Session: 03-03: Building Performance Simulations for Sustainable Solutions

Paper Number: 167553

167553 - Large Scale Building Energy Modeling With Energy Conservation Measures for Carbon Mitigation and Retrofit Paybacks 

Abstract: 

This study aims to estimate CO₂ emissions from commercial and residential buildings across the United States, with in-depth analyses focused on five metropolitan areas: New York City, Chicago, Phoenix, Atlanta, and Seattle. These regional case studies assess the influence of climate, infrastructure, and local policies on building-related CO₂ emissions. The research investigates the potential for emission reductions through electrification using low-carbon energy sources, improvements in energy efficiency, and on-site CO₂ capture technologies. Techno-economic and life-cycle analyses provide high-level estimates of the acquisition and operational costs of various decarbonization strategies, including building-integrated CO₂ capture and management systems. Quantitative estimates of emission reductions for each technology are compared and ranked based on effectiveness and feasibility. Additionally, the study identifies residual, hard-to-abate emissions and analyzes results in the context of regional, climatic, and infrastructural differences. The findings offer valuable insights for urban planners, enabling the prioritization of energy retrofitting strategies tailored to specific building types and vintages. For example, results indicate that a VAV with reheat system retrofit in high-rise apartments built to ASHRAE 90.1-2013 standards has a payback period of 6.78 years and CO₂ reduction of 20.77%.

Presenting Author: Hang Li Oak Ridge National Laboratory

Presenting Author Biography: Hang Li is a Postdoctoral Research Associate in the Grid-interactive Controls Group. Hang's research specializes in Building Energy Modeling (BEM) Automation and Building Information Modeling (BIM) Interoperability. He has strong technical background in developing methods and tools to support modeling automation for sustainable built environment, by leveraging artificial intelligence, data analytics, and information extraction.