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Prefeasibility Study of Photovoltaic Power Potential Based on a Skew-Normal Distribution

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  • Shin Young Kim

    (New and Renewable Energy Resource and Policy Center, Korea Institute of Energy Research, 152 Gajeong-ro, Yuseong-gu, Daejeon 34129, Korea
    School of Electrical Engineering, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul 02841, Korea)

  • Benedikt Sapotta

    (Karlsruhe Institute of Technology, Institute of Functional Interfaces, Hermann-von-Helmholtz-Platz 1, 76344 Eggenstein-Leopoldshafen, Germany)

  • Gilsoo Jang

    (School of Electrical Engineering, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul 02841, Korea)

  • Yong-Heack Kang

    (New and Renewable Energy Resource and Policy Center, Korea Institute of Energy Research, 152 Gajeong-ro, Yuseong-gu, Daejeon 34129, Korea)

  • Hyun-Goo Kim

    (New and Renewable Energy Resource and Policy Center, Korea Institute of Energy Research, 152 Gajeong-ro, Yuseong-gu, Daejeon 34129, Korea)

Abstract

Solar energy does not always follow the normal distribution due to the characteristics of natural energy. The system advisor model (SAM), a well-known energy performance analysis program, analyzes exceedance probabilities by dividing solar irradiance into two cases, i.e., when normal distribution is followed, and when normal distribution is not followed. However, it does not provide a mathematical model for data distribution when not following the normal distribution. The present study applied the skew-normal distribution when solar irradiance does not follow the normal distribution, and calculated photovoltaic power potential to compare the result with those using the two existing methods. It determined which distribution was more appropriate between normal and skew-normal distributions using the Jarque–Bera test, and then the corrected Akaike information criterion (AICc). As a result, three places in Korea showed that the skew-normal distribution was more appropriate than the normal distribution during the summer and winter seasons. The AICc relative likelihood between two models was more than 0.3, which showed that the difference between the two models was not extremely high. However, considering that the proportion of uncertainty of solar irradiance in photovoltaic projects was 5% to 17%, more accurate models need to be chosen.

Suggested Citation

  • Shin Young Kim & Benedikt Sapotta & Gilsoo Jang & Yong-Heack Kang & Hyun-Goo Kim, 2020. "Prefeasibility Study of Photovoltaic Power Potential Based on a Skew-Normal Distribution," Energies, MDPI, vol. 13(3), pages 1-12, February.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:3:p:676-:d:316502
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    References listed on IDEAS

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    1. Hyun-Jin Lee & Shin-Young Kim & Chang-Yeol Yun, 2017. "Comparison of Solar Radiation Models to Estimate Direct Normal Irradiance for Korea," Energies, MDPI, vol. 10(5), pages 1-12, April.
    2. Clifford M. Hurvich & Jeffrey S. Simonoff & Chih‐Ling Tsai, 1998. "Smoothing parameter selection in nonparametric regression using an improved Akaike information criterion," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 60(2), pages 271-293.
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    Cited by:

    1. Jie Zhu & Buxiang Zhou & Yiwei Qiu & Tianlei Zang & Yi Zhou & Shi Chen & Ningyi Dai & Huan Luo, 2023. "Survey on Modeling of Temporally and Spatially Interdependent Uncertainties in Renewable Power Systems," Energies, MDPI, vol. 16(16), pages 1-19, August.

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