This work provides a comprehensive framework for addressing key research gaps in bottom-up energy system modeling. While the field has experienced significant advancements in recent decades, largely due to improvements in computational capabilities and data availability, current models face persistent challenges in accuracy, robustness, and comprehensibility. While numerous review papers have examined specific aspects of energy system modeling challenges, no comprehensive framework exists that synthesizes all major challenges facing bottom-up energy system models under a unified structure. We propose a novel classification system that organizes these challenges into three fundamental categories, offering a structured approach to understanding and addressing them. Our conceptual framework, based on literature synthesis, proposes a thematic classification based on accuracy, robustness and comprehensibility as three pillars to map the challenges faced by bottom-up energy system models. For accuracy, we analyze the critical dimensions of temporal, spatial, techno-economic, and sector-coupling resolution, along with the importance of sector disaggregation. For robustness, we examine methods for addressing data-based and model-based uncertainties. For comprehensibility, we discuss the importance of transparency, participatory processes, behavior integration, environmental impact assessment, and multi-level modeling alignment. This holistic framework provides a roadmap of the overall challenges facing energy system models, equipping the field with a clearer path to close the persistent gap between modelling results and the concrete requirements of energy policy development and real-world energy transition implementation.
Prina et al. (Thu,) studied this question.