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Chaotic Evolutionary Algorithms for Multi-Reservoir Optimization

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  • R. Arunkumar
  • V. Jothiprakash

Abstract

The water sharing dispute in a multi-reservoir river basin forces the water resources planners to have an integrated operation of multi-reservoir system rather than considering them as a single reservoir system. Thus, optimizing the operations of a multi-reservoir system for an integrated operation is gaining importance, especially in India. Recently, evolutionary algorithms have been successfully applied for optimizing the multi-reservoir system operations. The evolutionary optimization algorithms start its search from a randomly generated initial population to attain the global optimal solution. However, simple evolutionary algorithms are slower in convergence and also results in sub-optimal solutions for complex problems with hardbound variables. Hence, in the present study, chaotic technique is introduced to generate the initial population and also in other search steps to enhance the performance of the evolutionary algorithms and applied for the optimization of a multi-reservoir system. The results are compared with that of a simple GA and DE algorithm. From the study, it is found that the chaotic algorithm with the general optimizer has produced the global optimal solution (optimal hydropower production in the present case) within lesser generations. This shows that coupling the chaotic algorithm with evolutionary algorithm will enrich the search technique by having better initial population and also converges quickly. Further, the performances of the developed policies are evaluated for longer run using a simulation model to assess the irrigation deficits. The simulation results show that the model satisfactorily meets the irrigation demand in most of the time periods and the deficit is very less. Copyright Springer Science+Business Media Dordrecht 2013

Suggested Citation

  • R. Arunkumar & V. Jothiprakash, 2013. "Chaotic Evolutionary Algorithms for Multi-Reservoir Optimization," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(15), pages 5207-5222, December.
    • Handle: RePEc:spr:waterr:v:27:y:2013:i:15:p:5207-5222
    DOI: 10.1007/s11269-013-0463-4
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    References listed on IDEAS

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    6. V. Jothiprakash & Ganesan Shanthi & R. Arunkumar, 2011. "Development of Operational Policy for a Multi-reservoir System in India using Genetic Algorithm," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 25(10), pages 2405-2423, August.
    7. 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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    4. Xinyu Wu & Shuai Yin & Chuntian Cheng & Zhiyong Chen & Huaying Su, 2023. "SSDP Model with Inflow Clustering for Hydropower System Operation," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(3), pages 1109-1123, February.
    5. Feng, Suzhen & Zheng, Hao & Qiao, Yifan & Yang, Zetai & Wang, Jinwen & Liu, Shuangquan, 2022. "Weekly hydropower scheduling of cascaded reservoirs with hourly power and capacity balances," Applied Energy, Elsevier, vol. 311(C).
    6. Mohammad Zounemat-Kermani, 2016. "Investigating Chaos and Nonlinear Forecasting in Short Term and Mid-term River Discharge," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(5), pages 1851-1865, March.
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