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Contribution for optimal sizing of grid-connected PV-systems using PSO

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  • Kornelakis, Aris
  • Marinakis, Yannis

Abstract

Particle Swarm Optimization (PSO) is an optimization algorithm considered to be highly efficient for the solution of complicated problems. This paper presents the application of this method for the design optimization of photovoltaic grid-connected systems (PVGCSs). The purpose of the proposed methodology is to locate the optimal number of system devices and the optimal values of the PV module installation details, such that the total net economic benefit achieved during the system operational lifetime period is maximized. The optimization's decision variables are the optimal number of the PV modules, the PV modules optimal tilt angle, the optimal placement of the PV modules within the available installation area and the optimal distribution of the PV modules among the DC/AC converters. The objective function of the proposed optimization process is the lifetime system's total net profit which is calculated according to the method of the Net Present Value (NPV). The methodology's resulting system structures are economically evaluated through the methods of the discounted payback time and the Internal Rate of Return (IRR). The PSO algorithm is compared to the application of Genetic Algorithms (GAs) in terms of efficiency for the current problem.

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  • Kornelakis, Aris & Marinakis, Yannis, 2010. "Contribution for optimal sizing of grid-connected PV-systems using PSO," Renewable Energy, Elsevier, vol. 35(6), pages 1333-1341.
    • Handle: RePEc:eee:renene:v:35:y:2010:i:6:p:1333-1341
    DOI: 10.1016/j.renene.2009.10.014
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    7. Appelbaum, Joseph & Aronescu, Avi, 2022. "Inter-row spacing calculation in photovoltaic fields - A new approach," Renewable Energy, Elsevier, vol. 200(C), pages 387-394.
    8. Ullah, Hayat & Kamal, Ijlal & Ali, Ayesha & Arshad, Naveed, 2018. "Investor focused placement and sizing of photovoltaic grid-connected systems in Pakistan," Renewable Energy, Elsevier, vol. 121(C), pages 460-473.
    9. Rodrigo, Pedro M. & Mouhib, Elmehdi & Fernandez, Eduardo F. & Almonacid, Florencia & Rosas-Caro, Julio C., 2024. "Comprehensive ground coverage analysis of large-scale fixed-tilt bifacial photovoltaic plants," Renewable and Sustainable Energy Reviews, Elsevier, vol. 192(C).
    10. Youssef, Ayman & El-Telbany, Mohammed & Zekry, Abdelhalim, 2017. "The role of artificial intelligence in photo-voltaic systems design and control: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 78(C), pages 72-79.
    11. Sommerfeldt, Nelson & Madani, Hatef, 2017. "Revisiting the techno-economic analysis process for building-mounted, grid-connected solar photovoltaic systems: Part one – Review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 74(C), pages 1379-1393.
    12. Hafez, Omar & Bhattacharya, Kankar, 2012. "Optimal planning and design of a renewable energy based supply system for microgrids," Renewable Energy, Elsevier, vol. 45(C), pages 7-15.
    13. Al-Sulttani, Ali O. & Ahsan, Amimul & Hanoon, Ammar N. & Rahman, A. & Daud, N.N.N. & Idrus, S., 2017. "Hourly yield prediction of a double-slope solar still hybrid with rubber scrapers in low-latitude areas based on the particle swarm optimization technique," Applied Energy, Elsevier, vol. 203(C), pages 280-303.
    14. Ghorbani, Narges & Kasaeian, Alibakhsh & Toopshekan, Ashkan & Bahrami, Leyli & Maghami, Amin, 2018. "Optimizing a hybrid wind-PV-battery system using GA-PSO and MOPSO for reducing cost and increasing reliability," Energy, Elsevier, vol. 154(C), pages 581-591.
    15. Yilmaz, Saban & Dincer, Furkan, 2017. "Impact of inverter capacity on the performance in large-scale photovoltaic power plants – A case study for Gainesville, Florida," Renewable and Sustainable Energy Reviews, Elsevier, vol. 79(C), pages 15-23.
    16. Varaha Satra Bharath Kurukuru & Ahteshamul Haque & Mohammed Ali Khan & Subham Sahoo & Azra Malik & Frede Blaabjerg, 2021. "A Review on Artificial Intelligence Applications for Grid-Connected Solar Photovoltaic Systems," Energies, MDPI, vol. 14(15), pages 1-35, August.
    17. Oliva H., Sebastian & MacGill, Iain & Passey, Rob, 2016. "Assessing the short-term revenue impacts of residential PV systems on electricity customers, retailers and network service providers," Renewable and Sustainable Energy Reviews, Elsevier, vol. 54(C), pages 1494-1505.
    18. Perez-Gallardo, J.R. & Azzaro-Pantel, C. & Astier, S. & Domenech, S. & Aguilar-Lasserre, A., 2014. "Ecodesign of photovoltaic grid-connected systems," Renewable Energy, Elsevier, vol. 64(C), pages 82-97.
    19. Nima Narjabadifam & Mohammed Al-Saffar & Yongquan Zhang & Joseph Nofech & Asdrubal Cheng Cen & Hadia Awad & Michael Versteege & Mustafa Gül, 2022. "Framework for Mapping and Optimizing the Solar Rooftop Potential of Buildings in Urban Systems," Energies, MDPI, vol. 15(5), pages 1-32, February.
    20. Kanters, Jouri & Wall, Maria, 2016. "A planning process map for solar buildings in urban environments," Renewable and Sustainable Energy Reviews, Elsevier, vol. 57(C), pages 173-185.
    21. Bin Ye & Minhua Zhou & Dan Yan & Yin Li, 2020. "Multi-Objective Decision-Making for Hybrid Renewable Energy Systems for Cities: A Case Study of Xiongan New District in China," Energies, MDPI, vol. 13(23), pages 1-25, November.
    22. Ma, Chao & Deng, Zexing & Xu, Ximeng & Pang, Xiulan & Li, Xiaofeng & Wu, Runze & Tian, Zhuojun, 2024. "Space optimization of utility-scale photovoltaic power plants considering the impact of inter-row shading," Applied Energy, Elsevier, vol. 370(C).
    23. Mayer, Martin János, 2022. "Impact of the tilt angle, inverter sizing factor and row spacing on the photovoltaic power forecast accuracy," Applied Energy, Elsevier, vol. 323(C).

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