YOUNG SCIENTIST LECTURE
Speaker: DR. Jing Wang
Title: Electrifying High-Efficiency Future Communities: Impact on Energy, Emissions, and Grid
Speaker bio : Dr. Jing Wang is a Postdoctoral Researcher at National Renewable Energy Laboratory, Colorado, USA. Her research is focused on applying advanced modeling and control techniques to help communities achieve flexibility, decarbonization, and resilience. She has a background in building energy engineering and received her Ph.D. degree from University of Colorado Boulder.
Supporting institution: National Renewable Energy Laboratory
ADAPEN paper link: https://doi.org/10.1016/j.adapen.2022.100095
Abstract: To combat climate change and meet decarbonization goals, the building sector is improving energy efficiency and electrifying end uses to reduce carbon emissions from fossil fuels. All-electric buildings are becoming a trend among new constructions, introducing opportunities for decarbonization but also technical challenges and research gaps. Further investigation is needed to understand how the adoption of energy efficiency measures (EEMs) and distributed energy resources (DERs) in all-electric communities would affect energy consumption, carbon emissions, and grid planning. This paper presents a case study of a mixed-use, all-electric community located in Denver, Colorado. We use URBANopt™, a physics-based urban energy modeling platform to model the community and then evaluate the impact of EEMs and DERs (i.e., photovoltaics [PV], electric vehicles [EVs], and batteries) on the community’s energy usage, carbon emissions, and peak demand. The results show that adding EEMs and PV led to both energy consumption and carbon emissions reductions across all building types. However, we saw fairly limited impact of EEMs and PV on buildings’ peak demand in our case. Additionally, due to overnight EV charging activities and higher grid carbon intensity at night, the carbon emissions in multifamily buildings have a noticeable increase compared to scenarios without vehicles. Finally, the addition of batteries helped reduce peak demand by 11%–29%. The modeling workflow and evaluation methods can be applied to similar communities to evaluate their performance and the effect of integrating EEMs and DERs.