High-performance districts and communities offer opportunities for reducing energy use, emissions, and costs, and can be instrumental in helping cities achieve their climate goals. The design of such communities requires identification of opportunities early on and their re-evaluation throughout the planning process. There is a need for energy modeling tools that connect 3D Computer-Aided Design (CAD) platforms to simulation engines, enabling detailed energy analysis of districts within the workflows and tools used by practitioners. This paper introduces the Dragonfly and URBANoptTM combined toolset that supports the creation of urban models from a range of geometry formats typically used by designers and planners, and provides an integrated pathway to simulate district-scale energy systems. The toolset is piloted by a global architecture and master planning firm to evaluate several key urban-scale technical questions for the design of a district in Chicago. The findings indicate that, while energy savings can be achieved through traditional architectural studies and enhancements to individual building efficiency, the modeling toolset helps identify additional savings and insights that can be achieved when considering district-scale energy systems. Finally, this study demonstrates how the Dragonfly/URBANopt toolset can integrate with master planning workflows, thereby enabling an iterative performance-based design process.

Housing retrofits are essential for meeting societal decarbonization goals, alongside addressing energy insecurity, improving public health, and creating new jobs. Yet, despite their multiple benefits and comprehensive government efforts to incentivize retrofits, adoption rates across the world remain low, usually less than 1% per year. Barriers to adoption among homeowners include lack of knowledge of what combination of energy retrofitting upgrades are most cost effective for their situation given available incentive programs. Similarly, cities lack urban-level analysis tools to optimize uptake of and predict carbon emissions reduction from existing incentive programs. To address the latter gap, we present a census-based Urban Building Energy Modeling framework that combines a technical energy saving potential analysis with a socioeconomic model that includes occupant demographics, local building regulations, and incentive eligibility criteria. We use the framework to evaluate the effectiveness of retrofit programs in two Boston neighborhoods with median incomes of $110,00 and $42,000. Results reveal that for the higher income, neighborhood predicted and actual adoption rates between 2014 and 2017 are comparable. In the lower income neighborhood, the proportion of households that would financially benefit from incentive offerings is higher. However, current participation rates do not reflect this difference suggesting that many viable projects do not happen for reasons that are not yet captured by the model. Urban planners, energy policy designers, and community advocates seeking to plan and evaluate energy incentive programs can use this framework to understand the breakdown of opportunities and barriers for different socio-demographic groups and geographic locations.