1) The document describes a methodology for simulating errors in renewable energy generation forecasts and sub-hourly variations using historical data.
2) The methodology involves simulating hourly wind and solar power production at different locations, then simulating the placement of renewable energy plants and generating forecasts errors based on numerical weather predictions.
3) It then discusses how the simulation tools can help evaluate reserve requirements for power systems with high shares of renewable energy.
The document summarizes research on simulating satellite brightness temperature (BT) data using land surface models and observations. Key points:
- Researchers developed a two-phase system to simulate gridded AMSR-E BT data using the Community Land Model (CLM), a microwave emissivity model, and calibration with SCE-UA algorithm and AMSR-E observations.
- The system calculates sub-grid land states from CLM, simulates BT from each patch, and calibrates wetland emissivity to minimize differences from observed BT.
- Results showed the calibrated wetland emissivities transferred well to another location and improved soil moisture estimates when assimilated using an ensemble Kalman filter
This document provides an overview and validation of Re.SunTM, a software tool for assessing solar resources worldwide using mesoscale weather modeling and coupling techniques. Re.SunTM runs the WRF mesoscale numerical weather model to generate meteorological data, then applies clear sky models incorporating aerosol and gas data to estimate solar radiation indices. It was validated against measurements from 9 complex sites, showing a mean bias of 0.64% and root mean square error of 4%. Statistics demonstrate Re.SunTM can estimate average annual global solar radiation to within 4.14% of measured for 80% of cases. The document concludes Re.SunTM is useful for solar applications requiring site-specific resource assessment.
Solar radiation ground measured data quality assessment reportIrSOLaV Pomares
This technical report analyzes radiometric data measured at a site in XXX from June 2011 to May 2012. It assesses data quality using various filters and comparisons to clear sky models. Global, diffuse, and direct normal irradiance were measured. The methodology section describes transforming time to true solar time, calculating hourly/daily averages, and quality analysis including physical limits checks and cross-component relationship checks. Graphs of measured and clear sky data are presented and used to visually inspect data quality.
At present, with the development of wind power project in China, there are more and more projects located at the complex terrain and complex environment. At the same time, since the large planned area of project, the complex mountain area, and limited number of met mast, even without met mast, in order to the reliable development of the wind power project, it is important that how to do the wind resource assessment without actual measurement wind data and other conditions such as less reliable wind data, and the met mast was not considered representative. This paper will use the atmospheric model to do mesoscale simulation calculation of wind resources, and then combine with CFD technology to downscaling computation to get high resolution wind power assessment result. Finally, in order to confirm the validity of this application in the actual project, the comparison between calculation values and measurement values is carried out. The verification result through the actual data of different met mast shows that the wind resource assessment method which combines the CFD and mesoscale technologies is reliable. The main contribution of the article is to provide the reference model and approach for regional planning and large scale wind resource assessment when there isn’t enough adequate and effective wind data.
This document summarizes a wind resource assessment of the Metropolitan Area of Barcelona conducted using computational fluid dynamics (CFD) software. Wind characteristics were first transferred from a weather station to 200m above the study area using TopoWind software accounting for topography. CFD modeling with UrbaWind then computed wind flow within the urban area accounting for buildings and terrain. Results include mean annual wind speed and energy production maps at various hub heights. Validation with weather station data found differences generally under 0.4m/s, with some overestimation where vegetation was not modeled. The assessment provides guidance on siting small wind turbines in urban environments.
The document discusses operational forest fire simulation tools for supporting decision making. It outlines several models for fire propagation, including empirical Rothermel-based models and computational fluid dynamics physical models. It then describes the InfoSIM system, which integrates these propagation models, high-resolution wind data, and fuel maps into a GIS-based platform to enable real-time 3D wildfire simulation and analysis to support wildfire management. The system has been operationally implemented and validated in several regions in Spain.
2013 ASPRS Track, Developing an ArcGIS Toolbox for Estimating EvapoTranspirat...GIS in the Rockies
Estimating water used by vegetated areas is very important for water resources management and water rights. Traditionally the amount of water delivered to an area is calculated by installing some measuring device (flumes, weirs, flow meters, etc.). The alternative approach presented here estimates the actual water use in a vegetated areas based on ground surface energy balance concept using the ReSET model (Remote Sensing of ET – ReSET developed by IDS group in Colorado state university) that uses satellite and Arial imagery with visible and thermal bands along with weather data to estimate daily actual crop Evapotranspiration (ET) for vegetated areas. Surface energy balance models have been proven to be a robust approach for estimating vegetation evapotranspiration. One of the main limitations of wider application of these models in water resources and irrigation management is the requirement of extensive back ground in surface energy modeling. This presentation shows the development and the application of an ArcGIS toolbox that runs an automated version of the ReSET model. The tool is compatible with NASA/USGS Landsat Legacy Project. The presented ArcGIS tool automates the model in all stages and requires minimum interference from user. The tool presented accommodates both basic and advanced users. The results using the tool were tested and validated using results from manual ReSET model runs.
The document summarizes research on simulating satellite brightness temperature (BT) data using land surface models and observations. Key points:
- Researchers developed a two-phase system to simulate gridded AMSR-E BT data using the Community Land Model (CLM), a microwave emissivity model, and calibration with SCE-UA algorithm and AMSR-E observations.
- The system calculates sub-grid land states from CLM, simulates BT from each patch, and calibrates wetland emissivity to minimize differences from observed BT.
- Results showed the calibrated wetland emissivities transferred well to another location and improved soil moisture estimates when assimilated using an ensemble Kalman filter
This document provides an overview and validation of Re.SunTM, a software tool for assessing solar resources worldwide using mesoscale weather modeling and coupling techniques. Re.SunTM runs the WRF mesoscale numerical weather model to generate meteorological data, then applies clear sky models incorporating aerosol and gas data to estimate solar radiation indices. It was validated against measurements from 9 complex sites, showing a mean bias of 0.64% and root mean square error of 4%. Statistics demonstrate Re.SunTM can estimate average annual global solar radiation to within 4.14% of measured for 80% of cases. The document concludes Re.SunTM is useful for solar applications requiring site-specific resource assessment.
Solar radiation ground measured data quality assessment reportIrSOLaV Pomares
This technical report analyzes radiometric data measured at a site in XXX from June 2011 to May 2012. It assesses data quality using various filters and comparisons to clear sky models. Global, diffuse, and direct normal irradiance were measured. The methodology section describes transforming time to true solar time, calculating hourly/daily averages, and quality analysis including physical limits checks and cross-component relationship checks. Graphs of measured and clear sky data are presented and used to visually inspect data quality.
At present, with the development of wind power project in China, there are more and more projects located at the complex terrain and complex environment. At the same time, since the large planned area of project, the complex mountain area, and limited number of met mast, even without met mast, in order to the reliable development of the wind power project, it is important that how to do the wind resource assessment without actual measurement wind data and other conditions such as less reliable wind data, and the met mast was not considered representative. This paper will use the atmospheric model to do mesoscale simulation calculation of wind resources, and then combine with CFD technology to downscaling computation to get high resolution wind power assessment result. Finally, in order to confirm the validity of this application in the actual project, the comparison between calculation values and measurement values is carried out. The verification result through the actual data of different met mast shows that the wind resource assessment method which combines the CFD and mesoscale technologies is reliable. The main contribution of the article is to provide the reference model and approach for regional planning and large scale wind resource assessment when there isn’t enough adequate and effective wind data.
This document summarizes a wind resource assessment of the Metropolitan Area of Barcelona conducted using computational fluid dynamics (CFD) software. Wind characteristics were first transferred from a weather station to 200m above the study area using TopoWind software accounting for topography. CFD modeling with UrbaWind then computed wind flow within the urban area accounting for buildings and terrain. Results include mean annual wind speed and energy production maps at various hub heights. Validation with weather station data found differences generally under 0.4m/s, with some overestimation where vegetation was not modeled. The assessment provides guidance on siting small wind turbines in urban environments.
The document discusses operational forest fire simulation tools for supporting decision making. It outlines several models for fire propagation, including empirical Rothermel-based models and computational fluid dynamics physical models. It then describes the InfoSIM system, which integrates these propagation models, high-resolution wind data, and fuel maps into a GIS-based platform to enable real-time 3D wildfire simulation and analysis to support wildfire management. The system has been operationally implemented and validated in several regions in Spain.
2013 ASPRS Track, Developing an ArcGIS Toolbox for Estimating EvapoTranspirat...GIS in the Rockies
Estimating water used by vegetated areas is very important for water resources management and water rights. Traditionally the amount of water delivered to an area is calculated by installing some measuring device (flumes, weirs, flow meters, etc.). The alternative approach presented here estimates the actual water use in a vegetated areas based on ground surface energy balance concept using the ReSET model (Remote Sensing of ET – ReSET developed by IDS group in Colorado state university) that uses satellite and Arial imagery with visible and thermal bands along with weather data to estimate daily actual crop Evapotranspiration (ET) for vegetated areas. Surface energy balance models have been proven to be a robust approach for estimating vegetation evapotranspiration. One of the main limitations of wider application of these models in water resources and irrigation management is the requirement of extensive back ground in surface energy modeling. This presentation shows the development and the application of an ArcGIS toolbox that runs an automated version of the ReSET model. The tool is compatible with NASA/USGS Landsat Legacy Project. The presented ArcGIS tool automates the model in all stages and requires minimum interference from user. The tool presented accommodates both basic and advanced users. The results using the tool were tested and validated using results from manual ReSET model runs.
New calculation methodology for solar thermal systems Aiguasol
The document describes a new methodology called METASOL for calculating the sizing of solar thermal systems in Spain. It was developed to address limitations of the existing F-Chart methodology. METASOL is based on over 69,000 detailed TRNSYS simulations of 7 different solar thermal system configurations across 5 locations in Spain. Monthly correlations were extracted from over 800,000 data points to generate a simple calculation method that considers factors like multiple system types, locations, loads, and temperatures not addressed by F-Chart. The new methodology is integrated into a free and easy-to-use graphical interface to standardize solar thermal system sizing in Spain.
This study implements a Bayesian statistical framework to calibrate the SCOPE process-based simulator for simulating gross primary production (GPP) and top-of-canopy reflectance at a spruce flux tower site in the Czech Republic. Markov chain Monte Carlo simulation is used to quantify the uncertainty in SCOPE input parameters by comparing simulated and measured reflectance and GPP data. The results show the posterior parameter distributions have lower uncertainty than the prior distributions. Simulated half-hourly GPP over the growing season using maximum a posteriori parameter estimates matches the measured data. Future work will estimate seasonal parameter variations to improve GPP simulation accuracy.
The document summarizes a comparison between large eddy simulations (LES) of methane plume dispersion from a point source with measurements taken during a field campaign. The LES simulation was able to reproduce key meteorological conditions on the measurement day and capture the spatial evolution and statistical properties of the measured plumes over time. While the LES matched measurements well given the simple terrain and boundary layer meteorology, the study concludes more comprehensive meteorological measurements are still needed to fully validate dispersion models against real-world field experiments.
Met Éireann has expanded from monitoring Irish climate to conducting climate modelling. It was initially involved in regional climate modelling through projects like C4I. It has since joined the EC-Earth consortium to run its own global climate model. EC-Earth simulations will be contributed to CMIP5 and used for national climate impact research. Met Éireann also maintains regional modelling capabilities and plans high-resolution regional simulations.
Time integration of evapotranspiration using a two source surface energy bala...Ramesh Dhungel
This document provides an outline for a dissertation on developing methodologies and models to estimate evapotranspiration (ET) using a two-source surface energy balance model. The objectives are to extrapolate ET between satellite overpass dates using gridded weather data and Landsat-based ET data. A resistance-based two-source surface energy balance model is developed that incorporates a soil water balance model. The model is tested against ET estimates from the METRIC model to estimate ET at higher temporal resolutions than satellite overpasses.
Optimal combinaison of CFD modeling and statistical learning for short-term w...Jean-Claude Meteodyn
After almost three decades of active research, short-term wind power forecasting is now considered as a mature field. It has been widely and successfully put into operation within the past ten years. Meteodyn with over a decade of experience in wind engineering has contributed to this spread with tens of wind farm equipped with forecast solutions around the world. Our next-generation short-term forecasting solution has been designed to makes the most of both a tailored micro-scale CFD modeling and advanced statistical learning. In the frame of our model design, various options have been considered and evaluated taking into account both model performance and operational constraints. Two main approaches for wind power forecasting are usually considered in the literature (and sometimes opposed): “physical” and “statistical”. It is widely admitted that an optimal combination of both is necessary to build a high performance forecasting system. However, behind "optimal combination" resides a wide variety of design options. We propose here to shed some light on what performances one should expect from several modeling options for combining physics (mesoscale/CFD modeling) and statistics (grey/black box statistical learning, phase/magnitude correction, data filtering). Case studies are taken from real wind farms in various climate and terrain conditions.
Improved Kalman Filtered Neuro-Fuzzy Wind Speed Predictor For Real Data Set ...IJMER
This document presents three new models for short-term (24 hours ahead) wind speed forecasting for Egypt's northwestern coast based on real data collected from the site. The first model predicts wind speed using the same month of data from seven consecutive years. The second model predicts using only one month of data with a time series prediction scheme. The third model applies a discrete Kalman filter to one month of data first to reduce noise before prediction using an adaptive neuro-fuzzy inference system (ANFIS). The Kalman filtered data provided more accurate predictions with a 64% reduction in error compared to the first model.
Solar Radiation Estimation based on Digital Image ProcessingPrashant Pal
This document discusses a method for estimating solar radiation using digital image processing of sky camera images. Sky camera images are divided into three areas and pixel values from clear and cloudy day images are analyzed. Correlations between pixel values and solar altitude are used to create databases for clear and cloudy conditions. Beam, diffuse, and global radiation values are estimated based on averages of pixel values from the three areas and compared to measured values, achieving reasonably low error rates. The method provides information that can improve solar plant performance and operation based on meteorological conditions.
Math 390 - Machine Learning Techniques PresentationDarragh Punch
This document discusses various machine learning techniques for modeling solar radiation, including artificial neural networks (ANNs), support vector machines (SVMs), radial basis functions (RBFs), support vector regression (SVR), Gaussian processes (GP), and numerical weather prediction (NWP). ANNs can predict optimal photovoltaic system layouts and "learn" from examples. SVMs using RBF kernels are more accurate than other models for solar radiation forecasting. SVR provides better representations than multi-class SVMs. GP is the best predictor of solar irradiance. And NWP samples current weather to predict future conditions up to 6 hours ahead, relevant for long-term solar farm planning.
1) Thermal waves in Saturn's atmosphere were analyzed using infrared observations from 2003-2013.
2) Maps were compiled from multiple instruments and analyzed using power spectral analysis to detect thermal waves.
3) Waves with different wavelengths were found to trace chemical species at different altitudes in Saturn's atmosphere. Large wave trains were detected in late 2003 and 2004.
Estimation of global solar radiation by using machine learning methodsmehmet şahin
In this study, global solar radiation (GSR) was estimated based on 53 locations by using ELM, SVR, KNN, LR and NU-SVR methods. Methods were trained with a two-year data set and accuracy of the mentioned methods was tested with a one-year data set. The data set of each year was consisting of 12 months. Whereas the values of month, altitude, latitude, longitude, vapour pressure deficit and land surface temperature were used as input for developing models, GSR was obtained as output. Values of vapour pressure deficit and land surface temperature were taken from radiometry of NOAA-AVHRR satellite. Estimated solar radiation data were compared with actual data that were obtained from meteorological stations. According to statistical results, most successful method was NU-SVR method. The RMSE and MBE values of NU-SVR method were found to be 1,4972 MJ/m2 and 0,2652 MJ/m2, respectively. R value was 0,9728. Furthermore, worst prediction method was LR. For other methods, RMSE values were changing between 1,7746 MJ/m2 and 2,4546 MJ/m2. It can be seen from the statistical results that ELM, SVR, k-NN and NU-SVR methods can be used for estimation of GSR.
Geothermal exploration using remote sensing techniquesSepideh Abadpour
The document discusses using remote sensing to identify geothermal regions. It describes common remote sensing techniques like identifying thermal anomalies and using satellite images to study land surface temperature. The document also discusses a case study in Mexico that used Landsat images to identify areas with potential geothermal activity by enhancing oxide and hydroxyl mineral spectral features while suppressing vegetation. Band ratios and subtractions were used to create color composites that effectively identified altered rocks and geological structures for further field exploration.
Typical Meteorological Year Report for CSP, CPV and PV solar plantsIrSOLaV Pomares
This technical report analyzes the solar resource available at a site in Northern Cape, South Africa selected to host a solar thermal power plant. It presents a typical meteorological year (TMY) developed using 12 years of hourly solar radiation data for the site. The TMY is generated using a methodology that selects the most representative month from each year for key meteorological variables. It is comprised of months from 2007 to 2010 that best match the long-term averages for global horizontal and direct normal solar radiation at the site. The TMY and long-term averages are presented and show a close match in monthly and daily solar radiation patterns for use in modeling solar power production at the site.
Validation of wind resource assessment process based on CFD Jean-Claude Meteodyn
Wind resource assessment requires nowadays more efficient tools to provide an accurate evaluation of production in order to reduce costs.As onshore wind farms are built in more complex terrains, it is necessary to find a new method to provide a fine evaluation of energy which reduces the error during the data extrapolation process. This explains why CFD models have become a standard for WRA in specific conditions.This presentation is focused on the wind speed and energy yield prediction carried out for a 29MW wind farm project. The accuracy of the wind modeling is investigated by the cross validation between the different met masts around the site. The net energy prediction P50 is compared against real wind farm performance data during a blind test organized by EWEA in 2013. More than 50 companies have been involved in order to compare methods results.
Use of mesoscale modeling to increase the reliability of wind resource assess...Jean-Claude Meteodyn
During wind farm design phase, the wind direction distribution is a crucial information for wind turbine layout optimization. However, in complex terrains, the wind rose at hub height of the wind turbines can be quite different from met mast measurement.The study shows that in complex terrains, the use of mesoscale modeling provides a complement to met mast measurement. It allows to better determine the turbine-specific wind rose and to reduce the uncertainty in wind resource assessment. The coupling of mesoscale and CFD model allows to produce high resolution wind map, by taking into account both mesoscale and microscale terrain effects.
Contribution to the investigation of wind characteristics and assessment of w...Université de Dschang
M. Bawe Gerard Nfor, Jr. a soutenu sa thèse de Doctorat/Phd en Physique, option Mécanique-Énergétique ce 19 mai 2016 dans la salle des conférences de l'Université de Dschang. A l'issue de la soutenance, le jury présidé par le Prof. Anaclet Fomethe lui a décerné, à l'unanimité de ses membres, la mention très honorable.
Voici la présentation powerpoint qu'il a effectuée dans le cadre de cette soutenance.
Advanced weather forecasting for RES applications: Smart4RES developments tow...Leonardo ENERGY
Recording at: https://youtu.be/45Zpjog95QU
This is the 3rd Smart4RES webinar that will address technological and market challenges in RES prediction and will introduce the Smart4RES strategy to improve weather forecasting models with high resolution.
Through wind and solar applications, Innovative Numerical Weather Prediction and Large-Eddy Simulation approaches will be presented.
Design of PV backup system for data centerMohamed Abbas
This document details the design of a backup PV system for a data center in Bogota, Colombia. Section I introduces the problem of weekly blackouts at the data center and the financial incentive to install a greener PV-storage backup system instead of a diesel generator. Section II discusses optimizing the panel tilt and azimuth for the location, determining the optimal configuration is 9 degrees tilt and 128 degrees azimuth. Section III analyzes the data center's load profile and effects of blackouts. Section IV provides details on selecting and sizing PV modules, charge controllers, batteries and inverters to meet the load requirements within constraints of the components.
New calculation methodology for solar thermal systems Aiguasol
The document describes a new methodology called METASOL for calculating the sizing of solar thermal systems in Spain. It was developed to address limitations of the existing F-Chart methodology. METASOL is based on over 69,000 detailed TRNSYS simulations of 7 different solar thermal system configurations across 5 locations in Spain. Monthly correlations were extracted from over 800,000 data points to generate a simple calculation method that considers factors like multiple system types, locations, loads, and temperatures not addressed by F-Chart. The new methodology is integrated into a free and easy-to-use graphical interface to standardize solar thermal system sizing in Spain.
This study implements a Bayesian statistical framework to calibrate the SCOPE process-based simulator for simulating gross primary production (GPP) and top-of-canopy reflectance at a spruce flux tower site in the Czech Republic. Markov chain Monte Carlo simulation is used to quantify the uncertainty in SCOPE input parameters by comparing simulated and measured reflectance and GPP data. The results show the posterior parameter distributions have lower uncertainty than the prior distributions. Simulated half-hourly GPP over the growing season using maximum a posteriori parameter estimates matches the measured data. Future work will estimate seasonal parameter variations to improve GPP simulation accuracy.
The document summarizes a comparison between large eddy simulations (LES) of methane plume dispersion from a point source with measurements taken during a field campaign. The LES simulation was able to reproduce key meteorological conditions on the measurement day and capture the spatial evolution and statistical properties of the measured plumes over time. While the LES matched measurements well given the simple terrain and boundary layer meteorology, the study concludes more comprehensive meteorological measurements are still needed to fully validate dispersion models against real-world field experiments.
Met Éireann has expanded from monitoring Irish climate to conducting climate modelling. It was initially involved in regional climate modelling through projects like C4I. It has since joined the EC-Earth consortium to run its own global climate model. EC-Earth simulations will be contributed to CMIP5 and used for national climate impact research. Met Éireann also maintains regional modelling capabilities and plans high-resolution regional simulations.
Time integration of evapotranspiration using a two source surface energy bala...Ramesh Dhungel
This document provides an outline for a dissertation on developing methodologies and models to estimate evapotranspiration (ET) using a two-source surface energy balance model. The objectives are to extrapolate ET between satellite overpass dates using gridded weather data and Landsat-based ET data. A resistance-based two-source surface energy balance model is developed that incorporates a soil water balance model. The model is tested against ET estimates from the METRIC model to estimate ET at higher temporal resolutions than satellite overpasses.
Optimal combinaison of CFD modeling and statistical learning for short-term w...Jean-Claude Meteodyn
After almost three decades of active research, short-term wind power forecasting is now considered as a mature field. It has been widely and successfully put into operation within the past ten years. Meteodyn with over a decade of experience in wind engineering has contributed to this spread with tens of wind farm equipped with forecast solutions around the world. Our next-generation short-term forecasting solution has been designed to makes the most of both a tailored micro-scale CFD modeling and advanced statistical learning. In the frame of our model design, various options have been considered and evaluated taking into account both model performance and operational constraints. Two main approaches for wind power forecasting are usually considered in the literature (and sometimes opposed): “physical” and “statistical”. It is widely admitted that an optimal combination of both is necessary to build a high performance forecasting system. However, behind "optimal combination" resides a wide variety of design options. We propose here to shed some light on what performances one should expect from several modeling options for combining physics (mesoscale/CFD modeling) and statistics (grey/black box statistical learning, phase/magnitude correction, data filtering). Case studies are taken from real wind farms in various climate and terrain conditions.
Improved Kalman Filtered Neuro-Fuzzy Wind Speed Predictor For Real Data Set ...IJMER
This document presents three new models for short-term (24 hours ahead) wind speed forecasting for Egypt's northwestern coast based on real data collected from the site. The first model predicts wind speed using the same month of data from seven consecutive years. The second model predicts using only one month of data with a time series prediction scheme. The third model applies a discrete Kalman filter to one month of data first to reduce noise before prediction using an adaptive neuro-fuzzy inference system (ANFIS). The Kalman filtered data provided more accurate predictions with a 64% reduction in error compared to the first model.
Solar Radiation Estimation based on Digital Image ProcessingPrashant Pal
This document discusses a method for estimating solar radiation using digital image processing of sky camera images. Sky camera images are divided into three areas and pixel values from clear and cloudy day images are analyzed. Correlations between pixel values and solar altitude are used to create databases for clear and cloudy conditions. Beam, diffuse, and global radiation values are estimated based on averages of pixel values from the three areas and compared to measured values, achieving reasonably low error rates. The method provides information that can improve solar plant performance and operation based on meteorological conditions.
Math 390 - Machine Learning Techniques PresentationDarragh Punch
This document discusses various machine learning techniques for modeling solar radiation, including artificial neural networks (ANNs), support vector machines (SVMs), radial basis functions (RBFs), support vector regression (SVR), Gaussian processes (GP), and numerical weather prediction (NWP). ANNs can predict optimal photovoltaic system layouts and "learn" from examples. SVMs using RBF kernels are more accurate than other models for solar radiation forecasting. SVR provides better representations than multi-class SVMs. GP is the best predictor of solar irradiance. And NWP samples current weather to predict future conditions up to 6 hours ahead, relevant for long-term solar farm planning.
1) Thermal waves in Saturn's atmosphere were analyzed using infrared observations from 2003-2013.
2) Maps were compiled from multiple instruments and analyzed using power spectral analysis to detect thermal waves.
3) Waves with different wavelengths were found to trace chemical species at different altitudes in Saturn's atmosphere. Large wave trains were detected in late 2003 and 2004.
Estimation of global solar radiation by using machine learning methodsmehmet şahin
In this study, global solar radiation (GSR) was estimated based on 53 locations by using ELM, SVR, KNN, LR and NU-SVR methods. Methods were trained with a two-year data set and accuracy of the mentioned methods was tested with a one-year data set. The data set of each year was consisting of 12 months. Whereas the values of month, altitude, latitude, longitude, vapour pressure deficit and land surface temperature were used as input for developing models, GSR was obtained as output. Values of vapour pressure deficit and land surface temperature were taken from radiometry of NOAA-AVHRR satellite. Estimated solar radiation data were compared with actual data that were obtained from meteorological stations. According to statistical results, most successful method was NU-SVR method. The RMSE and MBE values of NU-SVR method were found to be 1,4972 MJ/m2 and 0,2652 MJ/m2, respectively. R value was 0,9728. Furthermore, worst prediction method was LR. For other methods, RMSE values were changing between 1,7746 MJ/m2 and 2,4546 MJ/m2. It can be seen from the statistical results that ELM, SVR, k-NN and NU-SVR methods can be used for estimation of GSR.
Geothermal exploration using remote sensing techniquesSepideh Abadpour
The document discusses using remote sensing to identify geothermal regions. It describes common remote sensing techniques like identifying thermal anomalies and using satellite images to study land surface temperature. The document also discusses a case study in Mexico that used Landsat images to identify areas with potential geothermal activity by enhancing oxide and hydroxyl mineral spectral features while suppressing vegetation. Band ratios and subtractions were used to create color composites that effectively identified altered rocks and geological structures for further field exploration.
Typical Meteorological Year Report for CSP, CPV and PV solar plantsIrSOLaV Pomares
This technical report analyzes the solar resource available at a site in Northern Cape, South Africa selected to host a solar thermal power plant. It presents a typical meteorological year (TMY) developed using 12 years of hourly solar radiation data for the site. The TMY is generated using a methodology that selects the most representative month from each year for key meteorological variables. It is comprised of months from 2007 to 2010 that best match the long-term averages for global horizontal and direct normal solar radiation at the site. The TMY and long-term averages are presented and show a close match in monthly and daily solar radiation patterns for use in modeling solar power production at the site.
Validation of wind resource assessment process based on CFD Jean-Claude Meteodyn
Wind resource assessment requires nowadays more efficient tools to provide an accurate evaluation of production in order to reduce costs.As onshore wind farms are built in more complex terrains, it is necessary to find a new method to provide a fine evaluation of energy which reduces the error during the data extrapolation process. This explains why CFD models have become a standard for WRA in specific conditions.This presentation is focused on the wind speed and energy yield prediction carried out for a 29MW wind farm project. The accuracy of the wind modeling is investigated by the cross validation between the different met masts around the site. The net energy prediction P50 is compared against real wind farm performance data during a blind test organized by EWEA in 2013. More than 50 companies have been involved in order to compare methods results.
Use of mesoscale modeling to increase the reliability of wind resource assess...Jean-Claude Meteodyn
During wind farm design phase, the wind direction distribution is a crucial information for wind turbine layout optimization. However, in complex terrains, the wind rose at hub height of the wind turbines can be quite different from met mast measurement.The study shows that in complex terrains, the use of mesoscale modeling provides a complement to met mast measurement. It allows to better determine the turbine-specific wind rose and to reduce the uncertainty in wind resource assessment. The coupling of mesoscale and CFD model allows to produce high resolution wind map, by taking into account both mesoscale and microscale terrain effects.
Contribution to the investigation of wind characteristics and assessment of w...Université de Dschang
M. Bawe Gerard Nfor, Jr. a soutenu sa thèse de Doctorat/Phd en Physique, option Mécanique-Énergétique ce 19 mai 2016 dans la salle des conférences de l'Université de Dschang. A l'issue de la soutenance, le jury présidé par le Prof. Anaclet Fomethe lui a décerné, à l'unanimité de ses membres, la mention très honorable.
Voici la présentation powerpoint qu'il a effectuée dans le cadre de cette soutenance.
Advanced weather forecasting for RES applications: Smart4RES developments tow...Leonardo ENERGY
Recording at: https://youtu.be/45Zpjog95QU
This is the 3rd Smart4RES webinar that will address technological and market challenges in RES prediction and will introduce the Smart4RES strategy to improve weather forecasting models with high resolution.
Through wind and solar applications, Innovative Numerical Weather Prediction and Large-Eddy Simulation approaches will be presented.
Design of PV backup system for data centerMohamed Abbas
This document details the design of a backup PV system for a data center in Bogota, Colombia. Section I introduces the problem of weekly blackouts at the data center and the financial incentive to install a greener PV-storage backup system instead of a diesel generator. Section II discusses optimizing the panel tilt and azimuth for the location, determining the optimal configuration is 9 degrees tilt and 128 degrees azimuth. Section III analyzes the data center's load profile and effects of blackouts. Section IV provides details on selecting and sizing PV modules, charge controllers, batteries and inverters to meet the load requirements within constraints of the components.
Quantification of operating reserves with high penetration of wind power cons...IJECEIAES
The high integration of wind energy in power systems requires operating reserves to ensure the reliability and security in the operation. The intermittency and volatility in wind power sets a challenge for day-ahead dispatching in order to schedule generation resources. Therefore, the quantification of operating reserves is addressed in this paper using extreme values through Monte-Carlo simulations. The uncertainty in wind power forecasting is captured by a generalized extreme value distribution to generate scenarios. The day-ahead dispatching model is formulated as a mixed-integer linear quadratic problem including ramping constraints. This approach is tested in the IEEE-118 bus test system including integration of wind power in the system. The results represent the range of values for operating reserves in day-ahead dispatching.
The concepts related of the New Model of River Adige, and especially an analysys of the existing OMS components ready and their interpretation on the basis of travel time approaches
Embedded Applications of MS-PSO-BP on Wind/Storage Power ForecastingTELKOMNIKA JOURNAL
Higher proportion wind power penetration has great impact on grid operation and dispatching,
intelligent hybrid algorithm is proposed to cope with inaccurate schedule forecast. Firstly, hybrid algorithm
of MS-PSO-BP (Mathematical Statistics, Particle Swarm Optimization, Back Propagation neural network)
is proposed to improve the wind power system prediction accuracy. MS is used to optimize artificial neural
network training sample, PSO-BP (particle swarm combined with back propagation neural network) is
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CIRED Paper 1414 - Evaluation of the level of prediction errors
1. 23rd International Conference on Electricity Distribution Lyon, 15-18 June 2015
Paper 1414
CIRED 2015 1/4
Evaluation of the level of prediction errors and sub-hourly variability of PV and
wind generation in a future with a large amount of renewables
Robin Girard, Arthur Bossavy,
Loïc Le Gars, Georges Kariniotakis,
MINES ParisTech, PERSEE Center,
PSL Research University, France
Robin.girard@mines-paristech.fr
ABSTRACT
In this paper we propose a method for the simulation of
errors in renewable energy sources generation
forecasting (photovoltaic and wind) for use in power
system planning studies. The proposed methodology
relies on 5 elementary simulation steps. The first step is
the simulation of photovoltaic plant and wind farm power
production, with a sufficient spatial and temporal
resolution (few km and hourly time step), the second is
the simulation of the localisation of production sites, the
third step is the generation of forecast errors using
historic data of numerical weather predictions, and the
last step is the simulation of intra-hourly variations of
photovoltaic production. Finally, it is discussed how
these simulation tools can assist the evaluation of the
required tertiary reserves in a power system with a large
share of renewable energies into the mix.
INTRODUCTION
In a future with a large amount of renewable energy
sources (RES) integrated into the distribution system,
there are many kinds of new constraints that cannot be
neglected a priori in the power distribution system
operation and planning. This includes short term (few
hours in advance) forecast errors and sub-hourly variation
of the RES production. However, the importance of these
two quantities is subject to the so called aggregation
effect and cannot be considered as a linear function of the
installed capacity. Furthermore, this aggregation effect
highly depends on the considered geographical perimeter
of the area where the RES plants are located.
In this paper we present a methodology to estimate the
level of short-term forecast error and sub-hourly
production variations at any spatial scale. We apply the
methodology in the case of France considering three
scales: national, regional and down to the scale of the
distribution network.
Before simulating forecast errors and sub-hourly
variations, we describe in Section 2 the methodology
used to simulate hourly wind power and solar power
production at any location. In Section 3 our methodology
for simulating forecast errors is exposed while Section 4
is devoted to the simulation of intra-hourly variations.
The last Section gives conclusions.
SIMULATION OF THE RENEWABLE
ENERGY PRODUCTION
We describe here the methodology used to simulate
hourly wind power and solar power production at any
given location. Wind power is simulated through the use
of a statistically calibrated power curve and wind time
series resulting from the use of a meteorological model in
reanalysis mode. Solar power simulation is based on the
use of data from the SODA database, and namely on
estimations of the surface solar irradiation at an hourly
resolution and at a spatial scale of 3km. The SODA data
rely on the use of satellite images and physical modeling
of radiative transfer in the atmosphere. Both models used
to simulate production are validated on real data.
Simulation of wind power production
The simulation of wind power also relies on the
combination of a model and meteorological data from
MERRA refined spatially with a downscaling method,
relying on meteorological data from ECMWF. The
meteorological parameter used as input in the model here
is the wind speed at 50 m height. The model is a
statistical one that allows the conversion of wind speed
into power production. It is a piecewise model combining
a linear part, a locally polynomial part and a constant part
for the plateau and finally a linear part to model the cut-
off. A constraint is added to allow the reproduction of a
realistic temporal variability. In the application, the
parameters of the model are estimated for each region of
France with a least square estimation procedure. In the
end it is verified that the simulated production at the
regional scale has a temporal variability and a capacity
factor that is equal to that observed from the regional
RTE (French TSO) production curves in 2013 (available
at the RTE web site).
Simulation of solar power
Solar power production is simulated with a model of a
solar photovoltaic system taking as input meteorological
data: the irradiance corresponding to the inclination and
orientation of the system, and the temperature. The model
takes into account the effect of the temperature as well as
the different losses up to that of the transformation
station. Finally, the model is calibrated on several
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different real production sites.
The temperature is obtained through meteorological
reanalysis MERRA [1]. Meteorological reanalysis are the
combination of earth measurements and the use of the
physical equations of the atmosphere. They take the form
of a spatio-temporal field of wind speed/direction and
temperature for a temporal resolution of 1 h, and a spatial
horizontal resolution of about 50 km. Concerning the
irradiation data they are taken from the SODA database,
which contains 10 years of earth irradiation estimated
data obtained at 15 minute temporal resolution and about
3 km spatial horizontal resolution with satellite images
combined with a radiative transfer model.
SIMULATION OF THE RES PLANTS SITE
LOCATIONS
Any simulation of wind and solar power production at a
regional scale requires a localisation of the installed RES
power plants. We propose a method to generate
localisations and installed capacity over all sites in France
segmented with squares of 100m x 100m. This method
contains 4 steps:
1. Computation of exclusion areas, i.e. areas where it is
not possible to have PV or wind production sites due
to a strong constraint (such as relief, protected areas,
buildings for wind power, forest areas for PV…)
2. Application of an acceptability ratio: among the
available areas (those remaining after step 1) only a
fraction is selected and made available (10% for
wind power and 5% for solar power)
3. Selection of the site with sufficient potential of
energy production (the potential is computed with
the energy simulated thanks to the simulation process
described in the previous Section)
4. Selection among the remaining sites of those with the
best score and attribution of a capacity according to
the available surface. The score used is a
combination of a random term and a term providing
the energy potential of the site. The weight of the
random term is a parameter of the procedure called
Fp that allows measuring the proportion of the
variability of the score due to the variability of the
random term. In particular if Fp=1 the selection of
the site is random and if Fp=0 the selection is done
according to the energy potential.
An illustration of obtained results is shown in Figure 1
and in Figure 2 for PV production sites.
Figure 1: Spatial distribution of solar panels for Fp=0.8
(almost uniform)
Figure 2: Spatial distribution of solar panels for Fp=0.3
(more concentrated on locations endowed with the highest
energy potentials)
SIMULATION OF FORECAST ERRORS
Our methodology for simulating forecast errors mimics
real forecasting, performed on the simulated production
data. The real forecasting model is simplified in order to
allow a large number of computation repetitions. In the
case of PV forecasting as well as Wind Power
forecasting, it is used as input two explanatory variables:
meteorological forecast of ECMWF (wind speed for wind
power and surface solar irradiation for PV) and recent
past production measurements. The simulation is
performed along several years and thousands of different
implantation configurations. The distribution of errors is
obtained as a function of the spatial scale and the chosen
time horizon. An example of results for photovoltaic
3. 23rd International Conference on Electricity Distribution Lyon, 15-18 June 2015
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production is shown on Figure 3, according to the 1st
quantile, allowing to measure the level of the worst
errors. In Figure 4, the average error of wind power
production forecasted 12 hours in advance is represented
spatially. In Figure 5, we show the time series of the sum
of hourly forecast errors for three different temporal
horizons: 6 hours, 3 hours and 1 hour ahead. The
corresponding errors are the sum of PV and wind power
forecast errors at the scale of France, in a 100% of RES
scenario, including 63 GW of PV systems and 33 GW of
wind power.
Figure 3: level of the worst errors of PV production along
years 2007 up to 2012, as a function of the forecasting time
horizon, and the spatial dispersion of PV systems (Fp=0.1
represents a concentration of solar panels only on the best
sites and Fp=1 represents a uniform distribution over
France). Only the forecasts issued at 00:00 are used in this
Figure.
Figure 4: Average forecast errors of wind power made 12
hours in advance expressed in root mean square error per
unit of installed capacity.
Figure 5: Forecast errors at the level of France, given
according to three different forecasting time horizons: 1
hour (red), 3 hour (in orange), and 6 hours ahead (in
yellow), in a scenario including 33 GW of wind power and
63 GW of PV (the errors represented here are the sum of
PV and wind).
SUB-HOURLY VARIATIONS SIMULATION
Concerning the simulation of sub-hourly variations, we
propose a machine learning procedure, based on a data
set of several years of sub-hourly generation
measurements, on more than 30 different systems located
in different places in the south of France. The simulation
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method contains 3 steps, applied for each day, and each
site for which a simulation is performed:
1. An hourly simulation is produced with the
method proposed in Section 2.
2. Using the simulation of the first step, detection
of the K=20 nearest days in all the learning set
composed of the 30 different sites. (i.e. those
minimizing the Euclidian distance between the
simulation and the observation).
3. Random selection of a day in the set of K days
obtained at the step 2. The difference between
the hourly values and the 5 minutes resolution
values in the selected day is added to the hourly
value of the simulated day.
Figure 6: Evolution of correlations between the difference of
mean hourly production values, and 5 minutes resolution
production values, as a function of the distance between
sites.
CONCLUSION
In this paper we have presented the methodology that was
elaborated in the French project SmartReserve (founded
by ADEME) in order to evaluate the required volume of
tertiary reserve, in a future with a large share of
renewable energy in France. The methodology allows the
simulation of wind and solar power forecast errors as
well as the intra-hourly variation of PV production at any
geographical scale. For the case of France with a very
high penetration of renewable energy (i.e. more than 30
GW of wind power and 60GW of PV) the results
presented in Figure 5 show that the worst event won’t
exceed a few GW for the total forecast error calculated an
hour in advance. This output is however not sufficient for
the evaluation of tertiary reserve. Indeed in a future with
a large share of renewable energy, it is likely that the
installed capacity of flexibility such as hydro storage or
power 2 gaz (to handle variability of production) will
largely exceed the few GW required for the tertiary
reserve and that most of the time this capacity will be
available to handle the forecast errors and the intra-hourly
variations.
MISCELLANEOUS
Acknowledgments
The French agency ADEME is greatly acknowledged for
founding the French project SmartReserve in which this
work was undertaken.
REFERENCES
[1] Rienecker, M.M., M.J. Suarez, R. Gelaro, R. Todling,
J. Bacmeister, E. Liu, M.G. Bosilovich, S.D.
Schubert, L. Takacs, G.-K. Kim, S. Bloom, J. Chen,
D. Collins, A. Conaty, A. da Silva, et al.
2011, MERRA: NASA's Modern-Era Retrospective
Analysis for Research and Applications. J.
Climate, 24, 3624-3648. Link.