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Abstract
The novel power system urgently exploits demand-side flexible regulation resources. Cascade gate-pumping water diversion projects consume massive electricity and possess great potential for demand response. Most existing studies treat gate-pumping systems as homogeneous single loads without subdividing internal differentiated regulation resources, making it difficult to quantify the regulation capacity and synergistic benefits of various control measures. In this study, three types of flexible loads are classified based on the regulation mechanisms of hydraulic structures, and their corresponding mathematical models are established. A multi-objective optimization model balancing operational cost and grid peak shaving is constructed and solved using the Nondominated Sorting Genetic Algorithm II (NSGA-II). Six scheduling scenarios are designed for comparative analysis based on the Jiaodong Water Diversion Project. The results reveal that independent regulation of a single load has inherent limitations, while collaborative optimization of the three types of resources can realize complementary advantages. Compared with the baseline operating condition, the peak power load is reduced by 11.8%, and the total operational cost is cut by 7.9%. The proposed load classification and quantitative evaluation framework provides theoretical support for water diversion projects to participate in grid demand response and facilitates the improvement of water-electricity coordinated scheduling schemes.
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Funding
This study was supported by the National Natural Science Foundation of China (52579027), the Independent Research Project of the National Key Laboratory of Water Cycle and Water Security for Basins (SKL2025TDGG03), and the Special Fund Project for Basic Research Business Expenses of China Institute of Water Resources and Hydropower Research (WR110145B0122025).
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Xu, K., Wang, C. & Wang, H. Classification of flexible loads in cascade gate-pumping systems and multi-objective collaborative optimization for demand response. Sci Rep (2026). https://doi.org/10.1038/s41598-026-66518-w
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DOI: https://doi.org/10.1038/s41598-026-66518-w