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Reinforcement Learning for the Desig...

Pigott, Aisling Karen,

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Reinforcement Learning for the Design and Operation of Distribution Networks /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Reinforcement Learning for the Design and Operation of Distribution Networks // Aisling Karen Pigott.
Author:	Pigott, Aisling Karen,
Description:	1 electronic resource (93 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 85-11, Section: B.
Contained By:	Dissertations Abstracts International85-11B.
Subject:	Architectural engineering. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31149349
ISBN:	9798382718033

Reinforcement Learning for the Design and Operation of Distribution Networks /
Pigott, Aisling Karen,

Reinforcement Learning for the Design and Operation of Distribution Networks /Aisling Karen Pigott. - 1 electronic resource (93 pages)

Source: Dissertations Abstracts International, Volume: 85-11, Section: B.

Recent changes to the distribution electrical network such as increased consumer demand and various renewable resources have made the need to replace and upgrade electrical distribution equipment more dire than ever. This thesis introduces various frameworks for model-free, data-driven reinforcement learning to optimize energy system control across electrical and thermal energy domains. We consider control at various points of the distribution network including aggregator price setting, system operator generator control, and variations of demand response that address grid services beyond peak shaving used in conventional demand response programs (such as voltage regulation). In this thesis, we seek to address the primary concern of the combination of thermal and electrical models, ensuring the feasibility of these results for both the customer and the electric utility. Each control study that includes load shifting validates that the thermal comfort requirements of the end-user are satisfied and does so without adding extraneous hardware. Finally, we introduce a study that considers the use of reinforcement learning algorithms in distribution network design by placing distributed electrical resources at optimal points to support grid-level goals. Preliminary results indicate that reinforcement learning can be valuable for improving grid operation.

English

ISBN: 9798382718033Subjects--Topical Terms:

3174102
Architectural engineering.
Subjects--Index Terms:

Distribution networks

Reinforcement Learning for the Design and Operation of Distribution Networks /
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245 1 0 $a Reinforcement Learning for the Design and Operation of Distribution Networks / $c Aisling Karen Pigott.
264 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2024
300 $a 1 electronic resource (93 pages)
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500 $a Source: Dissertations Abstracts International, Volume: 85-11, Section: B.
500 $a Advisors: Baker, Kyri Committee members: Henze, Gregor; Bernstein, Andrey; Nagy, Zoltan; Zuo, Wangda.
502 $b Ph.D. $c University of Colorado at Boulder $d 2024.
520 $a Recent changes to the distribution electrical network such as increased consumer demand and various renewable resources have made the need to replace and upgrade electrical distribution equipment more dire than ever. This thesis introduces various frameworks for model-free, data-driven reinforcement learning to optimize energy system control across electrical and thermal energy domains. We consider control at various points of the distribution network including aggregator price setting, system operator generator control, and variations of demand response that address grid services beyond peak shaving used in conventional demand response programs (such as voltage regulation). In this thesis, we seek to address the primary concern of the combination of thermal and electrical models, ensuring the feasibility of these results for both the customer and the electric utility. Each control study that includes load shifting validates that the thermal comfort requirements of the end-user are satisfied and does so without adding extraneous hardware. Finally, we introduce a study that considers the use of reinforcement learning algorithms in distribution network design by placing distributed electrical resources at optimal points to support grid-level goals. Preliminary results indicate that reinforcement learning can be valuable for improving grid operation.
546 $a English
590 $a School code: 0051
650 4 $a Architectural engineering. $3 3174102
650 4 $a Energy. $3 876794
650 4 $a Environmental engineering. $3 548583
653 $a Distribution networks
653 $a Power systems
653 $a Reinforcement learning
653 $a Voltage regulation
653 $a Renewable resources
690 $a 0462
690 $a 0791
690 $a 0543
690 $a 0775
710 2 $a University of Colorado at Boulder. $b Civil, Environmental, and Architectural Engineering. $e degree granting institution. $3 3765764
720 1 $a Baker, Kyri $e degree supervisor.
773 0 $t Dissertations Abstracts International $g 85-11B.
790 $a 0051
791 $a Ph.D.
792 $a 2024
856 4 0 $u https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31149349