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Modeling Multi-objective Pareto-optimal Reservoir Operation Policies Using State-of-the-art Modeling Techniques

Author

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  • Aadhityaa Mohanavelu

    (Amrita School of Engineering, Amrita Vishwa Vidyapeetham)

  • Bankaru-Swamy Soundharajan

    (Amrita School of Engineering, Amrita Vishwa Vidyapeetham)

  • Ozgur Kisi

    (Ilia State University)

Abstract

A novel challenge faced by water scientists and water managers today is the efficient management of the available water resources for meeting crucial demands such as drinking water supply, irrigation and hydro-power generation. Optimal operation of reservoirs is of paramount importance for better management of scarce water resources under competing multiple demands such as irrigation, water supply etc., with decreasing reliability of these systems under climate change. This study compares six different state-of-the-art modeling techniques namely; Deterministic Dynamic Programming (DDP), Stochastic Dynamic Programming (SDP), Implicit Stochastic Optimization (ISO), Fitted Q-Iteration (FQI), Sampling Stochastic Dynamic Programming (SSDP), and Model Predictive Control (MPC), in developing pareto-optimal reservoir operation solutions considering two competing operational objectives of irrigation and flood control for the Pong reservoir located in Beas River, India. Set of pareto-optimal (approximate) solutions were derived using the above-mentioned six methods based on different convex combinations of the two objectives and finally the performances of the resulting sets of pareto-optimal solutions were compared. Additionally, key reservoir performance indices including resilience, reliability, vulnerability and sustainability were estimated to study the performance of the current operation of the reservoir. Modeling results indicate that the optimal-operational solution developed by DDP attains the best performance followed by the MPC and FQI. The performance of the Pong reservoir operation assessed by comparing different performance indices suggests that there is high vulnerability (~ 0.65) and low resilience (~ 0.10) in current operations and the development of pareto-optimal operation solutions using multiple state-of-the-art modeling techniques might be crucial for making better reservoir operation decisions.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:waterr:v:36:y:2022:i:9:d:10.1007_s11269-022-03191-4
    DOI: 10.1007/s11269-022-03191-4
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    1. K. Srinivasan & Kranthi Kumar, 2018. "Multi-Objective Simulation-Optimization Model for Long-term Reservoir Operation using Piecewise Linear Hedging Rule," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(5), pages 1901-1911, March.
    2. Thomas Saaty & Saul Gass, 1954. "Parametric Objective Function (Part 1)," Operations Research, INFORMS, vol. 2(3), pages 316-319, August.
    3. Mohsen Saadat & Keyvan Asghari, 2017. "Reliability Improved Stochastic Dynamic Programming for Reservoir Operation Optimization," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(6), pages 1795-1807, April.
    4. M. Reddy & D. Kumar, 2006. "Optimal Reservoir Operation Using Multi-Objective Evolutionary Algorithm," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 20(6), pages 861-878, December.
    5. Aida Tayebiyan & Thamer Ahmed Mohammed Ali & Abdul Halim Ghazali & M. A. Malek, 2016. "Optimization of Exclusive Release Policies for Hydropower Reservoir Operation by Using Genetic Algorithm," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(3), pages 1203-1216, February.
    6. Majid Mohammadi & Saeed Farzin & Sayed-Farhad Mousavi & Hojat Karami, 2019. "Investigation of a New Hybrid Optimization Algorithm Performance in the Optimal Operation of Multi-Reservoir Benchmark Systems," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(14), pages 4767-4782, November.
    7. Mohammad S. Khorshidi & Mohammad Reza Nikoo & Mojtaba Sadegh & Banafsheh Nematollahi, 2019. "A Multi-Objective Risk-Based Game Theoretic Approach to Reservoir Operation Policy in Potential Future Drought Condition," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(6), pages 1999-2014, April.
    8. Mohammad Heydari & Faridah Othman & Kourosh Qaderi, 2015. "Developing Optimal Reservoir Operation for Multiple and Multipurpose Reservoirs Using Mathematical Programming," Mathematical Problems in Engineering, Hindawi, vol. 2015, pages 1-11, February.
    9. 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.
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    2. Suwapat Kosasaeng & Nirat Yamoat & Seyed Mohammad Ashrafi & Anongrit Kangrang, 2022. "Extracting Optimal Operation Rule Curves of Multi-Reservoir System Using Atom Search Optimization, Genetic Programming and Wind Driven Optimization," Sustainability, MDPI, vol. 14(23), pages 1-14, December.

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