IEEE Power & Energy Magazine - May/June 2015 - 62

Resilience Metrics
Before, During, and
After an Event

Adaptation,
Transformation,
Long-Term Planning,
and Decision Making

Short-Term
Metrics

Long-Term
Metrics

Quantifying
Resilience

figure 3. Quantifying short-term and long-term resilience.

over the cost of realizing the measure. Following this analysis, the resilience actions can be ranked and implemented
based on both their resilience- and cost-efficiency indices,
which would help build a power infrastructure that satisfies
both resilience and cost efficiency requirements.
Based on this discussion, it is clear how adaptive management, as a learning procedure that is function of time, is
therefore another concept that distinguishes resilience from
reliability and is necessary for understanding and building
resilience. In fact, the knowledge of a power system and its

main resilience threats is often partial and incomplete, as it
is almost impossible to accurately and precisely predict the
future extreme events that would compromise power system
resilience. In this respect, the Intergovernmental Panel on
Climate Change defines adaptive management as "a process
of iteratively planning, implementing, and modifying strategies for managing resources in the face of uncertainty and
change. Adaptive management involves adjusting approaches
in response to observations of their effect and changes in the
system brought on by resulting feedback effects and other
variables." The adaptive management approach explicitly
recognizes the presence of uncertainty and allows decisions
to be made and resilience actions to be taken based on new
(and possibly incomplete) information, resulting in an infrastructure with built-in operational and planning flexibility. At
the end of the day, the risk of acting under uncertainty is
always lower than the risk of inaction!
There are several examples of adaptive behavior in power
systems for mitigating the impact of catastrophic events. In the
United Kingdom, for instance, the North Sea storm in December 2013 resulted in the flooding of 2,600 homes, but approximately 800,000 homes had been protected from flooding. By
comparison, a similar event in 1953 led to the loss of 307 lives,
while 30,000 people evacuated their homes and 24,000 properties were seriously damaged.

Quantifying Resilience

Failure Probability

Quantifying resilience is not a straightforward process
(quite the opposite, actually, as it
may prove the most challenging
task within a resilience analysis
framework) because, as discussed
Weather Profile
previously, resilience is a multidimensional, dynamic concept
with several intrinsic complexiFragility Curves
ties. However, quantifying resil1
ience is necessary to evaluate
0.8
the effectiveness of the resilience
strategies and amend them as
Resilience
0.6
Enhancement
necessary. Numerous resilience
metrics exist, but many times they
0.4
quantify only one or a few dimen0.2
sions of resilience. For example,
resilience is often quantified
0
based on the degree of robustness
Intensity of Weather Parameter
to the initial shock, the functionResilience
ality achieved during the event,
Enhancement
or the postevent recovery duraComponent's Weathertion. However, a comprehensive
Affected Failure Probabilities
approach for quantifying both the
short-term, i.e., before, during,
and after an event (Figure 1), and
Resilience Assessment
long-term (Figure 2) features of
resilience should be developed to
get a quantitative understanding
figure 4. Evaluating and enhancing resilience to weather events using fragility curves.

62

ieee power & energy magazine

may/june 2015



Table of Contents for the Digital Edition of IEEE Power & Energy Magazine - May/June 2015

IEEE Power & Energy Magazine - May/June 2015 - Cover1
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IEEE Power & Energy Magazine - May/June 2015 - Cover3
IEEE Power & Energy Magazine - May/June 2015 - Cover4
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