LAPSE:2023.26437
Published Article

LAPSE:2023.26437
Optimization Model for the Long-Term Operation of an Interprovincial Hydropower Plant Incorporating Peak Shaving Demands
April 3, 2023
Abstract
The increasing peak-to-valley load difference in China pose a challenge to long-distance and large-capacity hydropower transmission via high-voltage direct current (HVDC) lines. Considering the peak shaving demands of load centers, an optimization model that maximizes the expected power generation revenue is proposed here for the long-term operation of an interprovincial hydropower plant. A simulation-based method was utilized to explore the relationships between long-term power generation and short-term peak shaving revenue in the model. This method generated representative daily load scenarios via cluster analysis and approximated the real-time electricity price of each load profile with the time-of-use price strategy. A mixed-integer linear programming model with HVDC transmission constraints was then established to obtain moving average (MA) price curves that bridged two time-coupled operations. The MA price curves were finally incorporated into the long-term optimization model to determine monthly generation schedules, and the inflow uncertainty was addressed by discretized inflow scenarios. The proposed model was evaluated based on the operation of the Xiluodu hydropower system in China during the drawdown season. The results revealed a trade-off between long-term energy production and short-term peak shaving revenue, and they demonstrated the revenue potential of interprovincial hydropower transmission while meeting peak shaving demands. A comparison with other long-term optimization methods demonstrated the effectiveness and reliability of the proposed model in maximizing power generation revenue.
The increasing peak-to-valley load difference in China pose a challenge to long-distance and large-capacity hydropower transmission via high-voltage direct current (HVDC) lines. Considering the peak shaving demands of load centers, an optimization model that maximizes the expected power generation revenue is proposed here for the long-term operation of an interprovincial hydropower plant. A simulation-based method was utilized to explore the relationships between long-term power generation and short-term peak shaving revenue in the model. This method generated representative daily load scenarios via cluster analysis and approximated the real-time electricity price of each load profile with the time-of-use price strategy. A mixed-integer linear programming model with HVDC transmission constraints was then established to obtain moving average (MA) price curves that bridged two time-coupled operations. The MA price curves were finally incorporated into the long-term optimization model to determine monthly generation schedules, and the inflow uncertainty was addressed by discretized inflow scenarios. The proposed model was evaluated based on the operation of the Xiluodu hydropower system in China during the drawdown season. The results revealed a trade-off between long-term energy production and short-term peak shaving revenue, and they demonstrated the revenue potential of interprovincial hydropower transmission while meeting peak shaving demands. A comparison with other long-term optimization methods demonstrated the effectiveness and reliability of the proposed model in maximizing power generation revenue.
Record ID
Keywords
energy price, HVDC transmission, hydropower system, mixed-integer linear programming, peak shaving
Subject
Suggested Citation
Cao R, Shen J, Cheng C, Wang J. Optimization Model for the Long-Term Operation of an Interprovincial Hydropower Plant Incorporating Peak Shaving Demands. (2023). LAPSE:2023.26437
Author Affiliations
Cao R: Institute of Hydropower and Hydroinformatics, Dalian University of Technology, Dalian 116024, China
Shen J: Institute of Hydropower and Hydroinformatics, Dalian University of Technology, Dalian 116024, China
Cheng C: Institute of Hydropower and Hydroinformatics, Dalian University of Technology, Dalian 116024, China
Wang J: Department of Civil and Environmental Engineering, Utah State University, 4110 Old Main, Logan, UT 84322-4110, USA
Shen J: Institute of Hydropower and Hydroinformatics, Dalian University of Technology, Dalian 116024, China
Cheng C: Institute of Hydropower and Hydroinformatics, Dalian University of Technology, Dalian 116024, China
Wang J: Department of Civil and Environmental Engineering, Utah State University, 4110 Old Main, Logan, UT 84322-4110, USA
Journal Name
Energies
Volume
13
Issue
18
Article Number
E4804
Year
2020
Publication Date
2020-09-14
ISSN
1996-1073
Version Comments
Original Submission
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PII: en13184804, Publication Type: Journal Article
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LAPSE:2023.26437
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https://doi.org/10.3390/en13184804
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Apr 3, 2023
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