IEEE Electrification - December 2022 - 14
De-Silo Data
* Access Smart Meter Streams
* Improve T&D Communication
* Standardize Data Format and Quality
Transmission
* Day-Ahead and Intrahour Forecasting
* Prevent Infeasible Grid Edge Dispatch Orders
Access
Latent Value
* Improve AMI Utilization
* Stream Device Operational Status
* Train Analytics With Historical Data
Develop
New Sensor Tech
* Repurpose Weather Sensing
* Increase Sensing Below Substation
* Optimize Sensor Placement
Distribution
*Intrahour Forecasting
* High Spatiotemporal Resolution
* Prevent Infeasible Grid Edge Dispatch Orders
* Situational Awareness
*Improve Coordination Among Stakeholders
Figure 4. An illustration of how the various capability gaps can be addressed with broad strategies targeting their root issues. Doing so supports
the core needs of RTOs and distribution operators, as indicated on the right. AMI: advanced metering infrastructure.
constraints, so finding ways to extract additional value
from extant sensor networks might be attractive from the
distribution operator perspective. Many states, including
grid edge PV leaders such as California, have widespread
advanced metering infrastructure (AMI) installations. AMI
data tend to be used only for billing, however, so they are
typically collected at the daily time resolution, and they
are often not stored, shared, or used for advanced analytics.
Although AMI data generally have high latency, making
them unsuitable for real-time grid edge PV output
estimation and load disaggregation, the development of
analytic methods to estimate past grid edge output and
capacity as well as to detect unknown installations might
yield substantial value. These static data (as opposed to
dynamic output data)-such as location on the grid network,
capacity, and the system characteristics-can be
combined with other lower latency data sources (such as
advanced weather data products) to provide real-time
estimates and forecasts of grid edge output and demand.
In addition to forecasting, location data in particular
can improve state estimation techniques at
the
TABLE 1. The NWP forecast characteristics
of representative models.
Spatial
Model
Resolution
12 km
3 km
0.19°
Temporal
Resolution Horizon
ECMWF HRES 0.1°, 9 km 1 h
NOAA NAM
NOAA HRRR
JMA NWP
3 h
1 h
3 h
ten days
84 h
48 h
transmission level, which might not be configured to
appropriately handle negative current values observed at
the T&D interface. Understanding which interface nodes
might see high DER power injection and flexible loads
can help adjust this framework to reduce the state estimate
error.
IEEE Electrification - December 2022
Table of Contents for the Digital Edition of IEEE Electrification - December 2022
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IEEE Electrification - December 2022 - Cover2
IEEE Electrification - December 2022 - Contents
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