Long-Term Optimization of Large-Scale Hydropower System Operations Based on Decomposition-Coordination
Publication: Journal of Water Resources Planning and Management
Volume 146, Issue 10
Abstract
The basic objective of long-term optimization of large-scale hydropower system operations (LOLHSO) is to increase water resource use efficiency and construct a clean and low-carbon-intensity energy system. However, limited by rather stochastic inflows, large-scale hydropower systems, and complex objectives and constraints, LOLHSO is characterized by uncertainty, high dimensionality, nonlinearity, and nonconvexity, which pose great challenges in modeling. This paper presents an improved hybrid decomposition-coordination and discrete differential dynamic programming model (IDC-DDDP) for solving the LOLHSO problem. In IDC-DDDP, a decomposition strategy considering the adjustment potential of hydropower plants is designed to reduce the system size. Meanwhile, owing to the sensitivity of DDDP to initial trajectories, a data mining–based strategy is developed as a means of generating superior initial trajectories. A corridor generation strategy is presented to determine the discrete steps and enhance global search abilities. Case studies in a hydropower system in southwestwern China demonstrate the practicability and robustness of the proposed model.
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Data Availability Statement
Some data, models, or code that support the findings of this study, including the Java code of the proposed IDC-DDDP model, are available from the corresponding author upon reasonable request.
Acknowledgments
The writers are grateful to the editor, the associate editor, and two reviewers for their constructive suggestions. The work described in this paper was supported by the National Nature Science Foundation of China (No. 91547201) and the National Natural Science Foundation of China (No. U1765103).
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Published In
Journal of Water Resources Planning and Management
Volume 146 • Issue 10 • October 2020
Copyright
© 2020 American Society of Civil Engineers.
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Received: May 21, 2019
Accepted: May 27, 2020
Published online: Aug 14, 2020
Published in print: Oct 1, 2020
Discussion open until: Jan 14, 2021
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