Jonathan Dowds

Modeling the Impact of Increasing PHEV Loads on the Distribution Infrastructure

Numerous recent reports have assessed the adequacy of current generating capacity to meet the growing electricity demand from Plug-in Hybrid Electric Vehicles (PHEVs) and the potential for using these vehicles to provide grid support (Vehicle to Grid, V2G) services. However, little has been written on how these new loads will affect the medium and low-voltage distribution infrastructure. This paper briefly reviews the results of the existing PHEV studies and describes a new model: the PHEV distribution circuit impact model (PDCIM). PDCIM allows one to estimate the impact of an increasing number of PHEVs (or pure electric vehicles) on transformers and underground cables within a medium voltage distribution system. We describe the details of this model and results from its application to a distribution circuit in Vermont.

Estimating the Impact of Electric Vehicle Smart Charging on Distribution Transformer Aging

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.

Estimating the acceleration of transformer aging due to electric vehicle charging

This paper describes a method for estimating the additional aging in medium/low voltage distribution transformers caused by increasing demand from electric vehicle charging. The proposed method combines detailed travel demand data from the National Household Transportation Survey with a one-year model of transformer hottest spot temperature, based on on IEEE C57.91–1995 Annex G. To illustrate the the model outputs, we present results for 15kVA and 25kVA overhead distribution transformers using ambient temperatures from Burlington, VT and Los Angeles, CA. Our results illustrate the importance of ambient temperatures to impact of PEVs on transformer aging.

Travel Demand and Charging Capacity for Electric Vehicles in Rural States: Vermont Case Study

As the number of electric vehicles (EVs) increases, planners must consider not only how this fuel switch may affect the electrical power infrastructure but also mobility. The suitability and charging requirements of these vehicles may differ in rural areas, where the electrical grid may be less robust and the number of miles driven higher. Although other studies have examined issues of regional power requirements of EVs, none has done so in conjunction with the spatial considerations of travel demand. For the forecast of both the future spatial distribution of EVs and the ability of these vehicles to meet current daily travel demand, this work used three data sets: the National Household Travel Survey, geocoded Vermont vehicle fleet data, and a geocoded data set of every building in the state. The authors considered spatial patterns in daily travel and home-based tours to identify optimal EV-charging locations and any area types that are unsuited for widespread electric vehicle adoption. Hybrid vehicles were found to be more likely to be adjacent to other hybrids than were conventional vehicles. This apparent clustering of current hybrid vehicles, in both urban and rural areas, suggests that the distribution of future EVs may also cluster. The analysis estimated that between 69% and 84% of the states vehicles could be replaced by EVs with a 40-mi range, but that estimate was dependent on the availability of workplace charging. Problematic areas for EV adoption may be suburban areas, where both residential density (and potential clustering of hybrids) and miles driven are high. The results suggest that EVs are viable for rural mobility demand but require special consideration for power supply and vehicle-charging infrastructure.

Estimating the Impact of Electric Vehicle Charging on Electricity Costs Given Electricity-Sector Carbon Cap

A model estimates the short-run effect of plug-in hybrid electric vehicle (PHEV) charging on electricity costs, given a cap on carbon dioxide (CO2) emissions that covers only the electricity sector. In the short run, cap-and-trade systems that cover the electricity sector increase the marginal cost of electricity production. The magnitude of the increase in cost depends on several factors, including the stringency of the cap in relation to the demand for electricity. The use of PHEVs, which also has the potential to decrease net greenhouse gas emissions, would increase demand for electricity and thus would increase the upward pressure on marginal costs. The model described examines this effect for the New England electricity market, which as of January 2009 operates under the Regional Greenhouse Gas Initiative, a cap-and-trade system for CO2. The model uses linear optimization to dispatch power plants to minimize fuel costs given inelastic electric demand and constraints on nitrogen oxide and CO2 emissions. The model is used to estimate costs for three fleet penetration levels (1%, 5%, and 10%) and three charging scenarios (evening charging, nighttime charging, and twice-a-day charging). The results indicate that PHEV charging demand increases the marginal cost of CO2 emissions as well as the average and marginal fuel costs for electricity generation. At all penetration levels the cost increases were minimized in the nighttime-charging scenario.

Barriers to Implementation of Climate Adaptation Frameworks by State Departments of Transportation

Disruptive events caused by weather extremes are imposing significant and rising costs on transportation agencies. In response, federal and state transportation agencies and other organizations are exploring adaptation measures to reduce the adverse consequences of these events. Several existing adaptation frameworks are synthesized here into a simplified, core adaptation framework, and the study seeks to delineate the current barriers to the widespread implementation of adaptation programs by state departments of transportation in the United States. From interviews with transportation practitioners and a review of the results from FHWA pilot projects, it is found that uncertainty about future climate conditions, the need for additional vulnerability-modeling tools, conceptual uncertainty about evaluating asset criticality, and limited funding all inhibit implementation of adaptation measures.

Comparisons of Discretionary Passenger Vehicle Idling Behavior by Season and Trip Stage with Global Positioning System and Onboard Diagnostic Devices

This study addresses the comparisons of discretionary passenger vehicle idling by using field data collected from 20 volunteers in Vermont. Each volunteer participated in two 2-week data collection periods, one in the summer and one in the winter. Overall, 15.6% of vehicle-operating time was spent idling, consistent with the limited existing data on this topic. In addition, the paper describes a processing method used with an in-vehicle Global Positioning System and onboard diagnostic data that allows discretionary idling at the start and end of trips to be separated from the in-travel idling related to traffic or traffic control. Discretionary idling accounted for more than 6.5% of vehicle operating time. Discretionary winter idling events were found to be longer than summer idling events and, for idling events greater than 60 s, trip-start idling was found to be longer than trip-end idling. Both of these results reaffirm previous findings suggesting that there are opportunities for behavioral changes to reduce idling. The method used to extract discretionary idling is promising for widespread use and large sample data collection efforts. This method will be critical for the many communities that lack robust idling data when considering the costs and benefits of idling behavior change initiatives.

Impacts of Model Resolution on Transportation Network Criticality Rankings

Objective rankings of the criticality of transportation network infrastructure are essential for efficiently allocating limited adaptation resources and must account for network connectivity and travel demand. Road link criticality can be quantified by the total travel delay caused when the capacity of a road segment or link is disrupted or removed. These methods can use standard travel demand models, but the exclusion of lower-volume roads and the aggregate nature of traffic analysis zones may distort resulting criticality rankings. To test the impact of link exclusion and demand aggregation, the authors applied the network robustness index, a well-established link criticality measure, to a hypothetical network with varying levels of network resolution and demand aggregation. The results show a statistically significant change in criticality rankings when demand is aggregated and especially when links are excluded from the network, suggesting that criticality rankings may be distorted when estimated with typical demand models. Application to a road network in Vermont supports the finding on the impact of network resolution on criticality rankings.

Strategic Location of Satellite Salt Facilities for Roadway Snow and Ice Control

Roadway snow and ice control (RSIC) operations can account for as much as 10% of the annual budget of a states department of transportation (DOT) in the snowbelt of the United States. Important considerations for planning RSIC operations are the locations and quantities of surface treatment materials. This study examined the use of satellite salt facilities (SSFs) and developed a novel, real-world approach for locating SSFs. The paper demonstrates a method for ranking the effectiveness of individual SSFs in their reduction of the distance that vehicles must travel to reload salt. The approach is demonstrated with the federal aid roadway network for the state of Vermont, and a locally optimal SSF location is identified for each of the existing service territories in the state. The results of an informal survey of satellite salt-siting practices among snowbelt DOTs are also reported. A critical aspect to siting new SSFs is the ability to use existing right-of-way around Interstates; survey respondents noted the need to explore public–private partnerships with landowners adjacent to the state highway right-of-way who may be willing to sell or lease small portions of cleared land for use as SSFs. From the survey information, the study compared a smaller set of ready-to-use SSF locations (with adequate right-of-way) with the locally optimized SSF locations.