Synergistic Integration of Machine Learning and Mathematical Optimization for Unit Commitment-Reference-Cited by-同舟云学术

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Synergistic Integration of Machine Learning and Mathematical Optimization for Unit Commitment

Published:2023 Issue: Volume: Page:1-10
ISSN:0885-8950
Container-title:IEEE Transactions on Power Systems
language:
Short-container-title:IEEE Trans. Power Syst.

Author:

Wu Jianghua¹^ORCID,Luh Peter B.¹,Chen Yonghong²^ORCID,Yan Bing³^ORCID,Bragin Mikhail A.⁴

Affiliation:

1. Department of Electrical and Computer Engineering, University of Connecticut, Storrs, CT, USA

2. MISO, Carmel, IN, USA

3. Department of Electrical and Microelectronic Engineering, Rochester Institute of Technology, Rochester, NY, USA

4. Department of Electrical and Computer Engineering, University of California, Riverside, CA, USA

Funder

Midcontinent ISO

National Science Foundation

National Taiwan University

Yushan Scholar Program

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Energy Engineering and Power Technology

Link

http://xplorestaging.ieee.org/ielx7/59/4374138/10026495.pdf?arnumber=10026495

Cited by 9 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Transfer-Based Approximate Dynamic Programming for Rolling Security-Constrained Unit Commitment with Uncertainties;Protection and Control of Modern Power Systems;2024-09

2. Tackling rolling security-constrained unit commitment under uncertainty with Monte Carlo value search and empirical knowledge;Electric Power Systems Research;2024-09

3. Large-scale dynamic surgical scheduling under uncertainty by hierarchical reinforcement learning;International Journal of Production Research;2024-06-14

4. Combining Deep Learning and Branch-and-Cut Algorithm for Transmission-constrained Unit Commitment;2024 9th Asia Conference on Power and Electrical Engineering (ACPEE);2024-04-11

5. Toward efficient transportation electrification of heavy-duty trucks: Joint scheduling of truck routing and charging;Transportation Research Part C: Emerging Technologies;2024-03

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