Which model features matter? An experimental approach to evaluate power market modeling choices

TL;DR

This paper compares different power market models to understand how modeling choices affect decarbonization pathway predictions for Europe, providing practical recommendations for modelers to improve accuracy and computational efficiency.

Contribution

It introduces an experimental approach to evaluate the impact of model features on power market modeling, highlighting the significance of model type, planning horizon, and resolution choices.

Findings

Model type determines capacity expansion trajectories.

Planning horizon impacts results mainly at low carbon prices.

Lower resolutions favor wind power and storage modeling.

Abstract

A novel experimental approach of inter- and intramodel comparisons is conducted with five power market models to give recommendations for modelers working on decarbonization pathways of Europe until 2050. The experiments investigate the impact of model type (optimization vs. simulation), planning horizon (intertemporal vs. myopic), temporal resolution (8760 vs. 384 hours), and spatial resolution (28 countries vs. 12 mega-regions). The model type fundamentally determines the evolution of capacity expansion. Planning horizon (assumed foresight of firms) plays a minor role for scenarios with high carbon prices. For low carbon prices in turn, results from myopic models deviate considerably from those of intertemporal models. Lower temporal and spatial resolutions foster wind power via storage and via neglected transmission boundaries, respectively. Using simulation instead of optimization frameworks, a shorter planning horizon of firms, or lower temporal and spatial resolutions might be necessary to reduce the computational complexity. This paper delivers recommendations on how to limit the discrepancies in such cases.