  The growth in variable renewables such as solar and wind is increasing the
impact of climate uncertainty in energy system planning. Addressing this
ideally requires high-resolution time series spanning at least a few decades.
However, solving capacity expansion planning models across such datasets often
requires too much computing time or memory. To reduce computational cost, users
often employ time series aggregation to compress demand and weather time series
into a smaller number of time steps. Methods are usually a priori, employing
information about the input time series only. Recent studies highlight the
limitations of this approach, since reducing statistical error metrics on input
time series does not in general lead to more accurate model outputs.
Furthermore, many aggregation schemes are unsuitable for models with storage
since they distort chronology. In this paper, we introduce a posteriori time
series aggregation schemes for models with storage. Our methods adapt to the
underlying energy system model; aggregation may differ in systems with
different technologies or topologies even with the same time series inputs.
Furthermore, they preserve chronology and hence allow modelling of storage
technologies. We investigate a number of approaches. We find that a posteriori
methods can perform better than a priori ones, primarily through a systematic
identification and preservation of relevant extreme events. We hope that these
tools render long demand and weather time series more manageable in capacity
expansion planning studies. We make our models, data, and code publicly
available.
