Smart Grid Resilience Issues & Enhancements

International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 10 Issue: 05 | May 2023 www.irjet.net p-ISSN: 2395-0072
© 2023, IRJET | Impact Factor value: 8.226 | ISO 9001:2008 Certified Journal | Page 1403
Smart Grid Resilience Issues & Enhancements
Abdulla Alblooshi1, Dr. Abdulla Ismail2
1 Graduate Student, Dept. of Electrical Engineering, Rochester Institute of Technology, Dubai, UAE
2 Professor, Dept. of Electrical Engineering, Rochester Institute of Technology, Dubai, UAE
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Abstract - This paper talks about generation information
about the progress of smart grids, and the concepts of
resilience. Then it highlights resilience issues in smart grids,
in addition to solutions and enhancement techniques. The
paper highlights issues like cyber threats such as FDI and
DoS attacks. Natural disasters like earthquakes Typhoons,
floods, wildfires, and hurricanes. Then goes to talk about
some enhancement techniques such as using Energy Storage
Systems, Distributed Energy reousces, Electric Vehicles
,Microgrids, mobiles devices. The paper also include
application of Blockchain in smart grid.
Key Words: Smart Grid, Resilience, Cyber-Physical
Protection, Islanding, Microgrids, Natural Disaster,
Blockchain
1.INTRODUCTION
With the rapid increase of demand for high quality,
reliable, efficient, environmentally friendly, and affordable
electricity, the movement toward the adoption and the
implementation of smart grid technologies and concepts
become a priority for utilities, regulators, governments,
and researchers [1]. EPRI defines smart grid as a network
that uses sensors, communication, and processing
computers to control and enhance the functionality and
the grid’s ability to deliver electricity. It also is optimized
to use it infrastructure like generation plants,
transmission, distribution, distributed energy resources
(DER) and storage methods such as batteries and electric
vehicles (EV) to improve operation, decrease
environmental impact, and efficiently manage the grid
assets [2], as well as features like self-healing, self-
monitoring, two-way communications, and two-way
power flow supported by advanced metering
infrastructure (AMI) [3]. With such an dramatic change in
the design, behaviour and components of the grid, several
challenges raised such as renewable energy integration,
which due to their unpredictable nature, advanced and
expensive technologies need to be integrated to an already
old grid. Another issue related to cyber security. The
increased penetration of components such as smart
meters, Internet-of-Things (IoT), phasor measurement
unit (PMU) will require safe internet connection to be
reliably used to monitor and take actions that will affect
the operation of the grid [4]. Natural disasters or Low-
Frequency High Impact (LFHI) such as earthquakes and
tornados have always impacted the reliability of the grid.
With integration of DERs such as solar forms, or grid close
to the sea or exposed to the environment that are easily
affected by typhoons and heavy rainfall [5]. the
complexity and the interconnectedness of the grid makes
it interdependent on other auxiliary services such as
supply chains, fuel sources, and communication
infrastructure. Other challenges include physical attacks
such as sabotage and vandalism, social acceptance and
public participation, lack of standardization and protocols,
aging grid and equipment failure, interoperability, and
interdependencies [6].
The concept of smart grid resiliency was the
outcome of different bodies in the industry attempting to
resolve those issues and challenges. While there is lack of
consensus on the definition of power system resilience
due to its recent adoption, there is an agreement on its key
aspects such as, robustness, resourcefulness, rapid
recovery, and adaptability. The National Infrastructure
Advisory Council (NIAC) defines resilience as the system’s
ability to anticipate, prepare for and adopt to changes in
the system conditions, and can withstand and recover
from events such as attacks, accidents and natural disaster
in a timely manner [7][8]. The concept of resilience can be
observed clearly in the resilience curve, shown in Figure-1,
that is widely used to assess and quantify infrastructure
resiliency [9].
Fig -1: Curve for infrastructure resilience [9].
There are a lot of research that is being conducted to
tackle resiliency issues in smart grid, and with continuous
growth and new technology penetration to the grid, more
issues will continue to appear. Hence, this paper will
highlight some of significant published research in the

field of smart electric grid resilience enhancement and
impact reduction or elimination methods, especially in the
field of cyber-security, micro-grid islanding, equipment
failure and grid performance degradation, natural
disasters, and extreme weather events.
2. Cyber Security issues
There are many components of the smart grid that are
connected through the internet such as the smart meters,
IoTs, home appliances and CCTV. Other are connected
through private communication methods such as PMU and
SCADA systems and even substations. Attacks like False
Data Injected (FDI), or Distributed Denial of Service
(DDosS), Man in The Middle (MITM) can impact how those
component operation, and can cause severe consequences
such as blackouts and restoration impacting resilience ,
and might affect personal such as increase in monthly bills
due to lack of communication with utility. [11].
2.1 False Data Injection
The cyber security aspect of smart grid resilience
focuses on performing risk assessment, prevention,
detection, and mitigation of various cyber threats such as
false data injection (FDI) against wide-area monitoring,
protection, and control application (WAMPAC) [10]. FDI
allows users to manipulate data going to the grid’s data
centers to facilitate energy theft, load shedding and even
blocking the data from being obtained by the control
centers [12].
In [13], it is proposed that an optimized coding scheme
be used to detect FDIs more precisely and allow sufficient
resources to be allocated to mitigate its impact. Another
strategy is proposed by [14] to reduce the to detect and
isolate the source of corrupted data source without
impacting the operation of the grid using distributed
communication schemes that can act as a secondary
control system. Another scheme is suggested by [15] aimed
at increasing the cyber- resilience of microgrids (MG)
under FDI attacks against the grid’s sensors, actuators, and
communication links, by introducing a hidden control layer
in addition to the standard three layers of control
associated with MGs as shown in Figure-2. The hidden
layer will be responsible for the timely restoration of the
MG in addition to mitigating the impact of the attack to the
primary control layers.
Fig -2: Standard control layers of MG [14 ].
The authors of [16] created a multi-sensor based temporal
prediction algorithm that enhances cyber-resilience of
WAMPAC by detecting bad system parameters data
injected to the system such as wrong voltages that might
cause grid instability or blackouts. Another scheme is
designed by the authors of [17] that enables them to detect
sophisticated FDI, also called CFDI, aimed the PMUs used
by smart grid. Their paper introduces the concept of Secure
CFDI Detection and Mitigation scheme (SeCDM) that uses
decentralized computation schemes along side knowledge
sharing algorithms to calculate measurement residuals and
allow a centralized FDI detection scheme to analyze the
data and detect any CFDI in progress. A control based
defensive strategy is proposed by [18] where a sliding
mode adaptive controller will ensure the reliable operation
of the smart grid during an FDI by automatically updating
the parameters of the control system in a way it will
eliminate the effect of the FDI aimed at the actuators of the
generators, or any other external disturbances such as load
shedding. In [19], a lag compensation is introduced
between the primary and secondary control loops of the
MGs in order to create a timescale separation between
them. This allows better detection and mitigation timing
against multiplicative FDI attacks, in addition to increasing
the frequency and voltage stability margins without the
need for additional components which adds to the overall
system resilience and efficiency. A fast detection algorithm
is proposed by [20], using a time-varying dynamic model
that reduces the worst-case delays (WDDs) of FDI attacks.
This model can detect and capture the state transition
changes due to attacks through a state estimation
algorithm. The algorithm can differentiate between attacks
and normal grid transients allowing the reduction of delays
and increase grid availability and resilience. Another
proposed dynamic model aimed at optimizing wide area
damping controllers (WADC) to mitigate the impact of false
data injection on the PMU and the shared communication
methods. The strategy entitles switching the control loop
from the one being attacked another one. Furthermore, a
used-defined law can be programmed to detect and switch
the loops if attacks were hidden [21]. A false data injection
against the synchronization systems of a DER based
microgrid is investigated in [22]. They proposed a
mitigation strategy is to establish a system to detect rapid

change in frequency of the system -which is an indication
of a cyberattack- and giving signal to the governor control
to react for quick system performance restoration and
added resiliency. The prevention of FDI attack on Load
Frequency Control system (LFC) is discussed in [23]. The
work proposes training machine learning methods such as
support vector machine (SVM) on the historical data of
frequency, active power and load deviation in the grid to
enable them to detect any anomalies. Then a proposed
Kalman Filtering (KM) is used to mitigate the impact of the
false data and restore the LFC to normal operation by
calculating the optimal control signal. Another machine
learning model is proposed by [24] in order to classify bad
data and actual attempts of FDI attacks. The paper
proposes back-propagation neural networks to improve
the work of state estimation, in order to identify FDI. The
trained model allows reduction of action time and
computational power requirements. While the method of
detection is getting more sophisticated, the paper in [25]
suggests Remedial Action Scheme (RAS) in case detection
methods have failed, and swift recovery of the grid is
required. The proposed method include using deep
recurrent neural network (DRNN) to optimally located
FACT devices, formulate FDI attacks and combine the
results to create and RAS. Another defense strategy against
FDI attacks is proposed by [26]. The paper tackles FDI
attacks from a network and a system wise. It aims to design
a virtual hidden network connected to the physical
network to overcome the structural challenges that can be
exploited during an FDI attack and adds overall flexibility
to the system. In [27], the threat of FDI attack toward
Electric Vehicle (EV) connected to smart grid is studied to
attempt timely detection. The authors use machine
learning algorithms in order to distinguish between safe
data and compromised data flowing through the system
during peer-to-peer (P2P) transaction between EV owners,
and initiate mitigation protocols with the assistance of
available systems. The design of a micro–Automatic
Generation Control system (uAGC) for microgrids is
suggested in [28]. The proposed scheme allows microgrids
(MG) to be resilient against loss of frequency control from
the grid. Because of the size of the uAGC, it is able to react
quickly to changes in frequency and power fluctuations
during FDI attacks or normal load or renewable energy
changes. Another paper focuses on the cyber resilience of
AGC proposes the using of H-infinity filter to detect and
mitigate transferred signals in smart grid. The H-Infinity
based state estimator to detect the bad data injected into
the data going to the control center and use to control the
AGC [29]. A deep unsupervised machine learning algorithm
is developed in order to detect false data injection into
large amount of sensor across large portions of the smart
grid. The proposed method by [30] uses real time and
efficient algorithms to detect anomalies and causal
interaction between the different subs-systems in the grid.
2.2 Denial of Service Attack
Due to the vast natural of smart grids, and
interconnectedness of different components with different
manufacturing and protection standard, and the grids
reliance of fast and reliable internet and communication
method to deliver its services, it has become a usual target
for a Distributed Denial of Service attacks causing
reduction in the grid resilience by increase the data loss
and corruption , and therefore, the detection and
correction mitigation technique is required [31]. This is
done by overloading the system communication
infrastructure with scam data, or wrong signals going to
Remote Terminal Units (RMU) or Phasor Measurement
Units (PMU) [32]. Similar to FDI, DoS is also countered
using the method of Prevention, Detection, Isolation &
Mitigation [33].
In smart grid, one of the forms of denial of service is
denial of electric service. Smart grid allows the utility to
control the flow of electricity to user through the use of
Remote Connection or Disconnection (RCD) units in smart
meters. Attacker might hijack the signal going to the RCD
and cause service interruption to different user types like
individuals or commercial as shown in Figure-3. A vast
number of hijacked RCD might cause grid instability or
blackouts. The paper suggested setting a time delay
between the execution of signal from all RCD which allow
time for detection and isolation. Also, slow changes in the
load status will make it easy for the grid to stabilize. The
paper also suggested methodical time delays and delay
mechanisms for different cases of RCD usages and type of
loads [34]. When it comes to Dos attack against microgrids,
the authors of [35] discuss method to detect, location and
isolate faults associated with DoS attacks. This is done by
creating a discrete model that enable control centers to
estimate magnitude changes in the system which is
indicative of attack presence.
Fig -3: Typical RCD attack on customers [34]
The resilience of a load frequency system is enhanced
using a predictor-based scheme in [36]. The authors study
the effect of DoS attack on multi-region LFC and method of
resilient restoration. Using Lyapunov theory, the authors
established a set of conditions that will allow the LFC
system to remain stable for longest period during different
DoS attack, allowing detection and isolation time. A
piecewise observer is proposed by [37] to increase the

resilience of frequency regulators in microgrids through
providing real-time estimate of the system’s states and
unknown ongoing signal. Furthermore, it provides
criterion that allow the system to be stable during different
cyber-attacks using H∞ controller. A machine learning
based DoS detection method is proposed by [38]. The
method include collecting network data and using
algorithms like SVM to detect any abnormalities within this
data stream. The SVM model allow distinction between
normal data, bad data and data showing signs of sabotage
like DoS, illegal access to remote machines (R2L), and
illegal access by users (U2R). the SVM model show higher
detection rate, which improve the security of the
communication and the overall resilience of the grid. The
uncertainty to the system caused by DoS attacks is studied
by [39] to create a scheme that allows saving the
communication bandwidth which help with operation
continuity. Furthermore, an adaptive H∞ controller is
introduced based on Lyapunov-Krasovskii to improve
stability during DoS attacks as shown in Figure-4. By
implementing this scheme into the LFC system, it will allow
the triggering of mitigation method in early stages, while
maintaining the stability of the operation.
Fig -4: event-triggered LFC during DoS attack [39]
In [40], a protocol to provide resilience against DoS
attacking using Flocking-Based method is proposed. The
consider data packets inside the network interact similarly
as flock of birds. Studied flocks behavior like centering and
matching were associated with smart grid parameters like
phase angles, and frequency synchronization. This
similarity allowed the author to based their resilient
communication protocol to be based on flocking models. In
[41], the defense against load altering attacks is studied.
unlike typical methods, the authors propose a model-free
approach that deal with actuator faults using simplified
representations of the system. The paper showed that
model-free approach demonstrate great performance
against data packet losses caused by DoS.
3. Natural Disasters
Natural disasters are one of the main resilience
issues of smart grid, as many of the recent blackouts in the
world were accredit to natural disasters. Data from the
USA are shown in Table-1 [42].
Table -1: Large Blackout Causes in the USA [42]
Type
% of
events
Mean size
in MW
Mean size
in
customers
Earthquake 0.8 1,408 375,900
Tornado 2.8 367 115,439
Hurricane /
tropical storm 4.2 1,309 782,695
Ice storm 5 1,152 343,448
Lightning 11.3 270 70,944
Wind/rain 14.8 793 185,199
Other cold
weather 5.5 542 150,255
Fire 5.2 431 111,244
Other external
cause 4.8 710 246,071
In [43], the authors studied the effect of earthquakes on
smart grid with great DER penetration. They propose that
while increasing DER improves energy availability, it does
increase the chance of being impacted by localized
earthquakes. The paper [44] establishes a framework for
evaluating and improving the resilience of small grids or
Island City Integrated Energy system (IC-IEC) by analyzing
both failures on system and component wise to allow the
establishment of recovery strategy in case of earthquakes
and increase the resilience of the system. A dynamic
restoration strategy is proposed by [45] to minimize the
power outage after events like earthquakes, which helps
with the grid resilience. Their algorithm will be fed with
real-time data about the event happening, available DERs
and grid condition, allow it to dynamically produce a grid
reconfiguration plan that will provide optimal amount of
power for the impacted area. The paper [46] proposes a
economically practical approach to move from reliability
based network investment to resilience based one. This is
done by identifying the best network investment that have
resilience against risks like earthquakes or other HILP
events. A distribution network resilience enhancement
method based on energy storage is proposed by [47]. The
historical data of earthquake location and magnitude are
modeled and studied to determine the location and the
sizing of Battery Energy Storage Systems (BESS) are
determined in order to supply critical loads during
earthquakes in a resilience and timely manner as shown in
Figure-5.

Fig -5: Response of BESS due to earthquake [47]
In [48], a quick restoration of power supply using the
energy stored in local electric vehicles (EV) through the
concept of vehicle-to-grid (V2G) is proposed. The paper
introduced a recovery model that uses data of network
faults events, EV users behavior and travel patterns to
create a spatial and temporal distribution that serve as a
dispatch plan for post-disaster restoration strategy.
Addition enhancement strategies to the distribution
network were introduced in [49] by going through the
historical data of the region, and allocating distributed
generation (DG) units in certain nodes that will allow
power restoration to that area post-earthquake. Mobile
generation units such as Mobile Emergency Generator
(MEGs), Truck Mounted Energy Storage System (MESS)
and electric vehicles (EV) can be used to enhance the
resilience of the distribution network by offering rapid
restoration of power during high impact low probability
events like earthquakes [50].
Hurricanes are one of the natural disasters that had a
lot of impact on the grid causing large blackouts such as the
one in Texas winter blackout in 2021. By implementing the
fragility curve, the authors of [51] identified the probability
of line outage due to high wind speed associated with
hurricanes. In addition to that, they considered risk of
overloading lines, load shedding to create a risk-based
assessment. Another team created a spatio-temporal
hurricane impact analysis (STHIA) to quantity the damage
of hurricanes on the power grid based on the historical
data of the region and potential risk factors, which can be
used in the design of the grid [52]. A Markov based pro-
active dispatch process is proposed by [53] to prepare an
optimal generation location and dispatch plan before
incoming hurricane or typhoon and post-even operation
which reduces curtailment and increases resilience.
Another stochastic model is developed in order to predict
the chance of flood caused by the hurricanes that might
impact substation. Resilience is enhanced by protection
important energy distribution hubs from going offline due
to floods [54]. The authors of [55] took into consideration
minimizing the power curtailment during hurricane in
addition to considering the financial costs of creating a
hurricane resilient generation dispatch plant, and
considering the consequences of such strategies after
system returns to normal. Increase the resilience of the
distribution grid through optimal placement of switches.
The paper [56] simulates the impact of the hurricanes on
the availability of different components of the smart grid.
Then an optimization model is used to place the switches
where it is more resilient during HILP events. In [57], the
resilience of the transmission line and the generation
plants are simulated with the consideration of capacity lost,
restoration costs and the damage to the system. The paper
managed to demonstrate how increase of DER might
impact the resilience negatively without proper modeling
and placement. Another resilience enhancement method
during hurricanes is proposed by [58]. Using Dynamic
Micro Grid (DMG) to improve the resilience of a smart grid
partition during hurricane is proposed. The paper also use
simulation to highlight the difference in resilience
improvement between Fixed MGs and Dynamic MGs. The
proposed enhancement method uses optimization
formulations for deploying the two types of micro grids
during a hurricanes. The simulation showed the DMGs can
carry more load during grid failures than FMGs as shown in
Chart-6.
Chart -1: Picked up loads by two types of MGs during
restoration from hurricane [58]
By combining the data from hurricane prediction
algorithms, and the proactive prediction of grid line
outages during hurricanes, a hybrid model based on
component fragility curve, simulation, and scenario
reduction algorithm, is established. This model enhances
the planning and deployment of grid resources while
considering line outages [59]. Another proactive strategy is
suggested by [60] to optimally locate DERs and the
optimized formulation of the microgrids that can serve
optimal amount of loads during interruption. In [61], the
deployment of mobile emergency resources is considered
during hurricane to supply the islanded critical loads. The
proposed algorithm also produces the best deployment
location, path to location optimization, transportation and
any constraints in terms of available infrastructure such as
blocked roads or non-critical loads.

A method to enhance resilience during typhoon
disasters is highlighted by [62]. The proposed scheme
involved optimal preventative scheduling approaching in
addition to taking actions toward optimizing the topology
of the transmission line to maximize power delivery.
Furthermore, the authors of [63] attempt to tackle the
issue of uncertain path that typhoons takes and consider a
three-stage day-ahead unit commitment for the generation
unit. This will allow optimal dispatch during the disaster
and during restoration. This is done by implementing
stochastic model for typhoon paths. In [64], resilience
would be considered during the planning stages of
transmission system and components by creating a
probabilistic typhoon model incorporated into fragility
curve. A Regional Integrated Energy System (RIES) optimal
operation through coordinated optimization considering
available energy equipment and demand response is create
by [65]. The paper proposes an enhancement for RIES
during typhoons allowing optimal operation.
In [66], an overall review of the impact of wildfire on
different parts of the smart grid is conducted. The paper
highlights different actions that can be taken before, during
and after the wildfire impacting the grid. This includes
forecasting risks and damages, state estimation, preventive
and corrective actions, and restoration and the end of the
event. The authors of [67] and [68] discuss several impacts
of wildfires on the smart grid system damaging
transmission towards causing the collapse of the line. In
addition to the particles and smoke from the fire impacting
the properties of the insulation and the conductors. An
increase of conductor temperature would increase
resistance and cause expansion and sagging. The paper
[69] discusses protective generation plan and unit dispatch
strategy to allow the mitigation of the impact of the
wildfire. It also utilizes Markov decision process to model
the condition and provide the operator of the system to
take rapid decisions that ensure the resilience and the
reliability of the grid and reduce curtailment of power and
costs. The fire growth, progression and trajectory is also
simulated in [70] as a way to enhance understanding of
required preventative response and impact to the grid as
shown in Figure-6.
Fig -6: wildfire progression on simulated IEEE 30-Bus
system
In [71], the paper attempts to simulate a wildfire event and
its impact on the components of the power system. The
proposed method will allow mitigation of the impact of the
wildfire, in addition to identifying the behavior of the fire
such as intensity, arrival time, path and ignition point. This
allows better decision making, planning future strategies.
The authors of [72] studied the impact of wildfires on the
dynamics and performance of micro grids. The paper
models the wildfire in form of heat gain and heat loss, and
then uses optimization method to decrease the impact of
the fire on the power delivery with restraints on DERs,
ESSs and power balance. EV penetration can also impact
the grid’s resilience against wildfires. In [73], suggest the
impact of mass EV mobilization and charging requirements
during wildfires. A model is designed to simulate EV users
evacuating and charging their cars during an event. In [74],
the authors provide an optimal expansion strategy to the
stake holders of the grid supporting their decision to add
new transmission lines, or enhancing the resilience of the
current infrastructure in addition to development of new
Distributed Energy resources (DERs). The use of deep
reinforced learning to analyze that that can be used by the
operators to take decision during wildfires to improve the
resilience of the grid is investigated in [75]. Wildfire
propagation model, and Markov decision process is used
with IEEE test system showed this approach can help
reduce load by reducing power flow into damaged lines. In
[76], microgrids are used to improve the resilience of the
grid during wildfires. This is done by integrating several
micro grids in strategically placed location to allow
different operational mode to be entered easily. Similar
work is done by [77] which focuses on the optimal location
for micro grids to improve the resilience of the grid during
extreme weather while overbudgeting. Improving the
resilience of the existing grid against wildfires and other
natural disasters by replacing wood transmission poles and
vegetation management [78]. The authors of [79] propose
a flexible scheduling strategy using EV ax auxiliary power
source in case major DERs went offline due to wildfires or
other disasters. The optimal scheduling and location will be
determined using two-stage model considering the random
behavior of EV users. A multi spatio-temporal resilience
assessment strategy against extreme weather is suggested
by [80] using assessment techniques incorporating the
fragility curve and statistical simulation in order to build a
real-time mapping of the grid components and their failure
potential against events like high wind speed. A similar
planning strategy for the long-term resilience of the
electrical network that combats extreme weather events is
proposed by [81]. The framework will consider high
penetration of DERs, IRES and will include mitigation
techniques based on the improved design of the grid. A
simulation base don fragility curve is used to create a
strategy for defensive islanding during extreme events
[82]. The strategy includes dividing the system into stable
and safe sections, and sections that can be at risk of
cascade failure, or might impact the operation of other

sections of the grid. The use of microgrids to improve
resiliency is also proposed in [83]. The authors analyzed
the mechanism extreme events affect he resilience of the
smart grid, and used Markov and Monte Carlo simulation to
output a strategy for deploying microgrids to supply power
to load,. The effect of the microgrid can be seen in Figure-7.
In [84], the authors attempt to tackle the issue of reactive
power shortage during an extreme event because of DERs
inability to produce enough. The paper proposes different
optimization techniques that will optimally dispatch
networked microgrids to improve reactive power
magnitude and overall mitigate the impact of the extreme
conditions.
Fig -7: Grid operation with and without microgrid [83]
4. Smart Grid Resilience Enhancements
A frequency control method based on distributed
energy storage devices is proposed in [85] as a method to
increase the resilience of the grid during abnormal grid
transients enabling the grid to overcome rapid frequency
changes and accelerate synchronization and stability of the
system. In [86], a framework is established to use DESS to
overcome frequency and voltage transient issues during
cyber-attacks or natural disasters. The paper [87] reviews
the method of which battery and energy storage systems
(ESS) can improve the resilience of the smart grid. If the
operation of the grid was hindered due to severe weather,
sabotage of theft, battery systems can take over the lost
loads, and supply them with power. The battery systems
can be dispatch to damaged grid locations, and help create
and support microgrids to compensate for lost load,
speeding up restoration and increasing resilience at the
same time as shown in Figure-8.
Fig -7: Grid Resilience with and without ESSs [87]
An optimization method that can optimize the
economics of a new Energy Storage Systems (ESS) and the
best location and routing to be considered during a crisis is
proposed by [88]. The model will also provide information
about the optimal dispatch to create a high resilience
microgrid to support smart grid during failures. The
application of mobile energy storage device to enhance the
resilience of the smart grid is studied in [89]. The paper
shows the advantages of mobile energy storage such as fast
deployment time, better control schemes and
infrastructure, and load serving capabilities. Also batteries
are more environmentally friendly comparing to mobile
diesel generation for example. Furthermore, the mobility of
the system allow its installation in more economically and
technically feasible locations as demonstrate in the Figure-
8.
Fig -7: Effectiveness of Mobile vs. Fixed ESSs [89]
In [90], an optimization model is created to optimize the
location and the capacity of the energy storage system with
technical and economic restrictions such as operating
costs, accessibility for power demand and non-black-start
(NB-S). the work also extends to proper sizing and location
of photovoltaic cells (PV). In [91], the distributed energy
storage devices owned by individuals are examined to
perform a resilience enhancement task during crisis. The
paper creates a framework for optimizing the process of
selling and buying electricity from individuals and MGO to
increase microgrid availability [92].
In [93] proposes using Smart Contracts through
Blockchain technology to enhance cyber security by
decentralizing the energy management and data

processing hubs which makes it extremely difficult to
manipulate date it requires the verification of 50% of the
nodes in the network and passing different consensus
algorithms. Furthermore, it introduces using Smart
Contracts that facilitates P2P transactions between small
and commercial power producers without established
trust or prior communication and encouraged more small
producers to connect to the grids, which reduces
interruption times, and increases the resilience of the grid.
This allows rapid energy transaction during disaster and
peak demand, increase the grid overall resilience.
Blockchain is also used to enhance the security and
improve the communication between the Distributed
System Operators (DSO) and the localized AMIs during
power transaction between DERs owners and the grid. The
role of blockchain is to create a high security, transparent
and private communication between the DSOs and smart
meters that enable the exchange of real-time pricing,
energy consumption data and patterns, diagnosis, billing,
planning and prediction[94] . the peer-to-peer (P2P)
transaction in smart grid would also be more secure and
rapid allowing prosumers to inject energy from their DERs
and DESSs which help with supply electricity during peak
demand or loss of generation units due to crisis [95].
Taking advantage of the blockchains security features to
use smart contracts to buy and sell electricity in increased
speed, scale, and high security levels [96]. The authors of
[97] show the blockchain security features in the energy
trading between DSOs and independent energy suppliers
and prosumers, and how it can improve the resilience of
the grid due to available energy supplies. In [98] highlight
further advantages of blockchain to counter some cyber-
attacks by detecting abnormal data in the system, and due
to the decentralized property of the blockchain, countering
cyber attacks is facilitated through rapid detection and
mitigation techniques. It also provides real-time
transparent transactive environmental improving demand
and supply management and allows secure participation of
different parties in the grid like prosumers, EV owners, and
DSOs. Enhancing the resilience of the energy market using
blockchain is studied in [99]. The paper proposed
blockchain technology to establish self-sovereign Identify
(SSI) allowing user to control their market identity as a
method of secure trading energy through the blockchain
smart contract technology. By encouraging all grid
participants to become SSIs, the transaction will become
more secure, and faster since SSI provide trust and identify
customers during the creation of the identity. The
proposed framework is shown in Figure-8.
Fig -8: framework of using SSI in Blockchain as a
means to enhance energy trading and therefore, increase
resilience of the grid [98].
A distributed predictive control scheme using blockchain
as infrastructure is proposed by [100] to provide DC
microgrids with high levels of security for their two way
communication protocols to prevent any sabotages or
cyber-attacks. The control strategy will improve proactive
resilience and the robustness of the DC microgrid. It also
takes advantage of blockchain’s resilience toward hacking
to improve the security of the DC micro grids without
compromising the systems ability to intervene during peak
demand or during natural disasters or grids loss of
generation units.
Reliable and autonomous Microgrid are main part of a
resilient smart grid. It connects loads with DERs and
allows customers to have a secure and reliable source of
electricity, especially during anticipated disasters [101]. In
[102], the location of microgrid location is determined
using information about the critical infrastructure available
along a certain geographical area, such as gas lines, water
treatment facilities or hospitals. One method to improve
the resiliency of microgrids is to implement FACTS devices
into the design of microgrids to allow trouble-free
transition between grid-connected mode to islanded or
autonomous mode of MG operation [103]. This allow
maintaining frequency and voltage withing acceptable
range of operation, which can help loads to maintain
normal operation. If the network is very large, then
implementing microgrids would increase the resilience of
he grid while maintaining economical feasibility. The case
of the European grid [104], which require increase of
capacity through penetration of DERs, DESSs and
introduction of backup generation which are part of a
typical scheme of microgrids. In [105] different advantages
of microgrids are highlighted. During failure of certain
point of the transmission or distribution network, the load
connected to affected node can be fed from a different

microgrid connected through different routes.
Furthermore, if the microgrid was fully isolated from the
grid due to disaster of man-made failure, it can operate in
islanded mode to supply the local connected load reliably.
Different failure scenarios can be seen from Figure-8.
Fig -8: Resilience improvement of grids using MG during
different failure events [105].
In [106], the authors suggest proper planning and
deployment of microgrids in future smart grids in order to
counter the impact of natural and made-made disasters on
the power system. The study showed resilience against
issues like islanding, cybersecurity, and natural disasters.
The authors of [107] review different resilience aspects of
networked microgrids (NMG). The study shows that NMGs
improved grid resilience during large scale outage
compared to standalone microgrids or standard outage
management protocols. This is due to NMG’s coordination
that maximizes the utilization of the power supply
available. Furthermore, NMGs can take power from regular
utility network allowing more coverage of customers. In
[108] the resilience oriented microgrid formation and
scheduling is discussed. Formation help with determining
the switching plan and the boundaries of each MG to allow
maximum coverage of loads during different scenarios. The
scheduling part focuses on optimal energy dispatch in
terms of meeting demand and optimizing costs. Microgrids
are also flexible when it comes to location, operating
voltages and concepts, connected DERs and operational
modes as they can be connected to other microgrids, utility
gris or operated in standalone [109]. During accidents, the
DSO is able to convert the operation mode of microgrids to
islanded mode to serve local critical loads as studied in
[110]. The autonomous microgrid can manage the DER to
supply sufficient power to the connected loads. As a matter
of fact, if the microgrid has enough generating units, it
might become the primary source of energy while keeping
the utility as a secondary source. The paper [111]
highlights the role of microgrids in overall grid resilience
during disasters and peak demand, and its ability to
manage alternative energy sources optimally so that
changes in load in the main grid will not impact the normal
operation. In [112], the authors investigated the properties
that makes a microgrid more resilient to different natural
and man-made events. With he objective to recover and
supply critical loads the moment disaster happens, the
location and sizing of microgrids are important.
Furthermore, interconnecting the microgrids to different
and critical nodes in the grid will help increase the
resilience and the effectiveness of microgrid in restoring
loads. The paper [113] proposes a two-stage approach to
restore power to critical loads after a HILP event. The
stages include configuration of the connected microgrid to
supply damaged sectors, and the next stage is to use
demand response from the critical load to optimally supply
them with electricity. The algorithm proposed will also
plan to integrate the microgrid back to the main grids or
creating new circuits with the non-effect switches and lines
after the event passes. The small signal stability of the grid
might cause frequency propagation and eventually
blackouts if not controlled properly. In [114] microgrids
are used to improve the dynamic resilience of the
microgrid through the use of power-electronic interfaced
energy resources. This is done by using several
optimization techniques to find the optimal operational
points and stability margins. The stability of the microgrids
would add up to the overall resilience of the smart grid.
The paper [115] also introduces the concept of electric
springs that improve microgrid responses during
fluctuating renewable distributed energy resources, by
acting like an inertial mass like flywheel in transitional
generators.
In [116], the propose an optimization technique to select
the optimal location for the generators in microgrids. The
paper tackles issues related to the discrete nature of
selecting the location of the DG and the continuous nature
of sizing the generator in addition to integrating a cost
function to consider the economic factor. The authors of
[117] suggest Intentional Islanding Techniques (IIT) in
order to mitigate the impact of severe conditions and allow
better probability to serve critical loads. A machine
learning model is created to predict the chances of failure
of different components of the microgrid during disaster
using historical data , and suggest optimal location and

deployment scheme [118]. A Decentralized decision
making algorithm is proposed by [119] in order to allow
optimal decision making in case control centers were
impacted by HILP events through a consensus based
system.
Electromagnetic Pulse (EMP) is one of the rare natural
phenomena that would several damage the smart grid.
EMP can disable entire electrical and electronical
infrastructure in short period of time. The authors of [119 ]
study the impact of EMP on the smart grid. The impact can
vary from no effect at all, to complete destruction of the
Printed Circuit Board (PCB) that is a core part of every
device in the smart grid. The only method to restore
service is replacement of those PCBs. Furthermore, EMP
would bring a cascade damage to all connected equipment
such as transformers, communication and protection
devices, and generators. Table-2 shows the impact of
different type of EMP on the component of typical electrical
Grid. Possible mitigation techniques include installing
protectors on critical equipment like communication
devices, inverters, and power supplies. Protectors include
Faraday cages, mesh wires, or installing them deep
underground.
Equipment At Risk
EMP
(Nuclear)
Solar
Storm Cyber
Physical
Attacks
Generator Stations DPE DEU DPE DPE
SCADA / 1ndustrial
Controls DPE DPE DPE DPE
Utility Control
Centers DPE DPE DPE DPE
Transformers DPE DPE PPE+CE DPE
Telecommunication
s Including
Cellphones DPE DPE DPE CE
Internet DPE DPE DPE CE
Radio Emergency
Communications DPE TE CE CE
Emergency SATCOM
Communications DPE TE CE CE
GPS DPE TE CE CE
Transportation DPE CE CE CE
Water DPE CE PPE+CE CE
DPE = Direct Permanent Effects
EU = Direct Effects Uncertain.
CE = Cascading Effects (if no backup power).
DPE+CE = Potential Permanent Effects plus Cascading Effects.
TE = Temporary Effect (0.5-36 hours) assuming backup power.
The restoration speed is a significant part of the
resilience of the grid. In [121], the optimal strategy to
restore the grid after HILP event have passed is
determined using optimization algorithms that ensures
that power is restored to critical loads using secondary
network nodes through technical information about grid
condition, available equipment, future demand for the
loads and implementation of restorative computerized
procedure. Another restoration strategy is suggested by
[122]. It is assumed that after a major unplanned outage,
the energy sources by normal utility feeders will be scarce.
One method to rapidly restore electric power is the use of
available mobile emergency resources such mobile energy
storage system, or dynamic microgrid. A model is created
to optimally determine the path to travel to critical grid
nodes to be supplied by the MESs. When the best routes are
determined, the critical loads nearby are scheduled to be
fed. The restoration method proposed by [123]
implements a restoration strategy based on Electrical
Vehicles. Restoration using optimized location of DERs
considering typical load flow to critical loads are suggested
by [124] as a method of rapid restoration and resilience
increase of the smart grid. The paper [125] suggests a
Resilient Distribution Network (RDN)that consists of DERs
that support the effort to restore energy to all critical loads.
This is done by restoration area plans with partial load
restoration such as hospitals or military camps. Then each
area will have a deployed DERs that can sustain its energy
needs until utility is returned to service. A mathematical
optimizing problem is created to optimally create electrical
boundaries for the RDNs. The authors of [126] suggest
similar work utilizing dynamics microgrids and Markov
decision making algorithm to support critical loads.
3. CONCLUSIONS
In this paper, resilience issues facing smart grid were
surveyed and summarized. The topics ranged between
cyber threats such as FDI and DoS, Natural disasters like
earthquakes, typhoons, hurricanes and floods. In addition
to some general enhancement techniques to improve the
resilience against know issues in Smart Grid.
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