Deep-Learning-Based Predictive Control of Battery Management for Frequency Regulation-Reference-Cited by-同舟云学术

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Deep-Learning-Based Predictive Control of Battery Management for Frequency Regulation

Published:2022-03-16 Issue:24 Volume:61 Page:8432-8442
ISSN:0888-5885
Container-title:Industrial & Engineering Chemistry Research
language:en
Short-container-title:Ind. Eng. Chem. Res.

Author:

Li Yun¹,Wang Yixiu¹,Chen Yifu²,Lu Yifan¹,Hua Kaixun¹,Ren Jiayang¹,Mozafari Ghazaleh¹,Lu Qiugang³,Cao Yankai¹^ORCID

Affiliation:

1. Department of Chemical and Biological Engineering, University of British Columbia, Vancouver, British Columbia V6T1Z4, Canada

2. Department of Chemical and Biological Engineering, University of Wisconsin─Madison, Madison, Wisconsin 53706, United States

3. Department of Chemical Engineering, Texas Tech University, Lubbock, Texas 79409, United States

Funder

Natural Sciences and Engineering Research Council of Canada

Publisher

American Chemical Society (ACS)

Subject

Industrial and Manufacturing Engineering,General Chemical Engineering,General Chemistry

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.iecr.1c04903

Reference26 articles.

1. Data Centers as Dispatchable Loads to Harness Stranded Power

2. Value Analysis of Battery Energy Storage Applications in Power Systems

3. Optimal Bidding Strategy of Battery Storage in Power Markets Considering Performance-Based Regulation and Battery Cycle Life

4. Optimal Battery Participation in Frequency Regulation Markets

5. Optimal bidding strategy of a virtual power plant in day-ahead energy and frequency regulation markets: A deep learning-based approach

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2. Semantic Knowledge Integrated Graph Convolutional Network for Zero-Sample Tracing of Control Performance Degradation;Industrial & Engineering Chemistry Research;2023-12-04

3. Machine Learning and Data Science in Chemical Engineering;Industrial & Engineering Chemistry Research;2022-06-22

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