  Assessing the effects of the energy transition and liberalization of energy
markets on resource adequacy is an increasingly important and demanding task.
The rising complexity in energy systems requires adequate methods for energy
system modeling leading to increased computational requirements. Furthermore,
with complexity, uncertainty increases likewise calling for probabilistic
assessments and scenario analyses. To adequately and efficiently address these
various requirements, new methods from the field of data science are needed to
accelerate current methods. With our systematic literature review, we want to
close the gap between the three disciplines (1) assessment of security of
electricity supply, (2) artificial intelligence, and (3) design of experiments.
For this, we conduct a large-scale quantitative review on selected fields of
application and methods and make a synthesis that relates the different
disciplines to each other. Among other findings, we identify metamodeling of
complex security of electricity supply models using AI methods and applications
of AI-based methods for forecasts of storage dispatch and (non-)availabilities
as promising fields of application that have not sufficiently been covered,
yet. We end with deriving a new methodological pipeline for adequately and
efficiently addressing the present and upcoming challenges in the assessment of
security of electricity supply.
