Reducing Carbon Emissions: A Multi-Objective Approach to the Hydropower Operation of Mega Reservoirs

Optimizing the joint drawdown operation of mega reservoirs presents a significant opportunity to enhance the comprehensive benefits among hydropower output, water release, and carbon emission reduction. However, achieving the complementary drawdown operation of mega reservoirs while considering reservoir carbon emissions poses a notable challenge. In this context, this study introduces an innovative multi-objective optimization framework tailored for the joint drawdown operation of mega reservoirs. Firstly, a multi-objective optimization model, leveraging an intelligent evolutionary algorithm, is developed to minimize reservoir carbon emissions (Objective 1), maximize hydropower output (Objective 2), and maximize water release (Objective 3). Subsequently, a multi-criteria decision-making approach to search for the optimal scheme is employed. The proposed framework is applied to seven mega reservoirs within the Hanjiang River basin, China. The results show that the framework is effective in promoting comprehensive benefits, improving hydropower production by 8.3%, reservoir carbon emission reduction by 5.6%, and water release by 6.2% from the optimal solution under wet scenarios, compared to standard operation policies. This study not only provides a fresh perspective on the multi-objective drawdown operation of mega reservoirs but also offers valuable support to stakeholders and decision-makers in formulating viable strategic recommendations that take potential carbon emissions and advantages into account.

Authors

Institutions

Publication Details

Journal
Sustainability
Published
2025-03-20
DOI
https://doi.org/10.3390/su17062770
Citations
3
Primary Topic
Water resources management and optimization
Type
article
Field-Weighted Citation Impact
3.44
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Reducing Carbon Emissions: A Multi-Objective Approach to the Hydropower Operation of Mega Reservoirs

Yanlai Zhou, Fi‐John Chang, Chong‐Yu Xu, Qi Luo et al.
3 citations
Sustainability
Water resources management and optimization
3.44
article

Reducing Carbon Emissions: A Multi-Objective Approach to the Hydropower Operation of Mega Reservoirs

Yanlai Zhou, Fi‐John Chang, Chong‐Yu Xu, Qi Luo, Yuxuan Luo, Di Zhu
article en
3 citations

Abstract

Optimizing the joint drawdown operation of mega reservoirs presents a significant opportunity to enhance the comprehensive benefits among hydropower output, water release, and carbon emission reduction. However, achieving the complementary drawdown operation of mega reservoirs while considering reservoir carbon emissions poses a notable challenge. In this context, this study introduces an innovative multi-objective optimization framework tailored for the joint drawdown operation of mega reservoirs. Firstly, a multi-objective optimization model, leveraging an intelligent evolutionary algorithm, is developed to minimize reservoir carbon emissions (Objective 1), maximize hydropower output (Objective 2), and maximize water release (Objective 3). Subsequently, a multi-criteria decision-making approach to search for the optimal scheme is employed. The proposed framework is applied to seven mega reservoirs within the Hanjiang River basin, China. The results show that the framework is effective in promoting comprehensive benefits, improving hydropower production by 8.3%, reservoir carbon emission reduction by 5.6%, and water release by 6.2% from the optimal solution under wet scenarios, compared to standard operation policies. This study not only provides a fresh perspective on the multi-objective drawdown operation of mega reservoirs but also offers valuable support to stakeholders and decision-makers in formulating viable strategic recommendations that take potential carbon emissions and advantages into account.

SustainabilityVol. 17(6)
National Taiwan University (TW), University of Oslo (NO), Changjiang Water Resources Commission (CN), Wuhan University (CN)
Openalex Percentile: Top 9%
Water resources management and optimization
3.44
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.