Methodology for generating daily clearness index index values Kt starting from the monthly average daily value . Determining the daily sequence using stochastic models

Santos, Joaquín; Pinazo, J.M.; Cañada, J.

doi:10.1016/s0960-1481(02)00217-3

Cited by 46 publications

(18 citation statements)

References 16 publications

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“…It is called the clearness index because it decreases with increasing atmospheric attenuation of solar radiation, mostly determined by cloudiness. The true physical value of the clearness index is instantaneous although it can be used also with hourly values Ogunjobi and Kim, 2004), daily values (Martínez-Lozano et al, 1994a;Kudish and Ianetz, 1996;Ulgen and Hepbasli, 2002;Santos et al, 2003) or monthly values (Udo, 2000;Ulgen and Hepbasli, 2002;Kudish et al, 2005). In order to study the total UV radiation, Martínez-Lozano et al (1994b) defined an ultraviolet clearness index k tUV , with a similar form to k t but referring only to the UV spectral range and using the solar constant for UV radiation, I SCUV (81.13 Wm −2 ) obtained from the SUSIM Atlas 3 extraterrestrial spectrum (http://www.solar.nrl.navy.mil/susim atlas data.…”

Section: Introductionmentioning

confidence: 99%

The erythemal clearness index for Valencia, Spain

Tena

Martínez‐Lozano

Utrillas

et al. 2008

Intl Journal of Climatology

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ABSTRACT:The values of three clearness indices have been measured for the city of Valencia: k t , the clearness index for the whole solar spectrum, k tUV the clearness index for the ultraviolet range, and the erythemal clearness index for the erythemal radiation k tUVER , corresponding to the solar radiation weighted by the erythemal action spectrum on human skin, normalized with respect to the solar constant corresponding to that spectrum. The measurements of k tUV reached values that were approximately 54% of that corresponding to k t , while those of k tUVER were much smaller. Some statistics of the erythemal clearness index have been calculated both for monthly as well as hourly means. The shape of the distribution in both cases was nearly normal, so standard deviation and variance were used to analyse the dispersion of the data. The ratio of the erythemal clearness index and the corresponding solar irradiance was also studied. When only the central hours of the day were considered the relation between the two was markedly linear, giving a correlation coefficient that reached 99%.

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Section: Introductionmentioning

confidence: 99%

The erythemal clearness index for Valencia, Spain

Tena

Martínez‐Lozano

Utrillas

et al. 2008

Intl Journal of Climatology

Self Cite

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“…The synthetic solar radiation data are generated based on the cumulative frequency distribution of the daily clearness index, given as a function of the monthly clearness index. Santos et al [118] proposed a suitable model (ARIMA) for generating sequences of daily clearness index based on hourly clearness index.…”

Section: Overview On the Conventional Methods For Modeling And Predicmentioning

confidence: 99%

Artificial intelligence techniques for photovoltaic applications: A review

Mellit

Kalogirou

2008

Progress in Energy and Combustion Science

733

269

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“…After that, hourly wind speed data are transferred into hourly wind power data through the typical wind turbine power curve. As for solar output simulation, we use the methodology proposed in [40] to first generate the daily clearness index. Then, an hourly solar radiation in PV panel is obtained by combining the daily clearness index and incoming solar radiation data.…”

Section: Res Output Simulation Modulementioning

confidence: 99%

Multi-Period Generation Expansion Planning for Sustainable Power Systems to Maximize the Utilization of Renewable Energy Sources

Wang

Zhang

et al. 2020

Sustainability

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The increasing penetration of renewable energy brings great challenges to the planning and operation of power systems. To deal with the fluctuation of renewable energy, the main focus of current research is on incorporating the detailed operation constraints into generation expansion planning (GEP) models. In most studies, the traditional objective function of GEP is to minimize the total cost (including the investment and operation cost). However, in power systems with high penetration of renewable energy, more attention has been paid to increasing the utilization of renewable energy and reducing the renewable energy curtailment. Different from the traditional objective function, this paper proposes a new objective function to maximize the accommodation of renewable energy during the planning horizon, taking into account short-term operation constraints and uncertainties from load and renewable energy sources. A power grid of one province in China is modified as a case study to verify the rationality and effectiveness of the proposed model. Numerical results show that the proposed GEP model could install more renewable power plants and improve the accommodation of renewable energy compared to the traditional GEP model.

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Methodology for generating daily clearness index index values Kt starting from the monthly average daily value . Determining the daily sequence using stochastic models

Cited by 46 publications

References 16 publications

The erythemal clearness index for Valencia, Spain

The erythemal clearness index for Valencia, Spain

Artificial intelligence techniques for photovoltaic applications: A review

Multi-Period Generation Expansion Planning for Sustainable Power Systems to Maximize the Utilization of Renewable Energy Sources

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