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An Optimal Reservoir Operation Model Based on Ecological Requirement and Its Effect on Electricity Generation

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  • Na Yang
  • Yadong Mei
  • Chi Zhou

Abstract

Rather than optimizing water regimes for one or a few species, a better approach is to approximate the natural flow regime that maintains the entire panoply of species. RVA method generally describes flow regimes through 32 hydrologic indicators, quantifies the changes of the indicators post-dam, and distinguishes the highly changed indicators. Based on this function, an ecological objective which takes natural flow regimes into consideration is established. The objective synthesize highly changed indicators selected from the 32 hydrologic indicators and minimize their degree of change to approximate the natural status. The function of the hydrologic indicators are quantified through fuzziology according to their effect on river environment when changed. The model is applied to Xiangyang section of the Han River downstream the Danjiangkou reservoir, which maximizes the ecological objective to close to a natural flow regime. By optimization, model results indicate that the maximum synthetical membership of the selected indicators is 0.5, which means that the post-regulation optimum release is 50 % closer to the natural flow regime. It is much better than the traditional power generation regulation model, the membership of which is only 0.2. The result indicates that the ecological model is better in improving the river ecosystem, but with reduced power generation because of excessive abandon water. However, when deeply evaluate the effect of the new model, it shows the possibility of a win-win scenario between maintaining ecosystem health and power generation. Copyright Springer Science+Business Media B.V. 2012

Suggested Citation

  • Na Yang & Yadong Mei & Chi Zhou, 2012. "An Optimal Reservoir Operation Model Based on Ecological Requirement and Its Effect on Electricity Generation," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 26(14), pages 4019-4028, November.
    • Handle: RePEc:spr:waterr:v:26:y:2012:i:14:p:4019-4028
    DOI: 10.1007/s11269-012-0126-x
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    Citations

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    Cited by:

    1. Feng, Zhong-kai & Niu, Wen-jing & Cheng, Chun-tian, 2018. "Optimization of hydropower reservoirs operation balancing generation benefit and ecological requirement with parallel multi-objective genetic algorithm," Energy, Elsevier, vol. 153(C), pages 706-718.
    2. Han-Chung Yang & Jian-Ping Suen & Shih-Kai Chou, 2016. "Estimating the Ungauged Natural Flow Regimes for Environmental Flow Management," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(13), pages 4571-4584, October.
    3. Asmadi Ahmad & Ahmed El-Shafie & Siti Razali & Zawawi Mohamad, 2014. "Reservoir Optimization in Water Resources: a Review," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(11), pages 3391-3405, September.
    4. Jenq-Tzong Shiau & Chian-You Huang, 2014. "Detecting Multi-Purpose Reservoir Operation Induced Time-Frequency Alteration Using Wavelet Transform," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(11), pages 3577-3590, September.
    5. Philipp Emanuel Hirsch & Moritz Schillinger & Katharina Appoloni & Patricia Burkhardt-Holm & Hannes Weigt, 2016. "Integrating Economic and Ecological Benchmarking for a Sustainable Development of Hydropower," Sustainability, MDPI, vol. 8(9), pages 1-20, August.
    6. Shen, Jianjian & Cheng, Chuntian & Zhang, Xiufei & Zhou, Binbin, 2018. "Coordinated operations of multiple-reservoir cascaded hydropower plants with cooperation benefit allocation," Energy, Elsevier, vol. 153(C), pages 509-518.

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