A multistage distributionally robust optimization approach to water allocation under climate uncertainty
Abstract
This paper investigates a Multistage Distributionally Robust Optimization (MDRO) approach to water allocation under climate uncertainty. The MDRO is formed by creating sets of conditional distributions (called conditional ambiguity sets) on a finite scenario tree. The distributions in the conditional ambiguity sets remain close to a nominal conditional distribution according a ø-divergence (e.g., Kullback-Leibler divergence, Hellinger distance, Burg entropy, etc.). Here, the paper discusses a decomposition algorithm to solve the resulting MDRO with ø-divergences, which uses the dual formulation and solves only linear subproblems instead of convex ones. Some properties of the algorithm such as generating feasible policies and valid upper/lower bounds are established. The paper then applies the modeling and solution techniques to allocate water in a rapidly-developing area of Tucson, Arizona. Tucson, like many arid and semi-arid regions around the world, faces considerable uncertainty in its ability to provide water for its citizens in the future. The primary sources of uncertainty in the Tucson region include (1) unpredictable population growth, (2) the availability of water from the Colorado River, and (3) the effects of climate variability on water consumption. This paper integrates forecasts for all these sources of uncertainty into a single optimization model for robust and sustainablemore »
- Authors:
-
[2]
- Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)
- The Ohio State Univ., Columbus, OH (United States)
- Publication Date:
- Research Org.:
- Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)
- Sponsoring Org.:
- USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR); National Science Foundation (NSF)
- OSTI Identifier:
- 2282605
- Alternate Identifier(s):
- OSTI ID: 1907301
- Grant/Contract Number:
- AC02-06CH11357; AC02-05CH11231; CMMI-1563504
- Resource Type:
- Accepted Manuscript
- Journal Name:
- European Journal of Operational Research
- Additional Journal Information:
- Journal Volume: 306; Journal Issue: 2; Journal ID: ISSN 0377-2217
- Publisher:
- Elsevier
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 54 ENVIRONMENTAL SCIENCES; OR in environment and climate change; Multistage distributionally robust optimization; Phi-divergences; Water resources; Nested benders decomposition
Citation Formats
Park, Jangho, and Bayraksan, Güzin. A multistage distributionally robust optimization approach to water allocation under climate uncertainty. United States: N. p., 2022.
Web. doi:10.1016/j.ejor.2022.06.049.
Park, Jangho, & Bayraksan, Güzin. A multistage distributionally robust optimization approach to water allocation under climate uncertainty. United States. https://doi.org/10.1016/j.ejor.2022.06.049
Park, Jangho, and Bayraksan, Güzin. Mon .
"A multistage distributionally robust optimization approach to water allocation under climate uncertainty". United States. https://doi.org/10.1016/j.ejor.2022.06.049. https://www.osti.gov/servlets/purl/2282605.
@article{osti_2282605,
title = {A multistage distributionally robust optimization approach to water allocation under climate uncertainty},
author = {Park, Jangho and Bayraksan, Güzin},
abstractNote = {This paper investigates a Multistage Distributionally Robust Optimization (MDRO) approach to water allocation under climate uncertainty. The MDRO is formed by creating sets of conditional distributions (called conditional ambiguity sets) on a finite scenario tree. The distributions in the conditional ambiguity sets remain close to a nominal conditional distribution according a ø-divergence (e.g., Kullback-Leibler divergence, Hellinger distance, Burg entropy, etc.). Here, the paper discusses a decomposition algorithm to solve the resulting MDRO with ø-divergences, which uses the dual formulation and solves only linear subproblems instead of convex ones. Some properties of the algorithm such as generating feasible policies and valid upper/lower bounds are established. The paper then applies the modeling and solution techniques to allocate water in a rapidly-developing area of Tucson, Arizona. Tucson, like many arid and semi-arid regions around the world, faces considerable uncertainty in its ability to provide water for its citizens in the future. The primary sources of uncertainty in the Tucson region include (1) unpredictable population growth, (2) the availability of water from the Colorado River, and (3) the effects of climate variability on water consumption. This paper integrates forecasts for all these sources of uncertainty into a single optimization model for robust and sustainable water allocation. Then, it uses this model to analyze the value of constructing additional treatment facilities to reduce future water shortages. The results indicate that the MDRO approach can be very valuable for water managers by providing insights to minimize their risks and help them plan for the future.},
doi = {10.1016/j.ejor.2022.06.049},
journal = {European Journal of Operational Research},
number = 2,
volume = 306,
place = {United States},
year = {Mon Jun 27 00:00:00 EDT 2022},
month = {Mon Jun 27 00:00:00 EDT 2022}
}
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