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Quantifying the Effect of Plug-In Electric Vehicles on Future Grid Operations and Ancillary Service Procurement Requirements
Abstract
As plug-in electric vehicles (PEVs) grow in popularity, there is increasing research interest in the interaction between PEVs and the electric grid. Much of the previous work in the literature relies on an assumption that PEV charging will be scheduled, and that the duration and magnitude of charging loads can be modulated to suit the needs of the utility and the system operator. While access to the data or owner input necessary for charge scheduling and management might be technically feasible today, it is unclear whether vehicle owners will be amenable to providing these data or accepting utility control of their charging choices.
Because of these uncertainties in the future relationship between electric utilities and PEV owners, this study seeks to examine the market effects of vehicles in the absence of the additional data utilities would need to realize these alternate, “optimal” PEV charging scenarios. In particular, this study focuses on quantifying the potential uncertainty in vehicle charging loads on an energy and power basis.
Monte Carlo methods were applied to vehicle trip data from the National Household Travel Survey (NHTS) to generate simulated driving profiles for individual vehicles. Using these profiles, six PEV fleet sizes were studied, ranging from 1,000 to 500,000 vehicles, to determine whether fleet size had a linear effect on the stochasticity of vehicle charging loads. Following the Monte Carlo simulations, these loads were examined independent of and compared to net load (load minus wind generation).
Results from the Monte Carlo simulations indicate that even for the largest PEV fleet sizes studied, variability in average charging loads is on the order of 10 MW, less than 0.2% of the magnitude of charging load for those fleet sizes. In comparison with electricity demand in the Electric Reliability Council of Texas’ (ERCOT) operating area, these charging loads represent a 1% increase above typical summer peak loads. Unfortunately, while the relative increase in demand is small, the timing of peak charging load is nearly coincident with summer peak demand.
The simulation approach was validated by comparing the results against empirical vehicle charging data collected by the Pecan Street Research Consortium from households in Austin, Texas. Simulated and empirical vehicle charging trends showed generally good agreement, with similar charging times but slightly different charging durations. The alignment between the two charging profiles indicates that the simulation methodology applied here with NHTS travel data can be used to predict electric load for vehicle charging when empirical historical charging data are not available.
... This is particularly the case for travelling times, which people tended to report rounded to the hour, half hour and quarter hour. Only few authors have acknowledged the problem and have proposed solutions to offset this distortion [8,59,60]. This work employs Algorithm 1 to reduce the bias and realistically disperse departure and arrival times. ...
The impending uptake of electric vehicles (EV) in worldwide car fleets is urging stakeholders to develop models that forecast impacts and risks of this transition. The most common modelling approaches rely on car movements provided in household travel surveys (HTS), despite their large data bias towards internal combustion engine vehicles. The scientific community has long wondered whether this characteristic of HTSs would undermine the conclusions drawn for EV mobility. This work applies state-of-the-art modelling techniques to the Swiss national HTS to conclusively prove, by means of validation, the reliability of these commonly used approaches. The cars tracked in the survey are converted to EVs, either pure battery or plug-in hybrids, and their performance is simulated over 4 consecutive days randomly sampled from the survey. EVs are allowed to charge at both residential and public locations at an adjustable charging power. Charging events are determined by a finely calibrated plugging-in decision scheme that depends on the battery’s state of charge. The resulting charging loads corroborate the validation, as these successfully compare with measurements obtained from several EV field tests. In addition, the study includes a sensitivity analysis that highlights the importance of accurately modelling various input parameters, especially EVs’ battery sizes and charging power. This work provides evidence that conventional HTSs are an appropriate instrument for generating EV insights, yet it adds guidelines to avoid modelling pitfalls and to maximise the simulation accuracy.
This work uses a two-stage stochastic program (SP) to simulate a vehicle-to-grid (V2G) system operator participating in an electricity market to provide frequency regulation. The model developed for this work challenges four principal assumptions used in V2G models in the literature: the V2G system operator as a price taker; pre-scheduled vehicle use; deterministic vehicle state-of-charge; and deterministic regulation deployments. The results shown focus specifically on the effect of PEV battery size and PEV charging station (EVSE) power rating on V2G operator revenues and potential compensation for V2G program participants. These results indicate that battery size has a limited effect, but that increasing EVSE power rating can significantly increase revenues and compensation. Consequently, for the battery sizes in most PEVs, the V2G operator's market participation is constrained by available power, not energy, which is consistent with the magnitudes and time scales of typical frequency regulation deployments.
It is anticipated that plug-in electric vehicles (PEVs), which rely exclusively on a battery to provide some or all of their driving range, will grow in popularity in the coming decades. In anticipation of the growth in electricity demand to support PEV charging, extensive study has been devoted to assessing the effects of these loads. While optimal charging strategies could be applied to minimize the impact of PEV charging, these strategies have not been widely adopted, nor are there significant incentives for vehicle owners to adopt them. In this work, Monte Carlo simulations are performed to assess the effect of uncertainty in vehicle charging loads on frequency regulation procurement requirements in ERCOT. Results indicate that even for the largest fleet size simulated, 500,000 vehicles, the increase in frequency regulation required is typically less than 6 MW, which represents just over 1% of the average quantity procured in 2011.
With the emerging nationwide availability of battery electric vehicles (BEVs) at prices attainable for many consumers, electric utilities, system operators and researchers have been investigating the impact of this new source of energy demand. The presence of BEVs on the electric grid might offer benefits equivalent to dedicated utility-scale energy storage systems by leveraging vehicles' grid-connected energy storage through vehicle-to-grid (V2G) enabled infrastructure. It is, however, unclear whether BEVs will be available to provide needed grid services when those services are in highest demand. In this work, a set of GPS vehicle travel data from the Puget Sound Regional Council (PSRC) is analyzed to assess temporal patterns in vehicle use. These results show that vehicle use does not vary significantly across months, but differs noticeably between weekdays and weekends, such that averaging the data together could lead to erroneous V2G modeling results. Combination of these trends with wind generation and electricity demand data from the Electric Reliability Council of Texas (ERCOT) indicates that BEV availability does not align well with electricity demand and wind generation during the summer months, limiting the quantity of ancillary services that could be provided with V2G. Vehicle availability aligns best between the hours of 9 pm and 8 am during cooler months of the year, when electricity demand is bimodal and brackets the hours of highest vehicle use.
The air quality impacts of replacing approximately 20% of the gasoline-powered light duty vehicle miles traveled (VMT) with electric VMT by the year 2018 were examined for four major cities in Texas: Dallas/Ft Worth, Houston, Austin, and San Antonio. Plug-in hybrid electric vehicle (PHEV) charging was assumed to occur on the electric grid controlled by the Electricity Reliability Council of Texas (ERCOT), and three charging scenarios were examined: nighttime charging, charging to maximize battery life, and charging to maximize driver convenience. A subset of electricity generating units (EGUs) in Texas that were found to contribute the majority of the electricity generation needed to charge PHEVs at the times of day associated with each scenario was modeled using a regional photochemical model (CAMx). The net impacts of the PHEVs on the emissions of precursors to the formation of ozone included an increase in NOx emissions from EGUs during times of day when the vehicle is charging, and a decrease in NOx from mobile emissions. The changes in maximum daily 8 h ozone concentrations and average exposure potential at twelve air quality monitors in Texas were predicted on the basis of these changes in NOx emissions. For all scenarios, at all monitors, the impact of changes in vehicular emissions, rather than EGU emissions, dominated the ozone impact. In general, PHEVs lead to an increase in ozone during nighttime hours (due to decreased scavenging from both vehicles and EGU stacks) and a decrease in ozone during daytime hours. A few monitors showed a larger increase in ozone for the convenience charging scenario versus the other two scenarios. Additionally, cumulative ozone exposure results indicate that nighttime charging is most likely to reduce a measure of ozone exposure potential versus the other two scenarios.
Plug-in hybrid electric vehicles (PHEVs) have been touted as a transportation technology with lower fuel costs and emissions impacts than other vehicle types. Most analyses of PHEVs assume that the power system operator can either directly or indirectly control PHEV charging to coordinate it with power system operations. This paper examines the incentives of individual drivers making charging decisions with different electricity tariffs, and it compares the cost and emissions impacts of these charging patterns to the ideal case of charging controlled by the system operator. Our results show that real-time pricing performs worst among all of the tariffs we consider, because linear prices are inherently limited in signaling efficient use of resources in a system with nonconvexities. We also show that controlling overnight PHEV charging is significantly more important than limiting midday vehicle charging.
Plug-in electric hybrids will soon be introduced by several auto manufacturers. While the initial volume of plug-in hybrid electric vehicles will only constitute a very small fraction of total automobiles sold, “clustered” purchasing patterns for these vehicles may result in large localized loads on the power distribution network. Additionally, high-penetration deployment of variable generation sources will decrease flexibility in the power system. Previous research has indicated that plug-ins may be used as grid resources with no significant impact on fuel consumption. Deterministic 1- and 2-way communication architectures were modeled to show how real-time vehicle-grid interactions might occur. This analysis uses GPS travel survey data, grid-wide load data, and distribution-system load data to determine the effective power and energy storage capacity of a charging plug-in fleet, given actual driver behavior. The power utility controls vehicle charging rates based on the area control error and distribution transformer overheating. The cost of regulation and distribution transformer wear was calculated to determine the value potential of different data communication methods controlling a fleet of plug-in hybrids.
In travel surveys most respondents apply rounding of departure and arrival times to multiples of 5, 15 and 30 minutes: in the annual Dutch travel survey about 85-95 percent of all reported times are 'round' ones. We estimate rounding models for departure and arrival times. The model allows one to compute the probability that a reported arrival time m (say m=9: 15 am) means that the actual arrival time equals n (say m=9:21 am). Departure times appear to be rounded much more frequently than arrival times. An interpretation for this result is offered by distinguishing between scheduled and non-scheduled activities, and by addressing the role of transitory activities. We argue that explicitly addressing rounding of arrival and departure times will have at least three positive effects. 1. It leads to a considerably better treatment of variances of reported travel times. 2. It enables one to avoid biases in the computation of average transport times based on travel surveys. 3. It overcomes the problem that the use of travel survey data for the minute-per-minute records of the development of the number of persons in traffic displays erratic patterns.