Improving the Robustness of Reservoir Operations with Stochastic Dynamic Programming
Publication: Journal of Water Resources Planning and Management
Volume 147, Issue 7
Abstract
Reservoir operations should consider both adaptiveness and robustness to deal with two of the main characteristics of climate change: nonstationarity and deep uncertainty. In particular, robust operational strategies are distinguished from risk-neutral expected value optimization in the sense that they should be satisfactory over a wider range of uncertainty and improve the ability of a reservoir system to adapt to climate change. In this study, a new framework named robust stochastic dynamic programming (RSDP) is proposed that couples robust optimization (RO) with the formulations of objective function or constraints used in stochastic dynamic programming (SDP). Two main approaches of RO, namely feasibility robustness and solution robustness, are both considered in the optimization algorithm. Consequently, this study uses the Boryeong multipurpose dam to evaluate three SDP framings: conventional-SDP (CSDP), RSDP-feasibility robustness (RSDP-F), and RSDP-solution robustness (RSDP-S). These three SDP formulations were used to derive optimal monthly release rules for the Boryeong Dam, and their relative performances were evaluated using simulations of a broader range of inflow scenarios. The simulation-based re-evaluations of the resulting reservoir operational policies were quantified using a wide range of metrics that include reliability, resiliency, and vulnerability, as well as regret-based robustness metrics. The results of this study suggest that the RSDP-S model not only increases the range of possible solutions, but also yields more desirable operation outcomes under extreme climate conditions with respect to both traditional and robustness metrics.
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Data Availability Statement
All the data, models, or codes that support the findings of this study are available from the corresponding author upon reasonable request.
Acknowledgments
This research was supported by the Korea Environment Industry & Technology Institute (KEITI) through the Advanced Water Management Research Program, funded by the Korea Ministry of Environment (Grant No. 83082). The authors also wish to thank the Institute of Engineering Research, and Institute of Construction Environmental Engineering at Seoul National University and for providing research facilities for this work. The authors also thank Hae Na Yoon for providing the initial data sets and codes for this study.
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Journal of Water Resources Planning and Management
Volume 147 • Issue 7 • July 2021
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© 2021 American Society of Civil Engineers.
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Received: Jun 24, 2020
Accepted: Dec 29, 2020
Published online: Apr 19, 2021
Published in print: Jul 1, 2021
Discussion open until: Sep 19, 2021
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