Knowledge-Based Approach for Reservoir System Optimization
Publication: Journal of Water Resources Planning and Management
Volume 140, Issue 6
Abstract
This paper addresses a knowledge-based approach for reservoir system optimization. The approach takes a more detailed consideration of each turbine in a hydropower plant than the traditional constant output coefficient method. To use this approach, a knowledge expression and a knowledge function are defined for hydropower plant operation. The knowledge expression is extracted by dynamic programming to save all possibly optimal situations of unit commitment, and further, the knowledge function is formulated based on a two-dimensional interpolation of the knowledge expression. Through the use of the knowledge expression and the knowledge function, computer memory requirements can be reduced and unnecessary computations can be avoided in the reservoir operation optimization. To overcome the decomposition schemes in time and in space and guarantee finding the global optimum (in a discrete sense) with an extended time horizon, up to 400 CPU cores are used to run a parallel dynamic programming model, which applies the knowledge-based approach, to estimate the maximum energy production of the Three Gorges Project (TGP) and the Gezhouba Project (GZB) cascade hydropower plants in China in the year of 2010, with 1 day as the time step and 365 days as the time horizon. The case study results show that the maximum energy production of the TGP-GZB system would be in 2010 under the current operating rules. Thus, there is still room for improvement in energy production, with the maximum increase of 1.83% () from optimizing hydropower plant operation and 3.46% () from optimizing both hydropower plant operation and reservoir operation. The overall approach is effective in providing optimal assessment solutions with an acceptable computation time. Thus, it can be concluded that the approach can be applied for planning purposes or providing more reasonable boundaries for real-time operation.
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Acknowledgments
This study was supported by the National Key Technologies R&D Program #2009BAC56B03 and 2011BAZ03221B03 and the National Natural Science Foundation #51109114 in China. The first author is supported by a fellowship from the Chinese government for his visit to the University of California, Los Angeles. The authors are grateful to the associate editor and the two anonymous reviewers for their valuable and constructive comments and suggestions.
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Published In
Journal of Water Resources Planning and Management
Volume 140 • Issue 6 • June 2014
Copyright
© 2014 American Society of Civil Engineers.
History
Received: Jun 21, 2012
Accepted: May 17, 2013
Published online: May 21, 2013
Published in print: Jun 1, 2014
Discussion open until: Aug 12, 2014
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