NREL is a national laboratory of the U.S. Department of Energy, Office of Energy 

Efficiency & Renewable Energy, operated by the Alliance for Sustainable Energy, LLC.

Contract No. DE-AC36-08GO28308

Analysis of the Impacts of 

Distribution-Connected PV 

Using High-Speed Data Sets 

Preprint

Jason Bank and Barry Mather

To be presented at the IEEE Green Technologies Conference

Denver, Colorado

April 4-5, 2013

Conference Paper

NREL/CP-5500-57787

March 2013
NOTICE

The submitted manuscript has been offered by an employee of the Alliance for Sustainable Energy, LLC 

(Alliance), a contractor of the US Government under Contract No. DE-AC36-08GO28308. Accordingly, the US 

Government and Alliance retain a nonexclusive royalty-free license to publish or reproduce the published form of 

this contribution, or allow others to do so, for US Government purposes.

This report was prepared as an account of work sponsored by an agency of the United States government. 

Neither the United States government nor any agency thereof, nor any of their employees, makes any warranty, 

express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness of 

any information, apparatus, product, or process disclosed, or represents that its use would not infringe privately 

owned rights. Reference herein to any specific commercial product, process, or service by trade name, 

trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recommendation, 

or favoring by the United States government or any agency thereof. The views and opinions of authors 

expressed herein do not necessarily state or reflect those of the United States government or any agency thereof.

Available electronically at http://www.osti.gov/bridge

Available for a processing fee to U.S. Department of Energy

and its contractors, in paper, from:

U.S. Department of Energy

Office of Scientific and Technical Information

P.O. Box 62

Oak Ridge, TN 37831-0062

phone: 865.576.8401

fax: 865.576.5728

email: mailto:reports@adonis.osti.gov

Available for sale to the public, in paper, from:

U.S. Department of Commerce

National Technical Information Service

5285 Port Royal Road

Springfield, VA 22161

phone: 800.553.6847

fax: 703.605.6900

email: orders@ntis.fedworld.gov

online ordering: http://www.ntis.gov/help/ordermethods.aspx

Cover Photos: (left to right) PIX 16416, PIX 17423, PIX 16560, PIX 17613, PIX 17436, PIX 17721

Printed on paper containing at least 50% wastepaper, including 10% post consumer waste.
1

Analysis of the Impacts of DistributionConnected PV Using High-Speed Data Sets

Jason Bank, IEEE Member, Barry Mather, IEEE Member

Abstract -- High penetrations of distribution-connected 

photovoltaic (PV) systems are becoming more common. 

However, the impact of these variable generators on system 

voltage and automatic voltage regulation equipment is not well 

quantified. In contrast to load, which generally has some 

diversity, PV systems are often non-diverse over small 

geographic areas. Variability caused by PV can range from 

relatively slow changes in system voltage to high-frequency 

impacts on real and reactive power. These changes have the 

ability to impact the operation of a distribution circuit from a 

protection, voltage control, and load prediction/modeling point of 

view.

This paper utilizes information from high resolution data 

acquisition systems developed at the National Renewable Energy 

Laboratory and deployed on a high-penetration PV distribution 

system to analyze the variability of different electrical 

parameters. High-resolution solar irradiance data is also 

available in the same area which is used to characterize the 

available resource and how it affects the electrical characteristics 

of the study circuit. This paper takes a data-driven look at the 

variability caused by load and compares those results against 

times when significant PV production is present. Comparisons 

between the variability in system load and the variability of 

distributed PV generation are made. Additionally, the potential 

impacts of high-penetration PV on voltage regulation equipment 

such as capacitor banks and load tap changing transformers are

quantified.

I. INTRODUCTION

The Anatolia SolarSmart Community is a neighborhood 

southeast of Sacramento, California. It is an area in which 

each house has been built with highly energy efficient home 

features and an average of 2.5 kW of PV on each house. 

Because of this high concentration of PV generation the

Sacramento Municipal Utility District (SMUD) has several 

ongoing research projects in this neighborhood and 

distribution circuit. In order to support these research efforts 

the National Renewable Energy Laboratory (NREL) has been 

developing measurement devices and a supporting data 

collection network specifically targeted at distribution 

systems. This measurement network is designed to apply realtime and high-speed (sub-second) measurement principles to 

distribution systems that are already common for the 

transmission level in the form of phasor measurement units 

(PMU) and related technologies. Twelve of these meters have 

been installed on distribution transformers in the Anatolia 

neighborhood and voltage, current, and power measurements 

have been collected at a resolution of one second, covering 

almost two years. This paper aims to mine these extensive 

data sets to investigate some of the impacts high-penetration

PV has been having on this distribution circuit; specifically, 

the relative diversity factors of load on the circuit and PV 

generation at various points of aggregation and the impact on 

automatic voltage regulation equipment, both at the 

distribution and sub-transmission level.

II. METERING DEVICES AND SOURCE DATA

A. Distribution Transformer Metering

Over the past few years NREL has been developing highspeed, real-time metering hardware targeted specifically for 

installation at the distribution level [1],[2],[3]. These meters

implement phasor calculations and communications 

architecture similar to that of PMUs [4],[5]. Additionally,

these units include support for several power quality 

calculations. These meters are referred to as Distribution 

Monitoring Units (DMU) throughout this paper.

Design of the DMUs has been specifically targeted for 

installation at remote and disparate points within a distribution 

circuit, particularly at customer service voltage levels. The 

DMUs are equipped with a cellular modem that provides an 

Internet connection through the network of a cellular provider. 

Data is collected on a point-by-point basis in real time through 

this Internet connection. Servers at NREL are responsible for 

aggregating, correlating, and storing all of these data sets.

A picture of a DMU installed on the secondary of a 

transformer in Anatolia is shown in Fig. 1. Each of the voltage 

clips has been attached to the two hot and the neutral Z-bars,

and the current sensors are attached around the conductors 

going out to the homes. The DMU also requires an antenna for 

its GPS and cellular connections. In this case, a black hockeypuck style combination antenna was mounted to the top of the 

transformer case. 

NREL in conjunction with SMUD has deployed 12 DMUs 

on the secondary side of 50 kVA and 75 kVA pad mount,

split-phase residential transformers in the Anatolia 

neighborhood. DMU installation in this community began in 

March 2011, with installations taking place in several waves 

since that time. The DMUs have been collecting one-second 

data since their install dates, all of which has been stored on 

the NREL servers and made available for project team 

members. The distribution transformers in the Anatolia 

community generally feed between 8 and 12 single-family 

homes. Jason Bank (e-mail: jason.bank@nrel.gov) and Barry Mather (email: barry.mather@nrel.gov) are with the National Renewable 

Energy Laboratory, Golden, CO 80401 USA.
2

Fig. 1: DMU installed on a transformer secondary

Photo credit Jason Bank, NREL

B. Solar Resource Measurments

In addition to the above electrical metering, NREL has 

deployed a Global Horizontal Irradiance (GHI) sensor, 

weather station, and rotating shadow band radiometer (RSR) 

in the area of the Anatolia community. The GHI sensor 

provides one-second irradiance data and is situated at the 

north side of the neighborhood. The weather station and RSR 

provide one-minute data across several irradiance and 

environmental factors and are located about a mile to the 

northwest of the neighborhood in the Anatolia-Chrysanthy 

substation. Both of these units are part of the Measurement 

and Instrumentation Data Center (MIDC); more information 

on MIDC and publically-available data from several projects 

are available at www.nrel.gov/midc.

C. SCADA Data

Furthermore, data is being collected and stored through 

SMUD’s SCADA system. The SCADA data used here is 

taken at the feeder level where it enters the Anatolia 

neighborhood. Three-phase RMS voltage, RMS current, and 

real power and reactive power measurements are available at 

five-second resolution at this point.

III. POWER SPECTRAL DENSITY ANALYSIS

The high-resolution metering which has been installed at 

the distribution transformer level in the Anatolia 

neighborhood gives valuable insight in the nature of the loads 

and PV generation in the area. The available one-second 

resolution on the power measurements gives incredible detail 

into the variable nature of both the load and PV. Many of 

these variable features are periodic in nature due to the cycling 

of household loads. The interaction of these elements 

determines the total load seen by the utility and is thus 

important for advanced load modeling and assessment of the 

impacts of variable distributed generation.

Previous works have applied the concepts of Fourier 

Transforms and Power Spectral Density (PSD) to assess the 

variable nature of load and renewable generation [6],[7],[8]. 

These methods are utilized in the analysis detailed in this 

paper.

This method was applied to a set of DMU data to provide 

an example of how to use this analysis and to demonstrate the

results. The one-second real and reactive power measurements 

on August 16, 2012 from a transformer were used as the input 

data set. This transformer serves 10 single-family homes, so 

the measurements represent the summation of 10 residential 

customers. The results of the spectral analysis of this data are 

presented in Fig. 2. Here the higher order frequency terms 

from the Fourier Transform are truncated as they are of 

extremely low magnitude and primarily represent sampling 

and windowing error and show no discernible features.

Fig. 2: Power spectra of transformer power measurements

The points to the far left of Fig. 2 represent the lower order, 

higher magnitude components of the load waveform, i.e. the 

daily load profile. There are few points in this region because 

of the limited size of the input data set (a single day). Points 

further to the right on the x-axis represent periodic loads of 

successively higher switching frequencies. The y-axis gives 

the power drawn by the corresponding load component. Here 

the real power components are plotted in black, and the 

reactive power in red. The thick band running diagonally 

through Fig. 2 represents components resulting from 

measurement noise, windowing error, and aperiodic load 

elements. Periodic components of the measurement waveform 

show up as spikes above this band at the appropriate 

frequency value.

Inspection of this plot reveals two distinct spikes below the 

daily load and PV profiles. The first of these represents 

periodic loads that switch at a period of about 20 minutes (f ≈ 

8.5*10-4 Hz); both the real and reactive power demonstrate 

this behavior. The second feature is components with 

switching periods on the order of 100 seconds (f ≈ 1*10-2 Hz);

in this case, only the real power demonstrates this spike with 

the reactive power remaining relatively consistent in this 

frequency span. Examples of the load components which are 

driving these features of the spectral density are given in Fig. 

3 and Fig. 4.

20 minute components

100 second components
3

Fig. 3 gives an example of the load behavior which is 

driving the frequency components with a 20-minute period. 

This time series plot is a subset of the data set used to 

construct the power spectra of Fig. 2. A 4 kW, 2 kVAr load is 

switching on and off throughout the 2-hour window with a 

period on the order of 20 minutes as expected. Given the time 

of year, the size of the load, and the period, this is most likely 

residential air conditioning loads.

Fig. 3: Load component with 19-minute switching period

The other prominent component identified in Fig. 2 is a real 

power load switching at a period of about 100 seconds. Some 

of this behavior is demonstrated in the time series plot of Fig. 

4. Over this 10-minute span, a load of about 5 kW is switching 

on and off on at a 100-second interval. Additionally the 

reactive power over this span is relatively constant with little 

correlation to the switching in real power, indicating that the 

load in question has no reactive component. Once again this 

reflects the results of Fig. 2 which showed a load switching 

with a 100-second period that draws real power but not 

reactive.

Fig. 4: Load component with 100 sec switching period

The frequency domain analysis, as demonstrated in Fig. 2,

will be used throughout this paper to identify periodic features 

in the load and solar resource measurements. It is important to 

note that these load measurements represent the aggregation of 

10 residential customers, so the periodic waveforms of Fig. 3

and Fig. 4 may not necessarily be one particular load but up to 

10 similar loads switching in the same time frame. For 

example, the waveform of Fig. 3 may actually be up to 10 air 

conditioning units switching in the same time frame with each 

of the ‘on’ periods representing a different piece of physical 

equipment.

IV. PERIODIC LOADS AND AGGREGATION

The example data set demonstrated some of the periodic 

loads and how they are reflected in the power spectra. In that 

case, 1 transformer was considered which served 10 houses. In 

order to assess the effects of these components at the system 

level, several successively higher load aggregation levels were 

considered.

A set of data ranging from July 2 to July 8 2012 was chosen 

as the input. This particular set of days was selected because,

during this time, the solar resource measurements 

demonstrated very little variability. The low variability in 

irradiance should give a consistent PV output in the area and 

limit the short term variability to only effects caused by loads.

Data from 10 of the distribution transformers and the 

SCADA measurement point are available from this time span. 

The measured real power waveforms for all for these locations 

are used as the input data for the following power spectral 

analysis. The power spectra were computed for four different 

load waveforms representing different aggregation points of 

the load. These power spectra are given in Fig. 5. The black 

trace in Fig. 5 is the power spectra of a single transformer. The 

red trace is the power spectra of five transformers, with their 

power measurements summed on a point-by-point basis to 

produce the input for the Fourier Transform. The power from 

10 transformers is summed to produce the spectra given by the 

green trace. Finally, the blue trace gives the power spectra for 

the entire Anatolia neighborhood, as measured by the SCADA 

point, representing about 70 transformers. In order to put these 

results on the same vertical scale, all of the measurement data 

was normalized before performing the Fourier Transform.

Thus, in Fig. 5 the vertical axis does not give the absolute 

power of the component, but its value as a percentage of the 

total power of the source signal.

Fig. 5: Power spectra of load at multiple aggregation levels

Several prominent load components are prevalent in the 

power spectra of Fig. 5. The lower frequency components 

include load characteristics with 24, 12 and 6 hour periods. 

These represent the daily load profile, the daily PV production 

20 minutes

100 seconds

24 hour

12 hour

6 hour

20 min

100 sec
4

curves and other hourly trends. These components contain 

most of the power in the data set. The next major components 

are centered on a 20-minute period; these are the air 

conditioning loads discussed previously. While all 4 of the 

spectra demonstrate the 20-minute periodic load, they have a 

noticeable decrease in magnitude for larger aggregation levels.

Finally, a 100-second periodic load is also present but is only 

noticeable in the single transformer data set.

The results of Fig. 5 demonstrate some of the effects of 

load diversity. The longer term load characteristics in the 24, 

12 and 6 hour regions have successively higher magnitudes 

for larger aggregations of load. On the other end of the scale,

the single transformer demonstrates that more of its power is

concentrated in shorter term, more variable load components. 

As more and more transformers are included, the effects of 

smaller, variable loads are less prominent. Additionally, 

similar periodic loads may be out of phase with each other, 

effectively offsetting each other as more loads are included.

V. SEASONAL LOAD DIFFERENCES

Throughout the data sets presented so far, a 20-minute 

periodic load has been observed. Based on the size of the 

loads, the way they switch, and that the data has been from the 

summer months, it was claimed that these were residential air 

conditioning loads. In order to verify this, the power spectra 

for a set of real power SCADA data from January 2012 was 

computed. Once again, this data covered a week of clear days 

in order to limit the variability effects of the PV generation. 

This power spectrum is plotted in red on Fig. 6 against the 

previously-used spectra for the July SCADA data in black.

Fig. 6: Total load spectra in winter and summer

These two spectra track each other extremely closely and 

the only sizable difference occurs in the 20-minute region. The 

January measurement data shows no discernible spike at 20 

minutes as the results in that region fall into the noise band. 

This is as expected, since there should be no air conditioning 

load running in January.

VI. EFFECTS OF PV VARIABILITY

The Anatolia neighborhood incorporates a large amount of 

rooftop PV generation. Each house in the community is built 

and sold with integrated solar panels. On average throughout 

the community, each home has 2.5 kW of PV. Across the 

community of 343 houses, a total of 867 kW of PV capacity is 

installed. The variable nature of the solar resource introduces 

another level of variability into this circuit through the PV 

generation. 

Since the PV is installed on the customer rooftop level, both 

the distribution transformer and SCADA power measurements 

include the PV generation and the load netted together. To this 

point, clear sky days have been used for the analysis of load 

data to limit the impacts of solar resource variability on the 

results. In order to assess the impacts of solar resource 

variability, cloudy and clear days are compared. Here data sets 

from January are used because the winter months were found 

to contain the least variable load characteristics.

Seven clear days and seven cloudy days from between

January 1 and January 18, 2012 were selected for analysis. 

One-second global horizontal irradiance measurements from 

the north side of the neighborhood were compiled and the 

resulting power spectra were computed for this data. Fig. 7

presents these results, with the clear days plotted in red and 

the cloudy days in black.

Fig. 7: Solar resource spectra for clear and cloudy days

As before, the 24, 12 and 6 hour components are prevalent 

in both trends and contain most of the power in the signal. To 

the right of these points, the two spectra become markedly 

different though. The cloudy day demonstrates significantly 

higher power throughout the 2.7 hour (f ≈10-4 Hz) to 10 sec (f

≈10-1 Hz) band. This indicates significantly higher resource 

variability on the cloudy and overcast days as expected.

Neither spectrum has any prominent spikes in this region 

though, indicating the random, aperiodic nature of the cloud 

passage through this area.

The effect of the PV variability can be seen at the 

neighborhood level through the SCADA measurements. In 

Fig. 8, the spectra of the SCADA real power measurements is 

plotted in black for the cloudy days and in red for the clear 

days. The cloudy days demonstrate more power in the 2.7 

hour (f ≈10-4 Hz) to 20 sec (f ≈5*10-2 Hz) band than the clear 

days do. 

24 hour

12 hour

6 hour

20 min
5

Fig. 8: Power spectra of SCADA data on clear and cloudy days

Given the small area covered by this neighborhood, less 

than half a square mile, the irradiance throughout should be 

highly correlated and non-diverse. With all the PV panels 

experiencing very similar solar resource, the variability in 

irradiance should be well correlated with the variability in 

total power of the neighborhood. Up to a point, this is seen in 

Fig. 7 and Fig. 8; while the clear (low PV variability) and 

cloudy (high PV variability) days show drastic difference 

between the irradiances in Fig. 7, these differences are still 

present in Fig. 8 but are not as pronounced.

While the available resource is non-diverse across the 

neighborhood, several other factors are at play which can 

serve to limit the variability of the PV seen at the system level. 

The solar panels throughout the area are all roof-mounted on 

the homes and thus do not all have the same orientation, with 

most ranging from southeast facing to southwest. This 

diversity in PV panel orientation serves to lessen the impacts 

of non-diverse global horizontal irradiance as each panel is 

receiving slightly different incident irradiance. Additionally,

the load in the neighborhood is being netted against the PV 

generation before the measurement point and thus makes up a 

smaller percentage of the total signal power, reducing its 

prominence in the spectra of Fig. 8.

VII. EFFECTS ON VOLTAGE CONTROL ELEMENTS

The impact on automatic voltage regulation equipment such 

as voltage regulators located on a distribution circuit, as well 

as load tap changing transformers and switched capacitors

located at a distribution substation, has been previously 

studied through the simulation of circuit operation [9]. In this 

section, analysis of the high-quality data sets from the 

Anatolia neighborhood is used to gain an insight into how the 

variable nature of PV systems’ generation profiles impact the 

automatic voltage regulation equipment used to regulate the 

voltage on the distribution circuit. The analysis, described 

below, aims at developing a proxy for the general control 

profile of the load tap changing transformer and switched 

capacitor banks located at the Anatolia-Chrysanthy substation. 

The distribution circuit does not have a voltage regulator or 

switched capacitors located on the distribution circuit itself.

A. Description of Data Analysis used to Approximate the 

Impact of PV Variability on Automatic Voltage Regulation 

Equipment

Data was available for a total of 12 distribution 

transformers located in the Anatolia neighborhood. Each of 

these distribution transformers is connect to a single phase of 

the medium voltage three-phase system, and of the 12 units, 6 

are connected to phase A, 5 are connected to phase B, and 1 

unit is connected to phase C. Note – these phase assignments 

are somewhat arbitrary relative to the rest of the utility 

distribution system but will be used in the description of this 

analysis for simplicity. 

The goal of the data analysis was to develop an 

approximate waveform representing the control actions taken 

by automatic voltage regulation equipment. The analysis 

started with the determination of relatively large instantaneous 

voltage changes for each of the 12 DMUs. Fig. 9 shows an 

example of an instantaneous voltage change that occurred on 

all three phases on September 26, 2011 at about 11:47 p.m. 

This voltage change magnitude is approximately 2 VAC across 

the 240 VAC secondary winding on the split-phase distribution 

transformer. It is likely that this voltage step is due to a 

capacitor bank switching off at the substation, as the circuit 

load decreases in the late evening and voltage support from 

the capacitor bank is no longer needed. To determine voltage 

changes, the secondary winding voltage for the DMUs was 

processed using a forward-looking and backward-looking, 5-

second box-car moving average. One of the 5-second moving 

averages processed the past 5 seconds of voltage data, whereas 

the other averaged the subsequent 5 seconds of data. This data 

was then analyzed point-by-point, and if a voltage difference 

greater than 1 VAC was observed between the two 5-second 

averages, then a significant voltage regulation action may 

have occurred and an event was recorded noting the time at 

which the event occurred and the magnitude of the event.

Fig. 9: Plot of the three-phase voltage on the Anatolia distribution system 

for an instantaneous voltage step

In order to separate normal load events causing significant 

voltage changes on the measured distribution transformer, 

such as AC units cycling off and on from the voltage changes 

expected due to the action of an automatic voltage regulation 

device, it was necessary to correlate instantaneous voltage 

change events between various DMUs on the circuit. In this 
6

study, the data was processed such that a significant automatic 

voltage regulation control action was “detected” if 4 of the 6 

phase A units, 3 of the 5 phase B units and the sole phase C 

unit all experienced an instantaneous voltage change within 

three seconds of each other. It was necessary to allow some 

variation in event timing due to the five-second backward- and 

forward-looking moving averaging used to detect significant 

voltage changes, which added some time skew to the analysis.

The data analysis steps described above identify voltage 

regulation actions that take place on all three phases at the 

same time. The types of devices in which control actions 

would likely be recorded by this type of analysis are the 

switched capacitors at the substation and gang-operated, load 

tap changing transformers. As the circuit being studied has a 

non-ganged (single-phase voltage control) load tap changer,

the only control action that should be detected on the circuit is 

the switched capacitor banks located at the substation. The 

control of these capacitor banks is described in [10]. Generally 

speaking, the capacitors are switched to support the VAr 

requirements of the local loads and to support the subtransmission voltage.

B. Results from Automatic Voltage Regulation Equipment PV 

Impact Analysis

The analysis completed attempts to compare the threephase control action of the automatic voltage regulation 

equipment on the circuit for both clear days, when PV systems 

would be generating a predictable amount of energy, and 

cloudy days when the impacts of variable power generation 

due to PV would be noticeable. Fig. 10 shows the relative 

amount of voltage regulation, for all three phases, for a very

clear weather week from July 2-8, 2012. The daily voltage 

control action due to both the daily load curve of the 

neighborhood along with the smooth PV generation profile is 

evident. There is a small uptrend in the voltage regulation 

control waveform, meaning that the data analysis captures 

more positive voltage step changes than negative voltage step 

changes over the week. This small trend does not significantly 

affect the frequency of this signal for the frequencies of 

interest. 

Fig. 11 shows the relative voltage regulation waveform for 

a comprised week of cloudy days. The weather in Sacramento 

in the summer is generally clear so an entire week of cloudy 

weather data was not available. Instead, individual cloudy 

days were merged into a single week long data set. The days 

included in the analysis include days from June 4 through 

August 14, 2012. The relative voltage regulation actions 

during the cloudy days are markedly different than in the clear 

days shown in Fig. 10. Some days show more erratic control 

behavior as would be expected due to relatively highfrequency PV variability across the neighborhood. The entire 

week shows that the range of voltage control, as measured by 

the data analysis method described above, is lower than the 

week of clear data analyzed. No long-term trend is present in 

this waveform as each day, since they are not contiguous, 

starts at a relative voltage regulation of 0 VAC at 12:00:01 a.m.

Fig. 10: Relative voltage regulation control waveform for a week of clear 

weather from July 2-8, 2012

Fig. 11: Relative voltage regulation control waveform for seven days of 

cloudy weather that occurred between June 4 and August 14, 2012

As described in Section III, a power spectral density 

analysis was completed for the weekly relative voltage 

regulation waveforms shown in Fig. 10 and Fig. 11. The result 

of this power spectral density analysis is shown in Fig. 12. 

Additionally, in order to investigate the frequency components 

of a single cloudy day more accurately, a power spectral 

density of a typical cloudy day (August 3, 2012) was 

calculated and is shown in Fig. 12. As expected, more power 

is distributed at higher frequencies for the automatic voltage 

regulation equipment’s actions on cloudy days than for the 

clear days. This means that automatic control equipment is 

switching more often during these times due to PV variability 

or other load variability due to weather, which was not taken 

into account in this analysis. Additionally, the typical cloudy 

day represented by August 3, 2012 shows again that the power 

at higher frequencies and even at some relatively lower 

frequencies around 5*10-5 Hz are increased. Also, the 

variability of the power of the voltage regulation action is 

broadened at higher frequencies.
7

Fig. 12: Power spectra of the relative voltage regulation control 

waveforms

VIII.CONCLUSIONS AND FUTURE WORK

High-resolution data has been and continues to be collected 

in SMUD’s Anatolia distribution circuit. This community 

represents an area with high penetrations of distributed rooftop 

PV generation. The detailed nature of the data sets, both 

spatially and temporally, provides an excellent resource for 

assessing the grid impacts of distributed, variable generation. 

In this paper, power spectral density analysis has been applied 

to load and irradiance data in order to assess the nature of their 

variability from a frequency domain perspective. Using PSD 

analysis, several system characteristics have been identified.

These include the identification of specific periodic loads, the 

spectral density of solar resource variability, and how that 

affects the net load seen by the utility. Finally, the effect of 

added PV variability on voltage regulation equipment was 

analyzed.

Several ongoing related works are underway for this 

particular distribution circuit. Data collection in this area 

continues and new data sets are being compiled and processed. 

More complete spectral density analysis is being performed 

covering the entire data set as opposed to the few weeks 

covered in this analysis. This includes breakdowns for both 

seasonal and weather-related changes as they relate to both 

load and PV variability. This circuit also contains battery 

installations at the residential and distribution transformer 

levels as part of an ongoing demonstration project [11]. Future 

work with the power spectral analysis aims to quantify the 

effects that the battery firming algorithms are having on 

reducing the overall system variability. 

IX. ACKNOWLEDGMENT

This work was supported by the U.S. Department of Energy 

under Contract No. DOE-AC36-08-GO28308 with the 

National Renewable Energy Laboratory. Metering was 

installed into Sacramento Municipal Utility District’s service 

territory with the support of their research staff and 

maintenance crews. 

X. REFERENCES

[1] J. Bank, and B. Kroposki, “Development of a Real-Time, 

High-Speed Distribution Level Data Acquisition System,” Proc. IEEE Innovative Smart Grid Technologies (ISGT) 

Conference, Washington D.C. Jan 16-20 2012

[2] J. Bank, “Development of a Distribution Level Data Acquisition System and Preliminary Results,” Proc. IEEE 

Power and Energy Society General Meeting, San Diego, CA Jul 22-26 2012.

[3] J. Bank, “Deployment of High-Resolution Real-Time 

Distribution Level Metering on Maui,” Proc. of the Forty- Sixth Hawaii International Conference on System 

Sciences (HICSS), Wailea, HI, Jan 7-10 2013.

[4] A. G. Phadke, and J. S. Thorpe, “Synchronized Phasor Measurements and Their Applications,” Springer Science and Businees Media LLC, NewYork, 2010, pp. 5–103.

[5] "IEEE Standard for Synchrophasors for Power Systems," 

IEEE Std C37.118-2005 (Revision of IEEE Std 1344- 1995) , vol., no., pp.0_1-57, 2006

[6] J. Apt, “The Spectrum of Power from Wind Turbines,” 

Journal of Power Sources, vol. 169, pp. 369-374, Feb 26 

2007

[7] J. Cardell and C. Lindsay Anderson, “The Influence of 

Demand Resource Response Time in Balancing Wind and 

Load,” Proc. of the Forty-Sixth Hawaii International 

Conference on System Sciences (HICSS), Wailea, HI, Jan 

7-10 2013.

[8] D. Chassin, “Load Control Analysis for Intermittent Generation Mitigation,” Proc. of the Forty-Sixth Hawaii 

International Conference on System Sciences (HICSS), Wailea, HI, Jan 7-10 2013.

[9] B. Mather, “Quasi-Static Time Series Test Feeder for 

Integration Analysis on Distribtuion Systems,” Proc. of the IEEE Power and Energy Society General Meeting, San Diego, CA, July 22-26, 2012.

[10] P. McNutt and J. Hambrick, “Impact of SolarSmart 

Subdivisions on SMUD’s Distribution System,” NREL 

Technical Report TP-550-46093, July, 2009.

[11] DOE High-Penetration Solar Award: Sacramento 

Municipal Utility District, https://solarhighpen.energy.gov/project/sacramento_muni

cipal_utility_district

XI. BIOGRAPHIES

Jason Bank received his B.S., M.S., and Ph.D. degrees in 

Electrical Engineering from Virginia Tech in 2004, 2006, and 

2009 respectively. Graduate research topics in the power 

systems field included transmission-level dynamics, 

electromechanical wave propagation, wide-area 

measurements, data collection, and analysis. Work experience 

in these areas includes positions at ABB corporate research, 

and Oak Ridge National Laboratory.

He began working at the National Renewable Energy 

Laboratory in February 2010. Current research efforts focus 

on the continuing development of high-speed, real-time,

distribution-level data collection networks. Of particular study 

interest are those circuits containing high penetrations of 

distributed generation, PV, and smart grid technologies. He is 

an IEEE member.

Barry Mather earned the B.S. degree in Electrical 

Engineering in 2001 from the University of Wyoming, and 

M.S. and Ph.D. degrees, also in Electrical Engineering, in 

2008 and 2010 respectively from the University of Colorado

Boulder. Since March 2010, Barry has worked in the 

Distributed Energy Systems Integration Group at the National 

Renewable Energy Laboratory in Golden, Colorado where he 

researches the impacts of high-penetration PV integration on 

distribution systems and leads the Southern California Edison 

High-Penetration PV Integration Project.