IEEE Power & Energy Magazine - May/June 2018 - 51

performance are established and a sufficient amount of data is
available to represent these features. for the Btm pv simulations, the most salient factors are the localized weather conditions represented by the 4-km NsrDB weather data and the
assumed system design characteristics of the Btm pv fleet
used as inputs to the simulations. Note that this approach may
not be well suited for all applications. for example, in applications limited to localized, distribution-level analysis and forecasting, the Btm pv fleet is much smaller, which may warrant
greater consideration of, for example, more detailed pv system
design characteristics.
this statistical approach is similar to top-down load forecasting methods typically used by isos/rtos. for these

the estimated Btm pv output in that town and aggregate
the resulting town-level estimates to yield a total estimated
Btm pv power profile for a larger region of interest.
the primary rationale behind this approach is that once
Btm pv fleets mature and reach the tens or hundreds of
thousands of individual installations covering a large area,
a statistical approach to modeling their aggregate performance
characteristics is possible. in a sense, this type of approach is
a form of data compression, with the goal being the use of a
limited amount of information to infer the total Btm pv performance and, in turn, eliminate the need for any information
that is redundant. this kind of approach will work as long
as the most salient features that govern the Btm pv fleet's

1 May 2014 12:30 Hours

1 May 2014 12:30 Hours

1
0.9
0.8

46° N

46° N

0.7
0.6
0.5

44° N

44° N

0.4
0.3
0.2

42° N

42° N

0.1
72° W

70° W

72° W

68° W

70° W

68° W

0

(a)
15 October 2014 12:30 Hours

15 October 2014 12:30 Hours

1
0.9
0.8

46° N

46° N

0.7
0.6
0.5

44° N

44° N

0.4
0.3
0.2

42° N

42° N

0.1
72° W

70° W

68° W

(b)

72° W

70° W

68° W

0

figure 8. (Left) simulation and (right) measured BTM PV performance on (a) 1 May 2014 and (b) 15 October 2014.
(Graphic courtesy of ISO New England; Quantitative Business Analytics, Inc. is the data source for measured data.)
may/june 2018

ieee power & energy magazine

51



Table of Contents for the Digital Edition of IEEE Power & Energy Magazine - May/June 2018

Contents
IEEE Power & Energy Magazine - May/June 2018 - Cover1
IEEE Power & Energy Magazine - May/June 2018 - Cover2
IEEE Power & Energy Magazine - May/June 2018 - Contents
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IEEE Power & Energy Magazine - May/June 2018 - Cover3
IEEE Power & Energy Magazine - May/June 2018 - Cover4
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