IEEE Electrification - December 2022 - 12

Finally, most resource owners currently lack visibility of
their own products. PV smart inverters, for example,
might be able to make output measurements, but often,
these are not made available to customers or utilities
unless with an additional request. Exposing these data to
resource owner- or aggregator-facing applications can
empower those entities to make more effective decisions
as resource owners become active participants in the
energy market.
Grid Edge Visibility Gaps
The challenges outlined in the previous section are the
product of a variety of gaps, including missing communication
technologies and frameworks, as well as regulations
to structure coordination among entities. In this
section, we review the gaps in grid edge visibility. A gap in
this article is defined as a deficiency in the performance of
a grid entity's responsibilities (e.g., efficiency or reliability)
as a result of limitations in either the existing sensing and
measurement technologies or the surrounding institutional
frameworks or standards. Grid edge visibility gaps
can be divided into four broad and overlapping categories:
weather sensing and forecasting; grid edge monitoring
and forecasting; data standards and quality; and stakeholder
obstacles.
As shown in Figure 3, the three types on the top are
reinforced by the fourth type; therefore, addressing
these gaps requires not only technical solutions but also
involving RTOs, distribution operators, and resource
owners to uncover their specific needs; transfer
knowledge; and demonstrate the value proposition of
resolving the gaps. Gaps within each category often
share a root cause, so addressing one type of gap generally
supports goals in other areas (some examples are
indicated in Figure 4).
Weather Sensing and Forecasting
Some of the largest gaps in grid edge visibility are in the
area of weather sensing and forecasting. Accurate forecasts
and real-time knowledge of weather parameters are
essential to forecasting not only the grid edge DER output
but also forecasting the net load in general. One of the
largest areas where improvement is possible is in the spatiotemporal
resolution of these data. Operating entities
mainly rely on third-party weather services to monitor
and forecast grid edge DER output, and these vendors typically
adapt their data product from numerical weather
prediction (NWP) forecasts. This means that weather data
are typically at the >1-km spatial scale and the minute-tohour
timescale, and they might not fully characterize the
parameters that are most relevant to grid edge. Table 1
lists the forecast spatial and temporal characteristics of a
few widely used NWP models. Improving the resolution of
these data as well as decreasing their latency would
enable better management of weather-dependent grid
edge resources. These are ongoing efforts in most meteorology
agencies.
One avenue to address this gap is the development of
scalable, ground-based, low-cost, and customer-integrated
weather monitoring sensors. For example, sky
Resource
Owners
Smart Inverter
Transmission System
Operators
Wind Control Room
PV
Battery
ac
Transmission
Substation
Distribution
System
Operators
EV
Figure 1. An example power system infrastructure with grid edge components.
12
IEEE Electrification Magazine / DECEMBER 2022
Control Layer
Communication Layer
Power Systems Layer
IEEE Electrification - December 2022

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