IEEE Power & Energy Magazine - July/August 2016 - 23

Structure

Known

unknown

Modeling
Tools

Optimization
Tools

Examples

Less

Unknowable
Unforeseeable
Events
Divergent
Scenarios

Probabilistic or
Stochastic Description

Bounded by Sets

Stochastic
Programming

Robust and Fuzzy
Programming

Robustness
and Flexibility

Nodal Load, Yearly Demand
Growth, Wind Production

Investment Costs,
Facilities Outage

Long-Term Scenarios, Public Policy,
Earthquakes, Hurricanes, Terrorism

figure 5. The structure of uncertainties from known probabilistic or stochastic uncertainties to unknowable or scenario
uncertainty.

TEP, their existence must be acknowledged for completeness
since they are rigorous mathematical objects required for
rational decision making under Bayesian statistical theory.
Examples of subjective probabilities are those assigned to
future scenarios by expert elicitation.
Some uncertainties may be categorized differently or
move between categories depending on data availability,
modeling decisions, or knowledge, as we explain with the
following four examples. First, note that the failure of transmission facilities due to weather conditions may be regarded
as u and modeled by fuzzy techniques because accurate historical data is missing, even though repeatability may justify the application of probabilistic techniques if such data
were available (i.e., a K uncertainty). Second, even though
demand growth and nodal load are often labeled as random
and treated by probabilistic and mathematical forecasting
methods, other approaches such as fuzzy and robust programming have been applied for reasons such as tractability issues of stochastic optimization. Therefore, load growth
may be either K or u depending on modeling choices. Third,
although generation expansion is often modeled by scenarios, research on proactive models moves generation expansion from U or u to K, as discussed in the previous section.
Fourth, an increased understanding of regulatory uncertainties such as renewable portfolio standards targets will
probably never be enough to move such uncertainty from
U or u to K. Although KuU is connected to the nature of
uncertainty, KuU is more practical and thus also related to
practical considerations.
Unknowable uncertainties such as long-term state,
earthquakes, and medium-term black swans are highly
unpredictable. Unfortunately, these uncertainties are also
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ieee power & energy magazine

accompanied by huge risks from which hedging is difficult or
even impossible.
✔ First, the traditional approach for dealing with longterm uncertainties is based on a reduced number
of strategic scenarios with inherently high risks.
However, these scenarios do not describe every important future scenario. The purpose of strategic
scenarios is to bound uncertainty and understand
its impact rather than precisely forecasting future
outcome since accurate prediction is an impossible
task in the long term. Because scenarios are only
assumed to describe the future, it is not uncommon
to miss important future scenarios not perceived as
plausible at the time the scenarios were constructed.
Moreover, the actually realized future may well be
a combination of constructed scenarios with other
unforeseen events and processes; thus, plausible futures are not identifiable. Furthermore, huge risks
stem from the high degree of uncertainty in the long
term, even when only considering the set of constructed scenarios.
✔ Second, predicting the timing and location of earthquakes and hurricanes is impossible, and their occurrences have enormous impacts on electricity systems.
Scenarios seem to be the appropriate modeling tool
because of unpredictability and the need to hedge
against massive risks.
✔ Third, it is impossible to hedge against risks produced by completely unforeseeable black swans, such
as terrorist attacks, the financial crisis of 2008, or the
Chilean crisis of 2004 produced by the breach of gas
supply contracts by Argentina.
july/august 2016

Table of Contents for the Digital Edition of IEEE Power & Energy Magazine - July/August 2016