1. CROSS 2017 Symposium October 2017
pvlib-python et al
CROSS 2017 Symposium
October 2017
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2. Sunshine Analytics LLC
Introduction
Jessica Forbess
Sunshine Analytics
2013 – present
First Solar
EPC – testing
DNV GL
IE – due diligence
Electrical engineer, remote network monitoring for cell networks, leveraged into solar monitoring, leveraged into analytics
Sunshine Analytics LLC

3. PV Energy Modeling – What?
Location
Sun position / Shading
Meteo
Irradiance / Temperature / Wind
Precipitation (dust / snow soiling)
Orientation
Tilt / Azimuth
Equipment
Modules
Inverters
Other system losses
Energy Output
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4. PV Energy Modeling – Why?
Developers want a number to sell the project
EPCs need a number to test against
Debt providers need a conservative number to cover the loan
Equity providers want to know what the upside is
O&M needs to have a baseline to know if there are operational issues
Regulators / Off-takers increasingly care about power quality as well as energy – and forecasting
Independent Advisors are called upon to create models for each of these scenarios
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5. Consider the timescale – Annual/month
Annual / monthly
Overall value, cash flows
Metrics:
Yield
Performance Ratio
Tool:
PVWatts – NREL (mostly deprecated)
Annual and monthly models used to be pretty typical, with insolation for the location calculated on either of those timescales, and then fixed factors applied
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6. Consider the timescale – Subhourly
Hourly / subhourly
Testing against measured data
Troubleshooting
Metrics:
regressions
actual vs expected
peer comparisons
Tools:
PVsyst – the gold standard
SAM – NREL’s more detailed modeling tool
Helioscope / Aurora – C&I, portfolio based, some automated shading methods
Developer models – SPWR and FSLR
pvlib – Sandia developed
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7. Consider the timescale – Subsecond
Subsecond / subcycle
Power quality
Tools:
GridPV?
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8. Current PV Modeling Tools
Free as in Beer and/or speech
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9. Sunshine Analytics LLC
SAM
SAM – System Advisor Model
Slightly less tweakable than PVsyst
SDK for inclusion in applications
Open sourced soon?
Workflow designed to get annual energy models to estimate project value
Can extend to project lifetime value
Great documentation of modeling methods and calculations
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10. Sunshine Analytics LLC
pvlib
Sandia internal tool in MATLAB
Standardized analyses across the PV group
python library ported in 2013
Now maintained on github by Sandia and a assistant prof at U of Arizona
user base growing
conda and pip packages
BSD3 license
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11. Sunshine Analytics LLC
pecos
Sandia internal tool
Monitoring multiple experimental PV arrays
Now maintained on github by Sandia
pip package
modified BSD3 license
Designed to monitor and perform quality checks on time series data
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12. Sunshine Analytics LLC
pvlib – toolbox
Location
Sun position / Shading
Meteo
Irradiance / Temperature / Wind
Precipitation (dust / snow soiling)
Orientation
Tilt / Azimuth
Equipment
Modules
Inverters
Other system losses
Energy Output
module = sandia_modules['Canadian_Solar_CS5P_220M___2009_']
inverter = cec_inverters['SMA_America__SC630CP_US_315V__CEC_2012_']
system = SingleAxisTracker(module_parameters=module,
inverter_parameters=inverter,
series_modules=15, parallel_modules=300)
lat, lon = 45.5, # Portland, OR
start = pd.Timestamp.now()
end = start + pd.Timedelta(days=7)
for fx_class in [GFS, NAM, HRRR, RAP, NDFD]:
fx_model = fx_class()
fx_data = fx_model.get_processed_data(lat, lon, start, end)
irradiance = fx_data[['ghi', 'dni', 'dhi']]
weather = fx_data[['wind_speed', 'temp_air']]
mc = ModelChain(system, fx_model.location)
mc.run_model(fx_data.index, irradiance, weather)
mc.ac.plot()
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13. Sunshine Analytics LLC
pvlib – Forecasting
Forecasting is rare in standard PV modeling tools
Unidata’s Siphon library simplifies access to forecast data hosted on the Unidata THREDDS catalog
Estimated meteo parameters are provided by an external source, so the weather model is not part of the library
Irradiance components may be poor quality
Estimating irradiance from cloud cover is part of the pvlib forecast process
Experimentation is welcome here
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14. Typical process – Actual vs Expected
jupyter notebook
config file for system equipment and location
measured meteo data and energy
run meteo through model, compare to energy on hourly, daily, monthly basis
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15. Typical process – Hourly kW comparison
jupyter notebook
create typical year energy model (hourly)
calculate probabilities that a given hour of a given month would be at a given kW output
Use seaborn to visualize
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16. pvlib – clear sky detection
Standard library function to detect clear sky periods based on a paper by Matthew Reno
Clear sky periods better for certain analysis, peer to peer comparisons
Being able to automatically filter for those periods is very powerful
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17. Sunshine Analytics LLC
Further development
Shading
Row to row shading
Topographic impacts on row to row
External objects
Interaction with storage and the grid
Optimizing value of PV capacity, battery capacity based on local load and time-of-use rates
EV charging / discharging
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