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Long-Term Stochastic Reservoir Operation Using a Noisy Genetic Algorithm

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  • Ruan Yun
  • Vijay Singh
  • Zengchuan Dong

Abstract

To deal with stochastic characteristics of inflow in reservoir operation, a noisy genetic algorithm (NGA), based on simple genetic algorithms (GAs), is proposed. Using operation of a single reservoir as an example, the results of NGA and Monte Carlo method which is another way to optimize stochastic reservoir operation were compared. It was found that the noisy GA was a better alternative than Monte Carlo method for stochastic reservoir operation. Copyright Springer Science+Business Media B.V. 2010

Suggested Citation

  • Ruan Yun & Vijay Singh & Zengchuan Dong, 2010. "Long-Term Stochastic Reservoir Operation Using a Noisy Genetic Algorithm," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 24(12), pages 3159-3172, September.
    • Handle: RePEc:spr:waterr:v:24:y:2010:i:12:p:3159-3172
    DOI: 10.1007/s11269-010-9600-5
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    References listed on IDEAS

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    1. Juran Ahmed & Arup Sarma, 2005. "Genetic Algorithm for Optimal Operating Policy of a Multipurpose Reservoir," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 19(2), pages 145-161, April.
    2. Chun-Tian Cheng & Wen-Chuan Wang & Dong-Mei Xu & K. Chau, 2008. "Optimizing Hydropower Reservoir Operation Using Hybrid Genetic Algorithm and Chaos," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 22(7), pages 895-909, July.
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    Cited by:

    1. Chunxue Yu & Xinan Yin & Zhifeng Yang & Zhi Dang, 2019. "Sustainable Water Resource Management of Regulated Rivers under Uncertain Inflow Conditions Using a Noisy Genetic Algorithm," IJERPH, MDPI, vol. 16(5), pages 1-21, March.
    2. Md. Hossain & A. El-shafie, 2013. "Intelligent Systems in Optimizing Reservoir Operation Policy: A Review," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(9), pages 3387-3407, July.
    3. J. Sreekanth & Bithin Datta & Pranab Mohapatra, 2012. "Optimal Short-term Reservoir Operation with Integrated Long-term Goals," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 26(10), pages 2833-2850, August.
    4. Ali Assani & Raphaëlle Landry & Jonathan Daigle & Alain Chalifour, 2011. "Reservoirs Effects on the Interannual Variability of Winter and Spring Streamflow in the St-Maurice River Watershed (Quebec, Canada)," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 25(14), pages 3661-3675, November.
    5. V. Jothiprakash & R. Arunkumar, 2013. "Optimization of Hydropower Reservoir Using Evolutionary Algorithms Coupled with Chaos," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(7), pages 1963-1979, May.
    6. Xinyu Wu & Rui Guo & Xilong Cheng & Chuntian Cheng, 2021. "Combined Aggregated Sampling Stochastic Dynamic Programming and Simulation-Optimization to Derive Operation Rules for Large-Scale Hydropower System," Energies, MDPI, vol. 14(3), pages 1-15, January.

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