Analysis Of Correlation Of Climate Factors Affecting Solar Power Generation

 
 
 
  • Abstract
  • Keywords
  • References
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  • Abstract


     

    Background/Objectives: In designing the solar power generation, feasibility review and power generation volume prediction during guarantee phase after the completion are very important.

    Methods/Statistical analysis: The study compares the actual power generation volume obtained from solar power generation monitoring system and estimated volume calculated using overseas meteorological data from Meteonorm 7.1 and NASA-SSE and Korean data from the Korea Meteorological Administration, in order to understand their accuracy. The calculation using KMA data, with the highest prediction value, was used to analyze the correlation among solar radiation, temperature, and solar power generation volume.

    Findings: Previous solar power generation volume prediction was conducted only with solar radiation value, which caused errors between the actual and predicted solar power generation volume. The study found that the power generation volume and solar radiation have a high positive correlation coefficient of 0.8131 for Songam Power Plant. For correlation between power generation volume and temperature, the coefficient for Songam was 0.2843 and 0.4616 for Jipyeong Power Plant, showing lower influence than that of solar radiation. In sum, solar radiation influences the solar power generation volume more than temperature, but the current study indicates that both solar radiation and temperature must be considered for an accurate prediction of solar power generation volume.

    Improvements/Applications: Research to develop solar power generation volume prediction algorithm that takes into account both solar radiation and temperature must be conducted to expand the application of solar power generation system with more accurate estimation of power generation volume.

     


  • Keywords


    Photovoltaic power generation, Low carbon energy, Photovoltaics, PV system, Photovoltaic power generation prediction

  • References


      [1] Korea Meteorological Administration.(2008). An analysis of meteorological resources for optimal utilization of solar energy. Retrieved from :http://www.weather.go.kr/download_01/climate_energy.pdf

      [2] Cubas, J., Pindado, S., & De Manuel, C. (2014). Explicit expressions for solar panel equivalent circuit parameters based on analytical formulation and the lambert W-function. Energies, 7(7), 4098–4115.doi :10.3390/en7074098

      [3] LEE, H. H. (2009). Photovoltaic Power Generation for Low Carbon Green Growth, Seoul.Kidari Publishing Corp. Retrieved from :http://www.kidari.co.kr/front/php/product.php?product_no=12&main_cate_no=12&display_group=1

      [4] Tobnaghi, D. M., Madatov, R., &Naderi, D. (2013). The Effect of Temperature on Electrical Parameters of Solar Cells.International Journal of Advanced Research in Electrical Electronics and Instrumentation Engineering, 2(12), 6404–6407.

      [5] PVsyst.(2014). PVSYST USER’S MANUAL.Retrieved from :http://www.pvsyst.com/images/pdf/PVsyst_Tutorials.pdf

      [6] Yoo, B.J., Park, C.B. &Lee, J. (2016) A study on design of 1.5MW photovoltaics power generation system using Gwangmyeong railway station building, Journal of the Korean Society for Railway,19(5), 592–599.doi:10.7782/JKSR.2016.19.5.592

      [7] Yoo, B.J. &Lee, J. (2017) A study on photovoltaic power generation amount forecast at design stage for extended application in the field of railways, Journal of the Korean Society for Railway, 20(2), 182-189. doi:10.7782/JKSR.2017.20.2.182

      [8] Yoo, B. J., Park, C.B. & Lee, J.(2017) Comparative study to predict power generation using meteorological information for expansion of Photovoltaic power generation system for railway infrastructure, Journal of the Korean Society for Railway, 20(4), 474–481.doi:10.7782/JKSR.2017.20.4.474

      [9] Kim B.J., Park, J.W., Yun, J.H, &Shin.W.C. (2015) The development of performance evaluation program of building integrated photovoltaic system, Korea Institute of Ecological Architecture Environment Journal, 15 (4), 85-86.doi:10.12813/kieae.2015.15.4.085

      [10] Stein, J. S. (2017). PV Performance Modeling Methods and Practices Results from the 4th PV Performance Modeling Collaborative Workshop. Retrieved from :http://iea-pvps.org/fileadmin/dam/public/report/technical/T13_Report_PV_Performance_Modeling_Methods_and_Practices_FINAL_March_2017.pdf

      [11] PV Performance modeling collaborative. (2018). Retrieved from :https://pvpmc.sandia.gov/modeling-steps/2-dc-module-iv/module-temperature/faiman-module-temperature-model/


 

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Article ID: 22816
 
DOI: 10.14419/ijet.v7i3.24.22816




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