Balancing water, power, and carbon: A synergistic optimization framework for mega cascade reservoir operations

Zhihao Ning, Yanlai Zhou, Juntao He, Chun Tang, Chong-Yu Xu and Fi-John Chang

Renewable Energy, 2025, vol. 243, issue C

Abstract: This study addresses the critical intersection of renewable energy production and carbon emission reduction in the context of intensified human activities and global climate change by proposing an innovative optimization framework for mega cascade reservoirs. Unlike traditional approaches that often prioritize hydropower output or carbon emissions, our framework uniquely integrates a multi-objective optimization model that simultaneously minimizes carbon emissions, mitigates flood risk, and maximizes hydropower output within the physical constraints of cascade reservoir operations. To assess the performance of various impoundment schemes, we apply the Technique for Order Preference by Similarity to Ideal Solution, demonstrating the model's versatility across different inflow scenarios using seven cascade reservoirs in the Yangtze River as case studies. Our findings reveal that, compared to practical operation scheme, the optimal scheme enhances hydropower output by 5.82 billion kW·h/a (5.32 %), increases water supply by 2.68 billion m³ (8.00 %), reduces carbon emissions by 17.31 GgC/a (14.66 %), and lowers carbon intensity by 0.63 kgCO2e/MW·h (15.22 %). This research advances theoretical frameworks for reservoir operations and offers practical implications for policymakers, enabling more informed decision-making to achieve sustainable development goals. The novel integration of water-carbon synergies within reservoir management contributes significantly to the discourse on sustainable energy systems and climate resilience.

Keywords: Cascade reservoirs; Hydropower optimization; Carbon emission reduction; Multi-objective operation; Non-dominated sorting genetic algorithm II (NSGA-II); Sustainable water management (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:renene:v:243:y:2025:i:c:s0960148125002290

DOI: 10.1016/j.renene.2025.122567

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