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A Harvard-China Project Research Seminar with Christine Gschwendtner, Postdoctoral Fellow, Environment and Natural Resources Program (ENRP) and the Science, Technology, and Public Policy Program (STPP) at Harvard Kennedy School
Abstract: Electrification is a crucial measure for decarbonizing residential vehicles. This presentation provides insights into promising strategies for integrating electric vehicles (EVs) into the electricity system upon mass adoption. As the share of variable renewable energy sources increases, the electricity system faces greater challenges. Balancing electricity supply and demand becomes even more challenging as EV charging increases electricity demand peaks. Flexible EV charging offers a solution for integrating both EVs and variable renewable energy while reducing carbon emissions and avoiding substantial infrastructure costs, such as electricity grid reinforcements. This presentation discusses how the flexibility potential of EV charging can be harnessed by combining plug-in behavior, control strategies, and incentives. Using agent-based modeling, we account for diverse charging behaviors, including different plug-in behaviors at various locations. In addition to automated charging control strategies, we examine reactions to charging incentives that go beyond economic rationality, shedding light on how habits and convenience influence charging flexibility. With this approach, we reveal how time-varying electricity rates can incentivize charging flexibility across diverse locations. This work can support decision-makers in industry and policy to unlock the synergies between the electricity and transportation sectors.
Speaker Bio: Christine Gschwendtner is a Postdoctoral Researcher at Harvard University, focusing on sustainable energy systems. She is particularly interested in the intersection of infrastructure and human behavior, specifically related to decarbonizing energy systems across sectors, such as the transport and buildings sectors. She believes that interdisciplinary perspectives are crucial to find innovative solutions. Therefore, she combines her background in environmental engineering with data analytics and social sciences. Christine uses a variety of methods, e.g., agent-based modeling, geospatial analyses, choice experiments and interviews, working with large datasets and mostly using Python.
Sponsored by Harvard-China Project, Harvard Paulson School of Engineering and Applied Sciences.