Dealing with multiple decades of hourly wind and PV time series in energy models: A comparison of methods to reduce time resolution and the planning implications of inter-annual variability

TLDR

Significant inter‑year variability in wind and PV output across 25 years of data indicates that long time series are essential for accurate energy modeling. The authors aim to compare downsampling, clustering, and heuristic methods for reducing time resolution in a high‑resolution Great Britain power‑system model to enable robust planning with high shares of variable renewables. They implement these methods in the open‑source Calliope framework, applying them to 25 years of simulated wind and PV generation data. Results show that the best reduction technique depends on input data and model constraints, that heuristic approaches appear promising, and that relying on only a single or few years of data yields unreliable outcomes for high renewable penetration.

Abstract

Using a high-resolution planning model of the Great Britain power system and 25 years of simulated wind and PV generation data, this study compares different methods to reduce time resolution of energy models to increase their computational tractability: downsampling, clustering, and heuristics. By comparing model results in terms of costs and installed capacities across different methods, this study shows that the best method depends heavily on input data and the setup of model constraints. This implies that there is no one-size-fits-all approach to the problem of time step reduction, but heuristic approaches appear promising. In addition, the 25 years of time series demonstrate considerable inter-year variability in wind and PV power output. This further complicates the problem of time detail in energy models as it suggests long time series are necessary. Model results with high shares of PV and wind generation using a single or few years of data are likely unreliable. Better modeling and planning methods are required to determine robust scenarios with high shares of variable renewables. The methods are implemented in the freely available open-source modeling framework Calliope.