10 June 2014 High-Frequency Irradiance and Power Output for … · 2016. 10. 18. · Albany, New...

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10 June 2014

High-Frequency Irradiance and Power Output for Grid Integration and Storage Evaluations Jaclyn Frank

Senior Research Scientist

Dr. Michael Brower

Chief Technical Officer

Senior Research Scientist

Dr. Philippe Beaucage



Outline

• Background on solar integration study

• Technical approach:

– coupled mesoscale-microscale model + onsite measurements

• Validation

• Results

– Diurnal profiles

– Ramp frequency distribution

• Summary



Hawaii Solar Integration Study

• 2 years of 2-second PV output

• Utility-scale & distributed rooftop PV

• Single-axis tracking, fixed rooftop

• 500+ MW hypothetical sites

• Collaboration with HECO, GE, NREL

• Determine impact of increasing renewables on the electrical grid

• Oahu and Maui



Available Measured Data

• HECO’s “Sun Power for Schools”

– 6 Oahu Schools 2007-2008

– 15-minute average GHI and PV

• NREL Measurement and Instrumentation Data Center

– 4 Oahu stations with 1-second data since June 2009

– 2 stations with all components

• Oahu Pyranometer Array

– 17 instruments within 1 km2

– 1-second GHI since March 2010

• ASOS – 5-minute cloud cover

4 9

12

17



Challenges of Modeling High-Frequency PV

• Numerical models and satellite imagery won’t get high-frequency ramps correctly

• Point measurements do not represent plant-wide aggregate

Stochastic-kinematic cloud model developed to address these issues



Overview of Modeling Approach

Numerical Weather Prediction Model

• Solar irradiance

• Cloud cover fraction

• Cloud-base wind speed, direction

• 10-m wind speed, 2-m temperature (for power conversion)

Stochastic-Kinematic Cloud Model

• Cloud location

• Cloud size

• Cloud opacity

• Cloud overlap and edge effects

• Solar irradiance and power generation

MESOSCALE MICROSCALE

Reference: Beaucage, P., M.C. Brower, J.D. Frank and J.D. Freedman (2012). "Development of a Stochastic-Kinematic Cloud Model To Generate High-Frequency Solar Irradiance and Power Data”. Proceedings of the ASES conference, Boulder (CO), May 14th, 2012



Cloud Animation

Oahu • June 4th, 2007 • 6 AM – 7 PM

• Area ~100x100 km2 • Background = cloud cover

fraction from mesoscale model



Conversion to Power

• Extract irradiance at each point

• Convert to power with in-house software

• Aggregate over utility-scale plant or distributed PV area

1 km

1 km



Reality Check: Does Modeled Data Match Observations?

• An exact match with observations is impossible

• Synthesized data should be statistically indistinguishable from real data for parameters of interest

• What are the parameters of interest? – Mean output

– Diurnal, monthly patterns

– Dynamic behavior (spectrum of ramps) as a function of plant size/aggregation

– Correlation between sites

A typical pattern of direct and diffuse horizontal irradiance on Oahu



Validation - Irradiance Diurnal Mean Profiles

• January 2011

• Diurnal means, max match well for each component

• High bias in warm months, low bias in cool months

GHI DNI DHI

Model Mean Obs Mean Model Max Obs Max Clear Sky



HECO Station Validation: 2010-2011

• GHI bias (%)

• Small, acceptable low bias: 2%

• Model performs better on western side of Oahu

• Perhaps not fully capturing the impact of mountains?



Irradiance Ramps – Aggregation of Several Measurements

5 HECO stations

NREL17

6-sec 1-min 10-min

Modeled ramps slightly more variable than observed, but match distributions well



Applications for Grid Integration and Storage Evaluation

• Estimating sustainable penetration levels of solar energy in an electrical grid – Optimizing spatial distribution and

size of solar PV arrays

• Sizing of battery bank for storage to reduce:

– down ramps

– solar curtailment

http://www.solsystemscompany.com/blog/wp-content/uploads/2014/05/Solar+Battery.jpg



Summary

• Generated 2 years of 2-second PV output at 80+ hypothetical sites for the Hawaii Solar Integration Study

• Mean diurnal profiles fit well with available high-quality observations

• Ramp distributions agree well with observed

• Future work: – Test model in other climates

– Validate further with utility-scale PV data

Useful tool to estimate ramps at hypothetical (pre- construction) PV sites

New Approach: Mesoscale NWP model + microscale stochastic-kinematic cloud model



Thank You

+1 877-899-3463

[email protected]

awstruepower.com

Dr. Philippe Beaucage

[email protected]

Jaclyn Frank

[email protected]

mailto:[email protected]

mailto:[email protected]

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