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

Xiangyu Zhang, Huaiguang Jiang

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

2 Scopus Citations


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 languageAmerican English
Title of host publicationNew Technologies for Power System Operation and Analysis
EditorsH. Jiang, Y. Zhang, E. Muljadi
Number of pages44
ISBN (Electronic)9780128201688
StatePublished - 2020

Bibliographical note

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

NREL Publication Number

  • NREL/BK-5D00-77730


  • Artificial intelligence
  • Optimal control
  • Reinforcement learning
  • Smart grid


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