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New and improved methods to estimate day-ahead quantity and quality of solar irradiance

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  • Kang, Byung O
  • Tam, Kwa-Sur

Abstract

This paper proposes methodologies to estimate day-ahead quantity and quality of solar irradiance using the National Weather Service (NWS) sky cover forecast. The proposed methods use two parameters, the daily sky clearness index (KD) for quantity and the daily probability of persistence (POP–KD) for quality. POP–KD efficiently represents quality of daily solar irradiance. In addition, POP–KD can be applicable to indicate that solar irradiance variability is within ramp rates of common generators in power systems at a certain photovoltaic (PV) penetration level. For model development, this paper splits up a direct estimation process from cloud forecast to solar irradiance into two stages: forecast verification and cloud-to-irradiance conversion. Verification of the sky cover forecast shows an overestimation bias of approximately 20% on days with a high irradiance level. Thus, the NWS sky cover forecast needs to be adjusted based on the type of day. This paper also proposes new equations that provide accurate conversion from cloud observation to surface solar irradiance. Finally, this paper proposes a method for estimating day-ahead POP–KD and three methods for estimating day-ahead KD based on the NWS sky cover forecast. The proposed methods incorporate different schemes for dealing with the bias discovered in the cloud forecast. Estimation results demonstrate the effectiveness of the proposed methods at different irradiance levels.

Suggested Citation

  • Kang, Byung O & Tam, Kwa-Sur, 2015. "New and improved methods to estimate day-ahead quantity and quality of solar irradiance," Applied Energy, Elsevier, vol. 137(C), pages 240-249.
    • Handle: RePEc:eee:appene:v:137:y:2015:i:c:p:240-249
    DOI: 10.1016/j.apenergy.2014.10.021
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    References listed on IDEAS

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    1. Gianfranco Chicco & Valeria Cocina & Paolo Di Leo & Filippo Spertino & Alessandro Massi Pavan, 2015. "Error Assessment of Solar Irradiance Forecasts and AC Power from Energy Conversion Model in Grid-Connected Photovoltaic Systems," Energies, MDPI, vol. 9(1), pages 1-27, December.
    2. Dinesh, Chinthaka & Welikala, Shirantha & Liyanage, Yasitha & Ekanayake, Mervyn Parakrama B. & Godaliyadda, Roshan Indika & Ekanayake, Janaka, 2017. "Non-intrusive load monitoring under residential solar power influx," Applied Energy, Elsevier, vol. 205(C), pages 1068-1080.

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