Forecasting Tom Hoff
Integrating PV into Utility Planning and Operations 2014 Utility Solar Conference April 29th, 2014
Project Partners
Primary Funders
Power Industry
Project Lead 1
The Challenge of PV Integration PV production is subject to solar resource availability Many more generation sources: > 175,000 behind-the-meter PV systems now on the grid in CA
Two Goals for PV Integration Predict PV fleet production in the near-term
And in the long-term
• PV production needs to be time-synchronized with utility-load
Steps To Forecasting PV Fleet Output Step 1: Build PV Fleet from
and Other Sources
Step 2: Obtain Solar Resource Data from Forecast
Historical
Step 3: Simulate PV Fleet Production Using FleetView®
System Operation
Capacity Planning
Step 1 – Build PV Fleet PG&E Bay Area PG&E Non Bay Area SCE Coastal SCE Inland SDG&E
San Francisco
• 4.49 kW-AC • SunPower Inverter (SPR-5000X, 240V) • 27 Modules (SunPower 210 W, SPR-210-WHT) • 37.76281º N, 122.44313º W • Commissioned April 2008
Step 2: Obtain Solar Resource Data Satellite-derived time-series data from 1998 through the latest hour Forecasts up to 7-days in advance by combining cloud motion vector and numerical weather prediction
Credit: NASA
SolarAnywhere®: 1km x 1km Resolution Use SolarAnywhere API
Receive ½ Hour Data
Auto-Select 1 km Location 7
Step 3: Simulate PV Fleet Production
Note: Utility-sited systems include intertie systems in NV and AZ
Forecast Validation: 45 Systems on March 24, 2013 (Clear-Day) Time Horizon (Relative to Forecast Delivery) 0 – ¼ Hours
1 – 1¼ Hours
2 – 2¼ Hours
• Production forecast closely matches measured power production 9
Forecast Validation: 45 Systems on March 20, 2013 (Cloudy-Day) Time Horizon (Relative to Forecast Delivery) 0 – ¼ Hours
1 – 1¼ Hours
2 – 2¼ Hours
• Even on cloudy days 10
CAISO Fleet Results
Measured Data 18 PV systems Sept. 2011 to Aug. 2012 Half-hour data
Static Tuning
Relative Mean Absolute Error (%)
No Tuning
Dynamic Tuning
Capacity Planning: Hourly synchronized PV production and utility-load 60,000
1.7%/yr. growth
Demand (MW)
50,000 40,000 30,000 20,000 2020 Peak (w/12 GW PV) 2012 Peak (w/1.3 GW PV)
10,000
System Demand
Total Demand
0 0:00
6:00
12:00 Time of Day
18:00
0:00
Peak Day: August 13, 2012
Summary Fleet modeling used for both planning and operations Satellite-based resource data up to 1 km resolution Historical accuracy around 5% for half-hour forecasts (individual systems) Successful forecasting demonstration • Over 175,000 behind-the-meter PV systems • In operation for one year • Replicable throughout U.S.
13
Thank You Please feel free to contact us for any details or clarification related to presentation Tom Hoff Founder & Pres., Research [email protected]
Skip Dise SolarAnywhere Prod. Manager [email protected]
Adam Kankiewicz SolarAnywhere Research Spec. [email protected]
Copyright © 2012-2014 Clean Power Research, L.L.C. All rights reserved. The information herein is for informational purposes only and represents the current view of Clean Power Research, L.L.C. as of the date of this presentation. Because Clean Power Research must respond to changing market conditions, it should not be interpreted to be a commitment on the part of Clean Power Research, and Clean Power Research cannot guarantee the accuracy of any information provided after the date of this presentation. CLEAN POWER RESEARCH, L.L.C. MAKES NO WARRANTIES, EXPRESS, IMPLIED OR STATUTORY, AS TO THE INFORMATION IN THIS PRESENTATION.
Project Partners
Primary Funders
Power Industry
Project Lead 1
The Challenge of PV Integration PV production is subject to solar resource availability Many more generation sources: > 175,000 behind-the-meter PV systems now on the grid in CA
Two Goals for PV Integration Predict PV fleet production in the near-term
And in the long-term
• PV production needs to be time-synchronized with utility-load
Steps To Forecasting PV Fleet Output Step 1: Build PV Fleet from
and Other Sources
Step 2: Obtain Solar Resource Data from Forecast
Historical
Step 3: Simulate PV Fleet Production Using FleetView®
System Operation
Capacity Planning
Step 1 – Build PV Fleet PG&E Bay Area PG&E Non Bay Area SCE Coastal SCE Inland SDG&E
San Francisco
• 4.49 kW-AC • SunPower Inverter (SPR-5000X, 240V) • 27 Modules (SunPower 210 W, SPR-210-WHT) • 37.76281º N, 122.44313º W • Commissioned April 2008
Step 2: Obtain Solar Resource Data Satellite-derived time-series data from 1998 through the latest hour Forecasts up to 7-days in advance by combining cloud motion vector and numerical weather prediction
Credit: NASA
SolarAnywhere®: 1km x 1km Resolution Use SolarAnywhere API
Receive ½ Hour Data
Auto-Select 1 km Location 7
Step 3: Simulate PV Fleet Production
Note: Utility-sited systems include intertie systems in NV and AZ
Forecast Validation: 45 Systems on March 24, 2013 (Clear-Day) Time Horizon (Relative to Forecast Delivery) 0 – ¼ Hours
1 – 1¼ Hours
2 – 2¼ Hours
• Production forecast closely matches measured power production 9
Forecast Validation: 45 Systems on March 20, 2013 (Cloudy-Day) Time Horizon (Relative to Forecast Delivery) 0 – ¼ Hours
1 – 1¼ Hours
2 – 2¼ Hours
• Even on cloudy days 10
CAISO Fleet Results
Measured Data 18 PV systems Sept. 2011 to Aug. 2012 Half-hour data
Static Tuning
Relative Mean Absolute Error (%)
No Tuning
Dynamic Tuning
Capacity Planning: Hourly synchronized PV production and utility-load 60,000
1.7%/yr. growth
Demand (MW)
50,000 40,000 30,000 20,000 2020 Peak (w/12 GW PV) 2012 Peak (w/1.3 GW PV)
10,000
System Demand
Total Demand
0 0:00
6:00
12:00 Time of Day
18:00
0:00
Peak Day: August 13, 2012
Summary Fleet modeling used for both planning and operations Satellite-based resource data up to 1 km resolution Historical accuracy around 5% for half-hour forecasts (individual systems) Successful forecasting demonstration • Over 175,000 behind-the-meter PV systems • In operation for one year • Replicable throughout U.S.
13
Thank You Please feel free to contact us for any details or clarification related to presentation Tom Hoff Founder & Pres., Research [email protected]
Skip Dise SolarAnywhere Prod. Manager [email protected]
Adam Kankiewicz SolarAnywhere Research Spec. [email protected]
Copyright © 2012-2014 Clean Power Research, L.L.C. All rights reserved. The information herein is for informational purposes only and represents the current view of Clean Power Research, L.L.C. as of the date of this presentation. Because Clean Power Research must respond to changing market conditions, it should not be interpreted to be a commitment on the part of Clean Power Research, and Clean Power Research cannot guarantee the accuracy of any information provided after the date of this presentation. CLEAN POWER RESEARCH, L.L.C. MAKES NO WARRANTIES, EXPRESS, IMPLIED OR STATUTORY, AS TO THE INFORMATION IN THIS PRESENTATION.
