Prefeasibility study of photovoltaic power potential based on a skew-normal distribution

Shin Young Kim, Benedikt Sapotta, Gilsoo Jang, Yong Heack Kang, Hyun Goo Kim

Research output: Contribution to journalArticle

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.

Original languageEnglish
Article number676
JournalEnergies
Volume13
Issue number3
DOIs
Publication statusPublished - 2020 Jan 1

Keywords

  • Exceedance probabilities
  • Global horizontal irradiance (GHI)
  • Normal distribution
  • Photovoltaic power potential
  • Skew-normal distribution

ASJC Scopus subject areas

  • Renewable Energy, Sustainability and the Environment
  • Energy Engineering and Power Technology
  • Energy (miscellaneous)
  • Control and Optimization
  • Electrical and Electronic Engineering

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