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

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

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

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):

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

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:

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

Univ. of Jaén meteorological station

Radiometric station: DNI,GHI, DHI

Sky camera TSI-880

Ceilometer Jenoptik CHM 15 k

RS radiometer

Some data are freely available at: http://matras.ujaen.es

Univ. of Jaén meteorological station

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

2 k

m

150 m

…..

…..

…..

…..

MATRAS high density radiometric network

UJA

OPERATIONAL WEATHER FORECAST FOR ANDALUCIA

http://matras.ujaen.es

- 5 km spatial resolution

- 72 hours ahead

- Temp, prec, wind and GHI

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

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

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.

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

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

/07 1

2:0

0

8/2

/07 1

2:0

0

8/3

/07 1

2:0

0

8/4

/07 1

2:0

0

8/5

/07 1

2:0

0

8/6

/07 1

2:0

0

8/7

/07 1

2:0

0

8/8

/07 1

2:0

0

8/9

/07 1

2:0

0

8/1

0/0

7 1

2:0

0

8/1

1/0

7 1

2:0

0

8/1

2/0

7 1

2:0

0

8/1

3/0

7 1

2:0

0

8/1

4/0

7 1

2:0

0

8/1

5/0

7 1

2:0

0

0

200

400

600

800

DN

I (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)

• 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

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

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

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

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)

0

200

400

600

800

1000

1200

1

57

11

3

16

9

22

5

28

1

33

7

39

3

44

9

50

5

56

1

61

7

67

3

72

9

78

5

84

1

89

7

95

3

10

09

10

65

11

21

11

77

12

33

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)

DN

I (W/m

2)

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

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

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

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%)

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

What can be done?

1. Improve forecast of solar and wind power

2. Balancing studies 3. Future: hydrogen storage?

Solar

28

Balancing concept

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

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 DECADES

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

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.

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.

PW CSP=εturbine Asf (DNIεopt− LossHCE− LossSFP)(1− Lossparasitic)

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

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

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

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!!!

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:

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

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

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

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!!