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Downscaling Solar Power 

Output to 4-Seconds for 

Use in Integration Studies 

Marissa Hummon

3rd International Solar Power 

Integration Workshop

October 20-22, 2013

London, UK

NREL/PR-6A20-60336
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Executive Summary

High penetration renewable integration studies require solar power data with high 

spatial and temporal accuracy to quantify the impact of high frequency solar power 

ramps on the operation of the system. Our previous work concentrated on 

downscaling solar power from one hour to one minute by simulation. This method 

used clearness classifications to categorize temporal and spatial variability, and 

iterative methods to simulate intra-hour clearness variability. We determined that 

solar power ramp correlations between sites decrease with distance and the 

duration of the ramp, starting at around 0.6 for 30-minute ramps between sites that 

are less than 20 km apart. The sub-hour irradiance algorithm we developed has a 

noise floor that causes the correlations to approach ~0.005. Below one minute, the 

majority of the correlations of solar power ramps between sites less than 20 km 

apart are zero, and thus a new method to simulate intra-minute variability is needed. 

These intra-minute solar power ramps can be simulated using several methods, three 

of which we evaluate: a cubic spline fit to the one-minute solar power data; 

projection of the power spectral density toward the higher frequency domain; and 

average high frequency power spectral density from measured data. Each of these 

methods either under- or over-estimates the variability of intra-minute solar power 

ramps. We show that an optimized weighted linear sum of methods, dependent on 

the classification of temporal variability of the segment of one-minute solar power 

data, yields time series and ramp distributions similar to measured high-resolution 

solar irradiance data. 

Corresponding paper:

Hummon, M., Weekley, A., Searight, K., and Clark, K., “Downscaling Solar 

Power Output to 4-Seconds for Use in Integration Studies”, 3rd International 

Solar Power Integration Workshop, London, UK, October 20-22, 2013. (NREL 

CP-6A20-60335).
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A most significant figure...

The lack of availability of 

sub-hour solar data 

impedes the study and 

adoption of high 

penetration solar energy.

Planners turn to “worse 

case scenario” data: 

single measurement site 

scaled to desired 

penetration.

Result:

Extremely large reserves 

requirements; solar 

integration costs are 

inflated. greentech media cites this as one of the “most 

notorious and historically significant slides in our 

industry” and “have served as change agents in 

greentech circles”
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Solar Integration Studies

Solar Integration 

Study Phase

Impact Solar data requirements*

Time (span, 

resolution)

Space (span, 

resolution) Other

Site Selection

Site selection is a balancing act between 

resource quality, access to transmission, and 

coincidence of solar resource availability and 

system demand

10+ years, 1-hour Nationwide, 

~county/zip code

Capacity factor and 

variability weighting

Operating reserves

Carry too much operating reserves increases 

integration costs; carry too little operating 

reserves decreases system reliability, which in 

turn increases integration costs

2-3 years, 1-minute

Study region (e.g. 

Eastern 

Interconnect), <10 

km

Clear sky solar data 

for all location/time 

periods

Unit commitment 

modeling

Economics of system operation; Curtailment 

of solar energy; Changes in emissions, 

conventional fleet operation.

1 year, 1-hour

Study region (e.g. 

Eastern 

Interconnect), <10 

km

Requires forecast 

data: day-ahead, 4-

hour ahead

Economic dispatch 

modeling

Economics of 5-minute operation including 

deplyment of flexibility reserves for intra-hour 

changes in solar, wind, and load

1 year, 5-minute

Study region (e.g. 

Eastern 

Interconnect), <10 

km

Reserves 

adequacy/ 

reliability analysis

System reliability (measuring CPS-2 violations) 

with increasing renewable penetration; 

mitigation options (DR, storage)

1 year, 4-second

Study region (e.g. 

Eastern 

Interconnect), <10 

km

* Validated with ground measurements (of the appropriate 

temporal resolution)
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Frequency Regulation Requirements

System load following and regulation. Regulation (red) is the fast fluctuating component of total 

load (green) while load following (blue) is the slower trend 
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Frequency Regulation and Solar Power
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How do we measure/model solar radiation?

• Ground Measurements:

Pyranometers, Pyrheliometers, etc.

o Advantages: accurate, high temporal resolution.

o Disadvantages: local coverage, regular maintenance and 

calibration. 

• Satellite derived estimates:

Physical (e.g. GSIP), empirical (e.g. SUNY), etc.

o Advantages: global coverage, reasonably long time series,

o Disadvantages: spatial and temporal resolution, 

complicated retrieval process, accuracy depends on 

information content of satellite channels.

• Numerical Weather Prediction based estimates

NAM, GFS, ECMWF

o Advantages: global coverage, long time series (reanalysis 

data), increasing computing capability results in 

increasing resolution.

o Disadvantages: level of accuracy especially in cloud 

formation and dissipation (initialization and model physics 

issues).
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4-second Solar Power Data Algorithm

Andy Carter, View of clouds from above, South Africa.
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4-second Solar Power Data Algorithm

Andy Carter, View of clouds from above, South Africa.

Analytical Approach:

o 4-s Algorithm based on post processing on 

1-minute solar power data

o Assumptions:

– 4-second ramp distribution to be related to the 

1-minute ramp distribution, e.g. 

– Expect no correlation in 4-second ramps 

between sites
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Clearness Index

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Down scaling to one minute...

We used spatial variability to determine the most likely temporal variability. 

The overlapping spatial data results in the temporal data having a higher 

correlation with nearby sites than with sites far away.

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4-s Algorithm

• High frequency behavior 

can be estimated in 

frequency space using 

several methods.

• Some of the methods are 

better suited to particular 

variability classes than 

others.

• This step includes an 

optimal linear combination 

of the different downscaling 

methods for each variability 

class.
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Measured data sources

Oahu, Hawaii Irradiance 

Sensor Network

NREL campus Irradiance 

Sensor Network
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Downscaling to 4-s in frequency space

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Downscaling to 4-s in frequency space

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Downscaling to 4-s in frequency space

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Spectral amplitude from historic data

Spectral average from 

historic data shows 

distinct PSD for each 

class of temporal data.

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Downscaling to 4-s in frequency space

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Downscaling to 4-s in frequency space

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Solution

Weighting Downscaling 

Methods by Temporal Class Class of Temporal Variability (from 1-minute input data)

0 I II III IV V

Downscaling 

method

Spline 0.47 0.71 0.44 0.47 0.31 0.32

Linear fit over all 

frequencies 0.27 0.01 0.28 0.35 0.36 0.22

Linear fit over a subset of 

higher frequencies 0.11 0 0.11 0.1 0.16 0.45

Spectral amplitude 

estimated from historical 

average FFT

0.15 0.15 0.16 0.08 0.17 0.01

Error (MSE)

CI Time series 9.2E-05 6.3E-06 5.2E-04 1.8E-03 1.9E-03 1.6E-02

CI 4-s ramp distributions 6.6E-07 8.8E-11 3.6E-06 9.1E-06 1.0E-05 4.9E-06

Concluded: Different methods for different classes

Developed: Optimal weighting of methods for each class

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Application of 4-s Solar Power Data

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Results

E. Ela evaluated FERC 

Order 755 (the regulation 

market “pay for 

performance” order) in 

SMUD using the 4s solar 

data and fast response 

storage.

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Conclusions

• Presented a method of downscaling 1-minute solar data 

to 4-seconds by optimizing the linear combination of four 

methods for extending the PSD from f = 1/120 to 1/2 Hz 

(from 2-minutes to 2-seconds). The optimized weights 

vary by classification of the 1-minute temporal variability. 

• Demonstrated the quality of the fit by observing the mean 

squared error of the modelled data.

• Modelled data performs very well in comparing the 

distributions of ramps, which makes this dataset ideal for 

use in integration studies concerned with the rapid change 

in solar power output.

• Four-second solar power data enables researchers to 

investigate new methods of balancing the system under 

high penetrations of solar power. 
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Thank You

Questions?

Team: 

Marissa Hummon

Andrew Weekley

Keith Searight

Kara Clark

Contact: marissa.hummon@nrel.gov