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year is evaluated in any given scenario. Finally, only residential customers are considered, as initial adopters are expected to charge at their residence.

The project included an assessment of PEV charging effects on specific circuits in a utility’s distribution system—typically one or two representative feeders per utility—based on detailed simulations of distribution systems, customer load characteristics, and potential electric vehicle (EV) penetration and charging profiles. The results of the simulations were combined to develop summaries of general concerns and to identify assets most likely to be at risk, conditions that could require additional monitoring to avoid problems, and the impacts of different charging profiles.

As part of a PEV distribution impact collaborative project, EPRI developed a novel methodology to evaluate the impact of PEVs on distribution systems. The study methodology was designed to capture potential distribution system impacts in response to customer adoption of the new load type and was applied to 36 radial distribution feeders. The analytical framework was developed to evaluate the impacts of PEVs on distribution system thermal loading, voltage regulation, transformer loss of life, unbalance, distribution system losses, and harmonic distortion levels. These impacts are primarily determined by the assumed location of PEVs throughout the distribution network, the time of day that PEVs are expected to charge from the system, and the magnitude and duration of the charge cycle. In order to determine both system-level and individual component—level impacts, the framework provides for both deterministic and stochastic consideration of these key spatial and temporal variables (Figure 1). Specifically, the analysis identifies assets at risk of being affected and the likelihood and severity of impact.

•   Asset deterministic analysis examines each asset’s capacity to serve additional demand compared to the worst-case projected PEV demand under the defined scenario. Each asset’s capacity is determined via the circuit models and the projected PEV load is derived from probabilistic evaluations of PEV characteristics and number of customers served from each asset.

•   Stochastic analysis projects likely impacts considering the full projected diversity of the PEV charging through randomly generated system scenarios that model PEV charging and system response over a full calendar year. PEV load location and temporal demands are randomly determined using the PEV characteristic probability distributions discussed above. Results from the simulation and analysis of hundreds of these randomly generated cases provide indications of likely impacts and their severity.


The study is based on projected market penetration 1 to 5 years after PEV commercialization. Although the total penetration is assumed to be small, possible

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