David Pozo VázquezContributions from: F. Santos-Alamillos, V. Lara-Fanego, S. Quesada-Ruiz, J.A. Ruiz Arias, J. MartínezVa...
OUTLINE OF THE PRESENTATIONIntroduction. Solar radiation forecasting: a complex taskRecent research activities of the MATR...
INMEDIATE OPERATIONDISPACTHINGFORECASTS DAY AHEAD OPERATIONSMAINTENANCEAND OPERATIONSSTRATEGICPLANNINGRESOURCE EVALUATIONB...
Nowcasting (0-3hr): Usually based on both ground based (sky cameras, radiometers) and remote sensingmeasurementsHigh spa...
Ground based observationsSatelliteNumerical Weather Prediction ModelNowcasting Short-Term Forecasts ForecastingForecasting...
Nowcasting (0-3hr): Improvement of cloud tracking algorithms for sky camerasIntegration of radiometers+ sky cameras +cei...
OUTLINE OF THE PRESENTATIONIntroduction. Solar radiation forecasting: a complex taskRecent research activities of the MATR...
Univ. of Jaén meteorological stationRadiometric station: DNI,GHI, DHISky camera TSI-880Ceilometer Jenoptik CHM 15 kRS radi...
Some data are freely available at: http://matras.ujaen.esUniv. of Jaén meteorological station
1. Network of 25 radiometric stations (GHI) around de UJA campus2. ~150 m grid spatial resolution3. Validation of high spa...
OPERATIONAL WEATHER FORECAST FOR ANDALUCIAhttp://matras.ujaen.es- 5 km spatial resolution- 72 hours ahead- Temp, prec, win...
OUTLINE OF THE PRESENTATIONIntroduction. Solar radiation forecasting: a complex taskRecent research activities of the MATR...
DNI forecasting based on the WRF modelNUMERICAL WEATHER PREDICTION (NWP) MODELSPhysical-founded weather forecasting models...
DNI estimation methodologyNWPs do not provide DNI as a outputWe proposed a physical approach to derive the DNI based on th...
DNI and GHI forecast evaluationDNI and GHI WRF forecasts comprehensive evaluation in Southern Spain1 year of data, hourly ...
DNI FORECAST EVALUATION RESULTSDEPENDENCE ON THE SKY CONDITIONSAUGUST 2007, CORDOBA , DNI ONE-HOUR RES.WRF MODELForecastHo...
• Sensitivity study using the REST2clear-sky solar radiation model.• Uncertainty in DNI only due to AOD• Assumed SZA=30°• ...
The role of the aerosols in DNI forecasting• A method to reduce the uncertainties in aerosol load derived from MODIS hasbe...
OUTLINE OF THE PRESENTATIONIntroduction. Solar radiation forecasting: a complex taskMATRAS group presentation and faciliti...
Solar radiation nowcasting with sky-cameras• Meant for very high spatial resolution solar radiationforecasts (usually over...
Sector method over Cloud Index (CI)image for Cloud Tracking.PIV orientation is also shown (red line).Ladder method over Cl...
02004006008001000120015711316922528133739344950556161767372978584189795310091065112111771233Solar radiation nowcasting wit...
Solar radiation nowcasting with ceilometers• Ceilometers are able to detect high thin clouds• We are working in the use of...
SUNORACLE PROYECTSome of these developments are being used to obtain an operational DNIforecasting System for CSP plants:•...
OUTLINE OF THE PRESENTATIONIntroduction. Solar radiation forecasting: a complex taskMATRAS group presentation and faciliti...
Some facts:1) Solar and wind energy production are conditioned to weather and climateand, therefore, highly variable in sp...
Some facts (cont.):Currently in Spain: renewable production balanced withpumped hydro and combined cycle power plant (gas)...
What can be done?1. Improve forecast of solar and wind power2. Balancing studies3. Future: hydrogen storage?Solar28Balanci...
Spatial correlation of wind speed and solar radiation (to a lower extend)reduces with the distance.Spatial aggregation ten...
INMEDIATE OPERATIONDISPACTHINGFORECASTS DAY AHEADMAINTENANCEAND OPERATIONS STRATEGICPLANNINGRESOURCE EVALUATIONBANKINGPROJ...
Balancing concept311. We have analyzed the balancing between the solar (DNI/GHI) andwind energy resources in southern Spai...
Two steps:1.Canonical Correlation Analysis (CCA): daily integratedwind and solar (DNI) energy.2.Solar and wind power times...
Solar and wind power times series balancing analysis procedure:Reliability of the power obtained from the interconnection ...
First Spring modeCCAExplained varianceSolar: 34%Wind: 27%Canonical correl.: 0.66RESULTS1.Balancing effect between the sola...
First Spring modeSolar and wind powertime series analysisRESULTS35CSPCapacity factor ≠ 0: 35%Stad Capacity factor 0.21Wind...
36RESULTSDaily mean cycle of the hourly wind (continuous line), CSP (dashed line) and combinedCSP+WF (shaded areas) capaci...
Annual analysis (Std Dev):PV = 0.31Wind = 0.33PV+Wind = 0.21Winter analysis (Std Dev):PV = 0.34Wind = 0.27PV+Wind = 0.18PV...
OUTLINE OF THE PRESENTATIONIntroduction. Solar radiation forecasting: a complex taskMATRAS group presentation and faciliti...
University of JaénSynerMet Weather Solutions:• Spin-off company from MATRAS group UJAEN• Provide meteorological services r...
SynerMet DNI forecasting system: Based on the WRF model Up to 180 h forecasting horizon Up to 10 time resolution Aeros...
David Pozo VázquezContributions from: F. Santos-Alamillos, V. Lara-Fanego, S. Quesada-Ruiz, J.A. Ruiz Arias, J. MartínezVa...
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Future guidelines on solar forecasting the research view - David Pozo (University of Jaen)

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Future guidelines on solar forecasting the research view - David Pozo (University of Jaen)

  1. 1. David Pozo VázquezContributions from: F. Santos-Alamillos, V. Lara-Fanego, S. Quesada-Ruiz, J.A. Ruiz Arias, J. MartínezValenzuela, M. Laka-Iñurrategi , C. Arbizu-Barrena.SOLAR RADIATION ANDATMOSPHERE MODELLING GROUP (MATRAS)DEPARTMENT OF PHYSICSUNIVERSITY OF JAENUniversity of JaénWorkshop on Applications of solar forecastingMadrid, June 2013.Future guidelines on solar forecasting: the research view
  2. 2. OUTLINE OF THE PRESENTATIONIntroduction. Solar radiation forecasting: a complex taskRecent research activities of the MATRAS group:MATRAS group facilitiesDNI forecasting based on the WRF modelNowcasting based on sky cameras and ceilometersBalancing between CSP/PV solar plants and wind farmsSYNERMET WEATHER SOLUTIONS
  3. 3. INMEDIATE OPERATIONDISPACTHINGFORECASTS DAY AHEAD OPERATIONSMAINTENANCEAND OPERATIONSSTRATEGICPLANNINGRESOURCE EVALUATIONBANKING, PROYECT DEVELOPMENTOBSERVATIONS MADDEN-JULIAN OSCILLATION NAO ENSO CLIMATE CHANGEMINUTES HOURS DAYS WEEKS MONTHS SEASONS YEARS DECADESTIMEDETERMINISTICWEATHER FORECASTINGPROBABILISTIC FORECASTING CLIMATE CHANGE STUDIESSolar power plants times scales vs. weather and climate time scales
  4. 4. Nowcasting (0-3hr): Usually based on both ground based (sky cameras, radiometers) and remote sensingmeasurementsHigh spatial and temporal resolutions (~minutes)Meant to plant operation managementShort term forecast (3-6hr): Usually based on Numerical Weather Prediction Models (NWP) Up to ~km or spatial resolution and <1 hour temporal resol.Mean to plant operation management and participation in the electricity marketForecasting (6-72hr): Based on Numerical Weather Prediction Models (NWP) Up to ~km or spatial resolution and <1 hour temporal resol.Meant for participation in the electricity market and grid integration.Limits are nor really well defined !!Ground based observationsSatelliteNumerical Weather Prediction ModelNowcasting Short-Term Forecasts ForecastingDifferent time horizon are defined (COST WIRE definitions):
  5. 5. Ground based observationsSatelliteNumerical Weather Prediction ModelNowcasting Short-Term Forecasts ForecastingForecasting methodologies are really different depending on the forecasting horizon:Ceilometer:Cloud layers heightsSatellite (MSG)Total Sky Imager:Cloud trajectoryCombinations of differentmethods may producebetter forecasts!!!Numericalweatherprediction
  6. 6. Nowcasting (0-3hr): Improvement of cloud tracking algorithms for sky camerasIntegration of radiometers+ sky cameras +ceilometers to provide very highspatial resolution (~100 meters) and time resolutions (~minutes) DNI for. over solar power plantsShort term forecast (3-6hr): Improvement of cloud motion algorithms Integrations of NWP and satellite forecastsForecasting (6-72hr): DNI estimation from NWP forecasts The role of the aerosols The role of the cloudsMost important issue: combination of the different forecast (different time and spatialresolution) in an unified forecasting framework with a time horizon from minutes to days.Ground based observationsSatelliteNumerical Weather Prediction ModelNowcasting Short-Term Forecasts ForecastingSome current challenges to improve solar radiation forecasts:
  7. 7. OUTLINE OF THE PRESENTATIONIntroduction. Solar radiation forecasting: a complex taskRecent research activities of the MATRAS group:MATRAS group facilitiesDNI forecasting based on the WRF modelNowcasting based on sky cameras and ceilometersBalancing between CSP/PV solar plants and wind farmsSYNERMET WEATHER SOLUTIONS
  8. 8. Univ. of Jaén meteorological stationRadiometric station: DNI,GHI, DHISky camera TSI-880Ceilometer Jenoptik CHM 15 kRS radiometer
  9. 9. Some data are freely available at: http://matras.ujaen.esUniv. of Jaén meteorological station
  10. 10. 1. Network of 25 radiometric stations (GHI) around de UJA campus2. ~150 m grid spatial resolution3. Validation of high spatial res. solar radiation forecasts2km150 m…..…..…..…..MATRAS high density radiometric networkUJA
  11. 11. OPERATIONAL WEATHER FORECAST FOR ANDALUCIAhttp://matras.ujaen.es- 5 km spatial resolution- 72 hours ahead- Temp, prec, wind and GHI
  12. 12. OUTLINE OF THE PRESENTATIONIntroduction. Solar radiation forecasting: a complex taskRecent research activities of the MATRAS group:MATRAS group facilitiesDNI forecasting based on the WRF modelNowcasting based on sky cameras and ceilometersBalancing between CSP/PV solar plants and wind farmsSYNERMET WEATHER SOLUTIONS
  13. 13. DNI forecasting based on the WRF modelNUMERICAL WEATHER PREDICTION (NWP) MODELSPhysical-founded weather forecasting modelsProvides forecasts of weather variables: solar radiation, wind, temp., etc.Only tool able to provide 48 hours ahead forecastWeather and research forecasting (WRF) model:• Widely used around the world for renew. aplications.• Used both for weather operational forecasting and research• Wide range of physical parameterization: tuning for a specific areas or researchMATRAS: ~ 10 years of research activity in solar radiation forecasting based on WRF
  14. 14. DNI estimation methodologyNWPs do not provide DNI as a outputWe proposed a physical approach to derive the DNI based on the WRF outputsand satellite retrievals readily available (Ruiz-Arias et al., 2011)Aerosols Ozone Water vapor Water clouds Ice cloudsSatellite retrievals WRF-estimatedBroadband cloudless transmittance Clouds transmittanceTotal broadband atmospheric transmittanceRuiz-Arias, J. A., Pozo-Vázquez, D., Lara-Fanego, V. and Tovar-Pescador, J. (2011), A high-resolution topographiccorrection method for clear-sky solar irradiance derived with a numerical weather prediction model. Journal of AppliedMeteorology and Climatology.
  15. 15. DNI and GHI forecast evaluationDNI and GHI WRF forecasts comprehensive evaluation in Southern Spain1 year of data, hourly temporal resolution, 3 km spatial resolutionIndependent evaluation: seasons and sky conditionsLara-Fanego, V., Ruiz-Arias, J. A., Pozo-Vazquez, A. D., Santos-Alamillos, F. J. and Tovar-Pescador, J, 2012. Evaluation of theWRF model solar irradiance forecasts in Andalusia (southern Spain). Sol.Energy, doi:10.1016/j.solener.2011.02.014
  16. 16. DNI FORECAST EVALUATION RESULTSDEPENDENCE ON THE SKY CONDITIONSAUGUST 2007, CORDOBA , DNI ONE-HOUR RES.WRF MODELForecastHorizonRMSEW/M2 (%)MBEW/M2 (%)1 DAY AHEAD FORECAST0.4≤kt<0.65 183 (43) 93 (22)0.65≤kt 84 (11) -22 (-3)2 DAYS AHEAD FORECAST0.4≤kt<0.65 189 (45) 96 (22)0.65≤kt 123 (16) -60 (-8)3 DAYS AHEAD FORECAST0.4≤kt<0.65 197 (45) 68 (16)0.65≤kt 108 (14) -36 (-4)DNI, Cordoba, August 2007, hourly values8/1/0712:008/2/0712:008/3/0712:008/4/0712:008/5/0712:008/6/0712:008/7/0712:008/8/0712:008/9/0712:008/10/0712:008/11/0712:008/12/0712:008/13/0712:008/14/0712:008/15/0712:000200400600800DNI(W/M2)Measured valuesOne-day- ahead forecastsCloudy conditions: similar errors than for GHI forecast (RMSE ~45%)Clear-sky-conditions: errors about 2 times higher than for GHI forecasts (RMSE ~5% versus ~11%)Negative bias for clear conditions (tuning of the methodology to derive DNI)
  17. 17. • Sensitivity study using the REST2clear-sky solar radiation model.• Uncertainty in DNI only due to AOD• Assumed SZA=30°• The DNI uncertainty depends onthe AOD value.• For DNI: with average AOD values, theuncertainty keeps below 20%The role of the aerosols in DNI forecasting• Aerosol load for DNI forecasting mostly satellite estimates (MODIS): highuncertainties !!• Uncertainties in aerosols have a enormous impact on the reliability of the DNIforecasts, especially for high aerosol loads (common in summer in southernSpain)• Induced errors in the DNI may reach 30% for high AOD.(From Ruiz-Arias et al. 2013).DNI forecasting based on the WRF model
  18. 18. The role of the aerosols in DNI forecasting• A method to reduce the uncertainties in aerosol load derived from MODIS hasbeen developed (bias reduction based on AERONET stations comparison)• The method reduces the aerosol uncertainties error induced in DNI to ~ 5%.• Blue-shaded region: original L3M AOD uncertainty (as 1-std-dev)• Orange-shaded region: analysed AOD uncertainty (as 1-std-dev)• The analysed AOD has reduced bias and uncertainty for the typical AOD values(From Ruiz-Arias et al. 2013).DNI forecasting based on the WRF model
  19. 19. OUTLINE OF THE PRESENTATIONIntroduction. Solar radiation forecasting: a complex taskMATRAS group presentation and facilitiesRecent research activities of the MATRAS group:MATRAS group facilitiesDNI forecasting based on the WRF modelNowcasting based on sky cameras and ceilometersBalancing between CSP/PV solar plants and wind farmsSYNERMET WEATHER SOLUTIONS
  20. 20. Solar radiation nowcasting with sky-cameras• Meant for very high spatial resolution solar radiationforecasts (usually over solar plants) with time horizon ofabout 30 minutes• Based on statistical forecast of future cloud positions• Current algorithms (cloud motion): usually poor estimationof the cloud direction movement (cloud tracking)• As a result, forecasting errors increases enormously withthe forecasting time horizon
  21. 21. Sector method over Cloud Index (CI)image for Cloud Tracking.PIV orientation is also shown (red line).Ladder method over Cloud Index (CI)image for DNI Forecasting.Solar radiation nowcasting with sky-cameras• A new cloud tracking algorithm has been recently proposed: ladder• Sector method: cloud Fraction Change between each two consecutive imagesare computed. Cross-Correlation algorithm is applied to obtain the direction ofclouds moving towards the sun (marked blue in left figure).• Ladder method: no specific a priori (sector method) are assumed. Reducesforecasting errorFrom: A novel sector-ladder method for cloud tracking to forecast intra-hour DNI,S. Quesada et al, submitted to Solar Energy (2013)
  22. 22. 02004006008001000120015711316922528133739344950556161767372978584189795310091065112111771233Solar radiation nowcasting with ceilometers• High clouds (cirrus) may reduce DNI in ~20% from reference clear skyconditions• Very difficult to detect with sky cameras (thin clouds)DNI(W/m2)
  23. 23. Solar radiation nowcasting with ceilometers• Ceilometers are able to detect high thin clouds• We are working in the use of ceilometers to improve DNI forecastsbased on sky-cameras
  24. 24. SUNORACLE PROYECTSome of these developments are being used to obtain an operational DNIforecasting System for CSP plants:• Time horizon: 48 hours• Spatial resolution: variable from 100 m to 1 km• Time resolution: variable from 1 minutes to 15 minutes
  25. 25. OUTLINE OF THE PRESENTATIONIntroduction. Solar radiation forecasting: a complex taskMATRAS group presentation and facilitiesRecent research activities of the MATRAS group:MATRAS group facilitiesDNI forecasting based on the WRF modelNowcasting based on sky cameras and ceilometersBalancing between CSP/PV solar plantsand wind farmsSYNERMET WEATHER SOLUTIONS
  26. 26. Some facts:1) Solar and wind energy production are conditioned to weather and climateand, therefore, highly variable in space and time.2) Intermittent resources makes renewable electricity production fluctuating:therefore not reliable and expensive (..?)3) Storage and balancing with other energy sources are needed4) Today in Spain renewable power installed capacity:- Wind: 21 GWe (about 20% of the total)- Solar (PV+STPP): ~6 GWe (about 8% of the total)26Balancing concept Solar5) Low interconnection with other countries (about 6%)
  27. 27. Some facts (cont.):Currently in Spain: renewable production balanced withpumped hydro and combined cycle power plant (gas),based on solar and wind power forecasts.This is a inefficient and expensive approach for the futureLimit?. Many says about 30% of the installed power (nowclose in Spain). Depends on solar/wind power forecastaccuracy27SolarBalancing concept
  28. 28. What can be done?1. Improve forecast of solar and wind power2. Balancing studies3. Future: hydrogen storage?Solar28Balancing concept
  29. 29. Spatial correlation of wind speed and solar radiation (to a lower extend)reduces with the distance.Spatial aggregation tends to reduce fluctuations in the renewable production,but…Given a study region (power grid)……can above-normal wind speed at certain times and locations can becompensate with below-normal solar radiation at other locations?(negative spatial correlation between solar and wind resources).can be the location of the solar plants and wind farm optimally beselected in order to reduce as much as possible the temporalvariability of their combined electricity production?this optimal location will be end that the combined production of the windfarms and solar plants be reliable (even baseload) power?29Balancing concept
  30. 30. INMEDIATE OPERATIONDISPACTHINGFORECASTS DAY AHEADMAINTENANCEAND OPERATIONS STRATEGICPLANNINGRESOURCE EVALUATIONBANKINGPROJECT DEVELOPMENTOBSERVATIONS MADDEN-JULIAN OSCILLATION NAO ENSO CLIMATE CHANGEMINUTES HOURS DAYS-WEEKS MONTHS SEASONS YEARS DECADTIMEDETERMINISTICWEATHER FORECASTINGPROBABILISTIC FORECASTING CLIMATE CHANGE STUDIESELECTRIC POWER SYSTEM AND RENEWABLE ENERGYWEATHER AND CLIMATE SYSTEMS AND RENEWABLE ENERGYBalancing may occurs at different time scalesBalancing time scales
  31. 31. Balancing concept311. We have analyzed the balancing between the solar (DNI/GHI) andwind energy resources in southern Spain (Santos-Alamillos et al.,2012)2. Solar and wind resources obtained based on a WRF modelintegration: 3 years, 3 km spatial resolution. We included offshore(20 km from the coast) areas.
  32. 32. Two steps:1.Canonical Correlation Analysis (CCA): daily integratedwind and solar (DNI) energy.2.Solar and wind power times series balancing analysis:evaluation of the power variability of reference wind farmsand CSP plants allocated based on the CCA results.METHODOLOGY32Reference wind turbine:• Onshore VESTAS V90-2.0 MW• Offshore VESTAS V90-3.0 MW• Hub height 80 m.a.g.l.Reference CSP plant• 100 MWe parabolic trough plant (model Zhang and Smith 2008)• No storage.PWCSP=εturbine Asf (DNIεopt− LossHCE− LossSFP)(1− Lossparasitic)
  33. 33. Solar and wind power times series balancing analysis procedure:Reliability of the power obtained from the interconnection the CSP plantsand the wind farms, compared to that obtained based on standaloneCSP/wind farms were evaluated based on:1. Standard deviation of the hourly capacity factor, which is a measure ofthe reserves necessary for wind energy grid integration2. Percentage of time at which each value of the hourly capacity factor isavailable.METHODOLOGY33
  34. 34. First Spring modeCCAExplained varianceSolar: 34%Wind: 27%Canonical correl.: 0.66RESULTS1.Balancing effect between the solar energy in the whole region and the wind energy in thewhole region except the western part of the strait of Gibraltar.2.Synoptic patterns:• Positive solar and negative wind anomalies: north-easterly flow• Negative solar and positive wind anomalies: low pressure over France, frontal activity,southwesterly winds enhanced at the Cazorla mountains area.Solar (34%) Wind (27%)34
  35. 35. First Spring modeSolar and wind powertime series analysisRESULTS35CSPCapacity factor ≠ 0: 35%Stad Capacity factor 0.21WindCapacity factor ≠ 0: 70%Stad. Dev capacity factor: 0.35Combined CSP+WindCapacity factor ≠ 0: 85%Std. Dev. Capc. Factor : 0.1785% ~close to the availability of fossil fuel-based conventional thermal power plants!!
  36. 36. 36RESULTSDaily mean cycle of the hourly wind (continuous line), CSP (dashed line) and combinedCSP+WF (shaded areas) capacity factor values at the selected locations.WinterSpringSummerAutumnAnnual.1. All study periods, specially summer: lagbetween the CSP plant peak (12:00) andwind farm, about (20:00) h, i.e, a time lag ofabout 8 hours2. Overall, the best balancing between thesolar and wind energy production isobserved during spring. For this season,wind energy production is higher not onlyduring the afternoon (as in summer andautumn) but also during the night (period00:00 h to 6:00).Balancing studies may help toincrease the reliability ofaggregated solar and windelectricity yields, then reducingintegration costs and favoring ahigher penetration!!!
  37. 37. Annual analysis (Std Dev):PV = 0.31Wind = 0.33PV+Wind = 0.21Winter analysis (Std Dev):PV = 0.34Wind = 0.27PV+Wind = 0.18PV: dashed line;Wind: shaded area; PV+Wind: bold lineBalancing PV-WindSimilar results are found for PV and wind:
  38. 38. OUTLINE OF THE PRESENTATIONIntroduction. Solar radiation forecasting: a complex taskMATRAS group presentation and facilitiesRecent research activities of the MATRAS group:MATRAS group facilitiesDNI forecasting based on the WRF modelNowcasting based on sky cameras and ceilometersBalancing between CSP/PV solar plants and wind farmsSYNERMET WEATHER SOLUTIONS
  39. 39. University of JaénSynerMet Weather Solutions:• Spin-off company from MATRAS group UJAEN• Provide meteorological services related to renewable energy:1. Solar radiation forecasting (DNI / GHI)2. Solar and wind resources evaluation3. Balancing studieswww.synermet.com
  40. 40. SynerMet DNI forecasting system: Based on the WRF model Up to 180 h forecasting horizon Up to 10 time resolution Aerosol measures assimilated Cloud data assimilation system(under development) MOS postprocessing
  41. 41. David Pozo VázquezContributions from: F. Santos-Alamillos, V. Lara-Fanego, S. Quesada-Ruiz, J.A. Ruiz Arias, J. MartínezValenzuela, M. Laka-Iñurrategi , C. Arbizu-Barrena.SOLAR RADIATION ANDATMOSPHERE MODELLING GROUP (MATRAS)DEPARTMENT OF PHYSICSUNIVERSITY OF JAENUniversity of JaénWorkshop on Applications of solar forecastingMadrid, June 2013.Future guidelines on solar forecasting: the research viewThank you!!

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