IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v178y2019icp277-292.html
   My bibliography  Save this article

Sustainable and reliable design of reverse osmosis desalination with hybrid renewable energy systems through supply chain forecasting using recurrent neural networks

Author

Listed:
  • Li, Qian
  • Loy-Benitez, Jorge
  • Nam, KiJeon
  • Hwangbo, Soonho
  • Rashidi, Jouan
  • Yoo, ChangKyoo

Abstract

Renewable energy systems coupled with reverse osmosis (RO) desalination represent a great contribution to the fields of energy and sustainability. Especially in remote areas, where reliability issues associated with integrated systems result in problems of insufficient power supply or supply loss due to the intermittency of renewable sources and varying freshwater demand. This research aims to design a sustainable and reliable hybrid renewable energy system (HRES) coupled with RO desalination system (HRES-RO), considering different operational scenarios with fluctuating renewable energy supply, and changeable water demand. First, future energy supply from renewable sources, and water demand were forecasted to deal with the stochastic behavior of several variables including freshwater demand, ambient temperature, solar radiation, and wind speed by means of recurrent neural networks (RNN). Then, multi-criteria optimization was conducted using extended mathematical programming (EMP) with the aim of minimizing the total annual costs and greenhouse gas emission. Finally, the potential loss of power supply probability (PLPSP) was introduced as a tool to illustrate the sustainability of the proposed scenarios. The results showed that the proposed framework resulted in an HRES with optimized installation strategy using 111 photovoltaic panels and 5 wind turbines by considering three criteria (economic cost, environmental effect, and energy reliability). The designed system reduced PLPSP by 18.3% compared with the base case. Furthermore, the results demonstrated the contribution of advanced forecasting algorithms to address future uncertainties in the energy supply chain.

Suggested Citation

  • Li, Qian & Loy-Benitez, Jorge & Nam, KiJeon & Hwangbo, Soonho & Rashidi, Jouan & Yoo, ChangKyoo, 2019. "Sustainable and reliable design of reverse osmosis desalination with hybrid renewable energy systems through supply chain forecasting using recurrent neural networks," Energy, Elsevier, vol. 178(C), pages 277-292.
    • Handle: RePEc:eee:energy:v:178:y:2019:i:c:p:277-292
    DOI: 10.1016/j.energy.2019.04.114
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544219307479
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2019.04.114?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 search for a different version of it.

    References listed on IDEAS

    as
    1. Li, Yanfei & Shi, Huipeng & Han, Fengze & Duan, Zhu & Liu, Hui, 2019. "Smart wind speed forecasting approach using various boosting algorithms, big multi-step forecasting strategy," Renewable Energy, Elsevier, vol. 135(C), pages 540-553.
    2. Yang, Hongxing & Wei, Zhou & Chengzhi, Lou, 2009. "Optimal design and techno-economic analysis of a hybrid solar-wind power generation system," Applied Energy, Elsevier, vol. 86(2), pages 163-169, February.
    3. Kaboli, S. Hr. Aghay & Fallahpour, A. & Selvaraj, J. & Rahim, N.A., 2017. "Long-term electrical energy consumption formulating and forecasting via optimized gene expression programming," Energy, Elsevier, vol. 126(C), pages 144-164.
    4. Mohammad Rozali, Nor Erniza & Wan Alwi, Sharifah Rafidah & Abdul Manan, Zainuddin & Klemeš, Jiří Jaromír & Hassan, Mohammad Yusri, 2013. "Process integration of hybrid power systems with energy losses considerations," Energy, Elsevier, vol. 55(C), pages 38-45.
    5. Ekren, Orhan & Ekren, Banu Y., 2010. "Size optimization of a PV/wind hybrid energy conversion system with battery storage using simulated annealing," Applied Energy, Elsevier, vol. 87(2), pages 592-598, February.
    6. Bedi, Jatin & Toshniwal, Durga, 2019. "Deep learning framework to forecast electricity demand," Applied Energy, Elsevier, vol. 238(C), pages 1312-1326.
    7. Rajbongshi, Rumi & Borgohain, Devashree & Mahapatra, Sadhan, 2017. "Optimization of PV-biomass-diesel and grid base hybrid energy systems for rural electrification by using HOMER," Energy, Elsevier, vol. 126(C), pages 461-474.
    8. Amrollahi, Mohammad Hossein & Bathaee, Seyyed Mohammad Taghi, 2017. "Techno-economic optimization of hybrid photovoltaic/wind generation together with energy storage system in a stand-alone micro-grid subjected to demand response," Applied Energy, Elsevier, vol. 202(C), pages 66-77.
    9. Kyriakarakos, George & Dounis, Anastasios I. & Arvanitis, Konstantinos G. & Papadakis, George, 2012. "A fuzzy logic energy management system for polygeneration microgrids," Renewable Energy, Elsevier, vol. 41(C), pages 315-327.
    10. Janghorban Esfahani, Iman & Yoo, ChangKyoo, 2016. "An optimization algorithm-based pinch analysis and GA for an off-grid batteryless photovoltaic-powered reverse osmosis desalination system," Renewable Energy, Elsevier, vol. 91(C), pages 233-248.
    11. Zhao, Jing & Guo, Zhen-Hai & Su, Zhong-Yue & Zhao, Zhi-Yuan & Xiao, Xia & Liu, Feng, 2016. "An improved multi-step forecasting model based on WRF ensembles and creative fuzzy systems for wind speed," Applied Energy, Elsevier, vol. 162(C), pages 808-826.
    12. Hondo, Hiroki, 2005. "Life cycle GHG emission analysis of power generation systems: Japanese case," Energy, Elsevier, vol. 30(11), pages 2042-2056.
    13. Prasad, A. Rajendra & Natarajan, E., 2006. "Optimization of integrated photovoltaic–wind power generation systems with battery storage," Energy, Elsevier, vol. 31(12), pages 1943-1954.
    14. Kaboli, S. Hr. Aghay & Selvaraj, J. & Rahim, N.A., 2016. "Long-term electric energy consumption forecasting via artificial cooperative search algorithm," Energy, Elsevier, vol. 115(P1), pages 857-871.
    15. Ramli, Makbul A.M. & Hiendro, Ayong & Twaha, Ssennoga, 2015. "Economic analysis of PV/diesel hybrid system with flywheel energy storage," Renewable Energy, Elsevier, vol. 78(C), pages 398-405.
    16. Jing Huang & John Boland, 2018. "Performance Analysis for One-Step-Ahead Forecasting of Hybrid Solar and Wind Energy on Short Time Scales," Energies, MDPI, vol. 11(5), pages 1-12, May.
    17. Janghorban Esfahani, Iman & Ifaei, Pouya & Kim, Jinsoo & Yoo, ChangKyoo, 2016. "Design of Hybrid Renewable Energy Systems with Battery/Hydrogen storage considering practical power losses: A MEPoPA (Modified Extended-Power Pinch Analysis)," Energy, Elsevier, vol. 100(C), pages 40-50.
    18. Nag, Aditya Kumar & Sarkar, Shibayan, 2018. "Modeling of hybrid energy system for futuristic energy demand of an Indian rural area and their optimal and sensitivity analysis," Renewable Energy, Elsevier, vol. 118(C), pages 477-488.
    19. Olatomiwa, Lanre & Mekhilef, Saad & Ismail, M.S. & Moghavvemi, M., 2016. "Energy management strategies in hybrid renewable energy systems: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 62(C), pages 821-835.
    20. Rajkumar, R.K. & Ramachandaramurthy, V.K. & Yong, B.L. & Chia, D.B., 2011. "Techno-economical optimization of hybrid pv/wind/battery system using Neuro-Fuzzy," Energy, Elsevier, vol. 36(8), pages 5148-5153.
    21. Janghorban Esfahani, Iman & Lee, SeungChul & Yoo, ChangKyoo, 2015. "Extended-power pinch analysis (EPoPA) for integration of renewable energy systems with battery/hydrogen storages," Renewable Energy, Elsevier, vol. 80(C), pages 1-14.
    22. Gupta, R.A. & Kumar, Rajesh & Bansal, Ajay Kumar, 2015. "BBO-based small autonomous hybrid power system optimization incorporating wind speed and solar radiation forecasting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 41(C), pages 1366-1375.
    23. Alberto Dolara & Francesco Grimaccia & Sonia Leva & Marco Mussetta & Emanuele Ogliari, 2015. "A Physical Hybrid Artificial Neural Network for Short Term Forecasting of PV Plant Power Output," Energies, MDPI, vol. 8(2), pages 1-16, February.
    24. Chauhan, Anurag & Saini, R.P., 2016. "Techno-economic optimization based approach for energy management of a stand-alone integrated renewable energy system for remote areas of India," Energy, Elsevier, vol. 94(C), pages 138-156.
    25. Bordin, Chiara & Anuta, Harold Oghenetejiri & Crossland, Andrew & Gutierrez, Isabel Lascurain & Dent, Chris J. & Vigo, Daniele, 2017. "A linear programming approach for battery degradation analysis and optimization in offgrid power systems with solar energy integration," Renewable Energy, Elsevier, vol. 101(C), pages 417-430.
    26. Macedo, M.N.Q. & Galo, J.J.M. & de Almeida, L.A.L. & de C. Lima, A.C., 2015. "Demand side management using artificial neural networks in a smart grid environment," Renewable and Sustainable Energy Reviews, Elsevier, vol. 41(C), pages 128-133.
    27. Sebastian Pfaffel & Stefan Faulstich & Kurt Rohrig, 2017. "Performance and Reliability of Wind Turbines: A Review," Energies, MDPI, vol. 10(11), pages 1-27, November.
    28. Sawle, Yashwant & Gupta, S.C. & Bohre, Aashish Kumar, 2018. "Socio-techno-economic design of hybrid renewable energy system using optimization techniques," Renewable Energy, Elsevier, vol. 119(C), pages 459-472.
    Full references (including those not matched with items on IDEAS)

    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. Nguyen, Hai Tra & Safder, Usman & Nhu Nguyen, X.Q. & Yoo, ChangKyoo, 2020. "Multi-objective decision-making and optimal sizing of a hybrid renewable energy system to meet the dynamic energy demands of a wastewater treatment plant," Energy, Elsevier, vol. 191(C).
    2. Sinha, Sunanda & Chandel, S.S., 2015. "Review of recent trends in optimization techniques for solar photovoltaic–wind based hybrid energy systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 50(C), pages 755-769.
    3. Jiří Jaromír Klemeš & Petar Sabev Varbanov & Paweł Ocłoń & Hon Huin Chin, 2019. "Towards Efficient and Clean Process Integration: Utilisation of Renewable Resources and Energy-Saving Technologies," Energies, MDPI, vol. 12(21), pages 1-32, October.
    4. Klemeš, Jiří Jaromír & Varbanov, Petar Sabev & Walmsley, Timothy G. & Jia, Xuexiu, 2018. "New directions in the implementation of Pinch Methodology (PM)," Renewable and Sustainable Energy Reviews, Elsevier, vol. 98(C), pages 439-468.
    5. Theo, Wai Lip & Lim, Jeng Shiun & Ho, Wai Shin & Hashim, Haslenda & Lee, Chew Tin, 2017. "Review of distributed generation (DG) system planning and optimisation techniques: Comparison of numerical and mathematical modelling methods," Renewable and Sustainable Energy Reviews, Elsevier, vol. 67(C), pages 531-573.
    6. Ali Saleh Aziz & Mohammad Faridun Naim Tajuddin & Mohd Rafi Adzman & Makbul A. M. Ramli & Saad Mekhilef, 2019. "Energy Management and Optimization of a PV/Diesel/Battery Hybrid Energy System Using a Combined Dispatch Strategy," Sustainability, MDPI, vol. 11(3), pages 1-26, January.
    7. Nadjemi, O. & Nacer, T. & Hamidat, A. & Salhi, H., 2017. "Optimal hybrid PV/wind energy system sizing: Application of cuckoo search algorithm for Algerian dairy farms," Renewable and Sustainable Energy Reviews, Elsevier, vol. 70(C), pages 1352-1365.
    8. Talaat, M. & Farahat, M.A. & Elkholy, M.H., 2019. "Renewable power integration: Experimental and simulation study to investigate the ability of integrating wave, solar and wind energies," Energy, Elsevier, vol. 170(C), pages 668-682.
    9. Ahmad, Tanveer & Chen, Huanxin, 2019. "Deep learning for multi-scale smart energy forecasting," Energy, Elsevier, vol. 175(C), pages 98-112.
    10. Wissem, Zghal & Gueorgui, Kantchev & Hédi, Kchaou, 2012. "Modeling and technical–economic optimization of an autonomous photovoltaic system," Energy, Elsevier, vol. 37(1), pages 263-272.
    11. Aziz, Ali Saleh & Tajuddin, Mohammad Faridun Naim & Adzman, Mohd Rafi & Azmi, Azralmukmin & Ramli, Makbul A.M., 2019. "Optimization and sensitivity analysis of standalone hybrid energy systems for rural electrification: A case study of Iraq," Renewable Energy, Elsevier, vol. 138(C), pages 775-792.
    12. Tuan-Viet Hoang & Pouya Ifaei & Kijeon Nam & Jouan Rashidi & Soonho Hwangbo & Jong-Min Oh & ChangKyoo Yoo, 2018. "Optimal Management of a Hybrid Renewable Energy System Coupled with a Membrane Bioreactor Using Enviro-Economic and Power Pinch Analyses for Sustainable Climate Change Adaption," Sustainability, MDPI, vol. 11(1), pages 1-22, December.
    13. Norbu, Sonam & Bandyopadhyay, Santanu, 2017. "Power Pinch Analysis for optimal sizing of renewable-based isolated system with uncertainties," Energy, Elsevier, vol. 135(C), pages 466-475.
    14. William López-Castrillón & Héctor H. Sepúlveda & Cristian Mattar, 2021. "Off-Grid Hybrid Electrical Generation Systems in Remote Communities: Trends and Characteristics in Sustainability Solutions," Sustainability, MDPI, vol. 13(11), pages 1-29, May.
    15. Mahesh, Aeidapu & Sandhu, Kanwarjit Singh, 2015. "Hybrid wind/photovoltaic energy system developments: Critical review and findings," Renewable and Sustainable Energy Reviews, Elsevier, vol. 52(C), pages 1135-1147.
    16. Zia, Muhammad Fahad & Elbouchikhi, Elhoussin & Benbouzid, Mohamed, 2018. "Microgrids energy management systems: A critical review on methods, solutions, and prospects," Applied Energy, Elsevier, vol. 222(C), pages 1033-1055.
    17. Kumar, Jitendra & Suryakiran, B.V. & Verma, Ashu & Bhatti, T.S., 2019. "Analysis of techno-economic viability with demand response strategy of a grid-connected microgrid model for enhanced rural electrification in Uttar Pradesh state, India," Energy, Elsevier, vol. 178(C), pages 176-185.
    18. Das, Barun K. & Zaman, Forhad, 2019. "Performance analysis of a PV/Diesel hybrid system for a remote area in Bangladesh: Effects of dispatch strategies, batteries, and generator selection," Energy, Elsevier, vol. 169(C), pages 263-276.
    19. Zahraee, S.M. & Khalaji Assadi, M. & Saidur, R., 2016. "Application of Artificial Intelligence Methods for Hybrid Energy System Optimization," Renewable and Sustainable Energy Reviews, Elsevier, vol. 66(C), pages 617-630.
    20. Theo, Wai Lip & Lim, Jeng Shiun & Wan Alwi, Sharifah Rafidah & Mohammad Rozali, Nor Erniza & Ho, Wai Shin & Abdul-Manan, Zainuddin, 2016. "An MILP model for cost-optimal planning of an on-grid hybrid power system for an eco-industrial park," Energy, Elsevier, vol. 116(P2), pages 1423-1441.

    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:eee:energy:v:178:y:2019:i:c:p:277-292. 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: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/energy .

    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.