Solar Power Ramp Event Forewarning With Limited Historical Observations

Wen-li, Zhu; Zhang, Li; Yang, Ming; Wang, Bo

doi:10.1109/tia.2019.2934935

Cited by 11 publications

(1 citation statement)

References 40 publications

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“…The large-scale penetration of PV installations into the grid is hindered by this uncontrollable variability of radiation at ground level: since PV generation is strongly correlated with the solar irradiation incident on PV modules, the intrinsic variability of solar radiation reflects on to the variability of PV power production [7]. In more detail, the solar power fluctuations are primarily related to two aspects: the first one is the inherent radiation variation due to the diurnal pattern, which is determined by rotation and revolution of the Earth; the second one is related to the changes in meteorological conditions, such as the coverage given by clouds [8]. Due to a very short-term time horizon, the sudden variations of solar irradiance are primarily caused by the clouds' passage [9].…”

Section: Introductionmentioning

confidence: 99%

Very Short-Term Forecast: Different Classification Methods of the Whole Sky Camera Images for Sudden PV Power Variations Detection

et al. 2022

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Solar radiation is by nature intermittent and influenced by many factors such as latitude, season and atmospheric conditions. As a consequence, the growing penetration of Photovoltaic (PV) systems into the electricity network implies significant problems of stability, reliability and scheduling of power grid operation. Concerning the very short-term PV power production, the power fluctuations are primarily related to the interaction between solar irradiance and cloud cover. In small-scale systems such as microgrids, the adoption of a forecasting tool is a brilliant solution to minimize PV power curtailment and limit the installed energy storage capacity. In the present work, two different nowcasting methods are applied to classify the solar attenuation due to clouds presence on five different forecast horizons, from 1 to 5 min: a Pattern Recognition Neural Network and a Random Forest model. The proposed methods are tested and compared on a real case study: available data consists of historical irradiance measurements and infrared sky images collected in a real PV facility, the SolarTechLAB in Politecnico di Milano. The classification output is a range of values corresponding to the future value assumed by the Clear Sky Index (CSI), an indicator allowing to account for irradiance variations only related to clouds passage, neglecting diurnal and seasonal influences. The developed models present similar performance in all the considered time horizons, reliably detecting the CSI drops caused by incoming overcast and partially cloudy sky conditions.

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Section: Introductionmentioning

confidence: 99%