IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v223y2021ics0360544221002589.html
   My bibliography  Save this article

Performance analyses of various commercial photovoltaic modules based on local spectral irradiances in Malaysia using genetic algorithm

Author

Listed:
  • Seera, Manjeevan
  • Tan, Choo Jun
  • Chong, Kok-Keong
  • Lim, Chee Peng

Abstract

A novel comprehensive methodology integrated with genetic algorithm (GA) has been formulated by accounting for local spectral irradiance and the specifications of commercial photovoltaic (PV) module. Despite having the same solar irradiance, variation in power conversion efficiency (PCE) of PV module can be significant in different site locations by referencing to that of standard AM1.5 spectrum. We have carried out a case study using GA for a combination of three site locations in Peninsular Malaysia and twelve types of commercial PV modules. Type-9 PV module operating in Bangi has recorded the lowest gain with 0.1%, while type-10 PV module operating in Bandar Sungai Long has shown the best gain of up to 27%. For solving multi-objective problems, MmGA and NSGAII have been applied to optimize three objectives concurrently including PCE, PV weight and PV panel area. From the simulation, type-1, type-6, and type-12 PV modules show the best in at least two objectives for the categories of m-Si, p-Si, and thin film, respectively.

Suggested Citation

  • Seera, Manjeevan & Tan, Choo Jun & Chong, Kok-Keong & Lim, Chee Peng, 2021. "Performance analyses of various commercial photovoltaic modules based on local spectral irradiances in Malaysia using genetic algorithm," Energy, Elsevier, vol. 223(C).
    • Handle: RePEc:eee:energy:v:223:y:2021:i:c:s0360544221002589
    DOI: 10.1016/j.energy.2021.120009
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544221002589
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2021.120009?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. Daraban, Stefan & Petreus, Dorin & Morel, Cristina, 2014. "A novel MPPT (maximum power point tracking) algorithm based on a modified genetic algorithm specialized on tracking the global maximum power point in photovoltaic systems affected by partial shading," Energy, Elsevier, vol. 74(C), pages 374-388.
    2. Farangi, Mostafa & Asl Soleimani, Ebrahim & Zahedifar, Mostafa & Amiri, Omid & Poursafar, Jafar, 2020. "The environmental and economic analysis of grid-connected photovoltaic power systems with silicon solar panels, in accord with the new energy policy in Iran," Energy, Elsevier, vol. 202(C).
    3. Blaifi, S. & Moulahoum, S. & Colak, I. & Merrouche, W., 2016. "An enhanced dynamic model of battery using genetic algorithm suitable for photovoltaic applications," Applied Energy, Elsevier, vol. 169(C), pages 888-898.
    4. Chong, Kok-Keong & Khlyabich, Petr P. & Hong, Kai-Jeat & Reyes-Martinez, Marcos & Rand, Barry P. & Loo, Yueh-Lin, 2016. "Comprehensive method for analyzing the power conversion efficiency of organic solar cells under different spectral irradiances considering both photonic and electrical characteristics," Applied Energy, Elsevier, vol. 180(C), pages 516-523.
    5. Li, Jinze & Liu, Pei & Li, Zheng, 2020. "Optimal design and techno-economic analysis of a solar-wind-biomass off-grid hybrid power system for remote rural electrification: A case study of west China," Energy, Elsevier, vol. 208(C).
    6. Hong, Kai Jeat & Tan, Sin Tee & Chong, Kok-Keong & Yap, Chi Chin & Hj Jumali, Mohammad Hafizuddin & Loo, Yueh-Lin, 2019. "Numerical analysis with experimental verification to predict outdoor power conversion efficiency of inverted organic solar devices," Renewable Energy, Elsevier, vol. 135(C), pages 589-596.
    7. Fathy, Ahmed & Elaziz, Mohamed Abd & Sayed, Enas Taha & Olabi, A.G. & Rezk, Hegazy, 2019. "Optimal parameter identification of triple-junction photovoltaic panel based on enhanced moth search algorithm," Energy, Elsevier, vol. 188(C).
    8. Starke, Allan R. & Cardemil, José M. & Escobar, Rodrigo & Colle, Sergio, 2018. "Multi-objective optimization of hybrid CSP+PV system using genetic algorithm," Energy, Elsevier, vol. 147(C), pages 490-503.
    9. Zhang, Minhui & Zhang, Qin & Zhou, Dequn & Wang, Lei, 2021. "Punishment or reward? Strategies of stakeholders in the quality of photovoltaic plants based on evolutionary game analysis in China," Energy, Elsevier, vol. 220(C).
    10. Abdelkader, Abbassi & Rabeh, Abbassi & Mohamed Ali, Dami & Mohamed, Jemli, 2018. "Multi-objective genetic algorithm based sizing optimization of a stand-alone wind/PV power supply system with enhanced battery/supercapacitor hybrid energy storage," Energy, Elsevier, vol. 163(C), pages 351-363.
    11. Bastidas-Rodriguez, J.D. & Petrone, G. & Ramos-Paja, C.A. & Spagnuolo, G., 2017. "A genetic algorithm for identifying the single diode model parameters of a photovoltaic panel," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 131(C), pages 38-54.
    12. Ahmad, Lujean & Khordehgah, Navid & Malinauskaite, Jurgita & Jouhara, Hussam, 2020. "Recent advances and applications of solar photovoltaics and thermal technologies," Energy, Elsevier, vol. 207(C).
    13. Sirisamphanwong, Chattariya & Ketjoy, Nipon, 2012. "Impact of spectral irradiance distribution on the outdoor performance of photovoltaic system under Thai climatic conditions," Renewable Energy, Elsevier, vol. 38(1), pages 69-74.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Daxini, Rajiv & Wilson, Robin & Wu, Yupeng, 2023. "Modelling the spectral influence on photovoltaic device performance using the average photon energy and the depth of a water absorption band for improved forecasting," Energy, Elsevier, vol. 284(C).
    2. Daxini, Rajiv & Wu, Yupeng, 2024. "Review of methods to account for the solar spectral influence on photovoltaic device performance," Energy, Elsevier, vol. 286(C).
    3. Peng, Cheng & Chen, Heng & Lin, Chaoran & Guo, Shuang & Yang, Zhi & Chen, Ke, 2021. "A framework for evaluating energy security in China: Empirical analysis of forecasting and assessment based on energy consumption," Energy, Elsevier, vol. 234(C).
    4. Wilson Castillo-Rojas & Fernando Medina Quispe & César Hernández, 2023. "Photovoltaic Energy Forecast Using Weather Data through a Hybrid Model of Recurrent and Shallow Neural Networks," Energies, MDPI, vol. 16(13), pages 1-25, July.
    5. Belsky, A.A. & Glukhanich, D.Y. & Carrizosa, M.J. & Starshaia, V.V., 2022. "Analysis of specifications of solar photovoltaic panels," Renewable and Sustainable Energy Reviews, Elsevier, vol. 159(C).
    6. Sammy J. Martínez-Deusa & Carlos A. Gómez-García & Jaime Velasco-Medina, 2023. "A Platform for Outdoor Real-Time Characterization of Photovoltaic Technologies," Energies, MDPI, vol. 16(6), pages 1-16, March.
    7. Md. Imamul Islam & Mohd Shawal Jadin & Ahmed Al Mansur & Nor Azwan Mohamed Kamari & Taskin Jamal & Molla Shahadat Hossain Lipu & Mohd Nurulakla Mohd Azlan & Mahidur R. Sarker & A. S. M. Shihavuddin, 2023. "Techno-Economic and Carbon Emission Assessment of a Large-Scale Floating Solar PV System for Sustainable Energy Generation in Support of Malaysia’s Renewable Energy Roadmap," Energies, MDPI, vol. 16(10), pages 1-32, May.
    8. Siyu, Liu & Kangwen, Sun & Jian, Gao & Haoquan, Liang, 2023. "Receiving energy analysis and optimal design of crystalline silicon solar cell array on solar airship," Energy, Elsevier, vol. 282(C).
    9. Jiang, Meng & Ding, Kun & Chen, Xiang & Cui, Liu & Zhang, Jingwei & Cang, Yi & Yang, Hang & Gao, Ruiguang, 2024. "CGH-GTO method for model parameter identification based on improved grey wolf optimizer, honey badger algorithm, and gorilla troops optimizer," Energy, Elsevier, vol. 296(C).

    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. Jaszczur, Marek & Hassan, Qusay & Palej, Patryk & Abdulateef, Jasim, 2020. "Multi-Objective optimisation of a micro-grid hybrid power system for household application," Energy, Elsevier, vol. 202(C).
    2. Zurita, Adriana & Mata-Torres, Carlos & Cardemil, José M. & Guédez, Rafael & Escobar, Rodrigo A., 2021. "Multi-objective optimal design of solar power plants with storage systems according to dispatch strategy," Energy, Elsevier, vol. 237(C).
    3. Abdelkareem, Mohammad Ali & Sayed, Enas Taha & Nakagawa, Nobuyoshi, 2020. "Significance of diffusion layers on the performance of liquid and vapor feed passive direct methanol fuel cells," Energy, Elsevier, vol. 209(C).
    4. Xu, Xiao & Hu, Weihao & Cao, Di & Huang, Qi & Chen, Cong & Chen, Zhe, 2020. "Optimized sizing of a standalone PV-wind-hydropower station with pumped-storage installation hybrid energy system," Renewable Energy, Elsevier, vol. 147(P1), pages 1418-1431.
    5. Chennaif, Mohammed & Zahboune, Hassan & Elhafyani, Mohammed & Zouggar, Smail, 2021. "Electric System Cascade Extended Analysis for optimal sizing of an autonomous hybrid CSP/PV/wind system with Battery Energy Storage System and thermal energy storage," Energy, Elsevier, vol. 227(C).
    6. Shen-Tsu Wang, 2016. "Integrating grey sequencing with the genetic algorithm--immune algorithm to optimise touch panel cover glass polishing process parameter design," International Journal of Production Research, Taylor & Francis Journals, vol. 54(16), pages 4882-4893, August.
    7. Rezk, Hegazy & AL-Oran, Mazen & Gomaa, Mohamed R. & Tolba, Mohamed A. & Fathy, Ahmed & Abdelkareem, Mohammad Ali & Olabi, A.G. & El-Sayed, Abou Hashema M., 2019. "A novel statistical performance evaluation of most modern optimization-based global MPPT techniques for partially shaded PV system," Renewable and Sustainable Energy Reviews, Elsevier, vol. 115(C).
    8. Filipović, P. & Dović, D. & Horvat, I. & Ranilović, B., 2023. "Evaluation of a novel polymer solar collector using numerical and experimental methods," Energy, Elsevier, vol. 284(C).
    9. Zhang, Zumeng & Ding, Liping & Wang, Chaofan & Dai, Qiyao & Shi, Yin & Zhao, Yujia & Zhu, Yuxuan, 2022. "Do operation and maintenance contracts help photovoltaic poverty alleviation power stations perform better?," Energy, Elsevier, vol. 259(C).
    10. Arévalo, Paul & Benavides, Dario & Tostado-Véliz, Marcos & Aguado, José A. & Jurado, Francisco, 2023. "Smart monitoring method for photovoltaic systems and failure control based on power smoothing techniques," Renewable Energy, Elsevier, vol. 205(C), pages 366-383.
    11. Bhattacharjee, Vikram & Khan, Irfan, 2018. "A non-linear convex cost model for economic dispatch in microgrids," Applied Energy, Elsevier, vol. 222(C), pages 637-648.
    12. Papul Changmai & Sunil Deka & Shashank Kumar & Thanikanti Sudhakar Babu & Belqasem Aljafari & Benedetto Nastasi, 2022. "A Critical Review on the Estimation Techniques of the Solar PV Cell’s Unknown Parameters," Energies, MDPI, vol. 15(19), pages 1-20, September.
    13. Terfa, H. & Baghli, L. & Bhandari, R., 2022. "Impact of renewable energy micro-power plants on power grids over Africa," Energy, Elsevier, vol. 238(PA).
    14. Ning, Jiajun & Xiong, Lixin, 2024. "Analysis of the dynamic evolution process of the digital transformation of renewable energy enterprises based on the cooperative and evolutionary game model," Energy, Elsevier, vol. 288(C).
    15. Hannan, M.A. & Ali, Jamal A. & Mohamed, Azah & Hussain, Aini, 2018. "Optimization techniques to enhance the performance of induction motor drives: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P2), pages 1611-1626.
    16. El-Sattar, Hoda Abd & Kamel, Salah & Hassan, Mohamed H. & Jurado, Francisco, 2022. "An effective optimization strategy for design of standalone hybrid renewable energy systems," Energy, Elsevier, vol. 260(C).
    17. Guo, Lei & Meng, Zhuo & Sun, Yize & Wang, Libiao, 2018. "A modified cat swarm optimization based maximum power point tracking method for photovoltaic system under partially shaded condition," Energy, Elsevier, vol. 144(C), pages 501-514.
    18. Akhlaque Ahmad Khan & Ahmad Faiz Minai & Rupendra Kumar Pachauri & Hasmat Malik, 2022. "Optimal Sizing, Control, and Management Strategies for Hybrid Renewable Energy Systems: A Comprehensive Review," Energies, MDPI, vol. 15(17), pages 1-29, August.
    19. Yang, Yuqing & Bremner, Stephen & Menictas, Chris & Kay, Merlinde, 2022. "Forecasting error processing techniques and frequency domain decomposition for forecasting error compensation and renewable energy firming in hybrid systems," Applied Energy, Elsevier, vol. 313(C).
    20. Mohseni, Soheil & Brent, Alan C. & Burmester, Daniel, 2020. "A comparison of metaheuristics for the optimal capacity planning of an isolated, battery-less, hydrogen-based micro-grid," Applied Energy, Elsevier, vol. 259(C).

    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:223:y:2021:i:c:s0360544221002589. 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.