IEEE Electrification - December 2022 - 13

cameras are a promising type of local sensor to characterize
the impacts of clouds on PV for very short-term
output forecasting, especially considering their low cost
and high performance. In many other areas, weather
sensing is a technologically mature field, so the development
of sensors for grid edge visibility might be helped
by looking to other business applications of weather data
(e.g., agriculture) and adapting their sensing technology
for grid management purposes. Increasing the availability
of ground sensor data will also enable the validation of
satellite and NWP predictions as well as the creation of
high-resolution weather data products.
Developing and integrating these new sensors into
grid operations will require research to clarify weather
data requirements for grid applications and to assess
how varying data quality and reliability will affect application
performance. Beyond integrating them into existing
grid application models, developing advanced
" hybrid " models by combining physics-based models of
grid components with data-driven models of their relationship
to weather parameters will be important for grid
edge resources whose capacity and characteristics are
not fully known.
Grid Edge Resource Monitoring and Forecasting
T&D management systems require the real-time visibility
of grid edge devices for applications, including volt/VAR
control; fault location, isolation, and service restoration;
forecasting future grid edge DER output, demand, and net
load; and the procurement of ancillary services (at the
bulk power level). The lack of the real-time visibility of grid
edge components is a major gap in most grid edge frameworks.
Avenues to increase this visibility can be divided by
the type of device, such as DERs, and by load. Grid edge
DERs refer to energy-generating devices, such PVs and
energy storage, whereas grid edge load refers to energydemanding
devices, such as smart appliances and EVs.
Figure 5 demonstrates a list of sensors used for grid edge
monitoring at various aggregate levels.
One of the largest obstacles to improving grid edge visibility
cited by distribution system managers is budget
Largest
Scale
* Long-Term Trend
Analysis
TOs
* Day-Ahead Forecasting
* Intrahour Forecasting
* Real-Time Data
* Static Data
* Cost-Benefit Analysis
* DER Control +
Communications
* Local Distribution Grid
Visibility
Smallest
Scale
* Meeting Distribution
Grid Constraints
DOs
ROs
Figure 2. A schematic diagram of some of the highest priority
overlapping challenges faced by grid entities operating at different
scales. DO: distribution operators; TO: transmission operators;
RO: resource owners.
Weather Sensing and Forecasting
* Scalable Sensor Technology
* Short- and Long-Term Forecasting
Grid Edge Monitoring and Forecasting
* Real-Time and High Resolution Visibility
* Short- and Long-Term Forecasting
Data Standards and Quality
* Proprietary and Siloed Data Streams
* Communication Channels and Standards
Stakeholder Obstacles
* Costs
* Expertise
Figure 3. A schematic list of the four major areas of capability gap in grid edge visibility.
IEEE Electrification Magazine / DECEMBER 2022
13
IEEE Electrification - December 2022

Table of Contents for the Digital Edition of IEEE Electrification - December 2022
