8th PVPMC Workshop, May 9-10 2017

1. Simulating high-Frequency Solar PV
generation Profiles for Large Portfolios in
the SE US
Will Hobbs, Southern Company Services
Matt Lave, Sandia National Laboratories
1

2. Background
Southern Company (vertically integrated utilities in SE US)
needed solar profiles that are:
• Sub-hourly (10min interval down to 6sec interval)
• Multi-year and concurrent with recent load
• Adjustable by:
– Locations
– Capacities
– Type (fixed vs. tracking)
Applications include:
• Resource planning studies on 10 min regulation
requirements
• Fleet operation studies on 6 sec AGC cost and performance
Current solution: MATLAB toolbox that meets all
of these requirements

3. For each site and configuration (fixed, 1-axis tracking):
Repeat for all sites & configurations, then sum outputs
Model Overview
3
TBOM
(EPRI DPV)
Irrad30°S
(EPRI DPV)
Cloud
Speed
(NAM/UCSD)
Simple PV
Power Model
Wavelet
Variability Model
Irradiance
Translation
Portfolio Table
Site Config. MW
AC Power (Site, Config)
Plant size
Plant
Density
Input
Model/Calc
Output
Time Averaging
Method
Substitute “TAM” for “WVM”
to compare methods

4. Outline
4
TBOM
(EPRI DPV)
Irrad30°S
(EPRI DPV)
Cloud
Speed
(NAM/UCSD)
Simple PV
Power Model
Wavelet
Variability Model
Irradiance
Translation
Portfolio Table
Site Config. MW
AC Power (Site, Config)
Plant size
Plant
Density
Input
Model/Calc
Output
Time Averaging
Method
1 2
A.1
4
A.2
3
Results
5

5. Outline
5
TBOM
(EPRI DPV)
Irrad30°S
(EPRI DPV)
Cloud
Speed
(NAM/UCSD)
Simple PV
Power Model
Wavelet
Variability Model
Irradiance
Translation
Portfolio Table
Site Config. MW
AC Power (Site, Config)
Plant size
Plant
Density
Input
Model/Calc
Output
Time Averaging
Method
1 2 3
Results
5
4
A.1
A.2

6. Input Data (EPRI DPV Project)
6
• Clusters of pole-mounted
PV modules (with Tbom)
and POA pyranometers at
13 sites
• 4+ years of data (2012 –
much of 2016)
Photo credit: EPRI
(http://dpv.epri.com/)

7. Data Quality Challenges
7Photo credit: EPRI
Notable fixed shading at many sites.
Irrpoa plotted by solar azimuth, elevation (& filtered)

8. Outline
8
TBOM
(EPRI DPV)
Irrad30°S
(EPRI DPV)
Cloud
Speed
(NAM/UCSD)
Simple PV
Power Model
Wavelet
Variability Model
Irradiance
Translation
Portfolio Table
Site Config. MW
AC Power (Site, Config)
Plant size
Plant
Density
Input
Model/Calc
Output
Time Averaging
Method
1 2 3
Results
5
4
A.1
A.2

9. Cloud Speed
• Cloud speed data
obtained from NAM
• Daily, monthly, and
annual averages
computed
• Compared well with
radiosonde data
9
Mean 24.2 mph
Median 21.7 mph
Min 1.1 mph
Max 75.8 mph
Std_Dev 14.6 mph
10th %-tile 8.1 mph
90th %-tile 43.7 mph
Cloud speed statistics for Atlanta, 2014,
based on weather balloon soundings.
Cloud speed statistics across 13 DPV sites 2012-2016
NAM data (Jan Kleissl, Ellyn Wu, UCSD) .

10. Outline
10
TBOM
(EPRI DPV)
Irrad30°S
(EPRI DPV)
Cloud
Speed
(NAM/UCSD)
Simple PV
Power Model
Wavelet
Variability Model
Irradiance
Translation
Portfolio Table
Site Config. MW
AC Power (Site, Config)
Plant size
Plant
Density
Input
Model/Calc
Output
Time Averaging
Method
1 2 3
Results
5
4
A.1
A.2

11. Smoothing Models
• Wavelet Variability Model (WVM)
• Time Averaging Method (TAM)
– Smoothing window = sqrt(Plant Area) ÷ Cloud Speed
11
M. Lave, A. Ellis, J. Stein, Simulating Solar Power Plant Variability: A Review of Current Methods, SANDIA REPORT
SAND2013-4757, June 2013.

12. Outline
12
TBOM
(EPRI DPV)
Irrad30°S
(EPRI DPV)
Cloud
Speed
(NAM/UCSD)
Simple PV
Power Model
Wavelet
Variability Model
Irradiance
Translation
Portfolio Table
Site
AC Power (Site, Config)
Plant size
Plant
Density
Input
Model/Calc
Output
Time Averaging
Method
1 2 3
Config. MW
Results
5
4
A.1
A.2

13. Locational Capacities
253MW 1300MW - Fixed 1300MW -
Tracking
DPV Site Fixed MW Tracking MW Fixed MW Tracking MW
AL Eufaula 0 0 100 100
AL Hoover 0 0 100 100
AL Mobile 0 0 100 100
AL
Tuscaloosa
0 0
100 100
AL Wedowee 0 0 100 100
AL Wetumpka 0 0 100 100
GA Augusta 0 0 100 100
GA Columbus 29.9 0 100 100
GA Jonesboro 32.2 0 100 100
GA Macon 19.9 50.2 100 100
GA Rome 0 0 100 100
GA Savannah 0 0 100 100
GA Valdosta 17.2 103.3 100 100 13
• 253MW scenario
• 1300MW scenarios
For validation against
measured power (6
month overlap)
Sample application
for utility planning
Real plants
(253MW total)

14. Outline
14
TBOM
(EPRI DPV)
Irrad30°S
(EPRI DPV)
Cloud
Speed
(NAM/UCSD)
Simple PV
Power Model
Wavelet
Variability Model
Irradiance
Translation
Portfolio Table
Site
AC Power (Site, Config)
Plant size
Plant
Density
Input
Model/Calc
Output
Time Averaging
Method
1 2 3
Config. MW
Results
5
4
A.1
A.2

15. Sample Day Comparison
15
Clear Day:
Variable Day:
Overhead
shading at
DPV site(s)

16. Monthly Energy Comparison
16
Accurate energy estimate is not a primary goal,
but results are decent

17. Ramp Rate Comparisons at Different Time
Intervals
17
• WVM matches actuals very
well at 1 min
• TAM underestimates ramps
• WVM and TAM
overestimate ramps at 10
min, but WVM is closer
• Both match well to 95%-tile
• Mixed (but still good) results
Overestimation of ramps in WVM at 10
and 60min could be due to shading

18. Month by Hour 10min ramps, 95th %-tile*
18
Timing of 10 min
ramps:
TAM has notable
concentration of
high ramps:
WVM looks better:
NERC’s BAL standard (now replaced by BAAL)
required monthly CPS2 compliance of 90% on
10-minute Area Control Error
(we use 95% since solar generates ~1/2 of time)
*

19. Month by Hour 10min ramps, 95th %-tile
19
Timing of 10 min
ramps:
TAM has notable
concentration of
high ramps:
WVM looks better:
Maps of
difference from
actual ramps:
Morning
shading at
DPV site
NERC’s BAL standard (now replaced by BAAL)
required monthly CPS2 compliance of 90% on
10-minute Area Control Error
(we use 95% since solar generates ~1/2 of time)
*

20. Outline, part 2
20
TBOM
(EPRI DPV)
Irrad30°S
(EPRI DPV)
Cloud
Speed
(NAM/UCSD)
Simple PV
Power Model
Wavelet
Variability Model
Irradiance
Translation
Portfolio Table
Site Config. MW
AC Power (Site, Config)
Plant size
Plant
Density
Input
Model/Calc
Output
Time Averaging
Method
1 2b 3
Results
5
How important is this?
4
A.1
A.2

21. Daily, Monthly, or Annual Cloud Speed?
21
• How much benefit to using daily cloud speed over monthly
or annual avg.?
• Minimal change to broad
6-month ramp statistics
• What about seasonal
issues?
Daily cloud speed is best.
Overestimation gets worse in late
spring/early summer when using
monthly or annual avg. cloud
speeds.

22. Outline
22
TBOM
(EPRI DPV)
Irrad30°S
(EPRI DPV)
Cloud
Speed
(NAM/UCSD)
Simple PV
Power Model
Wavelet
Variability Model
Irradiance
Translation
Portfolio Table
Site
AC Power (Site, Config)
Plant size
Plant
Density
Input
Model/Calc
Output
Time Averaging
Method
1 2 3
Config. MW
Results
5b
(Sample Application)
4
A.1
A.2

23. 1300MW Portfolios
23
95th %-tile
• Summary for generic
planning scenario output
95%10min Ramp
(%)
253MW (WVM) 8.3 %
1300MW Fixed 4.6 %
1300MW
Tracking
6.8 %
95%10min Ramp
(%)
95% 10min Ramp
(MW)
253MW (WVM) 8.3 % 21.0 MW
1300MW Fixed 4.6 % 59.2 MW
1300MW
Tracking
6.8 % 88.9 MW

24. Conclusions & Next Steps
24
Implemented and partially validated a method for developing
solar profiles that are:
• High frequency
• Multi-year and concurrent with recent load
• Scalable/Adjustable
Next steps:
• Validate with more recent actual generation (~1000MW)
• Look at 6 second intervals
• Possibly better address shading
• Consider improved Tbom

25. Thanks to…
25
…EPRI for allowing us to use DPV data (Tom Key, Chris
Trueblood, David Freestate, others)
…UCSD for providing NAM cloud speed data (Jan Kleissl,
Ellyn Wu)
Questions?
whobbs@southernco.com
mlave@sandia.gov

26. Appendix
26
TBOM
(EPRI DPV)
Irrad30°S
(EPRI DPV)
Cloud
Speed
(NAM/UCSD)
Simple PV
Power Model
Wavelet
Variability Model
Irradiance
Translation
Portfolio Table
Site Config. MW
AC Power (Site, Config)
Plant size
Plant
Density
Input
Model/Calc
Output
Time Averaging
Method
1 2 3
Results
5
4
A.1
A.2

27. A.1 Plant Density
• Looked at range between 5 acres/MW and 100 acres/MW
• Primary focus on 55 acres/MW
– Assumption: max plant density of 5 acres per MW, 10% of land in
region around measurement site is used for PV  55 acres/MW
aggregate
27

28. A.1 Plant Density Impact
28
• 55 acres/MW is good for
1 min ramps
• 55 acres/MW causes small
overestimation at 10 min
• 100+ acres/MW is better
• Plant density has very little
impact at 60 min interval
WVM is intended to account for spatial
smoothing, not weather diversity. This could
explain the inconsistency here.

29. A.2 (Simple) Power Model
29
𝑃 𝐷𝐶,𝑚𝑜𝑑 = 𝑃𝑆𝑇𝐶
𝐺 𝑃𝑂𝐴
𝐺𝑆𝑇𝐶
1 −
𝛿
100
𝑇𝑆𝑇𝐶 − 𝑇𝐵𝑂𝑀
𝑃𝐴𝐶,𝑚𝑜𝑑 =
1 −
𝜇
100
𝑃 𝐷𝐶,𝑚𝑜𝑑 , 1 −
𝜇
100
𝑃 𝐷𝐶,𝑚𝑜𝑑 < 𝑃𝑖𝑛𝑣
𝑃𝑖𝑛𝑣, 1 −
𝜇
100
𝑃 𝐷𝐶,𝑚𝑜𝑑 > 𝑃𝑖𝑛𝑣
• PDC,mod is modeled average DC power (kW);
• PSTC is plant DC capacity (kW);
• GPOA is plane of array (POA) irradiance (W/m2);
• GSTC is test condition irradiance (1000 W/m2);
• δ is temperature coefficient for power of the modules (%/°C, typically negative);
• TSTC is standard test conditions cell temperature (25°C);
• TBOM is back of module (BOM) temperature (°C).
• PAC,mod is modeled average AC power (kW); and
• µ is a loss factor, including DC mismatch, DC wiring, inverter efficiency, etc. (%); and
• Pinv is inverter AC nameplate capacity (kW).