This paper systematically investigates how to represent intra-annual temporal variability in models of optimum electricity capacity investment. Inappropriate aggregation of temporal resolution can introduce substantial error in model outputs and in associated economic insight. The mechanisms by which inappropriate temporal resolution can introduce error are shown. How many representative periods are needed to fully capture the variability is then investigated. For a sample dataset, a scenario-robust aggregation of hourly (8760) resolution is possible in the order of 10 representative hours when electricity demand is the only source of variability. The inclusion of wind and solar supply variability increases the resolution of the robust aggregation to the order of 1000. A similar scale of expansion is shown for representative days and weeks. The associated set of ideas can be applied to any such temporal dataset, providing, at the least, a benchmark that any other aggregation method can aim to emulate. How prior information about peak pricing hours can potentially reduce resolution further is also discussed.
Published version: Energy Economics, Vol. 59 (September 2016), 261-274