  To manage computational complexity, models of macro-energy systems commonly
deploy reduced sets of time-series data. This paper evaluates the adequacy of
time-series reduction when modelling energy systems with fully renewable
generation and a consequent dependency on storage. Analysis includes various
methods to derive reduced time-series and to implement them into models, either
as time-slices, also referred to as representative days, or continuous
time-steps. All methods are tested with regard to unmet demand and accuracy of
estimated system costs using a simple capacity expansion model of the power
sector within a renewable energy system. Some methods achieve little unmet
demand, but instead their results regarding storage are biased and favour
seasonal at the expense of short-term storage. We conclude that renewable
energy systems limit the adequacy of time-series reduction and future research
should focus on alternative methods to reduce computational complexity.
