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Predictive control of an integrated PV-diesel water and power supply system using an artificial neural network

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  • Al-Alawi, Ali
  • M Al-Alawi, Saleh
  • M Islam, Syed

Abstract

This paper discusses the development of a predictive artificial neural network (ANN)-based prototype controller for the optimum operation of an integrated hybrid renewable energy-based water and power supply system (IRWPSS). The integrated system, which has been assembled, consists of photovoltaic modules, diesel generator, battery bank for energy storage and a reverse osmosis desalination unit. The electrical load consists of typical households and the desalination plant. The proposed Artificial Neural Networking controller is designed to be implemented to take decision on diesel generators ON/OFF status and maintain a minimum loading level on the generator under light load and high solar radiation levels and maintain high efficiency of the generators and switch off diesel generator when not required based on predictive information. The key objectives are to reduce fuel dependency, engine wear and tear due to incomplete combustion and cut down on greenhouse gas emissions. The statistical analysis of the results indicates that the R2 value for the testing set of 186 cases tested was 0.979. This indicates that ANN-based model developed in this work can predict the power usage and generator status at any point of time with high accuracy.

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  • Al-Alawi, Ali & M Al-Alawi, Saleh & M Islam, Syed, 2007. "Predictive control of an integrated PV-diesel water and power supply system using an artificial neural network," Renewable Energy, Elsevier, vol. 32(8), pages 1426-1439.
    • Handle: RePEc:eee:renene:v:32:y:2007:i:8:p:1426-1439
    DOI: 10.1016/j.renene.2006.05.003
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    References listed on IDEAS

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    1. Al-Alawi, S.M. & Al-Hinai, H.A., 1998. "An ANN-based approach for predicting global radiation in locations with no direct measurement instrumentation," Renewable Energy, Elsevier, vol. 14(1), pages 199-204.
    2. Kalogirou, Soteris A., 2001. "Artificial neural networks in renewable energy systems applications: a review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 5(4), pages 373-401, December.
    3. Wichert, B., 1997. "PV-diesel hybrid energy systems for remote area power generation -- A review of current practice and future developments," Renewable and Sustainable Energy Reviews, Elsevier, vol. 1(3), pages 209-228, September.
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    1. Gonçalves, F.V. & Costa, L.H. & Ramos, H.M., 2011. "Best economical hybrid energy solution: Model development and case study of a WDS in Portugal," Energy Policy, Elsevier, vol. 39(6), pages 3361-3369, June.
    2. Kashyap, Yashwant & Bansal, Ankit & Sao, Anil K., 2015. "Solar radiation forecasting with multiple parameters neural networks," Renewable and Sustainable Energy Reviews, Elsevier, vol. 49(C), pages 825-835.
    3. Mohammed Elsayed Lotfy & Tomonobu Senjyu & Mohammed Abdel-Fattah Farahat & Amal Farouq Abdel-Gawad & Atsuhi Yona, 2017. "A Frequency Control Approach for Hybrid Power System Using Multi-Objective Optimization," Energies, MDPI, vol. 10(1), pages 1-22, January.
    4. Mohammed, Ammar & Pasupuleti, Jagadeesh & Khatib, Tamer & Elmenreich, Wilfried, 2015. "A review of process and operational system control of hybrid photovoltaic/diesel generator systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 44(C), pages 436-446.
    5. Kebai Li & Tianyi Ma & Guo Wei, 2018. "Robust Economic Control Decision Method of Uncertain System on Urban Domestic Water Supply," IJERPH, MDPI, vol. 15(4), pages 1-15, March.
    6. Yap, Wai Kean & Karri, Vishy, 2015. "An off-grid hybrid PV/diesel model as a planning and design tool, incorporating dynamic and ANN modelling techniques," Renewable Energy, Elsevier, vol. 78(C), pages 42-50.
    7. Li, Chennan & Goswami, Yogi & Stefanakos, Elias, 2013. "Solar assisted sea water desalination: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 19(C), pages 136-163.
    8. Poompavai, T. & Kowsalya, M., 2019. "Control and energy management strategies applied for solar photovoltaic and wind energy fed water pumping system: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 107(C), pages 108-122.
    9. Jha, Sunil Kr. & Bilalovic, Jasmin & Jha, Anju & Patel, Nilesh & Zhang, Han, 2017. "Renewable energy: Present research and future scope of Artificial Intelligence," Renewable and Sustainable Energy Reviews, Elsevier, vol. 77(C), pages 297-317.
    10. 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.
    11. F. Gonçalves & L. Costa & Helena Ramos, 2011. "ANN for Hybrid Energy System Evaluation: Methodology and WSS Case Study," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 25(9), pages 2295-2317, July.
    12. Erdinc, O. & Uzunoglu, M., 2012. "Optimum design of hybrid renewable energy systems: Overview of different approaches," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(3), pages 1412-1425.
    13. Vasudevan, Krishnakumar R. & Ramachandaramurthy, Vigna K. & Venugopal, Gomathi & Ekanayake, J.B. & Tiong, S.K., 2021. "Variable speed pumped hydro storage: A review of converters, controls and energy management strategies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 135(C).
    14. Ussama Assad & Muhammad Arshad Shehzad Hassan & Umar Farooq & Asif Kabir & Muhammad Zeeshan Khan & S. Sabahat H. Bukhari & Zain ul Abidin Jaffri & Judit Oláh & József Popp, 2022. "Smart Grid, Demand Response and Optimization: A Critical Review of Computational Methods," Energies, MDPI, vol. 15(6), pages 1-36, March.
    15. Lagorse, Jeremy & Paire, Damien & Miraoui, Abdellatif, 2010. "A multi-agent system for energy management of distributed power sources," Renewable Energy, Elsevier, vol. 35(1), pages 174-182.
    16. Karabacak, Kerim & Cetin, Numan, 2014. "Artificial neural networks for controlling wind–PV power systems: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 29(C), pages 804-827.
    17. Upadhyay, Subho & Sharma, M.P., 2014. "A review on configurations, control and sizing methodologies of hybrid energy systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 38(C), pages 47-63.
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