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Modeling CSP and PV power output in WECC

Modeling CSP and PV power output in WECC. TEPPC Data Working Group Marissa Hummon October 11, 2011. What is covered in this presentation?. Brief Overview of the S ub-Hour S ynthesis A lgorithm Classes of temporal variability Satellite data Class selection Modeled Plants

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Modeling CSP and PV power output in WECC

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  1. Modeling CSP and PV power output in WECC TEPPC Data Working Group Marissa Hummon October 11, 2011

  2. What is covered in this presentation? • Brief Overview of the Sub-Hour Synthesis Algorithm • Classes of temporal variability • Satellite data Class selection • Modeled Plants • Filter for plant size • PV and CSP power output • Results & Analysis • Time series • Ramp distributions • Spatial – temporal correlations

  3. Definition: Clearness Index (ci)

  4. Classes of temporal variability Class 0 is a special case of Class I

  5. Downscaling to one minute

  6. Site Clearness Index Analysis Spatial satellite data is used to calculate the relative proportions of cloud cover in an area for each hour. This data is related to the sub-hourly measurements of irradiance. These figures show five consecutive hours of aerial satellite data (left) and corresponding minutely ground-based irradiance data (right).

  7. Satellite data Class selection

  8. Area Filter • Reduction in variability as the irradiance signal is aggregated over an area. The larger the area, the lower the variability. Evidence: • Mills et al. demonstrated this: Southern Great Plains ARM Network • Marcos et al. demonstrated this: Spain • Perez et al. demonstrated this: California • Gueymard et al.: United States

  9. Area Averaging A. Mills, R. Wiser, 2010, “Implications of Wide-Area Geographic Diversity for Short-Term Variability of Solar Power”, LBNL

  10. Point source versus PV plant Milagro (0.52 km2) fc = 0.0032 Hz (5.2 min) Sesma (0.042 km2) fc = 0.0088 Hz (1.9 min)

  11. Area Filter • PV plant footprint per PV capacity • Best Resource: 42 MW/km2 • Population PV: 38 MW/km2 • Rooftop: Capacity is evenly distributed over grid cell, 40-90 km2 • CSP plant footprint • 27.5 MW/km2 • Low pass filter (after Marcos et al.) • Cut-off frequency: fc = 0.0204 *sqrt(area) [hectares] • Range of plants affected: greater than 0.08 km2

  12. PSDs (all classes) Synthetic (unfiltered) global horizontal Utility PV from 30 to 220 MW Distributed PV f [Hz]

  13. Area Filter

  14. Irradiance to Power Conversion • System Advisor Model: • PVWatts, a physical model of a “generic” silicon solar panel with inputs: • direct normal • diffuse horizontal • wind speed (ground) • temperature • Physical Trough Model for CSP • Select the “default” positions for most of the plant. It scales the plant with nominal capacity. • Storage: either 0 or 6 hours • Dry cooled plants • Solar Multiplier of 2 with storage

  15. Analysis • Ramps (over all daylight hours) • Ramps with site aggregation • Site-to-site correlations

  16. Ramps (Synthetic and Measured)

  17. Regions of Sites for Ramp Correlations PV, best resource 1 CSP PV, population Rooftop 6 8 2 4 7 5 3

  18. Power Ramps Group 3: Southern California

  19. Ramp Correlations (2005)Area 2 Ramp correlations between sites a distance, d, apart, in a given region, follow this relationship, where t is duration of the ramp: c1 exp(-db1/t) + c2 exp(-db2/t) 1 6 8 2 4 7 5 3

  20. Ramp Correlations (2005)Area 5 Ramp correlations between sites a distance, d, apart, in a given region, follow this relationship, where t is duration of the ramp: c1 exp(-db1/t) + c2 exp(-db2/t) 1 6 8 2 4 7 5 3

  21. Ramp Correlations (2005)Area 7 Ramp correlations between sites a distance, d, apart, in a given region, follow this relationship, where t is duration of the ramp: c1 exp(-db1/t) + c2 exp(-db2/t) 1 6 8 2 4 7 5 3

  22. Questions & Comments • Feel free to contact me at: • Marissa Hummon • National Renewable Energy Laboratory • Strategic Energy Analysis Center • 303-275-3269 • marissa.hummon@nrel.gov

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