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Improving water quantity simulation & forecasting to solve the energy-water-food nexus issue by using heterogeneous computing accelerated global optimization method

Author

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  • Kan, Guangyuan
  • Zhang, Mengjie
  • Liang, Ke
  • Wang, Hao
  • Jiang, Yunzhong
  • Li, Jiren
  • Ding, Liuqian
  • He, Xiaoyan
  • Hong, Yang
  • Zuo, Depeng
  • Bao, Zhenxin
  • Li, Chaochao

Abstract

With continuous population increase and economic growth, challenges on securing sufficient energy, water, and food supplies are amplifying. Water plays the most important role in the energy-water-food (E-W-F) nexus issue such as energy supply (clean hydropower energy generation), water supply (drinking water), and food supply (agricultural irrigation water). Therefore, water quantity simulation & forecasting become an important issue in E-W-F nexus problem. Water quantity simulation & forecasting model, such as rainfall-runoff (RR) hydrological model has become a useful tool which can significantly improve efficiency of the hydropower energy generation, water supply management, and agricultural irrigation water utilization. The accuracy and reliability of the water quantity simulation & forecasting model are significantly affected by the model parameters. Therefore, demand of effective and fast model parameter optimization tool for solving the E-W-F nexus problem increases significantly. The shuffled complex evolution developed at University of Arizona (SCE-UA) has been recognized as an effective global model parameter optimization method for more than 20years and is highly suited to solve the E-W-F nexus problem. However, the computational efficiency of the SCE-UA dramatically deteriorates when applied to complex E-W-F nexus problem. For the purpose of solving this conundrum, a fast parallel SCE-UA was proposed in this paper. The parallel SCE-UA was implemented on the novel heterogeneous computing hardware and software systems which were constituted by the Intel multi-core CPU, NVIDIA many-core GPU, and PGI Accelerator Visual Fortran (with OpenMP and CUDA). Performance comparisons between the parallel and serial SCE-UA were carried out based on two case studies, the Griewank benchmark function optimization and a real world IHACRES RR hydrological model parameter optimization. Comparison results indicated that the parallel SCE-UA outperformed the serial one and has good application prospects for solving the water quantity simulation & forecasting model parameter calibration in the E-W-F nexus problem.

Suggested Citation

  • Kan, Guangyuan & Zhang, Mengjie & Liang, Ke & Wang, Hao & Jiang, Yunzhong & Li, Jiren & Ding, Liuqian & He, Xiaoyan & Hong, Yang & Zuo, Depeng & Bao, Zhenxin & Li, Chaochao, 2018. "Improving water quantity simulation & forecasting to solve the energy-water-food nexus issue by using heterogeneous computing accelerated global optimization method," Applied Energy, Elsevier, vol. 210(C), pages 420-433.
    • Handle: RePEc:eee:appene:v:210:y:2018:i:c:p:420-433
    DOI: 10.1016/j.apenergy.2016.08.017
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    3. Zeng, X.T. & Zhang, J.L. & Yu, L. & Zhu, J.X. & Li, Z. & Tang, L., 2019. "A sustainable water-food-energy plan to confront climatic and socioeconomic changes using simulation-optimization approach," Applied Energy, Elsevier, vol. 236(C), pages 743-759.
    4. Hua, En & Han, Xinxueqi & Bai, Yawen & Engel, Bernard A. & Li, Xin & Sun, Shikun & Wang, Yubao, 2023. "Synergy of water use in water-energy-food nexus from a symbiosis perspective: A case study in China," Energy, Elsevier, vol. 283(C).
    5. Yin, Linfei & Gao, Qi & Zhao, Lulin & Wang, Tao, 2020. "Expandable deep learning for real-time economic generation dispatch and control of three-state energies based future smart grids," Energy, Elsevier, vol. 191(C).
    6. Sadeghi, Seyed Hamidreza & Sharifi Moghadam, Ehsan & Delavar, Majid & Zarghami, Mahdi, 2020. "Application of water-energy-food nexus approach for designating optimal agricultural management pattern at a watershed scale," Agricultural Water Management, Elsevier, vol. 233(C).

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