Improving the Robustness of Reservoir Operations with Stochastic Dynamic ProgrammingSource: Journal of Water Resources Planning and Management:;2021:;Volume ( 147 ):;issue: 007::page 04021030-1DOI: 10.1061/(ASCE)WR.1943-5452.0001381Publisher: ASCE
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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contributor author | Gi Joo Kim | |
contributor author | Young-Oh Kim | |
contributor author | Patrick M. Reed | |
date accessioned | 2022-01-31T23:56:17Z | |
date available | 2022-01-31T23:56:17Z | |
date issued | 7/1/2021 | |
identifier other | %28ASCE%29WR.1943-5452.0001381.pdf | |
identifier uri | http://yetl.yabesh.ir/yetl1/handle/yetl/4270611 | |
description 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. | |
publisher | ASCE | |
title | Improving the Robustness of Reservoir Operations with Stochastic Dynamic Programming | |
type | Journal Paper | |
journal volume | 147 | |
journal issue | 7 | |
journal title | Journal of Water Resources Planning and Management | |
identifier doi | 10.1061/(ASCE)WR.1943-5452.0001381 | |
journal fristpage | 04021030-1 | |
journal lastpage | 04021030-14 | |
page | 14 | |
tree | Journal of Water Resources Planning and Management:;2021:;Volume ( 147 ):;issue: 007 | |
contenttype | Fulltext |