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6 results on '"Fangxing Fran Li"'

1. 2023 Best Papers, Outstanding Associate Editors, and Outstanding Reviewers

Author

Fangxing Fran Li

Subjects
Distribution or transmission of electric power, TK3001-3521, Production of electric energy or power. Powerplants. Central stations, TK1001-1841
Abstract

The Editorial Board of the IEEE Open Access Journal of Power and Energy (OAJPE) would like to recognize the following best papers selected from all papers published between October 1, 2020 and September 30, 2023 in OAJPE.

Published
2024
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2. Editorial Special Section on Invited Papers in 2022 on Emerging Topics in the Power and Energy Society

Author

Fangxing Fran Li

Subjects
Distribution or transmission of electric power, TK3001-3521, Production of electric energy or power. Powerplants. Central stations, TK1001-1841
Abstract

The 2020 and 2021 Special Sections on Invited Papers on Emerging Topics in the Power and Energy Society (PES) proved to be of broad interest to diverse readers in the PES community, and the publication of this new section marks the third year in a row for the special invited section. After a rigorous peer-review process, a total of 13 articles were accepted for publication in the 2022 invited section of the IEEE Open Access Journal of Power and Energy (OAJPE).

Published
2022
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3. 2021 Best Papers, Outstanding Associate Editor, and Outstanding Reviewers

Author

Fangxing Fran Li

Subjects
Distribution or transmission of electric power, TK3001-3521, Production of electric energy or power. Powerplants. Central stations, TK1001-1841
Abstract

The Editorial Board of the IEEE Open Access Journal of Power and Energy (OAJPE) would like to recognize the following best papers selected from all papers published between October 2018 and September 2021 in OAJPE and its preceding journal, IEEE Power and Energy Technology Systems Journal (PETS-J).

Published
2022
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4. Editorial Special Section on Invited Papers in 2021 on Emerging Topics in the Power and Energy Society

Author

Fangxing Fran Li and Joao P. S. Catalao

Subjects
Distribution or transmission of electric power, TK3001-3521, Production of electric energy or power. Powerplants. Central stations, TK1001-1841
Abstract

The publication of the 2020 Special Section of Invited Papers on Emerging Topics in the Power and Energy Society (PES) received broad interest from diverse readers of the PES community. We continued to publish the special section for another year. After a rigorous peer-review process, a total of 13 papers [A1]–[A13] were accepted for publication in this invited section of the IEEE Open Access Journal of Power and Energy (OAJPE) in 2021.

Published
2021
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5. 2020 Best Papers and Outstanding Reviewers

Author

Fangxing Fran Li

Subjects
Distribution or transmission of electric power, TK3001-3521, Production of electric energy or power. Powerplants. Central stations, TK1001-1841
Abstract

The Editorial Board of the IEEE Open Access Journal of Power and Energy (OAJPE) would like to recognize the following best papers published between October 2017 and September 2020 in OAJPE and its preceding journal, IEEE Power and Energy Technology Systems Journal (PETS-J). Also presents a list of reviewers who contributed to this publication in 2021.

Published
2021
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6. Encoding Frequency Constraints in Preventive Unit Commitment Using Deep Learning with Region-of-Interest Active Sampling

Author

Rui Yao, Tianqi Hong, Hantao Cui, Yichen Zhang, Jianzhe Liu, Feng Qiu, and Fangxing Fran Li

Subjects
FOS: Computer and information sciences, Frequency response, Mathematical optimization, Computer Science - Machine Learning, Test data generation, Computer science, business.industry, Deep learning, Energy Engineering and Power Technology, Sampling (statistics), Systems and Control (eess.SY), Electrical Engineering and Systems Science - Systems and Control, Machine Learning (cs.LG), Power (physics), Electric power system, Power system simulation, Encoding (memory), FOS: Electrical engineering, electronic engineering, information engineering, Artificial intelligence, Electrical and Electronic Engineering, business
Abstract

With the increasing penetration of renewable energy, frequency response and its security are of significant concerns for reliable power system operations. Frequency-constrained unit commitment (FCUC) is proposed to address this challenge. Despite existing efforts in modeling frequency characteristics in unit commitment (UC), current strategies can only handle oversimplified low-order frequency response models and do not consider wide-range operating conditions. This paper presents a generic data-driven framework for FCUC under high renewable penetration. Deep neural networks (DNNs) are trained to predict the frequency response using real data or high-fidelity simulation data. Next, the DNN is reformulated as a set of mixed-integer linear constraints to be incorporated into the ordinary UC formulation. In the data generation phase, all possible power injections are considered, and a region-of-interests active sampling is proposed to include power injection samples with frequency nadirs closer to the UFLC threshold, which significantly enhances the accuracy of frequency constraints in FCUC. The proposed FCUC is verified on the the IEEE 39-bus system. Then, a full-order dynamic model simulation using PSS/E verifies the effectiveness of FCUC in frequency-secure generator commitments.

Published
2021

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