IEEE Electrification - December 2022 - 20

By Nanpeng Yu , Wenyu Wang , and Raymond Johnson
Behind-the-Meter
Resources
Data-driven modeling, monitoring, and control.
EHIND-THE-METER
B
(BTM) resources are
distributed energy
resources (DERs),
such as rooftop solar
photovoltaics (PVs), electric vehicles,
and battery storage systems,
located on the customer side of
smart meters. Driven by monetary
incentives, declining costs, and
increasing electricity service interruptions,
the penetration of BTM
resources has been increasing
exponentially in the past few
years. For example, the smallscale
BTM solar PV capacity in
the United States has quickly
increased from 7,642 MWac in
September 2015 to 34,029 MWac
in February 2022.
On the one hand, in addition to
the energy they produce, BTM
resources provide a wide variety of grid services, such as
peak capacity reduction, frequency regulation, voltage
support, and three-phase load balancing. On the other
hand, the rising penetration of BTM resources in power
distribution systems causes increased voltage fluctuations,
extra wear and tear on the power equipment, the
degradation of power quality, escalated cybersecurity risk,
and the need for more complex protection systems. The
lack of visibility and high uncertainties associated with
BTM resources make it challenging for electric utilities to
properly model, monitor, and control these DERs. To make
Digital Object Identifier 10.1109/MELE.2022.3211016
Date of current version: 24 November 2022
20
matters worse, many electric utilities do not have the data
required to develop accurate secondary distribution network
models, which include the phase connectivity of the
service transformers, transformer-to-meter mapping, and
distribution system line parameters.
Most of the existing tools for system modeling, monitoring,
and control are based on physical models of distribution
systems. These tools rely heavily on accurate
models of distribution systems and BTM resources, which
could be difficult for electric utilities to develop and maintain.
Moreover, model-based tools often depend on optimization
and control algorithms that do not scale
efficiently with the number of BTM resources and the distribution
grid size. Recent advances in machine learning
IEEE Electrification Magazine / DECEMBER 2022
2325-5897/22©2022IEEE

https://www.orcid.org/0000-0002-0257-5890 https://orcid.org/0000-0001-5086-5465

IEEE Electrification - December 2022

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IEEE Electrification - December 2022 - Contents
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