Bankable Solar Resource Data for Energy Projects. Riaan Meyer, GeoSUN Africa, South Africa Marcel Suri, GeoModel Solar, Slovakia

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1 Bankable Solar Resource Data for Energy Projects Riaan Meyer, GeoSUN Africa, South Africa Marcel Suri, GeoModel Solar, Slovakia

2 Solar resource: fuel for solar technologies Photovoltaics (PV) Concentrated Solar Power (CSP) Concentrated PV (CPV) Global Horizontal Irradiation (GHI) Direct + Diffuse Direct Normal Irradiation (DNI)

3 Direct Normal Irradiation in the world context Values show annual longterm DNI average in kwh/m 2 Source: SolarGIS

4 Solar resource data: what is required? Planning a new solar power plant Solar and meteo data to be available at any location At least 10 years measurements High accuracy, no gaps in the data Control of existing power plant Systematic data supply for monitoring Forecasting up to 2 days ahead This is available with satellite-based models supported by high-quality ground measurements

5 Solar radiation how to measure 1. Ground sensors High accuracy information available for the site of a meteorological station 2. Solar models using satellite data Information calculated from satellite & atmospheric data Data are available almost everywhere

6 Option 1: Ground measurements ADVANTAGES High frequency measurements Higher accuracy (for high-standard sensors properly managed) TO BE CONSIDERED High accuracy sensors to be used Meteo station must be properly operated & maintained Data quality control Regular calibration Beneficial to combine measurements with satellite data Photo: courtesy of GeoSUN Africa, Kipp & Zonen

7 Option 2: Satellite data ADVANTAGES Available everywhere Map resolution 3 to 5 km Measurement every 15 (30) minutes TO BE CONSIDERED Lower accuracy for the instantaneous point estimate Consistency and stability High availability History of up to 20 years Data source: EUMETSAT, ECMWF, SolarGIS

8 Solar prospecting and site evaluation Typically data from satellite models are used representing at least 10 years Digital or paper maps can help in finding the most prospective areas for larger development Uncertainty of raw annual satellite values: DNI ±8 to 12% GHI ±3.5 to 7%

9 Project development and site evaluation Site-specific data from satellite model are used for preliminary evaluation Hourly time series Typical Meteorological Year (TMY) Meteorological station is installed to improve the accuracy of estimates Courtesy: GeoSUN Africa

10 Project development - later stages Due diligence Quality control of local measurements Site-adaptation of long history of satellite data with short term measurements Bankable report Local Annual uncertainty of site-adapted data can be reduced after 1-2 years of local measurement: DNI ±4 to 6% GHI ±2.5 to 3% Satellite

11 Project operation and performance assessment Quality controlled ground measurements Combined with satellite data to enhanced accuracy and no gaps Sustainable bankability of DNI and GHI data Every 1% reduced uncertainty helps in the technical and financial assessment of the power plant operation Source: P. Ineichen

12 Conclusions Using raw satellite-based solar resource data for first stage planning Uncertainty of raw annual values: DNI ±8 to 12% GHI ±3.5 to 7% History up to 19+ years in Africa Combined use of ground measurements and satellite data at a project for bankable assessment Reduces uncertainty of site-adapted annual values: DNI ±4 to 6% GHI ±2.5 to 3% Allows systematic quality control and sustainable bankability of solar resource data

13 DNI [Wh/m 2 ] What if I am uncertain about my DNI measurements? Client DNI Data /02/12 15/02/12 16/02/12 17/02/12 18/02/12 19/02/12 20/02/12 21/02/12 22/02/12

14 DNI [Wh/m 2 ] What if I am uncertain about my DNI measurements? Client DNI Data /02/12 15/02/12 16/02/12 17/02/12 18/02/12 19/02/12 20/02/12 21/02/12 22/02/12

15 DNI [Wh/m 2 ] Compare it to measured DNI obtained from a station with accurate readings local nearby Client DNI Data Nearby Measurement Station DNI /02/12 15/02/12 16/02/12 17/02/12 18/02/12 19/02/12 20/02/12 21/02/12 22/02/12

16 DNI [Wh/m 2 ] Compare it to calculated DNI obtained from the same station with accurate readings local nearby Client DNI Data Nearby Measurement Station DNI Nearby Measurement Station Calculated DNI /02/12 15/02/12 16/02/12 17/02/12 18/02/12 19/02/12 20/02/12 21/02/12 22/02/12

17 DNI [Wh/m 2 ] Compare it to satellite derived data (known to be good in that area) as an additional check Client DNI Data Nearby Measurement Station DNI Nearby Measurement Station Calculated DNI SolarGIS Satellite Derived DNI (At nearby measurement station) 0 14/02/12 15/02/12 16/02/12 17/02/12 18/02/12 19/02/12 20/02/12 21/02/12 22/02/12

18 Example 2 Something went wrong here Something went very wrong here Problem resolved Typical problems Tracker slight miss-alignment Tracker total miss alignment or not tracking Instruments not cleaned

19 Thank you! Riaan Meyer Marcel Suri GeoSUN Africa, South Africa GeoModel Solar, Slovakia