PV Measures Up for Fleet Duty

march/april 2013 ieee power & energy magazine 331540-7977/13/$31.002013IEEE

Digital Object Identifier 10.1109/MPE.2012.2234405

Date of publication: 20 February 2013

istockphoto.com/iaki antoana plaza

CConventional power plant performanCe metrics are designed for dispatchable generation. these can be difficult to apply to variable generators such as wind and solar power. this article describes additional metrics that can be applied to photovoltaic (pv) power plants and illus-trates these metrics using measured data collected from a 1-mw pv plant in tennessee over a one-year period. the article persuades that new metrics will be needed to measure and effectively employ pv for duty in a traditional genera-tion fleet.

over the last ten years, much attention has been given to operating the grid with large-scale wind resources. learn-ing from the wind experience has helped to acquaint grid operators with variable generation, and the north america electric reliability Corporation (nerC) has now initiated several efforts to address operating and balancing the grid with variable resources. Solar is included, but differences in wind and solar plant performances need to be considered.

PV Measures Up for Fleet Duty

By Chris Trueblood, Steven Coley, Tom Key, Lindsey Rogers, Abraham Ellis,

Cliff Hansen, and Elizabeth Philpot

Data from a Tennessee Plant Are Used to Illustrate Metrics That Characterize Plant Performance

34 ieee power & energy magazine march/april 2013

Solar pvs are much more distributed than wind, individual pv plants ramp more quickly, and has a different proximity and timing relative to end-use demand. Solar typically has a higher capacity value than wind, but defining the differences in a standard way is a work in progress.

Currently ieee 762-2006 is the standard for reporting individual power plant performance. it defines reliability, availability, and productivity indexes to report traditional, dispatchable power plant performance. the standard assumes fuel is available and indicates to what extent the rest of the plant performs relative to its rating and availability. in con-trast, performance metrics used for pv plants must take into account the fact that fuel is variable and is the main deter-mining factor of plant availability. this contrast, between traditional controllable generation and emerging variable generation, has led to some confusion and hesitation to incorporate solar plants into a conventional generation fleet.

in the future, metrics will be needed that are compat-ible with the traditional generation and that also allow easy comparison between new generation options. once metrics are defined, they need to be accepted by stakeholders and adopted into standards such as ieee and nerC. when both the pv industry and the utility industry speak the same lan-guage, assessments for bringing solar pv into a traditional generation fleet can be more consistent.

Sample PV Plant Sitepv plant metrics presented in this article are demonstrated by measured data from a 1-mw plant located in tennes-see. table 1 lists the plant specifics. this plant was selected because good-quality 1-s resolution data were available over a one-year period. it needs to be noted that plant perfor-mance is always site specific, and results should not be gen-eralized for other plant locations, fleets of plants, or plants with different design and size.

figure 1 shows a front view of the pv plant, and figure 2 shows the four 260-kw inverters and the distribution pole point-of-common-coupling with the utility for grid connec-tion. Data collected from this site include ac power, energy, voltage, temperature, and irradiance measurements. the sites irradiance instrumentation includes eight pyranometers located on the plane of array (poa), which is a fixed 25 tilt due south.

note that for this plant, the array dc and the inverter ac output powers are rated at approximately the same level (1.04 mw ac versus 1.03 mw dc). plant ac output is lim-ited to rated value. However, the dc rating refers to dc out-put at specific conditions and does not mean that dc output is limited to the rated value. for example, during low tem-peratures, with optimal sunlight incidence angles, and solar irradiance enhanced by cloud reflections, the array dc power significantly exceeds the dc rating. the relative sizes of these ratings affect plant performance metrics such as capacity factor, discussed later.

Measuring Solar Resourcethe solar resource at a given location is dependent on weather and time period examined. irradiance and insolation are two common, and well-defined, measures of solar resource.

irradiance is a measure of solar power on a given plane, e.g., a horizontal plane or poa, and is usually expressed in w/m2. the power output from a pv plant is generally propor-tional to the poa irradiance across the pv plants footprint. Because plant output is generally proportional to irradiance, variability in irradiance is informative about the variability in plant output power. for the results reported in this article, site

table 1. Tennessee plant specifications.

AC rating 1.04 MW

DC rating 1.03 MW

Inverters 4 260# kW

Modules 4,608 polycrystalline silicon modules rated 224 W dc at STC1

Mount Ground mounted, fixed 25 tilt, oriented due south

Commission date 2010

1STC stands for standard test conditions (1,000 W/m2 irradiance and 25 C module temperature) at which the performance of the module is measured and reported.

figure 1. A 1-MW PV array at the plant in Tennessee, view looking northeast.

figure 2. Inverters and utility service from local distribution.

march/april 2013 ieee power & energy magazine 35

irradiance was measured using typ-ical pyranometers having an aper-ture of roughly 1 cm2. the number of irradiance sensors required to accurately estimate irradiance over a plant footprint is the subject of active research.

insolation is defined as solar energy received over time, i.e., the integration of irradiance. typi-cal daily values range from 2 to 7 kwh/m2 depending on loca-tion, array tilt, time of year, and weather. figure 3 shows the mea-sured monthly poa insolation at the site (vertical bars) compared to calculated values (background). Calculated values are monthly and come from nrels pvwatts. they are based on site design details entered into the online calculator and a solar predic-tion for the site based on hourly weather history data in a typical meteorological year (tmY) from the national Solar radiation Database. Because insolation quantifies solar energy over a period of time, it is roughly proportional to the expected plant electrical energy output for the same period of time.

figure 4 shows a different view of the site irradiance, a solar power calendar based on poa irradiance averaged for each minute throughout the month of august 2012. as expected, the resource generally rises as the sun rises and falls as it sets. perhaps not as expected, the resource can be highly variable within time frames of seconds to minutes, changing quickly with passing clouds. no two days are the same. Some days are clear (e.g., 23 august), others are partly cloudy (e.g., 15 august), and others are overcast (e.g., 6 and 10 august).

looking day to day gives a perspective on the variation for a particular location and the time period. a method for classifying days as more or less variable is proposed. this method uses a combination of the classical daily clear-ness index and a new daily variability index, defined by Sandia national laboratories. research is being conducted to determine if distinguishing variability in this manner can be used by utility generation planners and grid operators in making decisions.

the daily clearness index is the ratio of solar energy mea-sured on a given surface to the theoretical maximum energy on that same surface during a clear sky day:

Daily Clearness Index

Calculated Clear Sky Solar InsolationMeasured Solar Insolation .=

the calculated clear sky solar insolation can be calculated from a number of clear sky models. typical values for the daily clearness index range from 0.0 to 1.1. values greater than 1.0 are obtained in practice because clear-sky models may not be exact for every hour at any given location.

the daily variability index is the variability in measured irradiance, relative to the variability of the calculated clear sky irradiance, with each quantified by the length of the irra-diance versus time plot for the day, where the curve length between two measurements is determined using a line seg-ment. typical values for daily variability index range from 1 to 30 and are determined using the equation

Daily Variability Index

Length of clear sky irradiance plotLength of measured irradiance plot

.=

Using combinations of the daily clearness index and the variability index, variability in irradiance can be quali-tatively categorized, as shown in figure 5, using five categories of variability conditions: high variability, mod-erate variability, mild variability, clear, and overcast days. examples of each type of day are shown with a corre-sponding value for variability index and clearness index. Quantifying variability of a single pv plant or fleet of pv plants over a given area may aid in power system opera-tions decisions, such as how to set the level of regulating reserves. Classifying days as having a specific variabil-ity allows a power system operator to determine the fre-quency at which each type of variable day occurs, which

figure 3. Monthly solar insolation is computed from measured irradiance using a POA pyranometer. The background shading is a monthly forecast for insolation at this site from NRELs PVWatts calculator and the horizontal line is the measured annual average.

Janu

ary

Febr

uary

Mar

ch

April

May

June July

Augu

st

Sept

embe

r

Oct

ober

Nov

embe

r

Dec

embe

r0

1

2

3

4

5

6

7

Inso

latio

n (kW

h/m2

per d

ay)

Month in 2012

PVWatts Simulated Output1 MW Plant in TN Measured (Monthly)1 MW Plant in TN Measured (Average)


figure 4. Measured solar irradiance profiles (blue areas) for each day in August 2012. One-min average data are shown from a POA pyranometer located at the Tennessee plant. The reference curve (thin orange line) is calculated from the Ineichen clear-sky model using Sandia National Laboratorys PV Performance Modeling Toolbox for MATLAB (http://pvpmc.org/pv-lib/).

August 2012

5 6 7 8 9 10 11

Sunday Monday Tuesday Wednesday Thursday Friday Saturday1 2 3 4

12 13 14 15 16 17 18

19 20 21 22 23 24 25

26 27 28 29 30 31

Clear Sky POA IrradianceMeasured POA Irradiance

Moderate

MildOvercast

Clear

VI 1 2CI $ 0.5

VI 1 2CI # 0.5

5 # VI 1 10

2 # VI 1 5

VI 2 10

High

figure 5. Categories for daily variability conditions are based on the clearness index (CI) and the variability index (VI).


may aid in determining the likelihood that the power system experiences challenging conditions from variable generation sources.

Use of a common set of variability metrics allows com-parison of the relative frequency power output changes between different plant sizes and weather regions. Using the U.S. irradiance map, shown in figure 6, the variable condi-tions of the tennessee plant are compared to a plant in ari-zona. the daily variability index is used to classify type and number of days each season of the year.

Power Plant and Fleet Standardsin north america, ieee and nerC are the two main sources for accepted practices to measure and report plant and fleet performance. Standard definitions for performance of generating units are provided by ieee while nerC covers the reliability of overall fleet operations, resource adequacy, and coordination between balancing authorities. nerC standards have relied on ieee to define individual

unit and plant performance indexes. in recent years, with a high growth in wind power, nerC has given special atten-tion to issues of variable generation resources. However, so far, ieee has not defined indexes for variable generation. Between the two efforts there remains some ambiguity on specific performance measures for variable and nondis-patchable generation.

IEEE Generator Performance StandardsSince 1980, ieee Standard 762 has provided standard defi-nitions for use in reporting generating unit performance. it defines performance factors for reliability, availability, and productivity. the standard provides 15 energy and capac-ity terms, 21 time designations and dates, 25 performance indexes for individual generating units, including availabil-ity, capacity, and outage factors. plant outages (forced or planned) determine unavailability. in the 2006 update, new indexes were added to pool groups of plants, including time-based and capacity-weighted indexes.

figure 6. Daily variability conditions for specific sites in Arizona and Tennessee. The background U.S. map shows average annual solar resource modeled by NREL (http://www.nrel.gov/gis/solar.html). (Used with permission from NREL.)

JanuaryMarch

AprilJune

JulySeptember

OctoberDecember

0

20

40

60

80

100

Perc

enta

ge o

f Day

s (%

) HighModerateMildClearOvercast

0

20

40

60

80

100

Perc

enta

ge o

f Day

s (%

)

> 6.56.06.55.56.05.05.54.54.03.54.04.04.53.03.5< 3.0

Season in 2012

Season in 2012

Variability Conditions: Arizona Site

Variability Conditions: Tennessee Site

JanuaryMarch

AprilJune

JulySeptember

OctoberDecember

AnnualIsolation

Latitude TiltkWh/m2 per Day


the ieee standard is used for traditional (dispatchable) generation that has a well-defined and consistent fuel source. Controllability, although not explicitly defined, is assumed to be an option in the plants operation. the power output of these traditional power plants can therefore be planned, controlled, and dispatched to meet expected daily load vari-ations and to balance or regulate energy delivered to trans-mission and distribution grids.

So far, ieee 762-2006 has not been used to character-ize variable generation such as wind and solar power. these generation resources depend heavily on the weather where sunlight and wind are, in effect, a variable fuel source. in the current ieee standard, the effects of weather are considered as a less-critical performance factor, for example, affecting the summer and the winter ratings of traditional thermal plants. adding more weather-related performance factors and related indexes to cover variable generation is expected to be a priority in the next revision of this ieee standard.

NERC Fleet Control Standardsin 2007, nerC created the integration of variable Genera-tion task force (ivGtf) to address issues related to variable generation from a fleet operation and flexibility perspective. one issue, output controllability, is a significant differen-tiator for variable power plants. the typical parameters that define controllability are start-up time, cycling range, rates, and minimums, as well as consideration for any expected and uncontrolled output variability. table 2 compares dif-ferent types of dispatchable and nondispatchable generation related to controllability.

previously, ieee 858-1993 (now withdrawn) defined terminology covering generator control parameters used in power operations. now, in ieee 762-2006, some level of plant controllability is assumed, but specific measures are

not defined. Since deregulation, nerC has taken on the responsibility to consider fleet controllability. independent system operators and utilities have set fleet operating prac-tices at the electricity balancing area level. tariffs define ancillary services such as regulation and ramping, and elec-tricity markets determine the value. everyday operators have to schedule in controllable power plants and to consider the overall flexibility of the fleet.

nerC has defined specific control performance stan-dards that apply between defined balancing areas. for exam-ple, measuring the fleets ability to support system frequency (CpS1) and to maintain planned energy exchanges (CpS2). the main objective is system reliability, flexibility, and resource adequacy. an individual solar or wind plant is usu-ally not considered controllable or dispatchable by operators. instead, these plants are assumed to operate at full power output and treated as must take energy producers.

there is active discussion on the extent to which a fleet of pv plants, with diversity of location and solar resource, can provide dependable capacity. for such variable gen-eration, nerC has recommended two dependability met-rics, effective load carrying capacity (elCC) and loss of load expectation (lole). in the nerC metrics, credit for capacity depends on the coincidence of the delivery profile relative to both high risk and peak demand periods. Some periods can be at high risk without necessarily being a peak demand period. elCC measures an individual generators contribution to the next increment of demand that a power system can reliably support. related, lole is a fleet-level resource adequacy metric. lole analysis, usually over a one-year period, forms the basis of calculating how much a particular generator, or group of generators, contribute to planning reserves and reserve margins, given a reliability target. for example, the traditional planning target for lole

table 2. Comparison of various generation technologies with respect to output controllability (Key, EPRI).

Generation Source

Startup Time

Minimum Cycling Time On/Off

Controlled Ramp Rate (Up or Down)

Uncontrolled Variability

Minimum Output % of Rated**

Typical Unit Size (MW)

Nuclear Hoursdays Days 0.22%/min Rarely 90100 7501,500

Coal Hoursdays Once/day 0.22%/min Rarely 4050 10750

Natural gas CT

Minutes More than twice/day

35%/min Rarely 5565 20250

Natural gas CC

Hours Twice/day 23%/min Rarely 4555 40400

Hydro power* Minutes Minutes 100% in 30 s to 2 min

Seasonal 10 1250

Wind* Minutes* Minutes* Seconds* 10%/min 25 0.015

Solar PV* Seconds* Minutes* Seconds* 10%/s 25


is .1 days/year, or maintaining genera-tion adequacy 99.97% of the time.

less formally, pv is given more credit than wind because of daytime and summer peaking even though demand typically peaks later in the day than solar noon. individual pv plants can be quite variable because of cloud movements. for example, when a cloud moves over a single pv plant, the amount of energy from that plant is reduced, perhaps significantly. How-ever, this variability can be reduced in very large plants or widely dis-tributed plants across the utility grid. pv mounting structures that track east to west are able to extract more energy later in the day and, therefore, may provide a better match of solar output to peak load times (typically 47 p.m.). additionally, variability is normally reduced when considering the net outputs of both wind and solar pv together.

it is clear that solar plants deserve some credit for daytime capacity and for contribution to resource adequacy. there is growing interest among utilities to find a standard way for assessing solar capacity credit. However, there remains both ambiguity and complexity in estimating this credit. not well defined are how to address multiple weather years, other plant components contributions to unreliability (such as inverter or tracker downtime), and statistical methods to account for overall uncertainty. Better calculation and reporting methods should be a goal of future standards development.

Measuring PV Plant PerformanceData from the 1-mw plant in tennessee, monitored for 12 months, are used to illustrate the metrics needed to char-acterize pv power and energy performance. Some metrics are traditional; others are new and are introduced in this article to address the challenge of measuring variable gen-eration. these metrics include plant capacity factor, normal-ized power and energy, and weather related variability. not covered are plant economics, operating reliability, output uncertainty, and resources forecasting. Sample performance results are specific to the one plant that was monitored, and general conclusions about other pv plants should not be drawn from these results. for the purpose of this article, only a single year of data is considered.

PV-Delivered Energy energy, or power output over a time period, is the primary mea-sure of plant productivity. for pv plants, the solar resource, or insolation, needs to be considered during the time period of interest. if carefully done, insolation can also provide a ref-erence point to help in determining the degradation of plant

output over time. Some of the factors currently defined in ieee 762 (conventional reporting factors) are planned and unplanned outages, availability, seasonal deratings, service, and capacity factors. the industry is proposing the measurement of the fol-lowing pv plant variables: energy production and solar resource factors (over a specified time period of interest), and solar plant energy performance, based on either inverter (ac) or array (dc) ratings. But first, we provide some discussion about the ambigu-ity surrounding solar power and capacity factor.

Conventional Capacity FactorCapacity factor is normally defined as the ratio of actual out-put of a plant over a period of time relative to the rated out-put, if operating at nameplate capacity over the same period. for example, coal and combined-cycle natural gas plants operate at about 7080%, nuclear at about 90%, gas turbines at less than 5%, and on-shore wind at about 2535%. Hydro in the United States (excluding pumped storage) operates at about 50% capacity factor.

Capacity Factor (CF)

System Rating (kW) Time Interval (hours)Total Energy Produced (kWh)

.#

=

the annual capacity factor for solar ranges from about 12 to 24%. figure 7 shows the monthly capacity factor for the 1-mw pv plant, based on inverter (ac) power rating. the average daytime hours for each month are included in the background to show the capacity factor relative to daytime hours (sunrise to sunset at a given geographical location). as can be seen, the capacity factor is highest in the spring months and ranges from 0.080.21 over this particular year. the one-year average capacity factor is 0.16 (noted by the

Janu

ary

Febr

uary

Mar

ch

April

May

June July

Augu

st

Sept

embe

r

Oct

ober

Nov

embe

r

Dec

embe

r 0

5

10

15

20

25

0

0.05

0.10

0.15

0.20

0.25

Capa

city

Fact

or

Aver

age

Dayt

ime

Hour

s

Month in 2012

Average Daytime HoursAverage Capacity FactorCapacity Factor

figure 7. Capacity factor shown by month (vertical bars), including average one-year capacity factor (horizontal line) and average daytime hours shown in the background (gray area).


horizontal red line). the annual capacity factor for other solar plants depends on many elements, including location, weather, array tracking, balance of plant efficiencies, and inverter sizing.

there are a few issues for using capacity factor with pv plants. the first issue is the choice of system rating. the utility industry uses the generator (inverter) ac rating when calculating capacity factor, while the pv industry has tra-ditionally used the collector (array) dc rating. as more pvs enter the fleet, the trend is to use ac rating as the system rat-ing. a second issue is, depending on the system, a potential economic advantage in over- or undersizing the inverter rat-ing (i.e., designing the dc array size relative to the ac inverter size). this design would affect the capacity factor, often sig-nificantly. new standards may help with these issues.

the time period considered for the capacity factor is also important. output from a pv plant follows the diurnal cycle of the sun, only producing during daytime hours (roughly 4,400 h per year), whereas conventional plants are available around the clock (less various downtimes). for conventional plants, the capacity factor can be a good indicator of how frequently a plant is economically dispatched and how often it is held in reserve. However, for nondispatchable solar plants, the capacity factor is most directly related to the solar resources, with the efficiency of the plant playing a second-ary role.

other normalized energy performance factors are pro-posed for pv plants. these should be considered in future revisions to ieee-762.

PV Energy Performancea different way to look at energy performance specific to pv plants is to normalize performance based on local sunlight

conditions (insolation). the idea of normalizing performance by solar insolation is not new. for example, pv production and solar reference ratios, identified as yields, were used to define plant performance in the 2005 conference paper (nrel/Cp-520-37358, 2005). evolving from this concept, a pv plant daytime plant energy performance factor is defined. this factor is dimensionless and can be used to compare perfor-mance with other plants.

energy performance factor is defined as a ratio of a daytime pro-duction factor and a sun factor. Daytime production factor is total daytime energy produced divided by the plant rated capacity times day-time hours. the plant rated capacity can be in terms of the inverter (ac rating) or the array (dc rating). Sun

factor is daytime insolation normalized for to a value repre-senting clear sky irradiance (1,000 w/m2) times the daytime hours. energy performance factor is defined:

Energy Performance FactorSun Factor

Production Factor .=

the factor is dimensionless, and typical values may range from 0.6 to 1.0. they can be computed over any time period, usually over month, quarter, or annual periods. lower values indicate lower performing systems. note that using daytime hours doesnt account for plant energy losses at night.

a monthly array (dc) energy performance factor and array temperature measured at the tennessee plant are shown in figure 8. as temperature increases the perfor-mance of the array generally decreases, due in large part to the less-efficient performance of modules operating in higher temperatures.

PV Output Powerthe plant output power is normalized to show relative output during a given time period, such as 15-min, hourly, daily, or monthly intervals. Depending on the application, output power can be computed using the raw data (e.g., 1-s mea-surements) or averaged data. an example is peak generation, based either on the 15-min or 1-h averages of the raw data. metrics computed from power output are useful to charac-terize plant impacts on the electric system. one traditional method used to characterize demand or generation is the annual duration curve.

Normalized Power figure 9 shows the annual power output duration measured at the tennessee plant. power output is based on 15-min

0

10

20

30

40

50

60

Tem

pera

ture

(cC)

0.6

0.7

0.8

0.9

1.0

1.1

Perfo

rman

ce F

acto

r

PerformanceFactor

Weighted Average ModuleTemperature (cC)

Janu

ary

Febr

uary

Mar

ch

April

May

June July

Augu

st

Sept

embe

r

Oct

ober

Nov

embe

r

Dec

embe

r

Month in 2012

figure 8. Daytime array energy performance factor (vertical bars) and weighted average module temperature (red line) by month.


interval averages during daytime only (4,454 total daytime hours). the irra-diance duration represents the avail-able solar resource at this site, and the power duration indicates corresponding ac plant output that exceeds a certain level for the specified duration.

irradiance is measured in the poa and is normalized to one sun (1,000 w/m2). plant output is normalized to the plants ac rating (1.04 mw ac). Duration (hours) of the power and irradiance are strongly correlated throughout the year. the pv plant does not reach full rated ac output (100%) because in this case, the ac capac-ity (1.04 mw ac) of the inverter is about the same as the dc capacity (1.03 mw dc) of the array. the temperature and the bal-ance of system losses prevent the plant from routinely exceeding its rating.

figure 10 shows the peak power output for each month. each bar represents the normalized peak power for 15-min (pink bar) and 1-h (blue bar) time intervals. in this particular example the highest peaks occur in late winter and spring, while the lowest peaks occur in summer and late fall. Seemingly counterintuitive, the graph illustrates that pv modules perform best when temperatures are lower and irradiance incident angles are more optimal for this array. all peaks occur near solar noon, typically between 11 a.m. and 1 p.m. local time. the highest one-year peak occurred in april: 88% for both 15-min and 1-h intervals.

another way to view pv output is to consider daily power output profiles by season. the daily power output profile for a given location is heavily dependent on the suns path across the sky and local cloud cover. figure 11 shows selected daytime power profiles in 15-min inter-vals for three days each season: a clear day, an overcast day, and a median day. each day is classified based on the amount of solar insolation.

the clearest day is the day within the season with the highest amount of insolation; the most overcast day is the day with the least amount of insolation; and a median day is the day that has the median amount of insolation. this view is useful to characterize the range of power output profiles observed by sea-son. in general, the fall and winter daily profiles are more elongated with higher power generation at midday, but those profiles have fewer total hours compared to the summer and spring profiles that

have more hours of daylight. During clear days in the spring, the power output is higher than during clear days in the sum-mer, partially because pv panels are more efficient at lower temperatures. the median day shows that power output from pvs can be variable throughout the day, mostly due to cloud movement overhead.

also of interest may be the plant output distributed statistically by time of day. figure 12 shows a plot by season for each hour of the day. the maximum and minimum val-ues for all seasons generally correspond to the clear sky and overcast conditions, respectively. for the tennessee plant, the inner quartile range is higher and also smaller in the spring and summer months compared to the winter and fall

figure 9. This duration curve shows the cumulative time that a measurement (ac power or POA irradiance) exceeds a certain level. For example, for 20% of the daytime hours the plants ac output exceeded 61% of its rating while the arrays irradiance input exceeded 73% of its rating.

10000

20

40

60

80

20 40 60 80

120

100

Mea

sure

d Va

lue

(% of

Rati

ng)

POA Irradiance

Percentage of 4,454 Daytime Hours in 2012 (%)

AC Output Power

figure 10. Peak powers per month (up to 99.7 percentile) are shown for 15-min and 1-h time intervals (another year would likely show differently).

60

70

80

90

100

May

June

July

Augu

st

Sept

embe

r

Oct

ober

Nov

em

ber

Dec

embe

r110

Pow

er

(% of

Sys

tem R

ating

) 15 min

Month in 2012

1 h

January

Febr

uary

Mar

ch

April


100

80

60

40

20

0

100

80

60

40

20

0

Pow

er

(% of

Rati

ng)

Hour of Day (Local Time)(c) (d)

Hour of Day (Local Time)5 10 15 20 5 10 15 20

WinterJanuaryMarch2012

SpringAprilJune2012

SummerJulySeptember2012

Clear DayOvercast DayMedian Day

(a) (b)FallOctoberDecember2012

figure 11. Power-output profiles of selected days for each season: the clearest day (tall green bars), the most over-cast day (short red bars), and the median energy day (yellow line), based on solar insolation. Note that array tilt, shade, terrain, Daylight Saving Time, and time of year, all affect the system start and stop time. (a) Winter (JanuaryMarch) 2012. (b) Spring (AprilJune) 2012. (c) Summer (JulySeptember) 2012. (d) Fall (OctoberDecember) 2012.

figure 12. Statistical distribution of a power output by hour of day for each season. Maximum and minimum values are black lines, the inner quartile range is a blue box, and the median value is a red line. (a) Winter (JanuaryMarch) 2012. (b) Spring (AprilJune) 2012. (c) Summer (JulySeptember) 2012. (d) Fall (OctoberDecember) 2012.

100

80

60

40

20

0

100

80

60

40

20

0

Pow

er

(% of

Rati

ng)

Hour of Day (Local Time) Hour of Day (Local Time)10 15 20 10 15 20

WinterJanuaryMarch2012

SpringAprilJune2012

SummerJulySeptember2012

FallOctoberDecember2012

Max and MinMedianInner Quartile

(c) (d)

(a) (b)


months. this difference indicates that spring and summer seasons experienced a greater number of hours with higher power generation and that power may be somewhat less vari-able in the spring and summer than in the fall or winter at 1-h time intervals.

PV Output Variabilitya method for quantifying pv plant output variability is not well defined. Here pv output variability is quantified by computing sequential changes in measured ac power output, either instantaneous or averaged, over multiple time intervals. these time-based changes form a ramp-rate data set that can be used to statistically characterize how often and to what extent output ramping occurs during daytime hours. Changes in power and rates of change in this data set can be closely related to the solar resource variability index presented earlier.

the data are presented in three ways, as the number of change in power (Dp) observations, as the percentile of extreme changes in power, and as the hours of Dp that exceed a value. for each presentation of data, four ramp rate intervals are included: 10 s, 1 min, 10 min, and 1 h. ramp rates are computed for each interval using an aver-aging method, which produces changes from differences between consecutive block averages (not a sliding scale). During daytime hours, the pv plant is assumed to be oper-ating when its ac power output exceeds 0.2% of the sys-tems rating.

Distributions of changes, the number of p observations, are plotted as a histogram to illustrate how often changes of different magnitudes were observed. for example, figure 13 shows distributions of change at the plant during the third quarter of 2012 (the summer season). of the four ramp rate intervals shown, higher-magnitude changes occur more often for 1-h intervals. this is because ramps at longer inter-vals are primarily caused by the suns rising or setting. also note that the directions of changes are separated to illustrate similarities between ramp-up and ramp-down observations. the relative frequency, which is the count of ramps at a certain magnitude divided by the total number of ramps, is scaled quite low, cropped to 5%, to emphasize that the most significant changes occur infrequently.

of primary interest to the utility industry are ramping events having extreme changes, even if they rarely occur. looking at the upper percentiles for both ramp-up and ramp-down magnitudes offers insight into the extreme cases. to illustrate the occurrence of extreme changes in summer 2012 data, figure 14 shows the Dp for upper percen-tiles (rare events) and for four time intervals. for example, the 99th percentile of the set of 10-min up-ramps is a change in output equal to 36% of the plants rating, indicating that a change in plant output of this magnitude or greater would be expected to occur approximately 74 times during the roughly 7,400 10-min intervals spanning the daytime hours, July through September.

another way to look at how frequently pv output ramps occur is illustrated in figure 15. Here the amount of movement, and the amount of time that pv is on the move, is plotted. the graph displays the percent of cumulative daytime hours during summer 2012 where Dp exceeds a specific value.

for example, 10-min ramps of 36% rated output, or more, occur approximately 1% of the time (12 h during the season). in other words, the 1-mw plants output was 36 kw or more per minute interval. a logarithmic axis is used to better see the larger changes in output that dont occur very often.

Currently, about 80% of pvs is connected to the distribu-tion grid. variability in distributed pvs, and the feeder host-ing capacity, have been a main focus of recent research. for example, epri has involved utilities, led by Southern Com-pany Services, to install more than 200 collection points of 1-s pv output data around the United States. Sandia

figure 13. Number of P observations relative frequency of changes in averaged power for four ramp rate intervals: 10 s, 1, 10 min, and 1 h. The period shown is summer 2012.

-100

1

2

3

4

5

50 1000-50

Rel

ative

Fr

equ

ency

(%)

Change in Power (% of System Rating)

1 h10 min1 min10 s

figure 14. Selected percentiles illustrate how increas-ing the ramp rate interval of interest affects ramp-up and ramp-down magnitude of change during third quarter 2012 (summer season). Percentiles are distinguished between up ( 0$ ) and down ( 0# ) ramp events. For example, the top of the yellow box indicates the 90th percentile of ramp-up events, while the bottom of the yellow box indicates the 90th percentile of ramp-down events.

10 s-100

-50

0

50

1 h10 min1 min

100

Chan

ge in

Pow

er

(% of

Rati

ng)

Ramp Rate Interval

99.99th99.9th99th95th90th


developed the solar resource variability index described here and has looked at output variability for a dispersed fleet of pv plants. epri, nrel and Sandia are currently investi-gating methods to determine feeder hosting capacity with California utilities. with higher penetration solar variability is at the distribution level, pv may affect voltage quality and power delivery to other utility customers. at the bulk level, the nerC ivGtf is leading efforts to characterize variable generation more from the system operators perspective.

results from all these efforts have contributed to defining pv plant performance in this article and will be shared with standards development organizations in the future.

Conclusionsnonconventional performance measures will be required to determine a pv plants fitness for fleet duty. Using high-resolution data collected from an operating pv system, the authors characterize plant performance and define metrics for power, energy, and variability. Such metrics are not yet incorporated into standards for reporting the unit perfor-mance of electric power plants and should be considered in the next revision of ieee StD 762. related, at the fleet level, grid operators will need better methods to determine the contribution of variable generation to resource ade-quacy. fortunately the nerC ivGtf has recently recom-mended methods to model and calculate capacity credit for variable generation.

together these metrics and methods can be used to deter-mine a pv plants performance relative to conventional generators. additionally, in the future simplified methods will be needed to estimate the uncertainty in pv plant out-put. also needed are methods for analyzing relationships between pv plant size, distance between plants, and power density to better understand the behavior of distributed pvs. as more solar generation deploys these performance

metrics and calculation methods will be useful for plant owners, distribution planners, and grid operators to better utilize variable pv plants in a traditional generation fleet.

For Further ReadingIEEE Standard Definitions for Use in Reporting Electric Generating Unit Re-liability, Availability and Productivity, ieee Standard 762-2006, mar. 2007.

nerC, methods to model and cal-culate capacity contributions of vari-able generation for resource adequacy planning, nerC task force report ivGtf1-2, mar. 2011.

G. Curley, power plant performance indices in new market environment: ieee Std 762 working group activities

and GaDS database, presented at ieee power engineering Society General meeting, feb. 2006, paper 1-4244-0493.

epri, photovoltaic plant output and cloud-induced vari-ability: issues and opportunities for enhancing plant produc-tivity and grid integration. epri, palo alto, Ca, tech. rep. 1023090, 2011.

t. Key, finding a bright spot, IEEE Power Energy Mag., vol. 7, no. 3, pp. 3444, may/June 2009.

nrel, performance parameters for grid-connected pv systems, nrel, Golden, Co, rep. nrel/Cp-520-37358, 2005.

nrel, procedure for measuring and reporting the per-formance of photovoltaic systems in buildings, nrel, Golden, Co, rep. nrel/tp-550-38603, oct. 2005.

t. Hoff and r. perez, modeling pv fleet output variabil-ity, Solar Energy, vol. 86, no. 8, pp. 21772189, aug. 2012.

m. reno, C. Hansen, and J. Stein, Global horizontal irradiance clear sky models: implementation and analy-sis, Sandia national laboratories, albuquerque, nm, rep. SanD20122389, 2012.

J. Stein, C. Hanson, and m. reno, the variability index: a new and novel metric for quantifying irradiance and pv output variability, presented at aSeS annual Conference, may 2012, rep. SanD2012-208.

BiographiesChris Trueblood is with the electric power research insti-tute (epri), Knoxville, tennessee.

Steven Coley is with epri, Knoxville, tennessee. Tom Key is with the epri, Knoxville, tennessee.Lindsey Rogers is with epri, Knoxville, tennessee. Abraham Ellis is with Sandia national laboratories.Cliff Hansen is with Sandia national laboratories.Elizabeth Philpot is with alabama power Company,

Birmingham. p&e

figure 15. Total time of occurrence for changes in power at selected ramp rate intervals: 10 s, 1 min, 10 min, and 1 h during third quarter 2012 (summer season). Daytime for this quarter totals 1,236 h.

0.010

20

40

60

80

100101.00.1

100

Chan

ge in

Pow

er

(% of

Rati

ng) 10 s

Percentage of Daytime (1,236 h)

1 min10 min1 h

PV Measures Up for Fleet Duty

Documents

Transcript of PV Measures Up for Fleet Duty