๏ Founded 2003, based on expertise acquired since the early ‘70s
๏ Provides high-end solar resource services to the solar industry:
๏ On-site measurement with advanced weather stations
๏ Satellite-derived solar resource data
๏ Aerosol data and analysis (SOLSUN)
๏ TMY data
๏ Specialized spectral irradiance modeling (SMARTS code)
๏ Circumsolar radiation
๏ Atmospheric attenuation (solar tower plants)
๏ Collaborates with major research institutions (NREL, NCAR, CNRS,
DLR, KISR, Universities…)
๏ Prepares aerosol data for major solar resource data providers
๏ Offers capacity-building workshops on resource assessment, etc.
๏ Involved in the development of solar irradiance standards (ASTM, IEC)
Solar Consulting Services
2
SOLSU N
๏ DNI: Direct Normal Irradiance, measured with pyrheliometer (≈2.5° acceptance radius)—may be different than what is actually usable by CSP!
๏ AOD: Aerosol Optical Depth, measured with sunphotomer/spectrometer
๏ CSI: CircumSolar Irradiance, measured with aureolemeter
๏ CSR: CircumSolar Ratio,
CSR =[CSI(2.5°) + DNI(0.25°)]/DNI(2.5°)
๏ Collaborative SE paper (2014) provides definitions and results: DNI, CSI, CSR, as affected by aerosols and clouds
Know Your Acronyms: DNI, AOD, CSI, CSR
CS
R
DNI
3
๏ New version of the U.S. National Solar Radiation Data Base (NSRDB) expected shortly (Dec. 2014)
๏ Will use the GSIP physics-based satellite model at 4 km resolution, in replacement of the 10-km SUNY/CPR empirical model; TMY/TBY files for each grid cell
๏ Solar Consulting Services (SCS) provides (i) calibrated aerosol data for North America, an important input of GSIP; and (ii) the best possible clear-sky radiation model, REST2
๏ NREL will help CSIRO improve the Australia solar resource maps, with aerosol data from SCS; new products expected in 2015
๏ Solar resource maps and databases were prepared for India in 2010 and 2012, with aerosol data also from SCS
๏ Another update expected for 2015/2016
๏ Getting aerosols right is a challenge over India; major source of uncertainty in DNI!
Solar Resource Developments at NREL
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DNI
๏ Global NASA SSE dataset (coarse 1x1° resolution currently) being revised
๏ Will use GIS-style web server, improved algorithm and 0.5x0.5° resolution, offer smartphone app—availability mid-2015
๏ Further developments (budget permitting): 10-km resolution, to be integrated into NREL tools for solar analysis and decision support
๏ Continued support of IRENA’s Global Atlas
Solar Resource Developments at NASA
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๏ Global Atlas for Renewable Energy portal, hosted by Masdar
๏ Various gridded maps of DNI, GHI, wind, population, infrastructures etc.; data integration possible (GIS-style)
๏ Variety of data providers, spatial resolutions, etc.
๏ Risk of inconsistency between databases from different sources
๏ Mostly for energy policy, preliminary potential studies and education
๏ Solar resource training workshops; first one to be held in Kuwait jointly with KISR, Nov. 16–20: 15 selected participants from MENA
Solar Resource Developments at IRENA
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๏ ESMAP program created by the World Bank to help emerging countries with their renewable energy resource assessments
solar and wind
modeled databases and resource maps
ground measurement campaigns
๏ Limited budget, provided by various donors
๏ Countries already being supported: Pakistan, Zambia, Malawi, Tanzania, Maldives
๏ Candidate countries (in process): Indonesia, Papua New Guinea, Vietnam, Morocco, Tunisia, Niger, East Asia Pacific region
๏ In discussion: Namibia, Somalia
๏ Namibia: First solar resource assessment done in 2012, based on aerosol data from SCS
๏ Showed very high potential for CSP!
Solar Resource Developments at ESMAP (World Bank)
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>2900 kWh/m2
Solar Forecasting Principles
๏ Irradiance forecasting becomes necessary, due to more variable generation
๏ Range of forecast horizons for various analyses: 1 min to 7 days; all methods actively researched: <1 min to 15+ min: sky imagers with cloud motion software
30 min to X hours: cloud motion vectors
Hours to days ahead: NWP modeling
AI, machine learning
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VALUE CHAIN
Solar Forecasting Developments—USA
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๏ Major DOE funding for 2 projects piloted by NCAR (physical forecasting) and IBM (AI, machine learning), period 2013–2015
๏ NCAR project (SunCast) is a public-private-academic partnership including many players (national labs, research groups, consultants, electric utilities, etc.); SCS is involved
Objectives: Irradiance forecasting (DNI and GHI), delivery mechanisms, validation, uncertainty quantification, real-world applications…
Development of an advanced WRF-Solar NWP model with improved cloud and irradiance modeling for high spatial/temporal resolution
Sophisticated data assimilation with AI blending, use of analog ensembles
๏ Many other research groups involved at various universities
• EU FP7 project with 12 participating groups from 7 countries, led by DLR
• 4-year project, 2013–2016; international Advisory Board
• Focused scope: forecast lead times of up to 4 hours (nowcasting)
• Looks into both temporal and spatial issues of DNI nowcasting
• Industry partners for direct applications and onsite validation
• Specific tasks on aerosol forecasting and circumsolar radiation prediction
Solar Forecasting Developments—DNIcast (EU)
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Among the very active groups in solar resource and forecasting: MATRAS (U. Jaen)
• Operational weather forecasts for Andalucia: 5-km spatial resolution, 72 hours ahead: T, precip, wind and GHI
• Solar nowcasting with sky imagers and advanced cloud-tracking algorithm
• DNI and GHI forecasts using WRF in Southern Spain, 3-km res.
• Independent evaluation for all seasons and sky conditions
• Professional forecasting services provided by spinoff SynerMet
Solar Forecasting Developments—Spain
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Solar Forecasting Developments—Japan
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๏ Many research groups involved at various universities
๏ Extremely competitive funding process for a national “Energy Management System”
๏ First phase, 2012–2015, 26 teams in competition; one of them is the TEEDDA group (Tokai U., Chiba U. and U. Tokyo), heavily relying on satellite data and physical models (spectral irradiance!); method to be ultimately applicable globally
๏ Second phase, 2015–2020, for one surviving team
Conclusion
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๏ New solar resource maps, databases and GIS products recently
proposed or in development, but effective “quality” still unknown other
than in broad/vague terms
๏ Lack of sufficient validation or benchmarking of these resource data
products; serious issues (>15% bias) over arid areas still unresolved;
better aerosol databases and products needed
๏ Lack of interest from the industry? Funding difficult!
๏ Strong developments in solar forecasting, many active teams around
the world
๏ Forces a win-win collaboration with the meteorology world; new
powerful products expected in 2016
๏ Day-ahead forecasting of clouds and aerosols still challenging
๏ Advanced ensemble forecasts combined with AI statistical methods
should bring improvements