  Recent studies indicate that the effects of inter-annual climate-based
variability in power system planning are significant and that long samples of
demand & weather data (spanning multiple decades) should be considered. At the
same time, modelling renewable generation such as solar and wind requires high
temporal resolution to capture fluctuations in output levels. In many realistic
power system models, using long samples at high temporal resolution is
computationally unfeasible. This paper introduces a novel subsampling approach,
referred to as "importance subsampling", allowing the use of multiple decades
of demand & weather data in power system planning models at reduced
computational cost. The methodology can be applied in a wide class of
optimisation-based power system simulations. A test case is performed on a
model of the United Kingdom created using the open-source modelling framework
Calliope and 36 years of hourly demand and wind data. Standard data reduction
approaches such as using individual years or clustering into representative
days lead to significant errors in estimates of optimal system design.
Furthermore, the resultant power systems lead to supply capacity shortages,
raising questions of generation capacity adequacy. In contrast, "importance
subsampling" leads to accurate estimates of optimal system design at greatly
reduced computational cost, with resultant power systems able to meet demand
across all 36 years of demand & weather scenarios.
