  The uncertainty quantification and risk modeling are hot topics in the
operation and planning of energy systems. The system operators and planners are
decision-makers that need to handle the uncertainty of input data of their
models. As an example, energy consumption has always been a critical problem
for operators since the forecasted values, and the actual consumption is never
expected to be the same. The penetration of renewable energy resources is
continuously increasing in recent and upcoming years. These technologies are
not dispatch-able and are highly dependent on natural resources. This would
make real-time energy balancing more complicated. Another source of uncertainty
is related to energy market prices which are determined by the market
participants behaviors. To consider these issues, uncertainty modeling should
be performed. Various approaches have been previously utilized to model the
uncertainty of these parameters such as probabilistic approaches, possibilistic
approaches, hybrid possibilistic-probabilistic approach, information gap
decision theory, robust and interval optimization techniques. This paper
reviews the research works that used information gap decision theory for
uncertainty modeling in energy and power systems.
