Scheduled Operation of PV Power Station Considering Solar Radiation Forecast Error

2011 ◽  
Vol 131 (3) ◽  
pp. 304-312 ◽  
Author(s):  
Satoshi Takayama ◽  
Ryoichi Hara ◽  
Hiroyuki Kita ◽  
Takamitsu Ito ◽  
Yoshinobu Ueda ◽  
...  
Keyword(s):  
Solar Radiation ◽  
Forecast Error ◽  
Power Station

A Study on the Scheduling of Large-Scaled PV Power Station Output based on Solar Radiation Forecast

2009 ◽  
Vol 129 (12) ◽  
pp. 1514-1521 ◽  
Author(s):  
Satoshi Takayama ◽  
Yuji Iwasaka ◽  
Ryoichi Hara ◽  
Hiroyuki Kita ◽  
Takamitsu Ito ◽  
...  
Keyword(s):  
Solar Radiation ◽  
Power Station

scholarly journals Investigations of dynamic characteristics of a tall industrial chimney due to light wind and solar radiation

2013 ◽  
Vol 12 (2) ◽  
pp. 087-094 ◽  
Author(s):  
Peter Breuer ◽  
Tadeusz Chmielewski ◽  
Piotr Górski ◽  
Eduard Konopka ◽  
Lesław Tarczyński
Keyword(s):  
Solar Radiation ◽  
Dynamic Characteristics ◽  
Field Tests ◽  
Power Station ◽  
Temperature Variations ◽  
Response Characteristics ◽  
Modal Damping ◽  
Wind Response ◽  
Satellite Signal ◽  
Air Temperature Variations

The present paper describes field tests conducted on the 300 m tall industrial chimney, located in the power station of Bełchatów (Poland), where the GPS rover receivers were installed at three various levels. The objectives of these GPS tests were to investigate the deformed vertical profile of this chimney, and its dynamic characteristics, i.e. the first natural frequency and the modal damping ratios. The results for the satellite signal receptions, the synopsis of recorded baselines and their ambiguity solutions, drifts of the chimney due to solar radiation and air temperature variations and dynamic wind response characteristics are presented.

scholarly journals Evaluating the spatio-temporal performance of sky-imager-based solar irradiance analysis and forecasts

2016 ◽  
Vol 16 (5) ◽  
pp. 3399-3412 ◽  
Author(s):  
Thomas Schmidt ◽  
John Kalisch ◽  
Elke Lorenz ◽  
Detlev Heinemann
Keyword(s):  
Solar Radiation ◽  
Forecast Error ◽  
Forecast Skill ◽  
Small Scale ◽  
Surface Solar Radiation ◽  
Data Set ◽  
Forecast Performance ◽  
Electric Power Supply ◽  
Sky Imager ◽  
Spatio Temporal

Abstract. Clouds are the dominant source of small-scale variability in surface solar radiation and uncertainty in its prediction. However, the increasing share of solar energy in the worldwide electric power supply increases the need for accurate solar radiation forecasts. In this work, we present results of a very short term global horizontal irradiance (GHI) forecast experiment based on hemispheric sky images. A 2-month data set with images from one sky imager and high-resolution GHI measurements from 99 pyranometers distributed over 10 km by 12 km is used for validation. We developed a multi-step model and processed GHI forecasts up to 25 min with an update interval of 15 s. A cloud type classification is used to separate the time series into different cloud scenarios. Overall, the sky-imager-based forecasts do not outperform the reference persistence forecasts. Nevertheless, we find that analysis and forecast performance depends strongly on the predominant cloud conditions. Especially convective type clouds lead to high temporal and spatial GHI variability. For cumulus cloud conditions, the analysis error is found to be lower than that introduced by a single pyranometer if it is used representatively for the whole area in distances from the camera larger than 1–2 km. Moreover, forecast skill is much higher for these conditions compared to overcast or clear sky situations causing low GHI variability, which is easier to predict by persistence. In order to generalize the cloud-induced forecast error, we identify a variability threshold indicating conditions with positive forecast skill.

scholarly journals Evaluating solar radiation forecast uncertainty

2018 ◽  
Author(s):  
Minttu Tuononen ◽  
Ewan J. O'Connor ◽  
Victoria A. Sinclair
Keyword(s):  
Solar Radiation ◽  
Cloud Cover ◽  
Spatial Scales ◽  
Forecast Error ◽  
Surface Radiation ◽  
Cloud Base ◽  
Strong Impact ◽  
Positive Bias ◽  
Radiative Balance ◽  
Clear Sky

Abstract. The presence of clouds, and their characteristics, has a strong impact on the radiative balance of the Earth and on the amount of solar radiation reaching the Earth's surface. Many applications require accurate forecasts of surface radiation on weather timescales, for example, solar energy and UV radiation forecasts. Here we investigate how operational forecasts of low and mid-level clouds affect the accuracy of solar radiation forecasts. Four years of cloud and solar radiation observations from one site – Helsinki, Finland, are analysed. Cloud observations are obtained from a ceilometer and therefore, we first develop algorithms to reliably detect cloud base, precipitation and fog. These new algorithms are widely applicable for both operational use and research, such as in-cloud icing detection for the wind energy industry and for aviation. The cloud and radiation observations are compared to forecasts from the Integrated Forecast System (IFS) run operationally and developed by the European Centre for Medium-Range Weather Forecasts (ECMWF). We develop methods to evaluate the skill of the cloud and radiation forecasts. These methods can potentially be extended to hundreds of sites globally. Over Helsinki, the measured Global Horizontal Irradiance (GHI) is strongly influenced by its northerly location and the annual variation in cloudiness. Solar radiation forecast error is therefore larger in summer than in winter, but the relative error in the solar radiation forecast is more or less constant throughout the year. The mean overall bias in the GHI forecast is positive (8 W m−2). The observed and forecast distributions in cloud cover, at the spatial scales we are considering, are strongly skewed towards clear-sky and overcast situations. Cloud cover forecasts show more skill in winter when the cloud cover is predominantly overcast; in summer there are more clear-sky and broken cloud situations. A negative bias was found in forecast GHI for correctly forecast clear-sky cases and a positive bias in correctly forecast overcast cases. Temporal averaging improved the cloud cover forecast and hence decreased the solar radiation forecast error, but made little impact on the overall bias. The positive bias seen in overcast situations occurs when the model cloud has low values of liquid water path (LWP). We attribute this bias to the model having LWP values that are too low or that the model optical properties for clouds with low LWP are incorrect.

scholarly journals Evaluating solar radiation forecast uncertainty

2019 ◽  
Vol 19 (3) ◽  
pp. 1985-2000 ◽  
Author(s):  
Minttu Tuononen ◽  
Ewan J. O'Connor ◽  
Victoria A. Sinclair
Keyword(s):  
Solar Radiation ◽  
Cloud Cover ◽  
Spatial Scales ◽  
Forecast Error ◽  
Surface Radiation ◽  
Cloud Base ◽  
Strong Impact ◽  
Positive Bias ◽  
Radiative Balance ◽  
Clear Sky

Abstract. The presence of clouds and their characteristics have a strong impact on the radiative balance of the Earth and on the amount of solar radiation reaching the Earth's surface. Many applications require accurate forecasts of surface radiation on weather timescales, for example solar energy and UV radiation forecasts. Here we investigate how operational forecasts of low and mid-level clouds affect the accuracy of solar radiation forecasts. A total of 4 years of cloud and solar radiation observations from one site in Helsinki, Finland, are analysed. Cloud observations are obtained from a ceilometer and therefore we first develop algorithms to reliably detect cloud base, precipitation, and fog. These new algorithms are widely applicable for both operational use and research, such as in-cloud icing detection for the wind energy industry and for aviation. The cloud and radiation observations are compared to forecasts from the Integrated Forecast System (IFS) run operationally and developed by the European Centre for Medium-Range Weather Forecasts (ECMWF). We develop methods to evaluate the skill of the cloud and radiation forecasts. These methods can potentially be extended to hundreds of sites globally. Over Helsinki, the measured global horizontal irradiance (GHI) is strongly influenced by its northerly location and the annual variation in cloudiness. Solar radiation forecast error is therefore larger in summer than in winter, but the relative error in the solar radiation forecast is more or less constant throughout the year. The mean overall bias in the GHI forecast is positive (8 W m−2). The observed and forecast distributions in cloud cover, at the spatial scales we are considering, are strongly skewed towards clear-sky and overcast situations. Cloud cover forecasts show more skill in winter when the cloud cover is predominantly overcast; in summer there are more clear-sky and broken cloud situations. A negative bias was found in forecast GHI for correctly forecast clear-sky cases and a positive bias in correctly forecast overcast cases. Temporal averaging improved the cloud cover forecast and hence decreased the solar radiation forecast error. The positive bias seen in overcast situations occurs when the model cloud has low values of liquid water path (LWP). We attribute this bias to the model having LWP values that are too low or the model optical properties for clouds with low LWP being incorrect.

scholarly journals Very-short term solar power generation forecasting based on trend-additive and seasonal-multiplicative smoothing methodology

2018 ◽  
Vol 51 ◽  
pp. 02003
Author(s):  
Stanislav Eroshenko ◽  
Alexandra Khalyasmaa ◽  
Rustam Valiev
Keyword(s):  
Solar Power ◽  
Forecast Error ◽  
Renewable Energy Sources ◽  
Power Station ◽  
Short Term ◽  
Operating Modes ◽  
Power Stations ◽  
Operational Forecasting ◽  
Solar Power Station ◽  
System Operating

In conditions of development of generating facilities on renewable energy sources, the technology runs up to uncertainty in the operational and short-term planning of the power system operating modes. To date, reliable tools for forecasting the generation of solar power stations are required. This paper considers the methodology of operational forecasting of solar power stations output based on the mathematical apparatus of cubic exponential smoothing with trend and seasonal components. The presented methodology was tested based on the measuring data of a real solar power station. The average forecast error was not more than 10% for days with variable clouds and not more than 3% for clear days, which indicates the effectiveness of the proposed approach.

scholarly journals Very-short term solar power generation forecasting based on trend-additive and seasonal-multiplicative smoothing methodology

2018 ◽  
Vol 51 ◽  
pp. 02003
Author(s):  
Stanislav Eroshenko ◽  
Alexandra Khalyasmaa ◽  
Rustam Valiev
Keyword(s):  
Solar Power ◽  
Forecast Error ◽  
Renewable Energy Sources ◽  
Power Station ◽  
Short Term ◽  
Operating Modes ◽  
Power Stations ◽  
Operational Forecasting ◽  
Solar Power Station ◽  
System Operating

In conditions of development of generating facilities on renewable energy sources, the technology runs up to uncertainty in the operational and short-term planning of the power system operating modes. To date, reliable tools for forecasting the generation of solar power stations are required. This paper considers the methodology of operational forecasting of solar power stations output based on the mathematical apparatus of cubic exponential smoothing with trend and seasonal components. The presented methodology was tested based on the measuring data of a real solar power station. The average forecast error was not more than 10% for days with variable clouds and not more than 3% for clear days, which indicates the effectiveness of the proposed approach.

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