Accuracy, Robustness and Comprehensibility – Challenges in bottom-up energy system models

Matteo Giacomo Prina , Michel Noussan 

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 uncertainty and comparing frameworks, models, and scenario results. For comprehensibility, we discuss the importance of transparency, participatory processes, behavioral integration, environmental impact assessment, and multi-level modeling alignment. This holistic framework not only synthesizes existing knowledge but also offers a new perspective on advancing the methodological frontier of decarbonization modeling. The result is a clear roadmap of the overall challenges facing energy system models, along with a mapping of the current state-of-the-art including innovative methods for addressing specific issues within each pillar. By addressing these interrelated challenges, researchers can develop more accurate, reliable, and policy-relevant models to better guide the global energy transition and decarbonization efforts, ultimately enhancing the field's contribution to sustainable energy development at multiple scales.