Infomorphism: Urban Planning For Renewable Energy Integration Via Simulated Energy Exchange Networks

Increasing renewable energy efficiency is a crucial part of developing a sustainable city. While current Urban Building Energy Modeling frameworks have been developed for analyzing and improving urban energy efficiency, these tools have not integrated systemic optimization modeling to develop and evaluate the performance of potential urban environments from generative planning models. In this study, we present Infomorphism, a computational planning framework that joins a morphological generative process with an energy network optimization model, to explore potential planning policies and constraints associated with renewable energy integration. This paper takes Manhattan as a case study to show local energy networks that maximize the city’s overall efficiency to share local renewable energy - generated thermal and electric energy - maximize renewable energy penetration rates and minimize energy exchange costs. We show how geothermal and solar drive a future city’s collective form and infrastructure to achieve up to 74% local renewable energy integration.