LAPSE:2023.20953
Published Article
LAPSE:2023.20953
Data-Driven Stochastic Scheduling for Energy Integrated Systems
March 21, 2023
As the penetration of intermittent renewable energy increases and unexpected market behaviors continue to occur, new challenges arise for system operators to ensure cost effectiveness while maintaining system reliability under uncertainties. To systematically address these uncertainties and challenges, innovative advanced methods and approaches are needed. Motivated by these, in this paper, we consider an energy integrated system with renewable energy and pumped-storage units involved. In addition, we propose a data-driven risk-averse two-stage stochastic model that considers the features of forbidden zones and dynamic ramping rate limits. This model minimizes the total cost against the worst-case distribution in the confidence set built for an unknown distribution and constructed based on data. Our numerical experiments show how pumped-storage units contribute to the system, how inclusions of the aforementioned two features improve the reliability of the system, and how our proposed data-driven model converges to a risk-neutral model with historical data.
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Keywords
data-driven, scheduling optimization, Stochastic Optimization, unit commitment
Subject
Suggested Citation
Yang H, Jin Z, Wang J, Zhao Y, Wang H, Xiao W. Data-Driven Stochastic Scheduling for Energy Integrated Systems. (2023). LAPSE:2023.20953
Author Affiliations
Yang H: State Key Laboratory of Simulation and Regulation of Water Cycles in River Basin, China Institute of Water Resources and Hydropower Research, Beijing 100038, China
Jin Z: Department of Production Engineering, KTH Royal Institute of Technology, 114 28 Stockholm, Sweden [ORCID]
Wang J: State Key Laboratory of Simulation and Regulation of Water Cycles in River Basin, China Institute of Water Resources and Hydropower Research, Beijing 100038, China
Zhao Y: State Key Laboratory of Simulation and Regulation of Water Cycles in River Basin, China Institute of Water Resources and Hydropower Research, Beijing 100038, China [ORCID]
Wang H: State Key Laboratory of Simulation and Regulation of Water Cycles in River Basin, China Institute of Water Resources and Hydropower Research, Beijing 100038, China
Xiao W: State Key Laboratory of Simulation and Regulation of Water Cycles in River Basin, China Institute of Water Resources and Hydropower Research, Beijing 100038, China
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Jin Z: Department of Production Engineering, KTH Royal Institute of Technology, 114 28 Stockholm, Sweden [ORCID]
Wang J: State Key Laboratory of Simulation and Regulation of Water Cycles in River Basin, China Institute of Water Resources and Hydropower Research, Beijing 100038, China
Zhao Y: State Key Laboratory of Simulation and Regulation of Water Cycles in River Basin, China Institute of Water Resources and Hydropower Research, Beijing 100038, China [ORCID]
Wang H: State Key Laboratory of Simulation and Regulation of Water Cycles in River Basin, China Institute of Water Resources and Hydropower Research, Beijing 100038, China
Xiao W: State Key Laboratory of Simulation and Regulation of Water Cycles in River Basin, China Institute of Water Resources and Hydropower Research, Beijing 100038, China
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Journal Name
Energies
Volume
12
Issue
12
Article Number
E2317
Year
2019
Publication Date
2019-06-17
Published Version
ISSN
1996-1073
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Original Submission
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PII: en12122317, Publication Type: Journal Article
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LAPSE:2023.20953
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doi:10.3390/en12122317
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[v1] (Original Submission)
Mar 21, 2023
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Mar 21, 2023
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