On representation of temporal variability in electricity capacity planning models

• A systematic investigation of the representation of temporal variability.
• Explanation of how temporal representation can misvalue technologies.
• Demonstration of temporal representations guaranteed not to introduce error.
• An approach to quantify the increased temporal variability introduced by renewables.

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 into model outputs and associated economic insight. The mechanisms underlying the introduction of this 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. These concepts 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.