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Hilshey, Alexander Doster

Electrical Engineering

2012

M.S.

Plug-in Electric Vehicles (PEVs) require a substantial electric load to charge batteries between travel tours. Given mass adoption of PEVs, battery charging may create new power load peaks during non-traditional times or may substantially increase the magnitude of pre-existing load peaks. These peaks are periods in which electric distribution infrastructure, specifically distribution transformers, may incur increased "aging," which may potentially lead to component failure. Understanding how PEV charging will impact distribution transformer aging is important to electric utilities who are preparing to support the PEV fleet. The first contribution presented in this thesis describes a method to estimate distribution transformer aging as a function of PEV charging. The method is used to evaluate transformer impacts under conditions which include varying quantity of PEVs, ambient temperature of differing climates, light or heavy base-line distribution transformer loading, schemes, and finally "slow" or ''fast''PEV charging. Additionally, a "smart charging" algorithm is described and evaluated for effectiveness in reducing distribution transformer aging given PEV charging. Results of the study show that trllJlsformer aging could increase by 6.3 times with the addition of six PEVs, using fast charging, within a twelve home neighborhood in a warm climate.
Smart charging is shown to reduce the transformer aging from PEVs by up to 68.5% while leaving only 2-3% of PEV owners with less than a 95% full battery charge. The latter portion of the thesis seeks to improve the method in which PEV charging demand is predicted by developing a trip-purpose PEV charging demand model which uses concepts from activity-based travel-demand modeling developed within the transportation engineering discipline. The trip-purpose approach will dynamically predict PEV travel behavior based on one-day travel survey data, from which charging load characteristics (charging start time and duration) may be obtained to forecast aggregate PEV load. Results of running the model for 1,000 iterations show an aggregate PEV charging load profile which approximates the shape of a load profile generated with pure sampling of empirical travel data, yet the modeled charging load exceeds the purely sampled load by an average of 0.038 kW with an aggregate time delay of 108 minutes. Explanations are provided to give insight into the discrepancies. Future work is outlined for improving the trip-purpose PEV charging demand model, adding provisions for generating multi-day load estimation, and for combining the method for estimating distribution transformer aging with the modeled PEV load data.