A State‐of‐the‐Art Review of Optimal Reservoir Control for Managing Conflicting Demands in a Changing World-Reference-Cited by-同舟云学术

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A State‐of‐the‐Art Review of Optimal Reservoir Control for Managing Conflicting Demands in a Changing World

Published:2021-12 Issue:12 Volume:57 Page:
ISSN:0043-1397
Container-title:Water Resources Research
language:en
Short-container-title:Water Resources Research

Author:

Giuliani M.¹^ORCID,Lamontagne J. R.²^ORCID,Reed P. M.³^ORCID,Castelletti A.¹^ORCID

Affiliation:

1. Department of Electronics, Information, and Bioengineering Politecnico di Milano Milano Italy

2. Department of Civil and Environmental Engineering Tufts University Medford MA USA

3. School of Civil and Environmental Engineering Cornell University Ithaca NY USA

Funder

National Science Foundation

Publisher

American Geophysical Union (AGU)

Subject

Water Science and Technology

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1029/2021WR029927

Reference285 articles.

1. Environmental hedging: A theory and method for reconciling reservoir operations for downstream ecology and water supply

2. Water and climate: Recognize anthropogenic drought

3. Extraction of Flexible Multi-Objective Real-Time Reservoir Operation Rules

4. Analysis of the Coordinated Operation of Reservoirs and Weirs during the Management of Nakdong River Water Resources

5. Optimizing Watershed Management by Coordinated Operation of Storing Facilities

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1. Explicit Stochastic Procedure for Developing Reservoir Storage-Yield-Reliability-Vulnerability Relationships;Water Resources Management;2024-09-09

2. Unlocking synergies of drawdown operation: Multi-objective optimization of reservoir emergency storage capacity;Journal of Environmental Management;2024-09

3. Free-Ride and Equilibriums in a Cooperative Game of Cascade Reservoirs;Journal of Water Resources Planning and Management;2024-09

4. Exploring the benefits of integrated energy-water management in reducing economic and environmental tradeoffs;Environmental Research: Energy;2024-09-01

5. Fill-and-Spill: Deep Reinforcement Learning Policy Gradient Methods for Reservoir Operation Decision and Control;Journal of Water Resources Planning and Management;2024-07

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