Abstract: This paper describes a method for estimating the impact of plug-in electric vehicle (PEV) charging on overhead distribution transformers, based on detailed travel demand data and under several different schemes for mitigating overloads by shifting PEV charging times (smart charging). The paper also presents a new smart charging algorithm that manages PEV charging based on estimated transformer temperatures. We simulated the varied behavior of drivers from the 2009 National Household Transportation Survey, and transformer temperatures based an IEEE standard dynamic thermal model. Results are shown for Monte Carlo simulation of a 25 kVA overhead distribution transformer, with ambient temperature data from hot and cold climate locations, for uncontrolled and several smart-charging scenarios. These results illustrate the substantial impact of ambient temperatures on distribution transformer aging, and indicate that temperature-based smart charging can dramatically reduce both the mean and variance in transformer aging without substantially reducing the frequency with which PEVs obtain a full charge. Finally, the results indicate that simple smart charging schemes, such as delaying charging until after midnight can actually increase, rather than decrease, transformer aging.
Abstract: This paper compares distribution transformer aging impacts resulting from plug-in electric vehicles charging under AC Level 1 versus AC Level 2 charging conditions. Additionally, we propose an algorithm for PEV smart charging and evaluate its effectiveness on transformer aging. We use a Monte Carlo simulation of a 25kVA distribution transformer, with ambient temperature data from Burlington, VT and Phoenix, AZ, to calculate transformer aging under both uncoordinated and smart charging conditions. The results indicate more substantial aging as a result of AC Level 2 charging compared to AC Level 1. Smart charging can significantly mitigate these effects. We also present a more decentralized approach to smart charging and compare two distributed automaton-based charge management strategies, which both prevent the transformer from becoming overloaded. These methods give vehicle owners the ability to select among charging priorities in an environment in which the vehicles manage their charging autonomously.