Streamline-based method for intra-day solar forecasting through remote sensing

Nonnenmacher, Lukas; Coimbra, Carlos F.M.

doi:10.1016/j.solener.2014.07.026

Cited by 61 publications

(29 citation statements)

References 44 publications

(78 reference statements)

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“…For both ASSIM1 and 2, the model assimilates radiances from the eight thermal infrared channels of SEVIRI (4)(5)(6)(7)(8)(9)(10)(11) at the start time (00 UTC). The comparison is done in the same manner of clear sky conditions, with the ARPA observations and using the distance weighted mean.…”

Section: Resultsmentioning

confidence: 99%

“…The second group relies on the analysis of satellite or ground based remote sensing to infer cloud motion and short-term forecasting from minutes to few hours [4][5][6][7][8]. Most of the works [9][10][11] shows that the second group outperforms the first one for forecasts within 4-5 h, with the time window depending on the localization. For a longer forecast horizon, numerical models are more appropriate [12].…”

Section: Introductionmentioning

confidence: 99%

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3D-VAR Data Assimilation of SEVIRI Radiances for the Prediction of Solar Irradiance in Italy Using WRF Solar Mesoscale Model—Preliminary Results

et al. 2020

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Solar power generation is highly fluctuating due to its dependence on atmospheric conditions. The integration of this variable resource into the energy supply system requires reliable predictions of the expected power production as a basis for management and operation strategies. This is one of the goals of the Solar Cloud project, funded by the Italian Ministry of Economic Development (MISE)—to provide detailed forecasts of solar irradiance variables to operators and organizations operating in the solar energy industry. The Institute of Methodologies for Environmental Analysis of the National Research Council (IMAA-CNR), participating to the project, implemented an operational chain that provides forecasts of all the solar irradiance variables at high temporal and horizontal resolution using the numerical weather prediction Advanced Research Weather Research and Forecasting (WRF-ARW) Solar version 3.8.1 released by the National Center for Atmospheric Research (NCAR) in August 2016. With the aim of improving the forecast of solar irradiance, the three-dimensional (3D-Var) data assimilation was tested to assimilate radiances from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) aboard the Meteosat Second Generation (MSG) geostationary satellite into WRF Solar. To quantify the impact, the model output is compared against observational data. Hourly Global Horizontal Irradiance (GHI) is compared with ground-based observations from Regional Agency for the Protection of the Environment (ARPA) and with MSG Shortwave Solar Irradiance estimations, while WRF Solar cloud coverage is compared with Cloud Mask by MSG. A preliminary test has been performed in clear sky conditions to assess the capability of the model to reproduce the diurnal cycle of the solar irradiance. The statistical scores for clear sky conditions show a positive performance of the model with values comparable to the instrument uncertainty and a correlation of 0.995. For cloudy sky, the solar irradiance and the cloud cover are better simulated when the SEVIRI radiances are assimilated, especially in the short range of the simulation. For the cloud cover, the Mean Bias Error one hour after the assimilation time is reduced from 41.62 to 20.29 W/m2 when the assimilation is activated. Although only two case studies are considered here, the results indicate that the assimilation of SEVIRI radiance improves the performance of WRF Solar especially in the first 3 hour forecast.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

3D-VAR Data Assimilation of SEVIRI Radiances for the Prediction of Solar Irradiance in Italy Using WRF Solar Mesoscale Model—Preliminary Results

et al. 2020

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“…We use dense optical flow applied to consecutive 540 satellite images to generate a CMV field (see also Nonnenmacher and Coimbra (2014)). We use the dense optical flow method of Horn-Schunck (Horn and Schunck, 1981), implemented as described in Sun et al (2010).…”

Section: Dense Optical Flow Forecastmentioning

confidence: 99%

Intra-hour cloud index forecasting with data assimilation

et al. 2019

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We introduce a computational framework to forecast cloud index (CI) fields for up to one hour on a spatial domain that covers a city. Such intra-hour CI forecasts are important to produce solar power forecasts of utility scale solar power and distributed rooftop solar. Our method combines a 2D advection model with cloud motion vectors (CMVs) derived from a mesoscale numerical weather prediction (NWP) model and sparse optical flow acting on successive, geostationary satellite images. We use ensemble data assimilation to combine these sources of cloud motion information based on the uncertainty of each data source. Our technique produces forecasts that have similar or lower root mean square error than reference techniques that use only optical flow, NWP CMV fields, or persistence. We describe how the method operates on three representative case studies and present results from 39 cloudy days.fields derived hourly from a mesoscale NWP model. These two data sources are assimilated into a background ensemble that is initialized with a NWP 25 CMV field. We refer to the system as ANOC for the Assimilation of NWP winds and Optical flow CMVs.Generically, DA is a Bayesian technique to update numerical models using sparse and noisy obser-30 vations (Reich and Cotter, 2015;Asch et al., 2016). We use an ensemble Kalman filter (EnKF) (see, e.g., Evensen (2009)) to perform our assimilations. EnKFs are computational tools for DA where forecast uncertainty is represented by an ensemble. 35Optical flow is a method to determine a velocity field from consecutive scalar fields. Numerical methods for optical flow can be divided into two categories: dense optical flow (Horn and Schunck, 1981), where an entire vector field is produced, 40 and sparse optical flow (Lucas and Kanade, 1981), where point estimates of a vector field are produced. We use both dense and sparse optical flow to determine CMVs in this study.Advection of satellite-derived cloud properties for 45 intra-hour CI or irradiance field forecasts for solar power applications has been considered in several

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“…For intra-week forecasts, Numerical Weather Prediction (NWP) models are known to generate good results. This usually holds true for horizons greater than 4 h. For intra-day forecasts below 4 h, mostly satellite image based prediction methods are used [11]. For intra-hour forecasts, local sky conditions and sky images are utilized along with other available meteorological data for the site [12e20].…”

Section: Solar Forecastingmentioning

confidence: 99%

Benefits of solar forecasting for energy imbalance markets

et al. 2016

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a b s t r a c tShort term electricity trading to balance generation and demand provides an economic opportunity to integrate larger shares of variable renewable energy sources in the power grid. Recently, many regulatory market environments are reorganized to allow short term electricity trading. This study seeks to quantify the benefits of solar forecasting for energy imbalance markets (EIM). State-of-the-art solar forecasts, covering forecast horizons ranging from 24 h to 5 min are proposed and compared against the currently used benchmark models, persistence (P) and smart persistence (SP). The implemented reforecast of numerical weather prediction time series achieves a skill of 14.5% over the smart persistence model. Using the proposed forecasts for a forecast horizon of up to 75 min for a single 1 MW power plant reduces required flexibility reserves by 21% and 16.14%, depending on the allowed trading intervals (5 and 15 min). The probability of an imbalance, caused through wrong market bids from PV solar plants, can be reduced by 19.65% and 15.12% (for 5 and 15 min trading intervals). All EIM stakeholders benefit from accurate forecasting. Previous estimates on the benefits of EIMs, based on persistence model are conservative. It is shown that the design variables regulating the market time lines, the bidding and the binding schedules, drive the benefits of forecasting.

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Streamline-based method for intra-day solar forecasting through remote sensing

Cited by 61 publications

References 44 publications

3D-VAR Data Assimilation of SEVIRI Radiances for the Prediction of Solar Irradiance in Italy Using WRF Solar Mesoscale Model—Preliminary Results

3D-VAR Data Assimilation of SEVIRI Radiances for the Prediction of Solar Irradiance in Italy Using WRF Solar Mesoscale Model—Preliminary Results

Intra-hour cloud index forecasting with data assimilation

Benefits of solar forecasting for energy imbalance markets

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