IEEE Power & Energy Magazine - May/June 2018 - 53

Capacity Factor (%)

24
22
20
18
16
14
12
10
8

makes it possible for long-term load forecasters to factor Btm
pvs into their forecasts, where and when necessary.

Concluding Remarks

1

2

3

4

5

6 7
Month

8

9

10 11 12

figure 10. The monthly average capacity factors in Massachusetts, 1998-2014. The upper and lower ends of each
box represent the first and third quartiles, respectively. The
band inside the box is the median value, and the ends of
the lines extending above and below the boxes ("whiskers")
reflect the degree of variability outside of the upper and
lower quartiles. The red circles reflect statistical outliers.

figure 9 illustrates the simulated per-unit Btm pv profile for the six New england states on 1-2 July 2007. these
profiles reflect an arithmetic mean of all town profiles within
each state and show the overall effect of weather in each
state on the simulated Btm pv. Based on the same set of
results, it is also possible to develop aggregate Btm pv profiles that reflect a specified town-level geographical distribution of installed Btm pvs by simply weighting the town
profiles by each town's installed capacity.
table 4 summarizes the average annual capacity factors for the six states over the entire 17-year NsrDB-based
simulation period, and figure 10 is a box plot showing the
variability of Btm pv's monthly capacity factors in massachusetts over the entire period. since New england typically
experiences snow events during winter and the effects of
snow cover were not explicitly modeled in the simulations,
the annual performance metrics are somewhat optimistic for
the winter months (and annually as a result). however, the
winter results are representative of the solar resource availability in winter, absent snow.
overall, the broad agreement between the granular, townlevel simulation results and measured data demonstrates that
the Btm pv fleet simulation yielded reasonable, realistic
results and highlights that Nrel's new NsrDB data set is not
only spatially and temporally comprehensive but also of a very
high quality. Given that the simulation period is 17 years, a
relatively robust understanding of the patterns of solar resource
availability is now possible in the area covered by NsrDB.
perhaps, more importantly, coupling the results with longterm forecasts of Btm pv and historical loads enables a thorough investigation of net load patterns as Btm pv penetrations increase. using this information, greater clarity is now
possible concerning the timing and magnitude of mitigation
measures that may be required to integrate increasingly larger
Btm pv penetrations in the future. lastly, the NsrDB now

may/june 2018

more than 70 years ago, researchers recognized temperature
as a driving factor of electricity demand. since then, temperature data has been widely used in load forecasting models.
Due to the increasing need of accurate energy forecasts, more
weather data are being adopted by the power industry. in
this article we presented two utility applications that rely on
weather data. We used some conventional and easily accessible weather data to offer a novel and probabilistic view of
saiDi, which helps reveal the utility's reliability trend. in the
Btm pv simulation study, we used some recently developed
comprehensive weather data to tackle an emerging problem
in renewable integration. researchers and practitioners have
created a wealth of knowledge in the science of meteorology
and atmosphere science in general. We hope that this article
will inspire you to try out more weather data and expand the
footprints of meteorology in the energy analytics arena.

For Further Reading
IEEE Guide for Electric Power Distribution Reliability Indices,
ieee standard 1366-2012 (revision of ieee standard 13662003), may 2012.
t. hong, J. Wilson, and J. Xie, "long term probabilistic
load forecasting and normalization with hourly information,"
IEEE Trans. Smart Grid, vol. 5, no. 1, pp. 456-462, Jan. 2014.
t. hong and s. fan, "probabilistic electric load forecasting: a tutorial review," Int. J. Forecasting, vol. 32, no. 3, pp.
914-938, July-sept. 2016.
National solar radiation database. (2017). National renewable energy laboratory. [online]. available: https://nsrdb.nrel
.gov/current-version#psm
National renewable energy laboratory. system advisor
model. [online]. available: https://sam.nrel.gov/
e. lorenz, t. scheidsteger, J. hurka, D. heinemann, and
c. Kurz, "regional pv power prediction for improved grid
integration," Progress Photovoltaics Res. Applicat., vol. 19,
no. 7, pp. 757-771, Nov. 2011.

Biographies
Jonathan Black is with iso New england, holyoke, massachusetts.
Alex Hofmann is with the american public power authority, arlington, virginia.
Tao Hong is with the university of North carolina at
charlotte.
Joseph Roberts is with iso New england, holyoke,
massachusetts.
Pu Wang is with the sas institute inc., cary, North
carolina.
p&e

ieee power & energy magazine

53


https://www.nsrdb.nrel.gov/current-version#psm https://www.nsrdb.nrel.gov/current-version#psm https://sam.nrel.gov/

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