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O P T I M I Z I N G l M A N A G E M E N T
EPRI‐Sandia PV System Symposium
Sanjay Shrestha & Mat TaylorSOLV Performance Team
THE FEEDBACK LOOPOperat iona l data a s an i nput to PV des i gn
Design
Build
TestRun
Analyze• Capacity testing• Performance modeling• Soiling analytics?• Clipping effects• Modeled versus Measured• Informing design and EPC
Start‐up
Commissioning
Capacity Testing
O P T I M I Z I N G l M A N A G E M E N T
Q U I Z : W H AT A R E T H E T H R E E M O S T C O M P E L L I N G FA C TO R S A F F E C T I N G P E R F O R M A N C E ?
• Inverter downtime • Grid conditions • Ground faults
O P T I M I Z I N G l M A N A G E M E N T
INTRODUCTIONObservat ions on how feedback m ight work
What we’ve learned so far…
• Sensor accuracy is always a risk
• PVsyst doesn’t know what a combiner is
• AC loss models available at back‐feed
• Performance confused with Production
• Things that matter are:
• Clipping
• POI limiting
• Curtailment
Commissioning Process
• 20 Plants / 8 methods
• Same data sets
• Same simulation methods
• Several inverters / modules
• With and without substations
• 1.5 to 80.0 MWAC nameplate
• Total of 279 MWAC / 363 MWDC
• 1,500 MWAC by December 2016
O P T I M I Z I N G l M A N A G E M E N T
MODELING TO TESTINGt rack ing models through commissioningTest Method Key metrics Evaluation
Performance Ratio • Output at POI• POA insolation
• Includes clipping• Includes night load
SRE “PR‐250” • Output at POI• POA irradiance
• POA > 250 W/m²• Excludes clipping
PRmadj • Output at POI• POA irradiance• Module temperature• Inverter output kW
• POA > 250 W/m²• Excludes clipping• Wind speed filter• Shade and snow filter• Unstable irradiance
• POA > 250 W/m²• Excludes clipping• Wind speed filter• Shade and snow filter• Unstable irradiance
Regression at STC
Regression at RCM
Regression at PTC • Output at POI• POA irradiance• Inverter output kW• Ambient temperature• Wind Speed
ASTM E2848
SOLV ASTM E2848
O P T I M I Z I N G l M A N A G E M E N T
REAL DATA – REAL PLANTSMapp ing capac i t y te s t data to r i s k
O P T I M I Z I N G l M A N A G E M E N T
REAL DATA – REAL PLANTSMapp ing capac i t y te s t data to r i s k
O P T I M I Z I N G l M A N A G E M E N T
CAPACITY TESTING Measured ve rsus mode led … as a MODEL?
Normalized production
Peer‐to‐peerComparisons
ComparativeInput current
Production Specific Production Comparative productionNormalized string current Cumulative energy inputCB Behavior Baseline Comparison to Baseline
O P T I M I Z I N G l M A N A G E M E N T
FEEDBACK TO DESIGNOperat iona l data to i n fo rm systems eng ineer ing
Calibrated Simulation
• Hourly resolution
• Generalized soiling
• “Availability” assumption
• (quality/mismatch check)
• AC loss assumptions
• Clipping frequency
Operational Data
• 5‐minute resolution
• Localized soiling
• Lost energy calculation
• DC characterization
• AC collection model
• Clipping behavior
