Medium-Term Multiobjective Operation Mode of Cascade Reservoirs Using Multisource Information
Publication: Journal of Hydrologic Engineering
Volume 27, Issue 9
Abstract
The incorporation of information on the current system states and forecasts makes reservoir operations more efficient; however, forecast uncertainties often occur. Therefore, excellent reservoir operations should integrate forecast uncertainties and current system states to achieve trade-offs among the objectives. This study proposed a medium-term multiobjective operation (MTMOO) mode, which combines the coupled rainfall forecast (CRF) and the current system state as multisource information to intuitively guide the multiobjective scheduling decision-making of cascade reservoirs. The CRF in different forecast periods was determined to mitigate forecast uncertainties. The MTMOO mode was formulated by establishing a multiobjective optimization model and then using the decision tree algorithm. For verification, the CRF, 5-day rainfall forecast, and observed rainfall were simulated using the MTMOO mode, and the results were compared to those of conventional scheduling. The case study shows that the electricity generation and water supply benefits under the MTMOO mode using CRF are much greater than those in conventional scheduling and slightly larger than in the MTMOO mode that directly uses rainfall forecast. This confirms the applicability and superiority of implementing the MTMOO mode with CRF as an input in multipurpose reservoirs.
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Data Availability Statement
Some or all data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request.
Acknowledgments
This work was supported by the Second Tibetan Plateau Scientific Expedition and Research (STEP) program, Grant No. 2019QZKK0203.
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History
Received: Oct 20, 2021
Accepted: Apr 14, 2022
Published online: Jun 24, 2022
Published in print: Sep 1, 2022
Discussion open until: Nov 24, 2022
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