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Physics-Informed Artificial Intellig...

Ferrando, Robert Louis,

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Physics-Informed Artificial Intelligence for Multi-Scale Energy Systems /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Physics-Informed Artificial Intelligence for Multi-Scale Energy Systems // Robert Louis Ferrando.
Author:	Ferrando, Robert Louis,
Description:	1 electronic resource (118 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 87-02, Section: B.
Contained By:	Dissertations Abstracts International87-02B.
Subject:	Applied mathematics. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=32170563
ISBN:	9798291503126

Physics-Informed Artificial Intelligence for Multi-Scale Energy Systems /
Ferrando, Robert Louis,

Physics-Informed Artificial Intelligence for Multi-Scale Energy Systems /Robert Louis Ferrando. - 1 electronic resource (118 pages)

Source: Dissertations Abstracts International, Volume: 87-02, Section: B.

The increase of renewable energy, combined with the increased vulnerability of critical infrastructure to natural and human disasters, requires efficient simulation and optimization of energy system operations. In addition, coordination of multiple technologies requires seamless bridging of vastly different time scales. In this thesis, we outline our efforts to use physics-informed artificial intelligence in a principled manner to accelerate steady-state optimization and market clearing in power systems and dynamic transient simulation in natural gas systems. We also offer some thoughts about how to couple these technologies towards higher-fidelity simulation and more strategic day-ahead and real-time decision making. The two main problems considered are DC-Optimal Power Flow, to which we apply active set learning, and Unit Commitment of dual-fuel generators, which we model as a Markov Decision Process. Finally, we opine on the role that emerging generative AI tools may play in advancing this work, and provide a small, yet illustrative case-study on unit commitment to illustrate the potential to use the Decision Flow algorithm to generate probabilistic samples of generator statuses.

English

ISBN: 9798291503126Subjects--Topical Terms:

2122814
Applied mathematics.
Subjects--Index Terms:

Energy systems

Physics-Informed Artificial Intelligence for Multi-Scale Energy Systems /
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500 $a Source: Dissertations Abstracts International, Volume: 87-02, Section: B.
500 $a Advisors: Chertkov, Michael Committee members: Sethuraman, Sunder; Venkataramani, Shankar; Bienstock, Daniel; Dvorkin, Yury.
502 $b Ph.D. $c The University of Arizona $d 2025.
520 $a The increase of renewable energy, combined with the increased vulnerability of critical infrastructure to natural and human disasters, requires efficient simulation and optimization of energy system operations. In addition, coordination of multiple technologies requires seamless bridging of vastly different time scales. In this thesis, we outline our efforts to use physics-informed artificial intelligence in a principled manner to accelerate steady-state optimization and market clearing in power systems and dynamic transient simulation in natural gas systems. We also offer some thoughts about how to couple these technologies towards higher-fidelity simulation and more strategic day-ahead and real-time decision making. The two main problems considered are DC-Optimal Power Flow, to which we apply active set learning, and Unit Commitment of dual-fuel generators, which we model as a Markov Decision Process. Finally, we opine on the role that emerging generative AI tools may play in advancing this work, and provide a small, yet illustrative case-study on unit commitment to illustrate the potential to use the Decision Flow algorithm to generate probabilistic samples of generator statuses.
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590 $a School code: 0009
650 4 $a Applied mathematics. $3 2122814
650 4 $a Energy. $3 876794
650 4 $a Alternative energy. $3 3436775
653 $a Energy systems
653 $a Machine learning
653 $a Optimization
653 $a Natural gas systems
653 $a Renewable energy
690 $a 0364
690 $a 0800
690 $a 0363
690 $a 0791
710 2 $a The University of Arizona. $b Applied Mathematics. $e degree granting institution. $3 3816363
720 1 $a Chertkov, Michael $e degree supervisor.
773 0 $t Dissertations Abstracts International $g 87-02B.
790 $a 0009
791 $a Ph.D.
792 $a 2025
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