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Impact of interconnection photovoltaic/wind system with utility on their reliability using a fuzzy scheme

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  • El-Tamaly, Hassan Hussein
  • Mohammed, Adel A. Elbaset

Abstract

Reliability analysis has been considered as an important step in any system design process. A reliable electrical power system means a system which has sufficient power to feed the load demand during a certain period or, in other words, has small Loss of Load Probability (LOLP). LOLP is defined as an expected fraction of load not met by its power needs from electrical power system during its lifetime. Photovoltaic (PV)/Wind Energy System (WES) Hybrid Electric Power System (PV/WES HEPS) differs considerably from the Utility Grid (UG) in its performance and operating characteristics. With the interconnection of PV/WES as a HEPS into the UG, the fluctuating nature of the energy produced by these systems has a different effect on the overall system reliability than that of the fluctuating nature of energy produced by UG. Therefore, this paper presents a complete study, from reliability point of view, to determine the impact of interconnecting PV/WES HEPS into UG. Four different configurations of PV/WES/UG have been investigated and a comparative study between these four different configurations has been carried out. The overall system is divided into three subsystems, containing the UG, PV and WES. The generation capacity outage table has been built for each configuration of these subsystems. These capacity outage tables of UG, PV/UG, WES/UG and PV/WES/UG are calculated and updated to incorporate their fluctuating energy production. This paper also presents a fuzzy logic technique to calculate and assess the reliability index for each HEPS configuration under study.

Suggested Citation

  • El-Tamaly, Hassan Hussein & Mohammed, Adel A. Elbaset, 2006. "Impact of interconnection photovoltaic/wind system with utility on their reliability using a fuzzy scheme," Renewable Energy, Elsevier, vol. 31(15), pages 2475-2491.
    • Handle: RePEc:eee:renene:v:31:y:2006:i:15:p:2475-2491
    DOI: 10.1016/j.renene.2005.11.012
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    Citations

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    Cited by:

    1. Akinyele, D.O. & Rayudu, R.K. & Nair, N.K.C., 2015. "Global progress in photovoltaic technologies and the scenario of development of solar panel plant and module performance estimation − Application in Nigeria," Renewable and Sustainable Energy Reviews, Elsevier, vol. 48(C), pages 112-139.
    2. Georgilakis, Pavlos S. & Katsigiannis, Yiannis A., 2009. "Reliability and economic evaluation of small autonomous power systems containing only renewable energy sources," Renewable Energy, Elsevier, vol. 34(1), pages 65-70.
    3. Meng Zhang & Shan Lu, 2014. "A reliability model of blade to avoid resonance considering multiple fuzziness," Journal of Risk and Reliability, , vol. 228(6), pages 641-652, December.
    4. Kannan, Nadarajah & Vakeesan, Divagar, 2016. "Solar energy for future world: - A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 62(C), pages 1092-1105.
    5. Nie, S. & Huang, Charley Z. & Huang, G.H. & Li, Y.P. & Chen, J.P. & Fan, Y.R. & Cheng, G.H., 2016. "Planning renewable energy in electric power system for sustainable development under uncertainty – A case study of Beijing," Applied Energy, Elsevier, vol. 162(C), pages 772-786.
    6. Zhou, P. & Jin, R.Y. & Fan, L.W., 2016. "Reliability and economic evaluation of power system with renewables: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 58(C), pages 537-547.
    7. Parida, Bhubaneswari & Iniyan, S. & Goic, Ranko, 2011. "A review of solar photovoltaic technologies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 15(3), pages 1625-1636, April.
    8. Chaiamarit, Kunjana & Nuchprayoon, Somboon, 2013. "Modeling of renewable energy resources for generation reliability evaluation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 26(C), pages 34-41.
    9. Suganthi, L. & Iniyan, S. & Samuel, Anand A., 2015. "Applications of fuzzy logic in renewable energy systems – A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 48(C), pages 585-607.
    10. 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.

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