IEEE Power & Energy Magazine - July/August 2020 - 43

It is evident that current planning standards need to be modified
to allow HILP events to be accounted for within the network
design and expansion decision-making process.

july/august 2020

(e.g., congestion costs, losses, and so on), carried out up to
the point where the marginal cost of additional investment
equals the marginal benefit of enhanced reliability. This is
graphically illustrated in Figure 1, where the reliability cost
is measured as the expected energy not supplied (EENS) ×
the VoLL. This probabilistic approach, however, presents a
fundamental limitation to properly addressing HILP events
and informs appropriate investment decisions as a hedge
against them, as we discuss further in this section.
HILP events are, by definition, very rare, and their impact
on average indicators such as the EENS is therefore very limited. For example, our analysis shows that, on average, it would
be economically optimal for Chile's power system to suffer
the consequences of very large earthquakes every 15 years
rather than invest in further assets to reinforce and harden
the power system. It is therefore worth asking ourselves why
we should be concerned about events that, on average, have a
relatively small effect. We argue that the answer to this question may be with the risk attitude of electricity consumers
and policy makers (and therefore network planners too, who
somehow "serve" both). In fact, as suggested by empirical
evidence, customers and policy makers generally dislike the
risks associated with the highly adverse consequences often
linked to HILP events and would thus like to reduce them as
much as possible. But how can we model this risk attitude,
and what is the underlying risk-attitude assumption in the
aforementioned probabilistic assessment?
In risk analysis, attitudes toward risk are usually classified into three categories, which we describe in this section.
Consider an electricity consumer who is given the choice
between the following two options. In the first option, the
consumer pays US$90 for a network service that hardly
ever fails, and, when it does, small amounts of energy are
unserved, totalizing an associated expected cost of ENS

Cost

with X being greater or much greater than 1 or 2 and even in
the order of hundreds or more. Indeed, here lies one of the
fundamental differences between reliability and resilience,
at least in the context of network planning. Planners may
then intuitively realize that, although it improves reliability,
more redundancy may not necessarily improve system resilience to extreme events. Alternatively, flexible ("smarter")
solutions could provide more viable options that enhance
resilience by helping to withstand the initial adverse impacts
of extreme events as well as by supporting the efficient
response and prompt recovery of the system.
In the new context outlined in this section, could hybrid
solutions (where hybrid refers to both infrastructure/network,
i.e., to provide redundancy and robustness, and noninfrastructure/non-network or smart operational solutions, that is,
to provide flexibility) constitute the optimal portfolio to boost
resilience to extreme events? Although this is still an open
research point and likely to be case specific, if we want to
keep the lights on or at least pursue an acceptable level of system operation under a large array of circumstances (beyond
so-called credible contingencies), it is evident that current
planning standards need to be modified to allow HILP events
to be accounted for within the network design and expansion
decision-making process. However, the key question here is
how? Even though no straightforward answer exists, in this
section, we discuss a few possible approaches while recognizing that it is technically unrealistic (and not economically viable) to consider targets of 100% reliable supply after
extreme events and, at the same time, acknowledge that the
system should meet classical (deterministic) reliability standards that consider only credible outages.
A first approach to incorporate HILP events within network investment planning could adopt probabilistic (or, in
mathematical programming terminology, stochastic) models that explicitly consider the associated probabilities and
resulting impacts of many states of the system, including
the intact system and simultaneous outage scenarios. These
impacts are usually measured in terms of energy not supplied (ENS) and valued through economic metrics such
as the value of lost load (VoLL). More specifically, in the
probabilistic reliability assessment pioneered by Billinton
and Allan, the resulting estimated costs from the ENS are
averaged (weighted by probability) across all of the modeled scenarios and optimized against additional investment
and operational costs. Network investments are thus well
justified as a tradeoff between economics and security of
supply and, if we neglect, for simplicity, operational costs

Total Cost

Investment
Cost

Reliability
Cost
Optimal Network

Network
Capacity

figure 1. The optimal balance between investment and
reliability costs.
ieee power & energy magazine

43



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
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IEEE Power & Energy Magazine - July/August 2020 - Cover3
IEEE Power & Energy Magazine - July/August 2020 - Cover4
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