IEEE Systems, Man and Cybernetics Magazine - July 2022 - 17

technique and the traveling-wave approach. Instead,
amplitudes of the lower frequency component can be
used. Figure 8 depicts an edge-based architecture for
fault location in a distribution system with a high penetration
of DGs.
Accordingly, in case of a fault incident, the zero-mode
transients are sampled at the device layer and then passed
to the adjacent edge node, where lower frequency amplitudes
are obtained. This procedure allows the fault section
to be located and, hence, rapidly isolated. The precise
location of the fault can be determined at the substation
cloud using the amplitude ratio of the two lower frequency
components at the fault section. In addition to the
above-mentioned advantages, edge-integrated platforms
are more immune to cyberattacks and, hence, can handle
processes even in the case of partial breakdowns.
This is absolutely strategic for critical applications such
as fault location [8].
Fog/Edge Computing
Along the lines of edge computing, the fog computing paradigm
was proposed by Cisco in 2012 to offer cloud capabilities
in the proximity of network edges, i.e., network
gateways. Since its introduction, industry has massively
invested in this concept; however, edge computing was
solely embraced by academia as a research topic for
years. As an instance, a number of giant industrial
groups, including but not limited to Cisco, Dell, Microsoft,
and Intel, partnered to found the OpenFog Consortium,
which has been collaborating with reputable
standard organizations, such as the European Telecommunications
Standards Institute and IEEE, to accelerate
the implementation of fog computing architectures. The
market of fog computing is assumed to mainly impact the
five sectors of energy, transportation, industry, agriculture,
and health care.
The overlap between fog and edge concepts has led to
their interchangeable use in the literature. However, we
would like to clarify the existing distinctions to highlight
the functionalities of each concept and their individual
contributions in complementing the cloud paradigm. Both
fog computing and edge computing enable intelligent decision
making and associated tasks in the vicinity of the network
edge. While edge computing often takes place at the
first point connected to IoT devices, fog computing occurs
one to two hops farther than edge computing. Furthermore,
edge computing features more energy and resource
limitations as the growing IoT applications create competition
over the available resources. Despite the subtle differences,
the two concepts are established on the same basis
of security, scalability, reliability, agility, and autonomy,
which has triggered the idea of integrating these two paradigms
as fog/edge computing. Similar to their individual
integration with cloud computing, the fog/edge paradigm
tends to unburden the cloud by providing services with
lower latency and jitter.
Fog/Edge Computing Applications in a Smart Grid
Pursuing the same path as edge computing, fog/edge paradigms
deployed in the smart grid domain aim to facilitate the
smooth integration of DERs in smart grids and encourage
the decentralized organization provisioned for these cyberphysical
systems. However, the augmented resources available
in the fog layer can further extend the applications or
considerably improve the functionality, privacy, security, and
the quality of service for both end users and operators. In
this section, we discuss the fields in smart grids where fog/
edge architectures can have beneficial impacts.
Smart Buildings
As discussed earlier in this article, in the near future, smart
grids will transform into a group of connected yet autonomous
entities, e.g., microgrids, operating in a coordinated
manner. As automation technologies advance and DERs take
over the grid, the current role of microgrids will be extended
to smaller self-contained units, e.g., nanogrids, as the building
blocks for future smart grids. Accounting for roughly 40%
of the total energy consumption in urban areas, smart buildings
are viable candidates to perform as nanogrids. Accordingly,
the energy consumption optimization within a smart
building is of significant importance. Moreover, until the realization
of the nanogrid paradigm, sensitive information of
residents must be shared with an MGC or the DSO for various
social, economic, and technical purposes. Load management
and forecasting, real-time monitoring, network
planning, and determining the electricity price are examples
of applications demanding consumer data.
Adversaries can access the fine-grained data on the
energy consumption of consumers, which can relay their
absence or presence, daily activities, and appliance use at
any time and can impair communication and compromise
meters. To address these concerns, it has been proposed to
release an aggregate value of energy consumption within
an area, such as a building. However, it's not guaranteed
that the aforementioned vulnerabilities would not occur,
particularly when daily aggregate values are disclosed.
Fog computing can be utilized to perform private aggregation
on data streaming from smart meters. Meanwhile,
some architectures take the privacy measures further and
add another privacy layer where edge nodes distribute
noise on smart meters so that just an aggregate value can
be derived, without disclosure of the exact metering values.
In addition to anonymization and aggregation of data,
fog nodes can be used besides edge devices to perform
local optimizations, e.g., energy cost minimization or preventive
maintenance planning based on the preference of
residents for each unit within a building. They can then
relay the local decisions to an upstream entity, such as a
cloud, for managing global optimizations.
Blockchain-Based Smart Energy Contracts
Subsequent to the major triumph of blockchain as a cybersecure
ledger for recording cryptocurrency transactions,
July 2022 IEEE SYSTEMS, MAN, & CYBERNETICS MAGAZINE 17
IEEE Systems, Man and Cybernetics Magazine - July 2022

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