IEEE Power & Energy Magazine - May/June 2020 - 66

domains of interest and extrapolating the sample and
introducing third-party customer demographics into
the analysis of AMI data to segment customers into
domains of interest.
✔✔ Big data management represents the challenges associated with managing the large amount of data coming
from AMI. One challenge is how to relate all the various sources of large data sets into a manageable relational model that allows for access by the various analytical tools used today, such as SAS, Python, and R.
✔✔ Solar-related issues focus on understanding the effect
of PVs on residential and commercial customer loads,
including forecasting behind-the-meter solar in real
time to better predict net loads.
✔✔ Market segmentation classifies customers based on
some observed attributes. The attributes could be contained in the customer billing system, characteristics
of the interval load, or based on a third-party data
source matched to utility billing records.
✔✔ Customer load characteristics use the interval load
data to isolate customers with a high load factor or a
high base load. The high load-factor customers have
a high value of the ratio of average demand to maximum demand. Typically, they can be put on a more
favorable rate because they provide a better utilization
of utility assets. The high base-load customers could
be good candidates for some utility intervention, such
as an energy efficiency program.

Tasks

Average

Number Number
Median of 5s
of 4s

Big data analytics

3.6

4

6

6

Big data
management

3.5

4

4

6

strategies for sizing transformers based on the diversity of customers served by a transformer.
✔✔ Theft detection focuses on using interval load data to
identify unanticipated changes in a customer's load
pattern; they can be the result of utility failure or deliberate action taken by the customer.
✔✔ Revenue protection is closely aligned with theft detection and associated with any analyses that look to
improve the financial performance of the utility.
✔✔ M&V 2.0 is a growing area of demand-response measurement and verification that uses interval load data
to estimate the savings associated with program intervention in real time.
✔✔ Cryptocurrency identification recognizes customers
running a server farm mining for cryptocurrency at
a time when utilities are beginning to explore what
drives residential customers with a high base load and/
or high load factor.
✔✔ Indoor agriculture investigates the likely load growth
resulting from indoor agriculture activities such as
cannabis production using hydroponics.
As an industry, we still have not advanced very far
beyond analyzing loads at the building or household level.
Although interest in end-use research persists, the business
case for collecting end-use data continues to be difficult to
justify due to the high cost of data collection. Some progress
is being made in the disaggregation of the whole building
or household load data into end-use components, such as
air conditioning, lighting, and refrigeration. However, with
more and more utilities rolling out AMI, applications such
as the evaluation of direct load control seems to be moving away from requiring end-use metering to being based on
AMI shapes. As an example, PJM requires utilities to provide whole-facility metering data, where available, to prove
the value of the utility demand-response programs.

Solar-related issues 3.5

3

6

2

The Next 10 Years of Load Research

Market
segmentation

3.4

4

5

6

Customer load
characteristics

3.4

3

6

2

Transformer sizing

2.2

1

2

2

Theft detection

1.9

1

2

0

Revenue
protection

1.9

1

2

0

M&V 2.0

1.8

1

1

0

Cryptocurrency
identification

1.6

1

1

1

Indoor agriculture
(cannabis)

1.6

1

1

0

table 2. The emerging analytical tasks
for load researchers.

M&V: measurement and verification.
66	

✔✔ Transformer sizing is a classic analysis that develops

ieee power & energy magazine	

What will load researchers be spending their time on 10 years
from now? Given the continuation of the utility/regulator
model, the increasing penetration of AMI and DER, and the
advancement of data technologies, we highlight some everexpanding load research applications.

Cost of Service and Innovative Rate Design
As listed in Table 1, cost of service and rate design are currently the two most important load research applications,
and we expect this to continue in the context of increased
penetration of DERs and the availability of interval data.
Collectively, load researchers will take fresh looks at these
core applications while getting used to the novel position
of having access to interval data from nearly all of their
customers. Because of the increased volatility in customer
demand at the meter associated with high penetrations of
DERs, an important application of load research in the near
may/june 2020



IEEE Power & Energy Magazine - May/June 2020

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