) received the Ph.D. degree with the University of Washington, Seattle, WA, USA, in 2016. He is currently an Associate Professor with the Department of Civil and Systems Engineering and Department of Electrical Computer Engineering, Johns Hopkins University (JHU), Baltimore, MD, USA, where he is also part of JHU's Ralph O'Connor Sustainable Energy Institute and Co-Director of the NSF Global Clima...Show More
Yury Dvorkin (Member, IEEE) received the Ph.D. degree with the University of Washington, Seattle, WA, USA, in 2016. He is currently an Associate Professor with the Department of Civil and Systems Engineering and Department of Electrical Computer Engineering, Johns Hopkins University (JHU), Baltimore, MD, USA, where he is also part of JHU's Ralph O'Connor Sustainable Energy Institute and Co-Director of the NSF Global Clima...View more
IEOR, Columbia University, New York, NY, USA
Daniel Bienstock (Member, IEEE) is currently a Liu Family Professor of operations research with joint appointments in applied math and applied physics, and in electrical engineering with Columbia University, New York City, NY, USA. His work focuses on methodology for mathematical optimizatin problems, with special interest on applications to power systems. He is also an INFORMS Fellow. He was the recipient of the 2022 Kha...Show More
Daniel Bienstock (Member, IEEE) is currently a Liu Family Professor of operations research with joint appointments in applied math and applied physics, and in electrical engineering with Columbia University, New York City, NY, USA. His work focuses on methodology for mathematical optimizatin problems, with special interest on applications to power systems. He is also an INFORMS Fellow. He was the recipient of the 2022 Kha...View more
Program in Applied Mathematics and Department of Mathematics, University of Arizona, Tucson, AZ, USA
Michael Chertkov (Senior Member, IEEE) received the M.Sc. degree in physics from Novosibirsk State University, Novosibirsk, Russia, in 1990, and the Ph.D. degree in physics from the Weizmann Institute of Science, Rehovot, Israel, in 1996. After his Ph.D., he spent three years with Princeton University, Princeton, NJ, USA, as a R.H. Dicke Fellow with the Department of Physics. In 1999, he joined Los Alamos National Lab, in...Show More
Michael Chertkov (Senior Member, IEEE) received the M.Sc. degree in physics from Novosibirsk State University, Novosibirsk, Russia, in 1990, and the Ph.D. degree in physics from the Weizmann Institute of Science, Rehovot, Israel, in 1996. After his Ph.D., he spent three years with Princeton University, Princeton, NJ, USA, as a R.H. Dicke Fellow with the Department of Physics. In 1999, he joined Los Alamos National Lab, in...View more
Contents Program in Applied Mathematics and Department of Mathematics, University of Arizona, Tucson, AZ, USA
Robert Ferrando received the B.S. degree in mathematics from the Macaulay Honors College at the College of Staten Island, City University of New York, New York City, NY, USA, and the M.S. degree in applied mathematics from The University of Arizona, Tucson, AZ, USA, where he is currently working toward the Ph.D. degree with the Graduate Interdisciplinary Program in applied mathematics. His research focuses on using physics-informed machine learning to construct efficient, interpretable algorithms for optimization, particularly in the context of power systems.
Robert Ferrando received the B.S. degree in mathematics from the Macaulay Honors College at the College of Staten Island, City University of New York, New York City, NY, USA, and the M.S. degree in applied mathematics from The University of Arizona, Tucson, AZ, USA, where he is currently working toward the Ph.D. degree with the Graduate Interdisciplinary Program in applied mathematics. His research focuses on using physics-informed machine learning to construct efficient, interpretable algorithms for optimization, particularly in the context of power systems.View more
Program in Applied Mathematics and Department of Mathematics, University of Arizona, Tucson, AZ, USA
Laurent Pagnier (Member, IEEE) received the M.S. and Ph.D. degrees in theoretical physics from École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland, in 2014 and 2019, respectively. He is currently a Visiting Assistant Professor with The University of Arizona, Tucson, AZ, USA. His research interests include developing new modeling and monitoring methods for power systems. He is also aiming at applying Machine Learning techniques to power systems. He is particularly interested in reinforcing the interpretablility and trustworthiness of ML methods which are paramount to increase their acceptance and usage inside this power system community.
Laurent Pagnier (Member, IEEE) received the M.S. and Ph.D. degrees in theoretical physics from École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland, in 2014 and 2019, respectively. He is currently a Visiting Assistant Professor with The University of Arizona, Tucson, AZ, USA. His research interests include developing new modeling and monitoring methods for power systems. He is also aiming at applying Machine Learning techniques to power systems. He is particularly interested in reinforcing the interpretablility and trustworthiness of ML methods which are paramount to increase their acceptance and usage inside this power system community.View more
Department of Industrial and Systems Engineering at Rutgers University, Piscataway, NJ, USA
Robert Mieth (Member, IEEE) received the Doctorate degree in engineering from the Technical University of Berlin, Berlin, Germany, in 2021. He is currently an Assistant Professor with the Department of Industrial and Systems Engineering, Rutgers University, New Brunswick, NJ, USA. Prior to joining Rutgers, he was a Postdoctoral Fellow with the Department of Electrical and Computer Engineering, Princeton University, Princeton, NJ, from 2022 to 2023, a Postdoctoral Researcher and Visiting Scholar with the Department of Electrical and Computer Engineering, New York University's Tandon School of Engineering from 2021 to 2022 and 2018 to 2020, respectively. His research interests include risk analysis, stochastic optimization, and data methods for modern power system operations and electricity markets.
Robert Mieth (Member, IEEE) received the Doctorate degree in engineering from the Technical University of Berlin, Berlin, Germany, in 2021. He is currently an Assistant Professor with the Department of Industrial and Systems Engineering, Rutgers University, New Brunswick, NJ, USA. Prior to joining Rutgers, he was a Postdoctoral Fellow with the Department of Electrical and Computer Engineering, Princeton University, Princeton, NJ, from 2022 to 2023, a Postdoctoral Researcher and Visiting Scholar with the Department of Electrical and Computer Engineering, New York University's Tandon School of Engineering from 2021 to 2022 and 2018 to 2020, respectively. His research interests include risk analysis, stochastic optimization, and data methods for modern power system operations and electricity markets.View more
Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD, USA
Zhirui Liang received the B.E. degree from North China Electric Power University, Beijing, China, in 2017, and the M.Sc. degree from Xi'an Jiaotong University, Xi'an, China, in 2020. She is currently working toward the Ph.D. degree in electrical and computer engineering with Johns Hopkins University, Baltimore, MD, USA. She began her Ph.D. study with the Tandon School of Engineering, New York University, New York City, NY, USA, in September 2020, and she transferred to Johns Hopkins University in September 2022. Her research focuses on the integration of machine learning in power systems and power markets.
Zhirui Liang received the B.E. degree from North China Electric Power University, Beijing, China, in 2017, and the M.Sc. degree from Xi'an Jiaotong University, Xi'an, China, in 2020. She is currently working toward the Ph.D. degree in electrical and computer engineering with Johns Hopkins University, Baltimore, MD, USA. She began her Ph.D. study with the Tandon School of Engineering, New York University, New York City, NY, USA, in September 2020, and she transferred to Johns Hopkins University in September 2022. Her research focuses on the integration of machine learning in power systems and power markets.View more
Department of Civil and Systems Engineering, Electrical and Computer Engineering and Ralph O'Connor Sustainable Energy Institute, Johns Hopkins University, Baltimore, MD, USA
Yury Dvorkin (Member, IEEE) received the Ph.D. degree with the University of Washington, Seattle, WA, USA, in 2016. He is currently an Associate Professor with the Department of Civil and Systems Engineering and Department of Electrical Computer Engineering, Johns Hopkins University (JHU), Baltimore, MD, USA, where he is also part of JHU's Ralph O'Connor Sustainable Energy Institute and Co-Director of the NSF Global Climate Center Electric Power Innovation for a Carbon-free Society. He was a graduate student Researcher with the Center for Nonlinear Studies, Los Alamos National Laboratory, Los Alamos, NM, USA, in 2014.
His research focuses on developing modeling and algorithmic solutions to assist society in accommodating emerging smart grid technologies (such as, intermittent generation, demand response, storage, smart appliances, and cyber infrastructure) using multi-disciplinary methods in engineering, operations research, economics, and policy analysis.
He was the recipient of the inaugural 2016 Scientific Achievement Award by Clean Energy Institute (University of Washington), 2019 NSF CAREER Award, Goddard Junior Faculty Fellowship (2019) Discovery Award (2023), Best Paper Award (IEEE PES GM in 2014), and IEEE PES Prize Paper Award (IEEE Transactions on Power Systems, 2023). He was also the recipient of the Best Reviewer Awards from IEEE Transactions on Power Systems in 2014, 2015, 2016, 2017, and 2018, IEEE Transactions on Sustainable Energy in 2014, 2015, and 2016, and IEEE Transactions on Smart Grids in 2016. He has been an Associate Editor for the IEEE Transactions on Smart Grid since 2019 and IEEE Transactions on Energy Markets, Policy and Regulation since 2022.
Yury Dvorkin (Member, IEEE) received the Ph.D. degree with the University of Washington, Seattle, WA, USA, in 2016. He is currently an Associate Professor with the Department of Civil and Systems Engineering and Department of Electrical Computer Engineering, Johns Hopkins University (JHU), Baltimore, MD, USA, where he is also part of JHU's Ralph O'Connor Sustainable Energy Institute and Co-Director of the NSF Global Climate Center Electric Power Innovation for a Carbon-free Society. He was a graduate student Researcher with the Center for Nonlinear Studies, Los Alamos National Laboratory, Los Alamos, NM, USA, in 2014.
His research focuses on developing modeling and algorithmic solutions to assist society in accommodating emerging smart grid technologies (such as, intermittent generation, demand response, storage, smart appliances, and cyber infrastructure) using multi-disciplinary methods in engineering, operations research, economics, and policy analysis.
He was the recipient of the inaugural 2016 Scientific Achievement Award by Clean Energy Institute (University of Washington), 2019 NSF CAREER Award, Goddard Junior Faculty Fellowship (2019) Discovery Award (2023), Best Paper Award (IEEE PES GM in 2014), and IEEE PES Prize Paper Award (IEEE Transactions on Power Systems, 2023). He was also the recipient of the Best Reviewer Awards from IEEE Transactions on Power Systems in 2014, 2015, 2016, 2017, and 2018, IEEE Transactions on Sustainable Energy in 2014, 2015, and 2016, and IEEE Transactions on Smart Grids in 2016. He has been an Associate Editor for the IEEE Transactions on Smart Grid since 2019 and IEEE Transactions on Energy Markets, Policy and Regulation since 2022.View more
IEOR, Columbia University, New York, NY, USA
Daniel Bienstock (Member, IEEE) is currently a Liu Family Professor of operations research with joint appointments in applied math and applied physics, and in electrical engineering with Columbia University, New York City, NY, USA. His work focuses on methodology for mathematical optimizatin problems, with special interest on applications to power systems. He is also an INFORMS Fellow. He was the recipient of the 2022 Khachiyan Prize in Optimization.
Daniel Bienstock (Member, IEEE) is currently a Liu Family Professor of operations research with joint appointments in applied math and applied physics, and in electrical engineering with Columbia University, New York City, NY, USA. His work focuses on methodology for mathematical optimizatin problems, with special interest on applications to power systems. He is also an INFORMS Fellow. He was the recipient of the 2022 Khachiyan Prize in Optimization.View more
Program in Applied Mathematics and Department of Mathematics, University of Arizona, Tucson, AZ, USA
Michael Chertkov (Senior Member, IEEE) received the M.Sc. degree in physics from Novosibirsk State University, Novosibirsk, Russia, in 1990, and the Ph.D. degree in physics from the Weizmann Institute of Science, Rehovot, Israel, in 1996. After his Ph.D., he spent three years with Princeton University, Princeton, NJ, USA, as a R.H. Dicke Fellow with the Department of Physics. In 1999, he joined Los Alamos National Lab, initially as a J. R. Oppenheimer Fellow with the Theoretical Division, and continued as a Technical Staff Member leading projects in physics of algorithms, energy grid systems, physics, and engineering informed data science and machine learning for turbulence. In 2019, he moved to Tucson to lead Interdisciplinary Graduate Program in applied mathematics, The University of Arizona, Tucson, AZ, USA, continuing to work for LANL part time. He has authored or coauthored more than 200 papers. His research interests include mathematics and statistics applied to physical, engineering, and data sciences. He is a Fellow of the American Physical Society.
Michael Chertkov (Senior Member, IEEE) received the M.Sc. degree in physics from Novosibirsk State University, Novosibirsk, Russia, in 1990, and the Ph.D. degree in physics from the Weizmann Institute of Science, Rehovot, Israel, in 1996. After his Ph.D., he spent three years with Princeton University, Princeton, NJ, USA, as a R.H. Dicke Fellow with the Department of Physics. In 1999, he joined Los Alamos National Lab, initially as a J. R. Oppenheimer Fellow with the Theoretical Division, and continued as a Technical Staff Member leading projects in physics of algorithms, energy grid systems, physics, and engineering informed data science and machine learning for turbulence. In 2019, he moved to Tucson to lead Interdisciplinary Graduate Program in applied mathematics, The University of Arizona, Tucson, AZ, USA, continuing to work for LANL part time. He has authored or coauthored more than 200 papers. His research interests include mathematics and statistics applied to physical, engineering, and data sciences. He is a Fellow of the American Physical Society.View more
