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Research Article
Kun Yang
Hohai University
Corresponding Author
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An improved binary-coded whale optimization algorithm (IBWOA) is proposed to solve the complex nonlinear problem of short-term hydropower generation scheduling (STHGS). The spatial optimal load distribution is combined with the temporal unit commitment combination model, and the binary array is used to represent the start/stop state of the unit. Sigmoid Function (SF) is used to solve the correspondence between binary array and real number. The whale algorithm's search mechanism is optimized, and the inertia weight and perturbation variation strategy are introduced to improve the algorithm's optimization ability. The unit commitment (UC) subproblem was solved by repairing the minimum uptime/downtime constraint and the spinning reserve capacity constraint, and the economic load scheduling (ELD) subproblem was solved by an optimal stable load distribution table (OSLDT). The Mutation mechanism and the Locally balanced dynamic search mechanism compensate for the non-convex problems caused by start-stop constraints and stable optimal table methods. The proposal is applied to solve the STHGS of the Three Gorges hydropower station. The results show that the method has good convergence, stability, fast calculation speed, and high optimization accuracy.
Keywords
Short-term hydropower generation scheduling, Whale optimization algorithm, Adaptive inertia weight, Perturbation mutation strategy, Optimal stable load distribution table
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View the published article at Water Resources Management.
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See the published version of this preprint at Water Resources Management.
This is a preprint, a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Learn more about In Review
Research Article
Kun Yang
Hohai University
Corresponding Author
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
Peer Review Timeline
CURRENT STATUS: Published
View the published article at Water Resources Management.
Version 1
Posted 22 Mar, 2021
No community comments so far
Editorial decision: Major revisions
On 03 Jul, 2021
Reviewers invited
Invitations sent on 19 Mar, 2021
Reviews received
Received 19 Mar, 2021
First submitted
On 20 Jan, 2021
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
Subject Areas
Hydrology
License:
This work is licensed under a CC BY 4.0 License. Read Full License
View the published article at Water Resources Management.
An improved binary-coded whale optimization algorithm (IBWOA) is proposed to solve the complex nonlinear problem of short-term hydropower generation scheduling (STHGS). The spatial optimal load distribution is combined with the temporal unit commitment combination model, and the binary array is used to represent the start/stop state of the unit. Sigmoid Function (SF) is used to solve the correspondence between binary array and real number. The whale algorithm's search mechanism is optimized, and the inertia weight and perturbation variation strategy are introduced to improve the algorithm's optimization ability. The unit commitment (UC) subproblem was solved by repairing the minimum uptime/downtime constraint and the spinning reserve capacity constraint, and the economic load scheduling (ELD) subproblem was solved by an optimal stable load distribution table (OSLDT). The Mutation mechanism and the Locally balanced dynamic search mechanism compensate for the non-convex problems caused by start-stop constraints and stable optimal table methods. The proposal is applied to solve the STHGS of the Three Gorges hydropower station. The results show that the method has good convergence, stability, fast calculation speed, and high optimization accuracy.
Figure 1
Figure 2
Figure 3
Figure 4
The full text of this article is available to read as a PDF.
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