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Gradient Evolution Optimization Algorithm to Optimize Reservoir Operation Systems

Author

Listed:
  • Arvin Samadi-koucheksaraee

    (Behbahan Khatam Alanbia University of Technology)

  • Iman Ahmadianfar

    (Behbahan Khatam Alanbia University of Technology)

  • Omid Bozorg-Haddad

    (University of Tehran)

  • Seyed Amin Asghari-pari

    (Behbahan Khatam Alanbia University of Technology)

Abstract

Population growth, environmental destruction, and climate change have all led to water scarcity on the available water resources. In this regard, reservoir systems have an important role to manage water resources. Thus, it is essential to optimize the management of water resources. Optimizing reservoir systems involves complications such as nonlinear functions, large number of sizing variables and numerous constraints. To solve complicated optimization problems, meta-heuristic optimization algorithms are reliable and powerful methods. Hence, the present paper applies gradient evolution (GE) algorithm to optimize reservoir operation systems. This algorithm is extracted from a gradient-based optimizer. In fact, the main novelty of this study is the application of GE algorithm to optimize single- and multi-reservoir systems. Accordingly, the GE is employed to optimize a four-reservoir system, the Khersan-1 reservoir and the Dez reservoir in Iran. The results confirm the high capacity of the GE to optimize the single and multi-reservoir systems as it can obtain solutions 99.99, 96 and 94% of global optimum for the four-reservoir, Khersan-1 reservoir and Dez reservoir operation problems respectively. The results of the GE are compared with those solutions calculated with linear programming (LP), non-linear programming (NLP) and genetic algorithm (GA), which corroborate the superior ability of GE to reach global optimum solution of reservoir operation systems.

Suggested Citation

  • Arvin Samadi-koucheksaraee & Iman Ahmadianfar & Omid Bozorg-Haddad & Seyed Amin Asghari-pari, 2019. "Gradient Evolution Optimization Algorithm to Optimize Reservoir Operation Systems," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(2), pages 603-625, January.
    • Handle: RePEc:spr:waterr:v:33:y:2019:i:2:d:10.1007_s11269-018-2122-2
    DOI: 10.1007/s11269-018-2122-2
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    Cited by:

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    2. Vartika Paliwal & Aniruddha D. Ghare & Ashwini B. Mirajkar & Neeraj Dhanraj Bokde & Andrés Elías Feijóo Lorenzo, 2019. "Computer Modeling for the Operation Optimization of Mula Reservoir, Upper Godavari Basin, India, Using the Jaya Algorithm," Sustainability, MDPI, vol. 12(1), pages 1-21, December.
    3. Aadhityaa Mohanavelu & Bankaru-Swamy Soundharajan & Ozgur Kisi, 2022. "Modeling Multi-objective Pareto-optimal Reservoir Operation Policies Using State-of-the-art Modeling Techniques," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(9), pages 3107-3128, July.
    4. Alan de Gois Barbosa & Alcigeimes B. Celeste & Ludmilson Abritta Mendes, 2021. "Influence of Inflow Nonstationarity on the Multipurpose Optimal Operation of Hydropower Plants Using Nonlinear Programming," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(8), pages 2343-2367, June.
    5. Iman Ahmadianfar & Omid Bozorg-Haddad & Xuefeng Chu, 2019. "Optimizing Multiple Linear Rules for Multi-Reservoir Hydropower Systems Using an Optimization Method with an Adaptation Strategy," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(12), pages 4265-4286, September.

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