IEEE Power & Energy Magazine - May/June 2020 - 68

a combination of level 1 and 2 chargers. Figure 3 shows the
EnergyPrint of the EV-only load following a super off-peak
TOU rate program. The vertical EnergyPrint shows the time
on the y-axis, the day of the year on the x-axis, and the load
as a color gradient, with low levels of load in the blue-toblack spectrum and high levels of load in the yellow-to-white
spectrum. The super off-peak generation plus distribution
charge was US$0.00695/kWh compared to the on-peak rate
of US$0.133. The transmission charge was the same for both
time periods: US$0.00970/kWh. Most of the energy (81%)
was consumed during the 1-5 a.m. time period. The average
annual use shown in the figure was calculated to be just over
4,000 kWh, with a noncoincident peak demand of 5.08 kW.

Predictive Load Analytics for Distribution
Operations and Planning
Even though population- and class-level load shapes will still
be relevant, load researchers will increasingly provide analysis
on localized subsets of customers, such as individual circuits, to
their internal clients within the utility. Importantly, they will be
expected to provide analytics relating to present or future conditions, rather than historical ones. These shifts will help distribution operators manage the grid more effectively by examining
future system conditions. To serve this need, load researchers will increasingly employ load forecasting models, either
at the individual customer level or at various ad hoc levels of
desired aggregation. This will allow for a better understanding
of loading issues in the distribution grid, proactively switching
loads for maintenance ahead of outages or after asset failures,
developing improved replacement and upgrade strategies, and
increasing transparency at the transmission and distribution
(T&D) interface. Figure 4 shows a localized distribution node
load forecast and its available capacity plots overlaid in green
and white, respectively. Offloading opportunities for portions
of the forecasted load from switching are shown in the bottom
right panel of the figure.
One planning strategy gaining popularity today is called
nonwires alternatives, namely, the grid investments or projects that use nontraditional T&D solutions such as distributed generation, energy storage, energy efficiency, demand
response, and grid controls that defer the need for distribution equipment upgrades in specific grid areas. It will be
paramount that the load research community be prepared to

0013: EV TOU Group, EV Load, EV

5.08 24
28
12
6
0.01 0
kW
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J

J

A

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figure 3. An EV-only charging station load on the super
off-peak TOU rate.
68	

ieee power & energy magazine	

provide the information and insight needed to support these
pending planning strategies.

Customer Targeting Using
Segmentation and Clustering
Utilities need to improve customer engagement through
analytics. Segmentation and clustering are two strategies
for identifying customers with similar attributes. Segmentation refers to a process that divides units into similar groups
and has been in use for years. With better customer overlays from improved customer records systems, the ability
to group customers and determine their hourly load variations from segment to segment are nearly unlimited. Some
common secondary sources include utility business intelligence databases, energy efficiency program-tracking systems, demand-response systems, census tract data, and tax
records. Machine learning algorithms can be used to identify customers with specific load characteristics, helping
to target marketing, pricing, energy efficiency, or demandresponse programs. Alternatively, the clustering process
finds similarities, in that load researchers can use clustering
algorithms to drive segmentation, with the differences based
on customer energy use or load shapes.
Figure 5 illustrates a simple strategy based on billing
determinants, e.g., low, medium, and high seasonal usage.
Another frequently used algorithm in practice, k-means
clustering breaks a population into k groups where the
within-group variance is minimized. These algorithms can
be designed to focus on customer load characteristics during specific periods, such as largest contributor during peak,
highest morning rise, and longest valley. They can also be
used to identify demand-response customers not contributing the anticipated load relief during event calls.

The Key Factors Needed for Load
Research to Continue to Thrive
As the primary study of customer demands over time, there
are several important foundational areas for load research to
ground itself as it advances to its future state.

Improving Data Quality
The future of load research will use considerable amounts
of interval load data from individual customers and supervisory control and data acquisition meters positioned
throughout the grid. This does not mean that all AMI data
will be flawless. The experience to date has shown deficiencies in automated systems used today and an almost
endless list of possible mistakes occurring throughout the
data-gathering process, ranging from initial AMI readings
to the formation of a data storage repository. The analytics process will continue to require careful management of
data inputs to ensure proper quality. Certainly, given the
volume of data being processed, we will need new, automated ways to track completeness and quality, positioning
data scientists to efficiently monitor and take appropriate
may/june 2020



IEEE Power & Energy Magazine - May/June 2020

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