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Modeling Solar Variability Effects on Power Plants
Matthew Lave and Jan KleisslSolar 2011
Variability Reduction through Aggregation
Rela
tive
Out
put
Large ramps are detectable real-time by satellite and ground stationsVariability models primarily needed for large central power plants / microgrids
• Method to estimate aggregated PV plant output variability given a single point sensor measurement
• Model requirements:• Universal: work for plants of any size, at any location, with any
arrangement of PV panels• Account for different variability at different timescales
• Applications:• Estimate variability of output from distributed and central PV plants to
determine grid impacts• Generate virtual PV output for renewable grid integration studies• Estimate benefits of various capacities of energy storage in reducing
ramp rates
Variability Model
3
Data
GHI recorded once per second using LICOR Li-200SZ silicon pyranometers.
4
Model Development
5
• Decompose GHI timeseries into variability at different time scales using the top hat wavelet
• Determine correlation of GHI fluctuations as a function of distance and timescale between different sites
• Use correlation relationship to model variability for a variety of solar PV plant types: distributed/central, large/small
• Setup a simple user interface: draw polygons around PV on Google map; input point sensor time series; output DC power for PV plant.
Model Development
6
Top Hat Wavelet Transform
7
• Wavelet decomposition using a “top hat” wavelet
• Shows fluctuations away from mean at each timescale
• Strong peaks (high variability) of duration 2048 sec (~34min) are detected at 10:30 and 11:00.
• Clear sky: No fluctuations from 12:00 to 15:30.
Correlation Between Sites
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1-0.2
0
0.2
0.4
0.6
0.8
1
corr
elat
ion
()
UCSD DEMROES year 2010
exp(-x/tavg
)
= 0.73 exp(-x/tavg
)
2-sec/4-sec8-sec/16-sec32-sec/64-sec128-sec/256-sec512-sec/1024-sec2048-sec/4096-sec
+0.27 exp(-x/tavg
)200
8*presentation by Tom Hoff at DOE/CPUC High Penetration Solar Forum, March 2, 2011
Large distance or small time
Small distance or large time
• Correlation at each timescale found by comparing the wavelet modes for two sites at a certain timescale• Normalized distance divided by time scale (Hoff, 2011*)
Case Study
9
UCSD1.2 MW
Distributed PV
UCSD1.2 MW
Central PV
Henderson, NV 48 MW
Central PV
Compare variability of PV plants:
Correlation -> Variability Ratio (VR)
• VR can be computed between each set of points (“containers”) for an entire plant over all timescales
• After a wavelet transform, each wavelet mode from a point sensor is divided by the VR at that timescale to create wavelet modes for the PV plant. An inverse transform creates the PV plant output.
N
i
N
jji
sitesallavg
sitex
dxt
N
t
ttVR
1 1,
2
2__
2_1
ˆ
),()(
)()(
10
UCSD distributed
PV plant
central PV plant of
same MW size
1.2 MW UC San Diego PV plant
11
48MW Henderson, NV PV plant
UCSD: 1.2 MW; 0.4 kW containersHenderson: 48 MW; 90 kW containers
12
UCSDDistributed PV
Henderson, NV Central PV
PV plant variability expressed in GHI units
• Power output smoothed compared to point sensor• UCSD distributed has noticeably smaller fluctuations than UCSD central.13
Acknowledgements•We very much appreciate funding from the DOE High PV Penetration Program
14
• Questions?• Please come visit us at solar.ucsd.edu• Contact: [email protected]
Backup Slides
Model Assumptions
16
• Correlations are solely a function of distance and timescale: no consideration for anisotropic variations
• PV is spaced evenly throughout the plant area
• “Containers” of varying size are appropriate to model PV plants
• PV plant average GHI can be converted into plant power output
Power Content of Fluctuations
• Power content = integral of wavelet mode at a certain timescale – shows variance at each timescale• Power content of one highly variable day (left) is much larger than the average power content of 48 days • Power content of average of 6 sites always less than power content of EBU2
17
Variability Ratio
N
VRrVR
2__
21_
tesavg_all_si
site_1
fpi
fpiVR
sitesallavg
site
1 vs. 48 days various N
to allow for easy visual comparison on same plot
VR is a function of timescale and number of sites (or, rather, correlation between sites)
18
Future Improvements
•Compare correlations found at UC San Diego to other locations
• Verify correlation relation at very short distances
•Compute power output with PV performance model
•Validate against actual power plant data
19