  This paper introduces a generalised version of importance subsampling for
time series reduction/aggregation in optimisation-based power system planning
models. Recent studies indicate that reliably determining optimal electricity
(investment) strategy under climate variability requires the consideration of
multiple years of demand and weather data. However, solving planning models
over long simulation lengths is typically computationally unfeasible, and
established time series reduction approaches induce significant errors. The
importance subsampling method reliably estimates long-term planning model
outputs at greatly reduced computational cost, allowing the consideration of
multi-decadal samples. The key innovation is a systematic identification and
preservation of relevant extreme events in modeling subsamples. Simulation
studies on generation and transmission expansion planning models illustrate the
method's enhanced performance over established "representative days" clustering
approaches. The models, data and sample code are made available as open-source
software.
