Evidence-Based Optimization of Urban Block Morphology for Enhanced Photovoltaic Potential

Urban morphology is a critical determinant of photovoltaic (PV) potential in cities, yet current design practices rarely incorporate this relationship systematically. Existing studies often struggle to balance analytical precision with computational efficiency and to translate data-driven insights into practical design implementation, limiting the role of morphological optimization in zero-energy urban transitions. To address these challenges, this study develops a three-stage computational workflow: (1) a lightweight surrogate model that replaces computationally intensive physical simulations to efficiently quantify multidimensional morphological impacts on PV potential; (2) an optimization algorithm that integrates the surrogate model to identify optimal urban configurations; and (3) a design translation framework that converts analytical outputs into actionable planning strategies. A case study in Tianjin demonstrates the method’s effectiveness, identifying floor area ratio (FAR) as the most influential parameter (β = 0.969, p < 0.001) and deriving optimal morphological values (FAR = 4.02; Shape Coefficient = 0.23) which yield substantial PV potential improvements of 13.9%–56.9% in new developments and 8.0% in retrofit scenarios. This generalizable method offers planners and policymakers an evidence-based tool applicable across diverse urban contexts, advancing the integration of morphological and energy optimization in the pursuit of zero-energy cities.