The document discusses solar radiation forecasting research conducted by the MATRAS solar radiation and atmosphere modelling group at the University of Jaen. The group has developed facilities for measuring and forecasting direct normal irradiance (DNI) using sky cameras, ceilometers and numerical weather prediction models. Their research aims to improve short-term DNI nowcasting and forecasting up to 72 hours ahead for applications such as solar power plant operation and electricity market participation. They are also investigating how to optimally balance solar and wind power resources to reduce production variability.

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:

7. 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

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

12. 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

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

25. 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

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:

38. 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

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!!