Risk and Resiliency Assessments of Renewable Dominated Edge of Grid Under High-Impact Low-Probability Events -A Review

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Risk and Resiliency Assessments of Renewable
Dominated Edge of Grid Under High-Impact
Low-Probability Events —A Review
Tossaporn Surinkaew, Rakibuzzaman Shah, Member, IEEE, and Syed Islam, Fellow, IEEE
Centre for New Energy Transition Research (CfNETR),Federation University, Mt Helen, VIC-3350, Australia
E-mail: t.surinkaew@federation.edu.au; m.shah@federation.edu.au; s.islam@federation.edu.au
Abstract—Low-probability high-impact (HILP) events such
as windstorms, earthquakes, wildfires, and floods, which can
cause significant damages to power systems, are inevitable and
unpredictable. Besides, uncertainties from distributed renewable
energy resources may prevent conventional techniques to improve
reliability of power grids. In previous research works, several
strategies have been introduced to investigate risk and resiliency,
and to find effective solutions to improve system reliability under
such extreme events. In this paper, a critical review of these
strategies is presented. Modelings of the HILP events are dis-
cussed. In the conclusion, this paper pinpoints significant findings
and give directions for robustly protecting power systems.
Index Terms—High-impact low-probability events, power sys-
tem resiliency, risk assessment, microgrids.
I. INTRODUCTION
Historical natural disasters such as earthquakes in countries
located closed to the edge of the Pacific Ocean, windstorms
in America, flooding events in the United Kingdom, bushfires
in Australia, etc., highlight their impacts on power system
reliability and resilience. For example, according to National
Centers for Environmental Information, America has experi-
enced 28 drought events, 33 flooding events, 9freeze events,
132 severe storm events, 52 tropical cyclone events, 18 wildfire
events, and 19 winter storm events in the last 40 years,
with severe impacts and major power system failures [1].
These phenomena are generally known as high-impact low-
probability (HILP) events [2]. Since the emergence of such
events is quite rare, power system planners, regulators, and
policy makers may not recognize them within network reliabil-
ity standards [3]. The HILP events are particularly challenging
to identify and understand. In comparison to ordinary power
supply disruptions, the relationships between cause and effect
are more complicate, the uncertainties are greater and it is
more challenging to deal with events, which are believed to
be unimaginable [4]. As reported by [5], Table I summarizes
critical power outages worldwide during 2011 −2016.
As a result, their impacts on techno-economic and reliability
aspects ignite electrical engineers, researchers, planner, and
those who are interested to seek suitable assessment methods
and resilient strategies to mitigate the HILP events.
On the one hand, modern power systems comprise dis-
tributed energy resources (DERs) with the ability of per-
forming individually, i.e., either in an islanded mode or a
grid-tied mode. These scenarios have been hailed as a game
TABLE I: Major power outages worldwide due to HILP events
during 2011 −2016 [5]
Date Location HILP events Affected
customers
February 2011 Christchurch,
New Zealand
Earthquake ≈160,000
June 2012 Ohio, United
States
Thunderstorm 3,800,000
July 2012 Northern
Area, India
Monsoon ≥600,000,000
October 2012 Eastern
Area, United
States
Hurricane
Sandy
8,100,000
January 2013 SE
Queensland,
Australia
Cyclone Os-
wald
250,000
February 2013 Northeastern
Area, United
States
The North
American
Blizzard
650,000
March 2013 Greater
Belfast,
Northern
Ireland
Heavy snow 200,000
April 2013 Poland Heavy snow 100,000
December 2013 Ontario,
Canada
Ice Storm ≈300,000
July 2014 Luzon,
Philippine
Typhoon
Rammasun
13,000,000
November 2015 Vancouver,
Canada
Windstorm 700,000
November 2015 Washington,
United
States
Windstorm >161,000
September 2016 Florida,
United
States
Hurricane
Hermine
300,000
September 2016 South
Australia,
Australia
The Blyth
Tornado
1,700,000
changer across energy industries in the transition to renewable-
dominated power systems [6]. Consequently, the effects of the
HILP events are intensified by the increasing complexity and
uncertainties of the power system due to integration of DERs.
It is ubiquitously accepted that the efficacy of a microgrid
significantly depends on the energy management systems [7].
Therefore, the optimal economic operations and electricity
generations can reduce the related costs of implementations
and planning, consequently enhancing the overall system re-
siliency [8].
Aiming at the HILP events, the number of publications

on techno-economic risk and resiliency assessment in mod-
ern power systems has relatively increased. These published
papers examine the technical and economical features of
modern power systems with uncertain DERs, and introduce
innovative methodologies for their effective operation man-
agements. Thus, the goal of this review paper is to explore
these publications focused on i) modelings of the HILP events,
ii) summarizing risk assessments and resilient strategies to
mitigate effects of the HILP events, and iii) suggesting future
research gaps and directions regarding on this timely topic.
II. MODELINGS OF HILP EV EN TS
Recently, world have been critically affected by catastrophic
phenomena such as windstorms, earthquakes, wildfires, and
floods. Major infrastructures including power systems are
severely damaged [9], [10]. During such events, the conse-
quences frequently go beyond conventional system design, as
these events lead to prolonged outages for large numbers of
customers and major loads in the systems [9], [10]. This may
eventually leads to wide-area blackouts. In this section, the
modelings of such HILP events are discussed.
Generally, risk assessment index (IR)can be formulated
according to [11] as,
RHI LP =SHI LP ×PHI LP ,(1)
where PHI LP ∈[0 1] is the probability of failure under an
HILP event and SHILP ∈Ris the severity of an HILP event.
In (1), PHI LP can be obtained by the facility curves
described in Section II-E, while SEis calculated from several
factors as demonstrated in Sections II-A, II-B, II-C, and II-D.
A. Windstorm Modeling
Current climate changes have created major windstorms,
which is able to cause high damages to power grids [12]. The
increased severity of windstorms will subject the power grids
to more serious levels of risk. Wind speed is the key factor and
it is the most destructive force of a windstorm. The probability
of failure of each branch/subsystem can be evaluated by
matching the wind speed at a peak value to the fragility
curves of feeders/transmission lines and/or towers in each
area [12]. In [13], very high wind speeds during windstorms
are gauged by means of a geographical random field, thus
allowing for spatial auto-correlation, which is adjusted with
earlier or historical data of the corresponding windstorm.
Besides, an uncertainty of zonal wind-speed-specific set can be
applied to create the spatiotemporal dynamics of windstorms
[12], [13]. Therefore, the windstorm hazard index (W HI )is
directly depend on wind speed (vw)and wind-related factors
(p1, p2,· · · ), as formulated by,
W H I =f(vw, vw(p1, p2,· · · )) .(2)
B. Earthquake Modeling
The earthquake disaster can ruin power supply networks,
substations, power transmission lines, and related electrical
apparatus [14]. System planners should activate effective mea-
sures to improve the earthquake-related reliability. To model
the hazard index from earthquake EHI, major factors such
as a proper characterization of the ground motions (GM)and
peak ground acceleration (P GA)at various sites/areas/zones
are considered as,
EH I =f(GM , P GA).(3)
In (3), the variable P GA can be observed from the ground
motion time-history at a site and it can be modeled as a non-
stationary random mechanism with probability distribution,
frequency content, and period of P GA [14]. Gaussian white
noise can be applied to explain the possibility of time-histories
ground movement [14]. Besides, the work in [15] also employs
a fault tree analysis to investigate impact of earthquake-related
reliability in power systems. The work in [16] represents a
recursive decomposition method for evaluating exclusive safe
and insecure paths of a corresponding power network under
earthquake.
C. Wildfire Modeling
Modeling the wildfire distribution is the key parts to investi-
gate its techno-economic risk on power networks. As reported
by [17], effects of wildfire on power networks (IW )can
be determined by observing the severity of wildfire events
(I W E), grid exposure (GE), and grid susceptibility (GV )
as follows,
IW =IW E ×GE ×GV. (4)
Recently, in [18], a fire weather index (FWI)is applied
to emulate wildfire risk distribution in a period of two years.
The FWI is a function of meteorological parameters such
as temperature (T),vw, relative humidity (Hr), and 24-hr
precipitation (P). A forest fire danger index (F F DI ), which
is a function of T,vw,Hr, drought factor (DF ), and rainfall
(RF ), can be alternatively use to evaluate the severity of the
wildfire. The FWI and F F DI are generally represented in
the following forms,
FWI =f(T, vw, Hr, P ),(5)
F F DI =f(T, vw, Hr, DF, RF ).(6)
where frepresents the nonlinear function.
In (5) and (6), the FWI ∈[0 ∞)and F F DI ∈[0 100]
can be categorized into six levels, i.e., very low, low, moderate,
high, very high, and extreme [19]. These indices also result
in the same warnings for wildfire. In [20], [21], [22], the
FWI can be used to evaluate fire risk irrespective of ignition
resources. Some resilient measures (i.e., losses of load and
energy expectations and probabilities, system resiliency during
a wildfire) with Monte Carlo simulation can be indirectly used
to evaluate the effects of wildfires [23]. In addition to the men-
tioned models, wildfire behavior can be created by physical,
quasi-physical, empirical, quasi-empirical, mathematical, and
simulation models [24].

TABLE II: Summary of HILP modelings
Model Considering Factors
Windstorm Wind speed, related factors to wind speed, etc.
Earthquake Ground motion and peak ground acceleration.
Wildfire Intensity of wildfire, gird exposure, grid vulnera-
bility, temperature, windspeed, relative humidity,
precipitation level, drought, rainfall
Flood Outdoor inundation, indoor electrical compo-
nents, indoor water depth, etc.
D. Flood Modeling
For reliability enhancement of power systems, alleviating
flooding aftermaths and quickly restoring to a pre-flood state
(or stable operating point) are to be regarded as high impor-
tance [25]. In this aspect, floods are considered as functions
of the changing rate of the water levels at the locations of grid
components [26]. The flood hazard causing failure of electrical
network components is developed in [27] by observing several
factors such as outdoor inundation (OI), indoor electrical
components (IEC), indoor water depth (I W D), and so on
[28], [29], [30]. In [31], the geographical information system
(known as GIS) is applied to examine the danger of flooding
events. Moreover, windstorms are correlated with flooding
events [2]. One can simply write flooding hazard index (F HI )
by,
F H I =f(OI , IEC, IW D, · · · ).(7)
Other factors can be used to determine the value of F HI
in (7) depending on available data.
Summary of the HILP modelings is given in Table II.
E. Fragility Curve
A fragility function is widely used to find the probability
of failure of either related structure or component relating to
the potential intensity of a hazard of each HILP event [2]. The
fragility curve can be acquired from i) identification of a big
data set of previous failures, ii) a structural simulation model,
iii) expert judgment, or iv) a combination of i) to iii) [2].
Examples of fragility curve of an HILP event is simply demon-
strated in Fig. 1. Let n=F actor 1, F actor 2,· · · , F actor n is
the counter for considered factor for the fragility curve of each
HILP event and k=V ariable 1, V ar iable2,· · · , V ariable k,
the probability vector of occurrence of event kwith factor n,
Pn
HI LP (k|n)in (1) can be obtained by,
Pn
HI LP (k|n) =









0,if k < k1
Pn
HI LP (k),if k2< k < k3
.
.
.
1,if k > k4
,(8)
where k1,· · · , k4is the range of k.
It should be noted in (8) that the calculation and conditions
of Pn
HI LP (k)are significantly different depending on the
corresponding HILP event. Formulations of the fragility curve
can be found in [27], [26], [31] for flooding events, [32],
[33], [12] for windstorms, [34] for wildfires, and [35], [36] for
earthquakes. Details regarding the fragility curve development
can be found in [2].
Fig. 1: Examples of fragility curve of an HILP event [2].
Fig. 2: Illustrative strategy toward resilient power system during
HILP events [37].
III. STR ATEG IE S TO MITIGATE EFF EC TS O F HILP EVENTS
Several previous research works proposed effective strate-
gies to mitigate the effects of the HILP events. Fig. 2
demonstrates an illustrative process of a resilient power system
subjected to HILP events. As summarized by [37], i) a resilient
power system is appropriately designed and expected to better
resist the HILP events than those of the traditional systems,
ii) the system can mitigate the effects of the disasters through
system hardening, and iii) resilient strategy applied to the
system can be recognized as efficient resource dispatching
methods, and more progressive restoration methods, such as
novel controls of islanded microgrid, will be applied in a
timely operation to recover the system to a new operating
point closing to normal performance level.
A. Mitigation of Windstorm Effects
Several research works have been conducted to mitigate
effects of windstorms. A double-layer robust and resilient
model for the optimal operation of an active distribution net-
work to enhance its operational resilience against windstorms
is utilized in [12]. The proposed method in [12] can pro-
vide higher resilience against windstorms compared to other
conventional methods, i.e., tri-level adaptive robust optimiza-
tion, and duality- and Karush–Kuhn–Tucker-based methods. In
[13], a criteria is defined to rank single components according

to windstorm influences on the overall system reliability.
It is found that component importance associated to initial
failures is ignited by the windstorms, which subsequently
leads to potential cascading failures. The obtained results in
[13] demonstrate that the proposed procedure can provide an
efficient basis for planning network improvements in terms of
i) strengthening vulnerable line segments against wind loads
and ii) increasing line capacities to limit cascading failures.
In [38], a demand side management program considered
weather condition in a real time pricing framework is proposed
to control the customer’s electricity consumption behavior
according to windstorm conditions. The outcome of [38] is
a better complement to the existing resilience enhancement
approaches, which can guide the utility to effectively employ
the existing power infrastructure and modern smart grid op-
eration and strategy to address the challenge of windstorm
hazards. Also, the proposed method is beneficial to the power
system resilience improvement and risk mitigation to lessen
adverse effects caused by severe windstorm conditions. Other
methods such as i) metrics and quantitative framework [39],
ii) component importance assessment [40], iii) resilience-
oriented proactive management approach [41], and iv) multi-
phase assessment and adaptation strategies, can be also used
to evaluate impacts of windstorms and mitigate their effects
on power system and microgrid reliability.
B. Mitigation of Earthquake Effects
Power system seismic design is used to mitigate earthquake
effects on power system reliability [42]. System performance
under earthquake characteristics depends on i) level of power
system, ii) regional coverage of power system, iii) character-
istics of power system network, iv) dynamic characteristics of
electrical equipment, and v) system robustness against external
disturbances [43], [16]. A systematic method incorporating
substation retrofit and imposing outages to the customers
under earthquake hazards is proposed in [44]. Here, a case
study of historical earthquake events is contemplated. In this
study, the Monte Carlo simulation is applied to track the
stochastic failures of affected substation components. The
results demonstrate that the affected power transformers and
circuit breakers at medium voltage level are the most critical
components in the retrofitting approaches. In [45], novel
assessment framework with component criticality, which can
rank the severity of earthquakes to the affected components,
is proposed. This method can substantially strengthen the
resilience of the system against seismic events.
C. Mitigation of Wildfire Effects
Mitigating the risk of wildfires is potentially one of the
biggest challenge of power system and microgrid reliability
[24]. Impacts of wildfire on power system in USA can cause
from around $M8to $M16,500 [46], [47], [24]. It is widely
known that wildfire can ignite from i) power systems by
an accidental fault or other catastrophic failures [48] (As
they are related to power system caused fire ignitions, this
phenomenon is reported and studied in Refs. [49], [50], [51])
TABLE III: Summary of wildfire mitigation techniques [24]
Techniques Fault of fail-
ure preven-
tion
Acr-ignition
prevention
Wildfire im-
pact mitiga-
tion
Moving line to
underground
✓ ✓
Structural hard-
ening
✓
Asset
inspections
✓
Waveform ana-
lytic
✓ ✓
Vegetation man-
agement
✓ ✓
Sensitive protec-
tion
✓
Disabled
reclosers
✓
Proactive opera-
tion
✓
Weather model-
ing
✓ ✓
Fire surveillance ✓
Emergency
planning
✓ ✓
and ii) and natural wildfires. According to [24], wildfire
mitigation techniques can be categorized into three groups,
i.e., fault prevention, arc-ignition prevention, and fire response
and impact mitigation. Table III summarizes techniques of
wildfire mitigation. Several research works have proposed
effective strategies to reduce the wildfire effects on power
systems. According to [52], the actions can be performed,
e.g., situational awareness by forecasting potential risks and
damages before the event, corrective action such as network
reconfiguration and power re-routing during the event, and
restoration such as black start and load restoration after the
event. A novel framework for resiliency improvement of a
power system with electric vehicles under wildfire event is
introduced in [23]. This method includes resiliency analysis
incorporated probabilistic models of load redistribution, which
also takes potential evacuation routes into consideration. A
probabilistic proactive generation re-dispatch strategy to en-
hance the operational resilience of power grids during wildfires
is proposed in [53]. The proposed approach in [53] can
enhance the power grid resiliency under various scenarios of
wildfires.
D. Mitigation of Flooding Effects
Flooding events are indeed problematic for previous trans-
mission substations, since they are installed on or close to
the ground level, resulting in the shutting down condition in
case of flooding [54]. The work in [55] proposes an extended
method with decision flow diagrams for external flooding risk
assessment of a nuclear power plant. The proposed framework
in [55] can facilitate the standardization of the evaluation on
the flooding risk assessment. The work in [31] develops a new
GIS-based tool for evaluating hazard of flood, related cost,
and reliability assessment of power networks. The counter-
measures applied in [31] can reduce the risk index from the

flooding scenario, failure probability, and provoked costs over
50% from their normal levels. Additionally, it is shown that
the levels of the electrical and water utilities are interrelated.
The proposed tool is very beneficial for future infrastructure
planning objectives by distinguishing critical risk areas, eval-
uating the potential costs, and supporting utilities, and finally
achieving their reliability goals in the cases of flooding events.
Moreover, other methods such as i) infrastructure hardening,
such as installing substations on elevated foundation slabs
and installing permanent physical barriers [56], [54], ii) using
energy management system, demand response, and network
reconfiguration to avoid load curtailment [57], iii) substation
failure estimation based on probabilistic approaches [58], iv)
robust protection strategies such as upgrading new structures
[59], and v) a stochastic resource allocation methods for
monitoring power substations in a day-ahead manner [60], are
presented. The advantages and disadvantages of such other
methods are discussed in [60].
IV. CONCLUSIONS AND FUTURE RESEARCH DIRECTIONS
A review of previous risk and resiliency assessments, mod-
elings, and mitigation methods considering the HILP events is
discussed in this paper. To investigate the risks and effects of
the HILP events, modeling of hazard or severity need to be
formulated according to available data. Since extreme events
rarely occur in actual power systems, previous research works
mostly apply probabilistic methods and fragility curves to
predict the HILP events. For future risk and resiliency assess-
ments, the following research directions can be considered as
follows:
1) Novel smart grid technologies such as real-time moni-
toring, smart meters, controllable loads, automatic data
acquisition systems, etc., lead to more flexible directions
for the power system and microgrid operation. However,
these technologies are not properly applied to the pro-
posed frameworks to use their contributions for future
resilience enhancements. Measurements and smart meters
should be applied to report the HILP events by using
dynamic data from the system,
2) Real-time tracking of the HILP events on power systems
in both distributed and centralized levels is important
for improving grid resiliency via targeted and quick re-
sponses. Therefore, more researches on high-performance
HILP, dynamic simulation, and equipment failure models
are indispensable,
3) Since DERs highly penetrate into future power systems,
using such distributed active elements to support elec-
tricity during the HILP events are alternative options and
should be well researched by taken into consideration
optimal techno-economical aspects,
4) A few research works incorporate the knowledge of
power system dynamics and stability into risk and relia-
bility assessment for sustainable system planning. Thus,
the dynamic constraints should be additionally considered
in this regard. Conventional risk and reliability assess-
ment methods may be ineffective under uncertain dynam-
ics of DERs. Few research works consider characteristics
and limitations of DERs under the HILP events. This
paves the way to design novel strategies for using DERs
to mitigate the effects of the HILP events.
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