IEEE Power & Energy Magazine - July/August 2020 - 42

While doing so, we highlight the fundamental physical and
decision makers' risk attitude features that distinguish
reliability-driven from resilience-driven investments.
A reliable power system would be operated in a secure way if
it were able to withstand these faults without threatening the
integrity of system operation while preserving the continuity
of supply to customers. However, the impact of recent extreme
events on power systems, e.g., bushfires in Australia, flooding events in the United Kingdom, storms in the Americas,
and earthquakes in countries located at the edge of the Pacific
Ocean, which have even led to chaotic societal situations, goes
far beyond N−1 or N−2 outages and clearly highlights the need
for rethinking current planning practices.
For example, during the last 10 years, Chile has, on an
annual basis, experienced more than 300 earthquakes above
4.5 Mw, with several hours of interruptions each year. In the
case of the United Kingdom, severe storms and floods
result in power outages for tens of thousands of customers per year. In 2016, for instance, floods were responsible
for power interruptions that lasted up to 56 h in Northwest
England. These are only a few examples worldwide where
the aftermath of catastrophic extreme events brought
resilience into discussions among power system planners,
regulators, and policy makers.
In this context, and within the broader framework of lowcarbon energy network planning being uncertain, in this
article, we analyze a set of key questions pertinent to the
resilience debate:
✔ How can we incorporate resilience thinking into power
system planning, thus going beyond traditional reliabilitydriven planning?
✔ Can the negative impacts of natural hazards on
electricity supply be mitigated through planning
measures?
✔ What is the optimum portfolio of measures for boosting power grid resilience to such extreme events?
✔ How can we build a power grid that is both robust and
flexible enough to withstand events that have possibly
never been experienced before?
Because such questions are troubling to decision makers around the world, our aim is to introduce a general,
quantitative framework that identifies optimal portfolios of
resilience-enhancing investments and demonstrates them
through several illustrative case studies. While doing so,
we highlight the fundamental physical and decision makers' risk-attitude features that distinguish reliability-driven
from resilience-driven investments. Our framework, which
was elaborated on during a United Kingdom-Chile joint
project and implemented in actual operation and planning
mechanisms in the Chilean power system, can thus be seen
42

ieee power & energy magazine

as a fundamental development to extend, in a transparent
and consistent way, current reliability practices toward more
resilient grids.

Incorporating Resilience
in Network Planning
With its growing relevance and interest to our IEEE Power &
Energy Society (PES), many definitions of power system
resilience have emerged lately. In the technical report PESTR65 published by the IEEE in April 2018, resilience was
defined as "the ability to withstand and reduce the magnitude and/or duration of disruptive events, which includes
the capability to anticipate, absorb, adapt to, and/or rapidly
recover from such an event." In general, these resilience
definitions mainly focus on characterizing the term extreme
event, which could threaten power systems, and on the key
features that a power system should possess within the multifaceted concept of resilience to minimize the risk exposure to these extreme events. In particular, the following two
points provide insight across all definitions and specific relevance to the network planners who aim to identify resilient
network enhancements:
1) an emphasis on extreme or catastrophic events, formally referred to as high impact and low probability
(HILP) (also known as black swan) events, which require some form of hedging
2) an emphasis on the time-varying nature of resilience,
including and quantifying the various phases before
and during a severe event as well as after it (when the
system recovers).

Capturing HILP Events Within Network
Planning: The Need for Risk-Averse Modeling
Historically, (deterministic) network reliability standards
have typically ignored any contingency beyond "credible"
ones, e.g., N−1/N−2. This has resulted in a bias toward building more and more infrastructure, mainly to provide redundancy to deal effectively with any outage that might threaten
the uninterrupted power supply. However, experience with
extreme events clearly shows that this reliability-driven
approach of making the infrastructure "bigger and stronger" through redundancy and reinforcements may not be
effective to hedge against multiple simultaneous outages or
outages occurring in rapid succession. In fact, very extreme
events cause outages well beyond credible ones, potentially
affecting hundreds of network components. In other words,
extreme events typically lead to considering N−X outages,
july/august 2020



IEEE Power & Energy Magazine - July/August 2020

Table of Contents for the Digital Edition of IEEE Power & Energy Magazine - July/August 2020

Contents
IEEE Power & Energy Magazine - July/August 2020 - Cover1
IEEE Power & Energy Magazine - July/August 2020 - Cover2
IEEE Power & Energy Magazine - July/August 2020 - Contents
IEEE Power & Energy Magazine - July/August 2020 - 2
IEEE Power & Energy Magazine - July/August 2020 - 3
IEEE Power & Energy Magazine - July/August 2020 - 4
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IEEE Power & Energy Magazine - July/August 2020 - Cover3
IEEE Power & Energy Magazine - July/August 2020 - Cover4
https://www.nxtbook.com/nxtbooks/pes/powerenergy_091020
https://www.nxtbook.com/nxtbooks/pes/powerenergy_070820
https://www.nxtbook.com/nxtbooks/pes/powerenergy_050620
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https://www.nxtbook.com/nxtbooks/pes/powerenergy_091019
https://www.nxtbook.com/nxtbooks/pes/powerenergy_070819
https://www.nxtbook.com/nxtbooks/pes/powerenergy_050619
https://www.nxtbook.com/nxtbooks/pes/powerenergy_030419
https://www.nxtbook.com/nxtbooks/pes/powerenergy_010219
https://www.nxtbook.com/nxtbooks/pes/powerenergy_111218
https://www.nxtbook.com/nxtbooks/pes/powerenergy_091018
https://www.nxtbook.com/nxtbooks/pes/powerenergy_070818
https://www.nxtbook.com/nxtbooks/pes/powerenergy_050618
https://www.nxtbook.com/nxtbooks/pes/powerenergy_030418
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https://www.nxtbook.com/nxtbooks/pes/powerenergy_111217
https://www.nxtbook.com/nxtbooks/pes/powerenergy_091017
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https://www.nxtbook.com/nxtbooks/pes/powerenergy_050616
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https://www.nxtbook.com/nxtbooks/pes/powerenergy_010216
https://www.nxtbook.com/nxtbooks/ieee/powerenergy_010216
https://www.nxtbook.com/nxtbooks/pes/powerenergy_111215
https://www.nxtbook.com/nxtbooks/pes/powerenergy_091015
https://www.nxtbook.com/nxtbooks/pes/powerenergy_070815
https://www.nxtbook.com/nxtbooks/pes/powerenergy_050615
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https://www.nxtbook.com/nxtbooks/pes/powerenergy_010215
https://www.nxtbook.com/nxtbooks/pes/powerenergy_111214
https://www.nxtbook.com/nxtbooks/pes/powerenergy_091014
https://www.nxtbook.com/nxtbooks/pes/powerenergy_070814
https://www.nxtbook.com/nxtbooks/pes/powerenergy_050614
https://www.nxtbook.com/nxtbooks/pes/powerenergy_030414
https://www.nxtbook.com/nxtbooks/pes/powerenergy_010214
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