IDEAS home Printed from https://ideas.repec.org/a/spr/waterr/v29y2015i13p4719-4734.html
   My bibliography  Save this article

A Surrogate Based Optimization Approach for the Development of Uncertainty-Aware Reservoir Operational Rules: the Case of Nestos Hydrosystem

Author

Listed:
  • I. Tsoukalas
  • C. Makropoulos

Abstract

Operation of large-scale hydropower reservoirs is a complex problem that involves conflicting objectives, such as hydropower generation and water supply. Deriving optimal operational rules is a challenging task due to the non-linearity of the system dynamics and the uncertainty of future inflows and water demands. A common approach to derive optimal control policies is to couple simulation models with optimization algorithms. This paper in order to investigate the performance of a future reservoir and safely infer about its significance employs stochastic simulation, thus long synthetically generated time-series and a multi-objective version of the Parameterization-Simulation-Optimization (PSO) framework to develop uncertainty-aware operational rules. Furthermore, in order to handle the high computational effort that ensues from that coupling we investigate the potential of a surrogate-based multi-objective optimization algorithm, ParEGO. The PSO framework is deployed with WEAP21 water resources management model as simulation engine and MATLAB for the implementation of optimization algorithms. A comparison between NSGAII and ParEGO optimization algorithms is performed to assess the effectiveness of the proposed algorithm. The aforementioned comparison showed that ParEGO provides efficient approximations of the Pareto front while reducing the computational effort required. Finally, the potential benefit and the significance of the future reservoir is underlined. Copyright Springer Science+Business Media Dordrecht 2015

Suggested Citation

  • I. Tsoukalas & C. Makropoulos, 2015. "A Surrogate Based Optimization Approach for the Development of Uncertainty-Aware Reservoir Operational Rules: the Case of Nestos Hydrosystem," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(13), pages 4719-4734, October.
    • Handle: RePEc:spr:waterr:v:29:y:2015:i:13:p:4719-4734
    DOI: 10.1007/s11269-015-1086-8
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1007/s11269-015-1086-8
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1007/s11269-015-1086-8?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to

    for a different version of it.

    References listed on IDEAS

    as
    1. Kleijnen, Jack P.C., 2009. "Kriging metamodeling in simulation: A review," European Journal of Operational Research, Elsevier, vol. 192(3), pages 707-716, February.
    2. Larson, S. & Larson, S., 2007. "Index-based tool for preliminary ranking of social and environmental impacts of hydropower and storage reservoirs," Energy, Elsevier, vol. 32(6), pages 943-947.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Xu Wu & Xiaojing Shen & Chuanjiang Wei & Xinmin Xie & Jianshe Li, 2024. "A Hybrid Particle Swarm Optimization-Genetic Algorithm for Multiobjective Reservoir Ecological Dispatching," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 38(6), pages 2229-2249, April.
    2. Gong, Yu & Liu, Tingxi & Liu, Pan & Duan, Limin, 2024. "Deriving joint operating rule curves for hydro–hydrogen–wind–photovoltaic hybrid power systems," Applied Energy, Elsevier, vol. 375(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Tian, Wei & Song, Jitian & Li, Zhanyong & de Wilde, Pieter, 2014. "Bootstrap techniques for sensitivity analysis and model selection in building thermal performance analysis," Applied Energy, Elsevier, vol. 135(C), pages 320-328.
    2. Zitrou, Athena & Bedford, Tim & Walls, Lesley, 2016. "A model for availability growth with application to new generation offshore wind farms," Reliability Engineering and System Safety, Elsevier, vol. 152(C), pages 83-94.
    3. Zhang, Wei & (Ato) Xu, Wangtu, 2017. "Simulation-based robust optimization for the schedule of single-direction bus transit route: The design of experiment," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 106(C), pages 203-230.
    4. WoongHee Jung & Alexandros A. Taflanidis & Norberto C. Nadal-Caraballo & Madison C. Yawn & Luke A. Aucoin, 2024. "Regional storm surge hazard quantification using Gaussian process metamodeling techniques," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 120(1), pages 755-783, January.
    5. Xuefei Lu & Alessandro Rudi & Emanuele Borgonovo & Lorenzo Rosasco, 2020. "Faster Kriging: Facing High-Dimensional Simulators," Operations Research, INFORMS, vol. 68(1), pages 233-249, January.
    6. Xin Wang & Xinchao Jiang & Hu Wang & Guangyao Li, 2025. "Manifold learning-assisted uncertainty quantification of system parameters in the fiber metal laminates hot forming process," Journal of Intelligent Manufacturing, Springer, vol. 36(3), pages 2193-2219, March.
    7. Wang, Zequn & Wang, Pingfeng, 2015. "A double-loop adaptive sampling approach for sensitivity-free dynamic reliability analysis," Reliability Engineering and System Safety, Elsevier, vol. 142(C), pages 346-356.
    8. Song, Zhouzhou & Zhang, Hanyu & Liu, Zhao & Zhu, Ping, 2023. "A two-stage Kriging estimation variance reduction method for efficient time-variant reliability-based design optimization," Reliability Engineering and System Safety, Elsevier, vol. 237(C).
    9. Puppo, L. & Pedroni, N. & Maio, F. Di & Bersano, A. & Bertani, C. & Zio, E., 2021. "A Framework based on Finite Mixture Models and Adaptive Kriging for Characterizing Non-Smooth and Multimodal Failure Regions in a Nuclear Passive Safety System," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    10. Menafoglio, Alessandra & Secchi, Piercesare, 2017. "Statistical analysis of complex and spatially dependent data: A review of Object Oriented Spatial Statistics," European Journal of Operational Research, Elsevier, vol. 258(2), pages 401-410.
    11. Mehdad, E. & Kleijnen, Jack P.C., 2014. "Classic Kriging versus Kriging with Bootstrapping or Conditional Simulation : Classic Kriging's Robust Confidence Intervals and Optimization (Revised version of CentER DP 2013-038)," Other publications TiSEM 4915047b-afe4-4fc7-8a1c-4, Tilburg University, School of Economics and Management.
    12. Stephen Ntiri Asomani & Jianping Yuan & Longyan Wang & Desmond Appiah & Kofi Asamoah Adu-Poku, 2020. "The Impact of Surrogate Models on the Multi-Objective Optimization of Pump-As-Turbine (PAT)," Energies, MDPI, vol. 13(9), pages 1-29, May.
    13. Zio, E., 2018. "The future of risk assessment," Reliability Engineering and System Safety, Elsevier, vol. 177(C), pages 176-190.
    14. Guo, Shuhao & Li, Xianyue & Šimůnek, Jirí & Wang, Jun & Zhang, Yuehong & Wang, Ya'nan & Zhen, Zhixin & He, Rui, 2024. "Experimental and numerical evaluation of soil water and salt dynamics in a corn field with shallow saline groundwater and crop-season drip and autumn post-harvest irrigations," Agricultural Water Management, Elsevier, vol. 305(C).
    15. Wen, Zhixun & Pei, Haiqing & Liu, Hai & Yue, Zhufeng, 2016. "A Sequential Kriging reliability analysis method with characteristics of adaptive sampling regions and parallelizability," Reliability Engineering and System Safety, Elsevier, vol. 153(C), pages 170-179.
    16. Gaspar, B. & Teixeira, A.P. & Guedes Soares, C., 2017. "Adaptive surrogate model with active refinement combining Kriging and a trust region method," Reliability Engineering and System Safety, Elsevier, vol. 165(C), pages 277-291.
    17. Palar, Pramudita Satria & Zuhal, Lavi Rizki & Shimoyama, Koji, 2023. "Enhancing the explainability of regression-based polynomial chaos expansion by Shapley additive explanations," Reliability Engineering and System Safety, Elsevier, vol. 232(C).
    18. Quan Wang & Zhaogang Zhang, 2025. "Airfoil Optimization Design of Vertical-Axis Wind Turbine Based on Kriging Surrogate Model and MIGA," Energies, MDPI, vol. 18(11), pages 1-22, June.
    19. J.-J. Sinou & L. Nechak & S. Besset, 2018. "Kriging Metamodeling in Rotordynamics: Application for Predicting Critical Speeds and Vibrations of a Flexible Rotor," Complexity, Hindawi, vol. 2018, pages 1-26, March.
    20. Mert Edali, 2022. "Pattern‐oriented analysis of system dynamics models via random forests," System Dynamics Review, System Dynamics Society, vol. 38(2), pages 135-166, April.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    JEL classification:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:waterr:v:29:y:2015:i:13:p:4719-4734. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.