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Maximizing energy production from hydropower dams using short-term weather forecasts

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  • Ahmad, Shahryar Khalique
  • Hossain, Faisal

Abstract

This study explores the maximization of hydropower generation by optimizing reservoir operations based on short-term inflow forecasts derived from publicly available numerical weather prediction (NWP) models. Forecast fields from the NWP model of Global Forecast System (GFS) were used to force the Variable Infiltration Capacity (VIC) hydrologic model to forecast reservoir inflow for 1–16 days lead time. The optimization of reservoir operations was performed based on the forecast of inflow. The concept was demonstrated for two dams in the United States. Results showed that a significantly greater amount additional hydroelectric energy benefit can be derived consistently than the traditional operations without optimization and weather forecasts. Goals of flood control and dam safety were also not compromised when exploring opportunities for hydropower maximization. An alternate data-based technique was also demonstrated to improve the forecasting skill and efficiency. The study clearly underscores the additional value of weather forecasts that are available publicly and globally from NWP models for any dam location for hydropower maximization. Given the on-going effort to coordinate strategies for sustainable energy production from renewable energy sources, it is timely that this concept be expanded further to current hydropower dam sites around the world.

Suggested Citation

  • Ahmad, Shahryar Khalique & Hossain, Faisal, 2020. "Maximizing energy production from hydropower dams using short-term weather forecasts," Renewable Energy, Elsevier, vol. 146(C), pages 1560-1577.
    • Handle: RePEc:eee:renene:v:146:y:2020:i:c:p:1560-1577
    DOI: 10.1016/j.renene.2019.07.126
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    Cited by:

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    2. Yang, Zhe & Wang, Yufeng & Yang, Kan, 2022. "The stochastic short-term hydropower generation scheduling considering uncertainty in load output forecasts," Energy, Elsevier, vol. 241(C).
    3. Zhou, Yuzhou & Zhao, Jiexing & Zhai, Qiaozhu, 2021. "100% renewable energy: A multi-stage robust scheduling approach for cascade hydropower system with wind and photovoltaic power," Applied Energy, Elsevier, vol. 301(C).
    4. Saber, Hossein & Mazaheri, Hesam & Ranjbar, Hossein & Moeini-Aghtaie, Moein & Lehtonen, Matti, 2021. "Utilization of in-pipe hydropower renewable energy technology and energy storage systems in mountainous distribution networks," Renewable Energy, Elsevier, vol. 172(C), pages 789-801.
    5. Andrej Predin & Matej Fike & Marko Pezdevšek & Gorazd Hren, 2021. "Lost Energy of Water Spilled over Hydropower Dams," Sustainability, MDPI, vol. 13(16), pages 1-17, August.
    6. Ifaei, Pouya & Nazari-Heris, Morteza & Tayerani Charmchi, Amir Saman & Asadi, Somayeh & Yoo, ChangKyoo, 2023. "Sustainable energies and machine learning: An organized review of recent applications and challenges," Energy, Elsevier, vol. 266(C).
    7. Iram Parvez & Jianjian Shen & Ishitaq Hassan & Nannan Zhang, 2021. "Generation of Hydro Energy by Using Data Mining Algorithm for Cascaded Hydropower Plant," Energies, MDPI, vol. 14(2), pages 1-28, January.

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