Aggregate Solar Variability

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Aggregate Solar Variability Jan Kleissl, Matthew Lave, Mohammad Jamaly, Juan Luis Bosch Department of Mechanical and Aerospace Engineering Center for Renewable Resources and Integration University of California, San Diego

Solar Variability and Geographic  Smoothing

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• Measured data anecdotally confirms large reduction in  aggregate relative PV variability • Need for modeling aggregate variability for distribution feeder  planning (voltage regulation) and ramp rate control on utility‐ scale plants

Perez and Hoff , 2013

Map of 86 PV Systems and 5 CIMIS Stations in SDG&E Territory

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Ramp Rates Results New Energy Horizons  Opportunities and Challenges

Largest step size of normalized aggregate PV output versus ramp time interval

Cumulative distribution of absolute value of 1-hour ramp rates of normalized aggregate PV output

The Day with the Largest Ramp Rate New Energy Horizons  Opportunities and Challenges

Aggregate Performance & Measured power of all 86 PV sites and Aggregate GHI of 5 CIMIS stations for the day with the largest 1-hour ramp rate in 2009

• The largest aggregated 1 hour ramp for this period was 50.6% of PV capacity and occurred from 900 to 1000 PST

GOES Satellite Images on Jun. 14, 2009 New Energy Horizons  Opportunities and Challenges

• The circles represent 86 PV systems • The area of the circles is proportional to the power rating of the PV system (the largest system is 939 kW) • Color bar shows ratio of 15-min averaged output to annual maximum output at that time of day (ToD)

Largest 1-hour Ramp Rates

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Histogram of the largest 1-hour ramp rates (30% and larger) of normalized aggregate PV output

Space and Time Scales of Variability

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1 hour 10 min 1 min

Time

EPRI

FPL

10 sec

1 sec

Distance Point

Yards / meters

km / miles

10 miles

1000 miles

Simulate PV Plant Output

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• For solar integration studies, we would like to simulate PV plant output profiles. • Geographic diversity will lead to a smoother plant output than point sensor. central plant

point sensor

distributed plant 10

Wavelet Variability Model (WVM)

WVM Inputs

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WVM Outputs

PV Plant Footprint Density of PV Plant Areal Average  Irradiance

Point Sensor  Timeseries Location/Day  Dependent “ ”  Coefficient

determine variability  reduction (smoothing) at  each wavelet timescale

irradiance to  power model

Plant Power Output

11

Wavelet Variability Model (WVM) New Energy Horizons  Opportunities and Challenges

 Estimate aggregated PV plant output variability  given only a single point sensor measurement.  Uses a wavelet decomposition by timescale to  account for different variability reductions (VRs) at  different timescales.  Universal application: works for plants at any  location, with any arrangement of PV modules.  distributed plant, a central plant, or combinations  of both. 12

Test cases New Energy Horizons  Opportunities and Challenges

Distributed PV Plant

Central PV Plant

Ota City, Japan 2MW rooftop PV ~550 houses with PV Varying tilt/azimuth

Copper Mountain, NV 48MW thin film PV Fixed tilt

13

Lave et al. UWIG 2011

Test WVM at Copper Mountain New Energy Horizons  Opportunities and Challenges

14

Ramp Rate Comparison

1/2 New Energy Horizons  Opportunities and Challenges

15

Main Input: Correlation as a function of time  and distance

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0.5

A = 5.64 1200 1000 800

0

W m-2

correlation

• WVM requires time/space correlation of solar irradiance – the smaller the correlation the larger the smoothing • value to fit correlation function • A depends on cloud speed, translating time scale to cloud size • Universal function results, applicable anywhere

1

600 400 200 0 06:00 08:00 10:00 12:00 14:00 16:00 18:00

-0.5 0

0.2

0.4 0.6       ,

̅ 16

time distance

)

0.8

1

Other Evidence for Universal  Correlation Function

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• From highly accurate satellite data, Hoff and Perez (2012) also found universal correlation function – Variety of meteorological conditions and geographical areas – Distance / time interval * cloud speed collapses all curves Δt = 1 hour

Δt = 2 hour

Δt = 4 hour

California

Southern Great Plains

Model Applications New Energy Horizons  Opportunities and Challenges

Planning • Determine power plant ramping requirements • (Combined with control strategy for energy storage) Assess  energy storage system required to comply with utility  maximum ramping requirement • Analyze the impact of no / perfect / realistic forecast on  PV+BESS ability to reduce ramps

Operations • Short term forecasting with total sky imager to anticipate  down ramps and pre‐curtail • Realize significant savings due to smaller energy storage needs

Simulating Geographic Smoothing • Example: UC San Diego Campus. • Distributed PV shows largest additional  reduction in variability at 1 to 5 minute ramps. • Simulate geographic smoothing. • Lave and Kleissl (2012) • Hoff and Perez (2012)

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Conclusions • Modeling of solar variability is mostly resolved – Universal correlation functions. – Cloud speed and local high‐resolution (sub 1 min)  irradiance required as input. • In balancing areas, minute‐to‐minute ramps benign – Hourly ramps can be significant – Intra‐hour and hour‐ahead solar forecasting 

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References New Energy Horizons  Opportunities and Challenges

• Lave, M., J Kleissl, J Stein, A Wavelet‐Based Variability Model  (WVM) for Solar PV Power Plants, IEEE Transactions on  Sustainable Energy, in press • Hoff, T. E., Perez, R., 2012. Modeling PV Fleet Output  Variability. Solar Energy, Forthcoming. • Hoff, T. E., Perez, R., 2013. “Solar Resource Variability” in  “Solar Resource Assessment and Forecasting”, Jan Kleissl  (Editor), Elsevier, 2013