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Modeling, control, and dynamic performance analysis of a reverse osmosis desalination plant integrated within hybrid energy systems

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  • Kim, Jong Suk
  • Chen, Jun
  • Garcia, Humberto E.

Abstract

An RO (reverse osmosis) desalination plant is proposed as an effective, FLR (flexible load resource) to be integrated into HES (hybrid energy systems) to support various types of ancillary services to the electric grid, under variable operating conditions. To study the dynamic analysis of such system, special attention is given here to the detailed dynamic modeling and control design of RO desalination process that employs a spiral-wound membrane module. In particular, the solution-diffusion model modified with the concentration polarization theory is applied to predict RO performance over a large range of operating conditions. Simulation results involving several case studies suggest that an RO desalination plant can provide operational flexibility to participate in energy management at the utility scale by dynamically optimizing the use of excess electrical energy. The incorporation of additional commodity (fresh water) produced from a FLR allows a broader range of HES operations for maximizing overall system performance and profitability.

Suggested Citation

  • Kim, Jong Suk & Chen, Jun & Garcia, Humberto E., 2016. "Modeling, control, and dynamic performance analysis of a reverse osmosis desalination plant integrated within hybrid energy systems," Energy, Elsevier, vol. 112(C), pages 52-66.
    • Handle: RePEc:eee:energy:v:112:y:2016:i:c:p:52-66
    DOI: 10.1016/j.energy.2016.05.050
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    References listed on IDEAS

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    6. Lee, Sangkeum & Cho, Hong-Yeon & Har, Dongsoo, 2018. "Operation optimization with jointly controlled modules powered by hybrid energy source: A case study of desalination," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P2), pages 3070-3080.
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    8. Soleimanzade, Mohammad Amin & Kumar, Amit & Sadrzadeh, Mohtada, 2022. "Novel data-driven energy management of a hybrid photovoltaic-reverse osmosis desalination system using deep reinforcement learning," Applied Energy, Elsevier, vol. 317(C).
    9. Zeyad A. Haidar & Mamdooh Al-Saud & Jamel Orfi & Hany Al-Ansary, 2021. "Reverse Osmosis Desalination Plants Energy Consumption Management and Optimization for Improving Power Systems Voltage Stability with PV Generation Resources," Energies, MDPI, vol. 14(22), pages 1-21, November.
    10. Epiney, A. & Rabiti, C. & Talbot, P. & Alfonsi, A., 2020. "Economic analysis of a nuclear hybrid energy system in a stochastic environment including wind turbines in an electricity grid," Applied Energy, Elsevier, vol. 260(C).
    11. Alipour, Manijeh & Mohammadi-Ivatloo, Behnam & Moradi-Dalvand, Mohammad & Zare, Kazem, 2017. "Stochastic scheduling of aggregators of plug-in electric vehicles for participation in energy and ancillary service markets," Energy, Elsevier, vol. 118(C), pages 1168-1179.
    12. Esmaeil Ahmadi & Benjamin McLellan & Behnam Mohammadi-Ivatloo & Tetsuo Tezuka, 2020. "The Role of Renewable Energy Resources in Sustainability of Water Desalination as a Potential Fresh-Water Source: An Updated Review," Sustainability, MDPI, vol. 12(13), pages 1-31, June.
    13. Ariana M. Pietrasanta & Mostafa F. Shaaban & Pio A. Aguirre & Sergio F. Mussati & Mohamed A. Hamouda, 2023. "Simulation and Optimization of Renewable Energy-Powered Desalination: A Bibliometric Analysis and Highlights of Recent Research," Sustainability, MDPI, vol. 15(12), pages 1-28, June.
    14. Zhao, Yang & Noori, Mehdi & Tatari, Omer, 2017. "Boosting the adoption and the reliability of renewable energy sources: Mitigating the large-scale wind power intermittency through vehicle to grid technology," Energy, Elsevier, vol. 120(C), pages 608-618.
    15. Garcia, Humberto E. & Chen, Jun & Kim, Jong S. & Vilim, Richard B. & Binder, William R. & Bragg Sitton, Shannon M. & Boardman, Richard D. & McKellar, Michael G. & Paredis, Christiaan J.J., 2016. "Dynamic performance analysis of two regional Nuclear Hybrid Energy Systems," Energy, Elsevier, vol. 107(C), pages 234-258.

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