IEEE Electrification Magazine - June 2018 - 77

data users can rely on blockchain technology to keep and
access a locally stored ledger that records all transactions by
all of the users.
another way of enhancing cybersecurity is to incorporate the characteristics of underlying physical models and
laws that cannot be changed by nature. for example,
state estimation is used extensively in electric power systems to identify any bad measurement data based on
Kirchhoff's laws. Even through an attacker can penetrate
the cybersystem and manipulate the measurement values, a state estimator can still identify the manipulated
data sources.
While it is known that no cyberdefense strategies can
guarantee that all attacks will be prevented, a resilient
cyberinfrastructure can be achieved by developing corresponding mitigation strategies in response to successfully
penetrated cyberattacks. robust mitigation strategies can
be achieved in both cyber and physical layers. in addition
to isolating and blocking the penetrated data sources in
the cyberlayer, it is important to develop application-specific
mitigation practices in the physical layer. realizing that
every infrastructure of a smart city is designed to serve
people living in the physical world, simply cutting off the
affected data sources sometimes is no better than doing
nothing. therefore, mitigation strategies must consider
the interdependency between cyber and physical layers
and aim to minimize the impact of cyberincidents.

Physical layer with Diversified Energy
Resources and Reconfigurable architecture
Characterization of Energy Resources in Smart Cities
in the physical layers of a smart city, the electric grids feature a diversified generation portfolio compared to conventional power systems. in addition to traditional
centralized-generation units, various types of distributed
energy resources (dErs) are used in the generation mix.
the deployment of dErs in smart cities enables a paradigm shift from centralized generation to a flexible

framework with distributed generation (dG) and localized
source and load-power balance. Based on the north american Electric reliability corporation report, dErs are
defined as any resource on the distribution system that
produces electricity and is not otherwise included in the
definition of the bulk electric system. meanwhile, dErs
can be classified into the seven categories listed in table 2.
considering the stochasticity and low-inertia characteristics of many types of dErs, it is necessary to coordinate the operation between emerging dErs and
conventional-generation units and manage smart cities
with multiple types of sources and loads collaboratively. in
this regard, the modeling and characterization of dErs
play a critical role. Particularly, the modeling of dErs can
be accomplished using an analytical or computational
way to describe the physical characteristics of a certain
type of dEr and also account for the control requirements
as set forth by the corresponding operation standards.
meanwhile, as an emerging technology, data-driven
approaches are being investigated and deployed in dEr
modeling and characterization, with which the dEr under
study is regarded as a black box. the input and output signals of the black box are collected, and the characteristics
of dErs are captured using data mining or machine-learning technologies.

Resiliency Enhancement in a Secured
Energy Delivery System
for decades, electric-power systems have been designed to
possess a high level of reliability and resiliency to withstand typical threats, but when facing disastrous events,
current industry practices may not be adequate. the challenges are due to the unique features of power outages
caused by disastrous events, as shown in table 3. for
example, multiple outages may happen during a storm
when trees at several locations topple utility poles, and the
outage locations depend on the path of the storm, whereas
a typical outage is caused by one or a few faults in a random manner. during extreme events, even transmission

TablE 2. Typical classifications of DERs in smart cities.
Type of DER

Descriptions

Distribution generation

Nonbulk electric system generation units owned or operated by a utility or a third-party entity.

Behind the meter
generation

Generation units at the customer side that support the customer's
load behind the retail meter.

Energy storage

Various types of energy-storage units at the utility or the customer's side.

Aggregated DER

A virtual-generation unit with multiple DERs aggregated at different
points of interconnection (POIs).

Microgrids

Aggregated DERs and loads at a single POI.

Cogeneration

Generation units with the combined traditional process of electricity production and its byproduct.

Emergency and back-up
generation

Generation units at the utility side and used in emergency.

IEEE Elec trific ation Magazine / J U N E 201 8

Table of Contents for the Digital Edition of IEEE Electrification Magazine - June 2018