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CSP Training series : solar resource assessment 1/2

CSP Training series : solar resource assessment 1/2






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    CSP Training series : solar resource assessment 1/2 CSP Training series : solar resource assessment 1/2 Presentation Transcript

    • Christian A. Gueymard
    • Overview • DNI: Definitions and general considerations • DNI measurement: instruments, calibration, maintenance, spectral corrections and accuracies • DNI prediction: various types of radiative models • Sources of modeled DNI data for the world: Why do they differ so much, what accuracy can we expect? • Short-term, interannual and long-term variability in DNI • Frequency distributions as a function of climate • Can DNI data from TMY time series be trusted? • Resource assessment for large projects: local measurements are important!
    • DNI: Definition and General Considerations • The “fuel” of CSP/CPV plants is DNI: Direct Normal Irradiance. Two possible definitions for DNI: • Irradiance received from the sun’s disc only (theoretical def.) • Direct irradiance from the sun’s disc plus some circumsolar diffuse irradiance within a cone of 2.5° around the sun center (practical def.) • DNI is what 2-axis tracking concentrators can utilize fully; 1-axis trackers (e.g., parabolic troughs) get somewhat less. • CSP systems use the complete solar spectrum, so only the broadband DNI is of interest. • CPV systems use solar cells that have pronounced spectral sensitivity. DNI is still what matters most, but spectral effects also come into play. • Specialized topics (e.g., spectral effects and circumsolar radiation) will be covered in the next webinar…
    • DNI Measurement (1) • DNI can be measured directly or indirectly • Direct measurements • Active cavity (reference “lab” instrument; not for continuous monitoring). • Thermopile pyrheliometer (robust field instrument, mounted on a tracker; most common models: Eppley NIP and Kipp & Zonen CHP1). • Rotating shadowband pyranometer [RSP] (field instrument, fast response, does not need tracker, nor as much electricity or maintenance as thermopiles; but needs corrections for temperature, cosine errors and spectral sensitivity). CAVITY THERMOPILES SILICON SENSORS NIP Active cavity NIP CHP1 RSR2 RSP (Eppley) (Eppley) (Kipp & Zonen) (Irradiance Inc.) (Solar Millenium)
    • DNI Measurement (2) • Indirect measurement Consists in using one pyranometer for Global Horizontal Irradiance (GHI) and another one (with fixed shadowring or tracking shade-disc) for Diffuse (DIF), and applying the fundamental closure equation Instantaneous DNI = (GHI – DIF)/cosZ [Z: Zenith angle] This method was very common in the past, and may still be in some countries, but has typically much higher uncertainties than direct measurements, depending on the type of pyranometer and sun shade. Ref.: C.A. Gueymard & D.R. Myers, Solar Energy 83, 171-185, 2009. Pyranometers PSP + shadowband 8-48 & PSP + shadeball CM22 + shadeball (Eppley) (Eppley) (Eppley) (Kipp & Zonen)
    • DNI Measurement (3) • Calibration—Modern methods of calibration of pyrheliometers and pyranometers against the WRR are explained in: C.A. Gueymard & D.R. Myers, Solar Radiation Measurement: Progress in Radiometry for Improved Modeling. In Modeling Solar Radiation at the Earth Surface, Springer 2008. • Performance issues—The NIP appears sensitive to a small daytime bias and/or thermal effects, which make its response vary during the day, with higher relative errors early AM and late PM. J. Michalsky et al., An extensive comparison of commercial pyrheliometers under a wide range of routine observing conditions. Submitted to J. Atmos. Ocean. Tech., 2010. DNI measured with RSP must be corrected for various shortcomings • F. Vignola, Removing systematic errors from Rotating Shadowband Pyranometer data. ASES Conf., 2006. • N. Geuder et al., Validation of direct beam irradiance measurements from rotating shadowband pyranometers in a different climate. SolarPACES Conf., 2010. • Measurement uncertainties—Typically, 2 to 5% uncertainty under field conditions, if well WRR maintained. Significantly larger uncertainty for indirect measurement with conventional setup (shadowband for diffuse and GHI uncorrected for thermal imbalance).
    • DNI Measurement (4) • Sources of measured data Unfortunately, not many sites measuring DNI with high-quality instrumentation provide publicly available data. The only international network of research-class stations is that of BSRN, http://www.bsrn.awi.de/en/home/bsrn/ In the U.S., only 4 high-quality stations have accumulated more than 25 years of DNI data: Hermiston, OR (1979); Eugene, OR (1977); Burns, OR (1979); and Golden, CO (1981). Data from national weather services are usually not in the public domain.
    • DNI Prediction with Radiative Models (1) • Since DNI measurements are much too scarce on a global scale, modeling is necessary! • Various types of radiative models exist. See general typology in: C.A. Gueymard & D.R. Myers, Validation and Ranking Methodologies for Solar Radiation Models. In Modeling Solar Radiation at the Earth Surface, Springer 2008. This reference also proposes various quality criteria, validation methods, and performance/ranking metrics. • To calculate irradiances, atmospheric scientists use radiative transfer models that evaluate fluxes wavelength by wavelength. These are too cumbersome for general use; thus, only “engineering-type” broadband models are used in practice, unless specific spectral effects (on PV/CPV) need be evaluated [next webinar…] • Some simple models calculate DNI with a daily or monthly time step. This is good only for rough design purposes. For serious resource assessment, hourly or sub-hourly data are necessary.
    • DNI Prediction with Radiative Models (2) • To obtain realistic DNI time series with hourly or sub-hourly time steps, two possible methods are currently used. 1. Physical method 2. Semi-physical method
    • DNI Prediction with Radiative Models (3) • Examples of semi-physical or physical models: • MAC3 (Canada) and METSTAT (USA), using hourly human cloud obs (now discontinued in North America) • GSIP (USA), using satellite cloud retrievals (in progress) • Examples of semi-physical/empirical models: • Perez/SUNY (USA) • DLR (Germany) • 3Tier (USA) • HelioSat (Europe) • A weak point of semi-physical models is the empirical derivation of DNI from GHI. 50 years after the pioneering work of Liu & Jordan, there is still no accurate or universal method to do this. Ref.: C.A. Gueymard, Progress in direct irradiance modeling and validation. ASES Conf., 2010.
    • DNI Prediction with Radiative Models (4) • Accuracy of modeled clear-sky DNI The REST2 model’s performance is currently unsurpassed. Assuming good input data is available, it can predict DNI within the uncertainty of high-quality irradiance measurements. • C.A. Gueymard, Solar Energy 82, 272–285, 2008. • C.A. Gueymard, Progress in direct irradiance modeling and validation. ASES Conf., 2010. 1200 REST2 Model Predictions 100 DNI Cumulative Air Mass 1.50 ± 0.05 Frequency Distribution 1100 90 Measured 1000 80 Bird Cumulated Frequency (%) Ineichen DNI Predicted (W/m ) 2 900 70 METSTAT REST2 60 Yang 800 50 700 40 600 Bondville 30 Golden 500 Mauna Loa 20 ±5% Solar Village 400 1-Min Clear-Sky DNI 10 Mauna Loa, HI (2008) 300 0 300 400 500 600 700 800 900 1000 1100 1200 800 900 1000 1100 1200 2 2 DNI Measured (W/m ) Irradiance (W/m )
    • DNI Prediction with Radiative Models (5) To obtain accurate modeled DNI predictions, the chosen model must be as physical as possible. Beware of too conveniently simple algorithms. Example—ASHRAE (1972) model: DNI = A exp(-B/cosZ) [A and B: monthly constants] vs. REST2 (2008)
    • DNI Prediction with Radiative Models (6) • In validation tests, do not dismiss apparent “outliers” too fast: they might reveal a problem in a part of the algorithm. Revisiting and expanding the validation data is how the “Eugene syndrome” affecting the Perez/SUNY model was eventually discovered and explained. C.A. Gueymard and S.M. Wilcox, Spatial and temporal variability in the solar resource: Assessing the value of short-term measurements at potential solar power plant sites. ASES Conf., 2009. 100 80 Global Direct Monthly Bias Error (%) 60 40 The Eugene 20 syndrome 0 -20 Eugene, OR Bias in monthly SUNY-modeled irradiations -40 1998 1999 2000 2001 2002 2003 2004 2005 2006 Date
    • DNI Prediction with Radiative Models (7) • Availability of high-quality inputs is crucial, with as little spatial or temporal interpolation as possible for the most important atmospheric variables, particularly clouds and aerosols. Use of long-term monthly-average aerosol data leads to significant errors in modeled DNI, and incorrect frequency distributions. • R. George et al., National solar radiation database (NSRDB)—10 km gridded hourly solar database. ASES Conf., 2007. • C.A. Gueymard, Variability in direct irradiance around the Sahara: Are the modeled datasets of bankable quality? SolarPACES Conf., 9 100 Long-term mean DNI 2010. DNI (kWh/m ) 8 Tamanrasset 90 2 7 80 6 70 DN 5 3Tier 60 ISIS 4 GeoModel 50 Meteonorm 3 SSE 40 SWERA 2 30 % Difference 1 20 0 10 -1 0 -2 -10 -3 -20 -4 -30 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 Yr Month
    • Factors Affecting DNI Atmospheric factors: • Clouds 1100 SMARTS Model Precipitable • Aerosols (AOD, etc.) Direct normal irradiance Rural Aerosol water (cm) Elevation Elevation • Water vapor (PW) 1000 0m PW 1500 m • Ozone, pressure, NO2… 0.470 • Air mass (m) 900 1.416 4.250 Aerosols: 800 Irradiance (W m-2) m •  Main cause of DNI extinction m=1 700 DUSTY under cloudless skies. •  DNI strongly decreases from 600 clean (850 W/m2) to dust-storm ASTM G173 conditions (300 W/m2), for 500 m = 1.25. m = 1.5 •  DNI is 3–5 times more 400 sensitive to AOD than GHI. CLEAN 300 m=2 200 0 0.2 0.4 0.6 0.8 1 1.2 AOD Aerosol Optical Depth at 500 nm
    • Importance of Aerosols Aerosol sources are highly variable: • Vegetation • Ground dust • Sea spray • Sand storms • Smoke (fires) • Industrial pollution • Volcanic plumes • Urban pollution Sarychev volcano eruption, June ‘09 Sahara dust storm
    • Optimal Siting for CSP/CPV plants Optimal siting of a CSP/CPV plant results from a compromise between many technical, environmental and solar resource constraints. Maximum solar resource (DNI) requires five Minimums: 1. Sustained clear skies (i.e., minimum cloudiness) 2. Absence of haze (i.e., minimum atmospheric turbidity) 3. Dry atmosphere (i.e., minimum water vapor) 4. Minimal air mass (i.e., minimum latitude) 5. High site elevation (i.e., minimum pressure)
    • The Sun Belt—Where CSP/CPV is advisable Approximate evaluation of DNI is possible with free datasets such as NASA-SSE. Sun Belt: mean daily DNI > 5.5 kWh/m2 or mean annual DNI > 2000 kWh/m2. Maps and data of much higher spatial resolution are needed for serious resource assessment. > 5.5
    • Sources of Modeled DNI Data Free data sources Commercial data sources • NASA-SSE (world) • 3Tier • DLR-ISIS (world) • AWS Truepower • NREL-NSRDB (USA) • Clean Power Research • UNEP-SWERA (various countries) • DLR-SOLEMI • European Solar Radiation Atlas Free solar resource maps and • Focus Solar geospatial toolkits • GeoModel-SolarGIS • NREL (for various countries) • Meteonorm http://www.nrel.gov/international/ • SoDa-HelioClim3 global_energy.html • Univ. Oldenburg-EnMetSol • NREL-NSRDB (USA) http://rredc.nrel.gov/solar/old_data/nsrdb/ • UNEP-SWERA (various countries) Solar resource for CSP: handbook http://swera.unep.net http://www.nrel.gov/docs/fy10osti/ 47465.pdf
    • Example of DNI Resource Map NREL’s DNI map for India (2010) • Based on satellite data for aerosols (SCS), clouds (Meteosat), and SUNY model. • High resource in the Himalayas • Elsewhere, the DNI resource is limited by strong haze and monsoon cloudiness. http://www.nrel.gov/international/ra_india.html
    • Differences in Resource Maps (1) Differences in DNI resource maps are much larger than those in GHI maps. Typically, ±5–10% differences over regions with good density of weather stations, ±30% or more elsewhere. Extreme differences have been found over parts of Africa, in particular. Such uncertainties can slow down the development of large CSP projects, which has actually happened recently in Abu Dhabi. NREL-SWERA NASA-SSE Kenya: NREL vs. DLR NREL map
    • Differences in Resource Maps (2) Q: What can explain inconsistencies and large disagreement between resource maps? • Cloud data obtained from different sources or different periods • Widely different aerosol data • Use of long-term monthly-average vs. mean daily aerosol data • Use of empirical algorithms, with degraded performance in some areas • Lack of validation against ground-truth DNI (since such data is rare) • Use of validation data (measured DNI) of low quality • Undocumented tweaking of some models or input data • Lack of scientific consensus on various modeling techniques and quality control methods • Lack of transparency from some commercial vendors, since their methods are proprietary, at least in part. To remedy this situation, the International Energy Agency (Task 36) and SolarPACES have launched research projects to validate or benchmark various datasets. A preliminary task is to identify and obtain high-quality DNI measurement data for the whole world, to be used as ground truth.
    • Solar Resource Variability vs. Financing • Solar resource is variable, and therefore so are the produced power and the revenues it generates. This directly affects cash flow. • To account for such variability in revenue, reserve accounts are generally necessary for debt service and to limit cash flow fluctuations. • Financing is often offered based on a restrictive revenue model, conservatively using a high probability (90, 95 or 99%) to exceed some minimum power production and revenue. • Lenders need to assess risks due to failure or bad years. • Incorrect evaluation of risk mitigation may lead to rejection of good projects, or to financial distress of risky projects. • Financing projects based on the nth percentile production is traditional and appears to work well, provided the production variability, and hence the solar resource, can be quantified probabilistically. Q: How can percentiles be calculated accurately?
    • Short-term DNI Variability (1) DNI varies smoothly under clear skies, but can vary extremely fast under partly cloudy skies, e.g., from 0 to 1000 W/m2 in a second, and vice versa. These fast transient conditions did not show easily in the past, when only hourly data were available. Time steps of 1-min become the norm for first- class stations. Some research stations use 1-sec to 3-sec time steps. Example Oahu, Hawaii 2 1654 W/m “Typical” partly-cloudy day 1600 5 July 2010 3-sec data ETHI for Oahu, HI GHI DNI Irradiance (W/m ) 2 1200 • GHI up to 25% more than ETHI* during lensing effect peaks, around noon. 800 • DNI also increases by a few %, due to large transient circumsolar diffuse. 400 * Extraterrestrial horizontal irradiance 0 6 7 8 9 10 11 12 14 15 16 17 18 19 LST
    • Short-term DNI Variability (2) Short-term DNI variability is no problem if the plant’s operation tolerates DNI fluctuations, incl. no DNI. But many CSP plants can’t operate below some threshold DNI. Q: Can these transient effects be correctly accounted in the daily, monthly or annual solar resource if DNI is not measured fast enough? Example “Typical” day in Oahu, HI 1 0 100 • Various thresholds: 200 0, 100, 200 and 300 W/m2 Relative daily irradiation 300 • Various measurement 0.95 time steps considered: 3 sec, 1 min, 15 min, 1 hr Threshold (W/m ) 2 • Some commercial data vendors provide data at 0.9 15-min intervals Oahu, Hawaii • Hourly time step may be 5 July 2010 2 Total DNI: 7 kWh/m too coarse for accurate 0.85 system simulation 1 100 • No gain in accuracy 3 sec 1 min 15 min 1 hr likely for steps < 1 min Time step (sec) • This topic needs further research, so an optimum data time step can be defined.
    • Interannual DNI Variability (1) There are good years and bad years in everything, particularly in DNI, due to: Climate cycles (El Niño, La Niña…), changes in release of natural aerosols, increase or decrease in pollution, volcanic eruptions, climate change… For GHI, it might take only 2–3 years of measurement to be within ±5% of the long- term mean. For DNI, it takes much longer, up to 5–15 years. Short measurement periods (e.g. 1 year) are not sufficient for serious DNI resource assessment! 20 Annual Resource Special techniques must be used to correct long-term modeled data using 10 short-term measured data. Anomaly (%) 0 20 -10 Convergence time 10 -20 Eugene, OR 1978–2009 5% Anomaly (%) 0 DNI GHI -30 -10 1975 1980 1985 1990 1995 2000 2005 2010 Eugene, OR Year -20 1978–2009 -30 13 years DNI GHI Eugene data: http://solardat.uoregon.edu/ 1975 1980 1985 1990 1995 2000 2005 2010 Year
    • Interannual DNI Variability (2) Interannual variability in DNI is much higher (at least double) than that in GHI. This variability is higher in cloudier climates (low Kn), but still significant in clearer regions (high Kn), which are targeted by CSP/CPV. Plots and maps provide this variability in terms of Coefficient of Variation (COV): COV = St. Dev. / Mean 14 Annual Resource This is significant at only a 66% Interannual Variability 12 NSRDB Data, 1961–1990 probability level. For a “bankable” Coefficient of Variation (%) DNI GHI 95% probability, double the COV 10 results. 8 6 4 2 0 0.05 0.15 0.25 0.35 0.45 Kn C.A. Gueymard, Fixed or tracking solar collectors? Helping the decision process with the Solar Resource Enhancement Factor. SPIE Conf. #7046, 2008. S. Wilcox and C.A. Gueymard, Spatial and temporal variability in the solar resource in the United States. ASES Conf., 2010. http://rredc.nrel.gov/solar/new_data/variability