Advances in Solar Radiation Prediction for Concentrating
Solar Thermal Technology
Dr. Lourdes Ramírez Renewable Energy Division
CIEMAT (Spain)
SFERA Networking 7th SFERA Summer School
Hosted by PSA-CIEMAT Almeria, 9-10 June 2016
Innovative R+D subjects for
Concentrating Solar Systems
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Advances in Solar Radiation Prediction for Concentrating Solar Thermal Technology
1. Introduction 2. Main forecasting techniques 3. Forecasting systems for CST power plants 4. Solar radiation forecasting base line 5. DNI and CST power plants forecasting main challenges 6. Conclusions
Content
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• The accurate solar energy forecasting
produced by a solar thermal power plant improve the profitability at different moments of the project.
• Direct normal irradiance (DNI) is the main input for energy production and then, DNI forecasting is the base for energy forecasting at CST power plants
Why and What?
1. Introduction
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1. Introduction
How?
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1. Introduction
When the installed power arise a significance impact in the electricity grids, the accurate DNI forecasting starts having an important role. The role is important in the both sides of the electricity production: • Utility side related to:
• the need to predict the total electricity in the network (up to 24 h) • the impact of the intermitency in the grid (intra-daily)
• Promoter side related to: • technical shutdowns (long-term) • plant operation (up to 24 h - medium-term) • with the dispatchability (intra-daily)
When? Who?
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1. Introduction
• is the possibility of modulating the energy dumped to the grid, • is a requested capability that aims to increase the price of the dumped
electricity. interest in the development of methods for energy price forecasting Improvements in solar forecasting can help for a better consideration of the energy provided by CST power plants
Dispatchability
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Advances in Solar Radiation Prediction for Concentrating Solar Thermal Technology
1. Introduction 2. Main forecasting techniques 3. Forecasting systems for CST power plants 4. Solar radiation forecasting base line 5. DNI and CST power plants forecasting main challenges 6. Conclusions
Content
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2. Main forecasting techniques
different forecasting’s horizon and with different spatial resolutions
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2. Main forecasting techniques
different project moments and different time resolutions
Project moment Forecasting role
Typical forecasting’s names
Time period ahead Main techniques Typical time
resolution
Before to build the plant • Project profitability Long-term Years
Statistical models
Months
Power plant exploitation
• Maintenance planning Medium-term Months Days
• Maintenance planning
10 days
Days Numerical weather prediction models (NWPM)
Half days
• Operation • Electricity market
3 days Hourly
Nowcasting
(4h-6h) 30 min
(0-6h min) Statistical models 15 min
(0-120 min) Satellite images 15 min
(0-60 min) Sky cameras 1 min
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2. Main forecasting techniques
Used and developed for weather forecasting purposes In a very simple way, • they have to know given initial conditions and then, • the differential equations describing the evolution of the atmosphere are
resolved In order to test their behavior these models can be used as well for “predicting the past” => reanalysis; and eval the forecasted horizons using ground measurements. Ground measurements can also be: • assimilated by the model or • taken into account in a post-process treatment
Numerical weather prediction models
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2. Main forecasting techniques
Numerical weather prediction models
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2. Main forecasting techniques
Relevant improvement when using new parametrizations (Ruiz-Arias et al., 2014) This new parametrization of the aerosol optical properties contributes to remove seasonal biases in the predicted GHI and also DNI.
Numerical weather prediction models
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2. Main forecasting techniques
The only pure forecasting technique The only way to obtain reliable information about the rest of meteorological variables needed for the system simulation
Numerical weather prediction models
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2. Main forecasting techniques
IMAGE DERIVED SOLAR FORECASTING
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2. Main forecasting techniques
IMAGE DERIVED SOLAR FORECASTING
In the case of satellite-derived models, • detectors are placed at geostationary satellite • faced to the earth surface, • seen an Earth part typically of 60 degrees around the sub-satellite point • with a resolution in a range from 1 up to 10 km. • Satellite images time frequency use to be between 15-30 minutes.
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2. Main forecasting techniques
IMAGE DERIVED SOLAR FORECASTING
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2. Main forecasting techniques
IMAGE DERIVED SOLAR FORECASTING
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2. Main forecasting techniques
IMAGE DERIVED SOLAR FORECASTING
In the case of sky-cameras, • detectors are placed at ground level faced to the sky, • seen the sky equivalent to 2 km around the camera locations • with a resolution between 1-10 m per grid point. • Sky images time frequency use to be configured between 1-60 seconds.
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2. Main forecasting techniques
IMAGE DERIVED SOLAR FORECASTING
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2. Main forecasting techniques
The first set of techniques applied to the solar radiation forecasting (Jensenius, 1981) MOS to NWPM.
STATISTICAL FORECASTING
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2. Main forecasting techniques
• No exogenous variables:
• Time series models • Machine learning techniques
• Exogenous variables: • Output from NWPM • Data related to another position • MOS • Machine learning techniques
STATISTICAL FORECASTING
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Advances in Solar Radiation Prediction for Concentrating Solar Thermal Technology
1. Introduction 2. Main forecasting techniques 3. Forecasting systems for CST power plants 4. Solar radiation forecasting base line 5. DNI and CST power plants forecasting main challenges 6. Conclusions
Content
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3. Forecasting systems for CST power plants
Typical prediction scheme
NWPM
Global Forecast System
Statistical approach
Statistical approach
Daily predictions
Intr-daily predictions
Meteorological prediction
Historical ground measures
Sky images
Satellite images
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3. Forecasting systems for CST power plants
Storage: can works as a peaks absorber
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Advances in Solar Radiation Prediction for Concentrating Solar Thermal Technology
1. Introduction 2. Main forecasting techniques 3. Forecasting systems for CST power plants 4. Solar radiation forecasting base line 5. DNI and CST power plants forecasting 6. Conclusions
Content
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4. Solar radiation forecasting baseline
Law EW, Prasad AA, Kay M, Taylor RA. Direct normal irradiance forecasting and its application to concentrated solar thermal output forecasting – A review. Solar Energy 2014;108:287–307. doi:10.1016/j.solener.2014.07.008.
100 Watts 1000 Watts
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Advances in Solar Radiation Prediction for Concentrating Solar Thermal Technology
1. Introduction 2. Main forecasting techniques 3. Forecasting systems for CST power plants 4. Solar radiation forecasting base line 5. DNI and and CST power plants forecasting main challenges 6. Conclusions
Content
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5. DNI and and CST power plants forecasting main challenges
Long-term forecasting: assessment and evaluations
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5. DNI and and CST power plants forecasting main challenges
Long-term forecasting: balancing with wind parks
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5. DNI and and CST power plants forecasting main challenges
Nowcasting: dispatchability
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5. DNI and and CST power plants forecasting main challenges
DNICast EU project
Nowcasting: harmonization
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5. DNI and and CST power plants forecasting main challenges
Nowcasting: spatial resolution at CST power plant level
DNICast EU project
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Advances in Solar Radiation Prediction for Concentrating Solar Thermal Technology
1. Introduction 2. Main forecasting techniques 3. Forecasting systems for CST power plants 4. Solar radiation forecasting base line 5. DNI and and CST power plants forecasting main challenges 6. Conclusions
Content
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6. Conclusions
1. Objective: improve project’s profitability, matching generation to expectations or energy load peaks
2. Solar energy output forecasting sensitivity is >95% related to DNI forecasting.
3. New aerosols parametrizations on for DNI from NWPM. 4. A forecasting system: different tools varying in temporal horizon,
temporal resolution, spatial resolution. AND LOCAL MEASUREMENTS. 5. Mean forecasting errors
– 100 wats < 10 min – 1000 wats 1-3 days
Baseline
CHANCE FOR IMPROVEMENTS!!
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6. Conclusions
1. Long-term – Incorporate ICCP scenarios – Convince decision makers on
optimal site selection for RE balancing
2. Medium-term (1-3 days) – Days classification
3. Nowcasting – Patterns identification and
forecasting – Signals and patterns situations
harmonization – Improvement od spatial and
thermal resolution at CST plant level
Main challenges
