Chapter Nine - Automated Optimal Control in Energy Systems: The Reinforcement Learning Approach

Xiangyu Zhang; Huaiguang Jiang

doi:10.1016/B978-0-12-820168-8.00015-8

Chapter Nine - Automated Optimal Control in Energy Systems: The Reinforcement Learning Approach

Xiangyu Zhang, Huaiguang Jiang

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

2 Scopus Citations

Abstract

With the development of smart grid technologies an increasing number of new devices and participants have joined modern energy systems and are inevitably making them more complicated and interdependent than ever. Optimally controlling such a complex energy system and maintaining its operation in a high-efficient, secure, and resilient manner are challenging tasks to the system operators. Fortunately, the revolution in deep learning and artificial intelligence (AI), both from hardware and algorithms perspectives, has provided new ideas and solutions to many previously intractable problems. As a result, this advance in computer science also sparked great research interests in utilizing AI in solving engineering problems related to the modern energy systems.

Original language	American English
Title of host publication	New Technologies for Power System Operation and Analysis
Editors	H. Jiang, Y. Zhang, E. Muljadi
Publisher	Elsevier
Pages	275-318
Number of pages	44
ISBN (Electronic)	9780128201688
DOIs	https://doi.org/10.1016/B978-0-12-820168-8.00015-8 https://doi.org/10.1016/B978-0-12-820168-8.00015-8
State	Published - 2020

Bibliographical note

Publisher Copyright:
© 2021 Elsevier Inc. All rights reserved.

NREL Publication Number

NREL/BK-5D00-77730

Keywords

Artificial intelligence
Optimal control
Reinforcement learning
Smart grid

Access to Document

Cite this

@inbook{5839e6d6ecff44a6892b264be0f7f37f,

title = "Chapter Nine - Automated Optimal Control in Energy Systems: The Reinforcement Learning Approach",

abstract = "With the development of smart grid technologies an increasing number of new devices and participants have joined modern energy systems and are inevitably making them more complicated and interdependent than ever. Optimally controlling such a complex energy system and maintaining its operation in a high-efficient, secure, and resilient manner are challenging tasks to the system operators. Fortunately, the revolution in deep learning and artificial intelligence (AI), both from hardware and algorithms perspectives, has provided new ideas and solutions to many previously intractable problems. As a result, this advance in computer science also sparked great research interests in utilizing AI in solving engineering problems related to the modern energy systems.",

keywords = "Artificial intelligence, Optimal control, Reinforcement learning, Smart grid",

author = "Xiangyu Zhang and Huaiguang Jiang",

year = "2020",

doi = "10.1016/B978-0-12-820168-8.00015-8",

language = "American English",

pages = "275--318",

editor = "H. Jiang and Y. Zhang and E. Muljadi",

booktitle = "New Technologies for Power System Operation and Analysis",

publisher = "Elsevier",

}

TY - CHAP

T1 - Chapter Nine - Automated Optimal Control in Energy Systems: The Reinforcement Learning Approach

AU - Zhang, Xiangyu

AU - Jiang, Huaiguang

PY - 2020

Y1 - 2020

N2 - With the development of smart grid technologies an increasing number of new devices and participants have joined modern energy systems and are inevitably making them more complicated and interdependent than ever. Optimally controlling such a complex energy system and maintaining its operation in a high-efficient, secure, and resilient manner are challenging tasks to the system operators. Fortunately, the revolution in deep learning and artificial intelligence (AI), both from hardware and algorithms perspectives, has provided new ideas and solutions to many previously intractable problems. As a result, this advance in computer science also sparked great research interests in utilizing AI in solving engineering problems related to the modern energy systems.

AB - With the development of smart grid technologies an increasing number of new devices and participants have joined modern energy systems and are inevitably making them more complicated and interdependent than ever. Optimally controlling such a complex energy system and maintaining its operation in a high-efficient, secure, and resilient manner are challenging tasks to the system operators. Fortunately, the revolution in deep learning and artificial intelligence (AI), both from hardware and algorithms perspectives, has provided new ideas and solutions to many previously intractable problems. As a result, this advance in computer science also sparked great research interests in utilizing AI in solving engineering problems related to the modern energy systems.

KW - Artificial intelligence

KW - Optimal control

KW - Reinforcement learning

KW - Smart grid

UR - http://www.scopus.com/inward/record.url?scp=85123811911&partnerID=8YFLogxK

U2 - 10.1016/B978-0-12-820168-8.00015-8

DO - 10.1016/B978-0-12-820168-8.00015-8

M3 - Chapter

AN - SCOPUS:85123811911

SP - 275

EP - 318

BT - New Technologies for Power System Operation and Analysis

A2 - Jiang, H.

A2 - Zhang, Y.

A2 - Muljadi, E.

PB - Elsevier

ER -

Chapter Nine - Automated Optimal Control in Energy Systems: The Reinforcement Learning Approach

Abstract

Bibliographical note

NREL Publication Number

Keywords

Access to Document

Other files and links

Fingerprint

Cite this