Past Event

CEEM Seminar Series | Professor Ricardo Daziano | Unveiling preferences for smart electric-vehicle charging programs

October 10, 2023
2:00 PM - 3:00 PM
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Large-scale charging of electric vehicles using limited and intermittent grid resources is an imminent problem. Smart electric vehicle charging programs offer a solution to alleviate peak demand pressures on the electrical grid, optimize renewable energy integration, enhance grid stability, and promote cost-effective, sustainable, and equitable electrified transportation solutions. Unidirectional smart charging (V1G) can effectively help to defer loads during critical periods (e.g., very hot summer afternoon), align EV charging with availability of renewable power, and shift demand to ultimately flatten loads. However, modeling demand response to smart EV charging requires a deep understanding of customer preferences for charging flexibility. In this work, we leverage unique data from a recent market research study to examine how customers respond to smart EV charging programs aimed at better managing EV loads. Using logit-type discrete choice models, we evaluate EV users' valuation of features of demand response, managed charging, and fixed schedule programs. Results indicate that EV users positively value emission reductions that are associated with decisions such as delaying when energy is delivered to the vehicles. We also find that scheduling EV charging to maximize renewable energy is more preferred than allowing priority charging in managed charging programs, further showing EV users’ support of environmental protection.



Ricardo Daziano, PhD in Economics and Associate Professor of Civil and Environmental Engineering at Cornell University, is a choice modeler working on deriving Bayesian and semiparametric estimators for economic models of customer decisions in engineering contexts. He leads research with a unique focus on applied econometrics of consumer choices around energy efficiency. In fact, his technical goal is to derive and apply statistical learning algorithms that are econometrically, behaviorally, and computationally superior to extant tools. Dr. Daziano’s empirical goal is to better understand the interaction of consumer behavior and engineering, investment, and policy choices for energy-efficient technologies and sustainability, especially in transportation.

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Scott Kelly