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

1. David Pozo Vázquez Contributions from: F. Santos-Alamillos, V. Lara- Fanego, S. Quesada-Ruiz, J.A. Ruiz Arias, J. Martínez Valenzuela, M. Laka-Iñurrategi , C. Arbizu-Barrena. SOLAR RADIATION AND ATMOSPHERE MODELLING GROUP (MATRAS) DEPARTMENT OF PHYSICS UNIVERSITY OF JAEN University of Jaén Workshop on Applications of solar forecasting Madrid, June 2013. Future guidelines on solar forecasting: the research view

2. OUTLINE OF THE PRESENTATION Introduction. Solar radiation forecasting: a complex task Recent research activities of the MATRAS group: MATRAS group facilities DNI forecasting based on the WRF model Nowcasting based on sky cameras and ceilometers Balancing between CSP/PV solar plants and wind farms SYNERMET WEATHER SOLUTIONS

3. INMEDIATE OPERATION DISPACTHING FORECASTS DAY AHEAD OPERATIONS MAINTENANCE AND OPERATIONS STRATEGIC PLANNING RESOURCE EVALUATION BANKING, PROYECT DEVELOPMENT OBSERVATIONS MADDEN-JULIAN OSCILLATION NAO ENSO CLIMATE CHANGE MINUTES HOURS DAYS WEEKS MONTHS SEASONS YEARS DECADES TIME DETERMINISTIC WEATHER FORECASTING PROBABILISTIC FORECASTING CLIMATE CHANGE STUDIES Solar power plants times scales vs. weather and climate time scales

4. Nowcasting (0-3hr):  Usually based on both ground based (sky cameras, radiometers) and remote sensing measurements High spatial and temporal resolutions (~minutes) Meant to plant operation management Short 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 market Forecasting (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 observations Satellite Numerical Weather Prediction Model Nowcasting Short-Term Forecasts Forecasting Different time horizon are defined (COST WIRE definitions):

5. Ground based observations Satellite Numerical Weather Prediction Model Nowcasting Short-Term Forecasts Forecasting Forecasting methodologies are really different depending on the forecasting horizon: Ceilometer: Cloud layers heights Satellite (MSG) Total Sky Imager: Cloud trajectory Combinations of different methods may produce better forecasts!!! Numerical weather prediction

6. Nowcasting (0-3hr):  Improvement of cloud tracking algorithms for sky cameras Integration of radiometers+ sky cameras +ceilometers to provide very high spatial resolution (~100 meters) and time resolutions (~minutes) DNI for. over solar power plants Short term forecast (3-6hr):  Improvement of cloud motion algorithms  Integrations of NWP and satellite forecasts Forecasting (6-72hr):  DNI estimation from NWP forecasts  The role of the aerosols  The role of the clouds Most important issue: combination of the different forecast (different time and spatial resolution) in an unified forecasting framework with a time horizon from minutes to days. Ground based observations Satellite Numerical Weather Prediction Model Nowcasting Short-Term Forecasts Forecasting Some current challenges to improve solar radiation forecasts:

8. Univ. of Jaén meteorological station Radiometric station: DNI,GHI, DHI Sky camera TSI-880 Ceilometer Jenoptik CHM 15 k RS radiometer

9. Some data are freely available at: http://matras.ujaen.es Univ. of Jaén meteorological station

10. 1. Network of 25 radiometric stations (GHI) around de UJA campus 2. ~150 m grid spatial resolution 3. Validation of high spatial res. solar radiation forecasts 2km 150 m ….. ….. ….. ….. MATRAS high density radiometric network UJA

11. OPERATIONAL WEATHER FORECAST FOR ANDALUCIA http://matras.ujaen.es - 5 km spatial resolution - 72 hours ahead - Temp, prec, wind and GHI

13. DNI forecasting based on the WRF model NUMERICAL WEATHER PREDICTION (NWP) MODELS Physical-founded weather forecasting models Provides forecasts of weather variables: solar radiation, wind, temp., etc. Only tool able to provide 48 hours ahead forecast Weather 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 research MATRAS: ~ 10 years of research activity in solar radiation forecasting based on WRF

14. DNI estimation methodology NWPs do not provide DNI as a output We proposed a physical approach to derive the DNI based on the WRF outputs and satellite retrievals readily available (Ruiz-Arias et al., 2011) Aerosols Ozone Water vapor Water clouds Ice clouds Satellite retrievals WRF-estimated Broadband cloudless transmittance Clouds transmittance Total broadband atmospheric transmittance Ruiz-Arias, J. A., Pozo-Vázquez, D., Lara-Fanego, V. and Tovar-Pescador, J. (2011), A high-resolution topographic correction method for clear-sky solar irradiance derived with a numerical weather prediction model. Journal of Applied Meteorology and Climatology.

15. DNI and GHI forecast evaluation DNI and GHI WRF forecasts comprehensive evaluation in Southern Spain 1 year of data, hourly temporal resolution, 3 km spatial resolution Independent evaluation: seasons and sky conditions Lara-Fanego, V., Ruiz-Arias, J. A., Pozo-Vazquez, A. D., Santos-Alamillos, F. J. and Tovar-Pescador, J, 2012. Evaluation of the WRF model solar irradiance forecasts in Andalusia (southern Spain). Sol.Energy, doi:10.1016/j.solener.2011.02.014

16. DNI FORECAST EVALUATION RESULTS DEPENDENCE ON THE SKY CONDITIONS AUGUST 2007, CORDOBA , DNI ONE-HOUR RES. WRF MODEL Forecast Horizon RMSE W/M2 (%) MBE W/M2 (%) 1 DAY AHEAD FORECAST 0.4≤kt<0.65 183 (43) 93 (22) 0.65≤kt 84 (11) -22 (-3) 2 DAYS AHEAD FORECAST 0.4≤kt<0.65 189 (45) 96 (22) 0.65≤kt 123 (16) -60 (-8) 3 DAYS AHEAD FORECAST 0.4≤kt<0.65 197 (45) 68 (16) 0.65≤kt 108 (14) -36 (-4) DNI, Cordoba, August 2007, hourly values 8/1/0712:00 8/2/0712:00 8/3/0712:00 8/4/0712:00 8/5/0712:00 8/6/0712:00 8/7/0712:00 8/8/0712:00 8/9/0712:00 8/10/0712:00 8/11/0712:00 8/12/0712:00 8/13/0712:00 8/14/0712:00 8/15/0712:00 0 200 400 600 800 DNI(W/M2 ) Measured values One-day- ahead forecasts Cloudy 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. • Sensitivity study using the REST2 clear-sky solar radiation model. • Uncertainty in DNI only due to AOD • Assumed SZA=30° • The DNI uncertainty depends on the AOD value. • For DNI:  with average AOD values, the uncertainty keeps below 20% The role of the aerosols in DNI forecasting • Aerosol load for DNI forecasting mostly satellite estimates (MODIS): high uncertainties !! • Uncertainties in aerosols have a enormous impact on the reliability of the DNI forecasts, especially for high aerosol loads (common in summer in southern Spain) • 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. The role of the aerosols in DNI forecasting • A method to reduce the uncertainties in aerosol load derived from MODIS has been 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. OUTLINE OF THE PRESENTATION Introduction. Solar radiation forecasting: a complex task MATRAS group presentation and facilities Recent research activities of the MATRAS group: MATRAS group facilities DNI forecasting based on the WRF model Nowcasting based on sky cameras and ceilometers Balancing between CSP/PV solar plants and wind farms SYNERMET WEATHER SOLUTIONS

20. Solar radiation nowcasting with sky-cameras • Meant for very high spatial resolution solar radiation forecasts (usually over solar plants) with time horizon of about 30 minutes • Based on statistical forecast of future cloud positions • Current algorithms (cloud motion): usually poor estimation of the cloud direction movement (cloud tracking) • As a result, forecasting errors increases enormously with the forecasting time horizon

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 images are computed. Cross-Correlation algorithm is applied to obtain the direction of clouds moving towards the sun (marked blue in left figure). • Ladder method: no specific a priori (sector method) are assumed. Reduces forecasting error From: A novel sector-ladder method for cloud tracking to forecast intra-hour DNI, S. Quesada et al, submitted to Solar Energy (2013)

22. 0 200 400 600 800 1000 1200 1 57 113 169 225 281 337 393 449 505 561 617 673 729 785 841 897 953 1009 1065 1121 1177 1233 Solar radiation nowcasting with ceilometers • High clouds (cirrus) may reduce DNI in ~20% from reference clear sky conditions • Very difficult to detect with sky cameras (thin clouds) DNI(W/m2)

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 forecasts based on sky-cameras

24. SUNORACLE PROYECT Some of these developments are being used to obtain an operational DNI forecasting 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

26. Some facts: 1) Solar and wind energy production are conditioned to weather and climate and, 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 needed 4) 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) 26 Balancing concept Solar 5) Low interconnection with other countries (about 6%)

27. Some facts (cont.): Currently in Spain: renewable production balanced with pumped hydro and combined cycle power plant (gas), based on solar and wind power forecasts. This is a inefficient and expensive approach for the future Limit?. Many says about 30% of the installed power (now close in Spain). Depends on solar/wind power forecast accuracy 27 Solar Balancing concept

28. What can be done? 1. Improve forecast of solar and wind power 2. Balancing studies 3. Future: hydrogen storage? Solar 28 Balancing concept

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 be compensate 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 be selected in order to reduce as much as possible the temporal variability of their combined electricity production? this optimal location will be end that the combined production of the wind farms and solar plants be reliable (even baseload) power? 29 Balancing concept

30. INMEDIATE OPERATION DISPACTHING FORECASTS DAY AHEAD MAINTENANCE AND OPERATIONS STRATEGIC PLANNING RESOURCE EVALUATION BANKING PROJECT DEVELOPMENT OBSERVATIONS MADDEN-JULIAN OSCILLATION NAO ENSO CLIMATE CHANGE MINUTES HOURS DAYS-WEEKS MONTHS SEASONS YEARS DECAD TIME DETERMINISTIC WEATHER FORECASTING PROBABILISTIC FORECASTING CLIMATE CHANGE STUDIES ELECTRIC POWER SYSTEM AND RENEWABLE ENERGY WEATHER AND CLIMATE SYSTEMS AND RENEWABLE ENERGY Balancing may occurs at different time scales Balancing time scales

31. Balancing concept 31 1. We have analyzed the balancing between the solar (DNI/GHI) and wind energy resources in southern Spain (Santos-Alamillos et al., 2012) 2. Solar and wind resources obtained based on a WRF model integration: 3 years, 3 km spatial resolution. We included offshore (20 km from the coast) areas.

32. Two steps: 1.Canonical Correlation Analysis (CCA): daily integrated wind and solar (DNI) energy. 2.Solar and wind power times series balancing analysis: evaluation of the power variability of reference wind farms and CSP plants allocated based on the CCA results. METHODOLOGY 32 Reference 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. Solar and wind power times series balancing analysis procedure: Reliability of the power obtained from the interconnection the CSP plants and the wind farms, compared to that obtained based on standalone CSP/wind farms were evaluated based on: 1. Standard deviation of the hourly capacity factor, which is a measure of the reserves necessary for wind energy grid integration 2. Percentage of time at which each value of the hourly capacity factor is available. METHODOLOGY 33

34. First Spring mode CCA Explained variance Solar: 34% Wind: 27% Canonical correl.: 0.66 RESULTS 1.Balancing effect between the solar energy in the whole region and the wind energy in the whole 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. First Spring mode Solar and wind power time series analysis RESULTS 35 CSP Capacity factor ≠ 0: 35% Stad Capacity factor 0.21 Wind Capacity factor ≠ 0: 70% Stad. Dev capacity factor: 0.35 Combined CSP+Wind Capacity factor ≠ 0: 85% Std. Dev. Capc. Factor : 0.17 85% ~close to the availability of fossil fuel- based conventional thermal power plants!!

36. 36 RESULTS Daily mean cycle of the hourly wind (continuous line), CSP (dashed line) and combined CSP+WF (shaded areas) capacity factor values at the selected locations. Winter Spring Summer Autumn Annual. 1. All study periods, specially summer: lag between the CSP plant peak (12:00) and wind farm, about (20:00) h, i.e, a time lag of about 8 hours 2. Overall, the best balancing between the solar and wind energy production is observed during spring. For this season, wind energy production is higher not only during the afternoon (as in summer and autumn) but also during the night (period 00:00 h to 6:00). Balancing studies may help to increase the reliability of aggregated solar and wind electricity yields, then reducing integration costs and favoring a higher penetration!!!

37. Annual analysis (Std Dev): PV = 0.31 Wind = 0.33 PV+Wind = 0.21 Winter analysis (Std Dev): PV = 0.34 Wind = 0.27 PV+Wind = 0.18 PV: dashed line;Wind: shaded area; PV+Wind: bold line Balancing PV-Wind Similar results are found for PV and wind:

39. University of Jaén SynerMet 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 evaluation 3. Balancing studies www.synermet.com

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. David Pozo Vázquez Contributions from: F. Santos-Alamillos, V. Lara- Fanego, S. Quesada-Ruiz, J.A. Ruiz Arias, J. Martínez Valenzuela, M. Laka-Iñurrategi , C. Arbizu-Barrena. SOLAR RADIATION AND ATMOSPHERE MODELLING GROUP (MATRAS) DEPARTMENT OF PHYSICS UNIVERSITY OF JAEN University of Jaén Workshop on Applications of solar forecasting Madrid, June 2013. Future guidelines on solar forecasting: the research view Thank you!!

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