Urban energy system modeling as strategic tool for integrated spatial and energy planning: A multi-node, multi-stage optimization approach

Urban energy system modeling intended to support strategic decision-making in integrated spatial and energy planning must integrate multiple sectors and at high spatio-temporal resolutions to account for the complexities of urban environments. Decision-making must account for spatial constraints, heterogeneity, and uncertainty in socio-economic and technological development trajectories. Few modeling approaches are sufficiently holistic to support such complex planning needs by themselves and modeling tool combinations are often applied case specific. This work contributes to bridging this gap by presenting a novel urban energy system modeling framework (UESMF) that combines spatially resolved, cross-sectoral energy demand projections with techno-economic optimization of energy transition pathways using Linear Programming (LP), while accounting for detailed spatial and temporal constraints of the built environment. The UESMF couples City Energy Analyst (CEA), MAED-City, and MESSAGEix, leveraging their respective strengths. CEA is used for building-level modeling of current useful energy demands in the built environment, MAED-City provides cross-sectoral projections for these energy demands based on their link to socio-economic and technological development pathways, and MESSAGEix delivers techno-economically optimized energy supply capacity investment pathways that correspond to scenario assumptions, local technical constraints, and policy constraints. The novelty lies in the developed streamlined tool coupling, which is implemented via automated Python scrips and allows fast feedback loops for the exploration of different development pathways and planning alternatives. The UESMF is demonstrated through a real urban energy case study, showcasing how it enables the exploration of trade-offs between different planning and policy scenarios. • Modeling framework for integrated spatial and energy planning. • Cross-sectoral energy demand projection based on socio-economic and technological development scenarios. • Multi-node, multi-stage techno-economic optimization of energy transition pathways. • Consideration of local spatial constraints and policy goals. • Method for formulation of clean energy transition strategies for carbon neutral cities.