the effects of electric vehicles on residential households...
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Energy Policy 49 (2012) 442–455
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Energy Policy
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The effects of electric vehicles on residential householdsin the city of Indianapolis
Shisheng Huang a, Hameed Safiullah b, Jingjie Xiao b, Bri-Mathias S. Hodge a, Ray Hoffman c, Joan Soller c,Doug Jones c, Dennis Dininger c, Wallace E. Tyner d, Andrew Liu b, Joseph F. Pekny a,n
a School of Chemical Engineering, Purdue University, 480 Stadium Mall Dr., West Lafayette, IN 47907, USAb School of Industrial Engineering,Purdue University, 315N. Grant Street, West Lafayette, IN 47907, USAc Indianapolis Power and Light Company, One Monument Circle, Indianapolis, IN 46204, USAd Department of Agricultural Economics, Purdue University, 403W State Street, West Lafayette, IN 47907, USA
H I G H L I G H T S
c Traffic flow modeling is used to accurately characterize EV usage in Indianapolis.c EV usage patterns are simulated to determine household electricity usage patterns.c Economic costs are calculated for the households for electric vehicles.c Possible public charging locations are examined.
a r t i c l e i n f o
Article history:
Received 11 December 2011
Accepted 21 June 2012Available online 2 August 2012
Keywords:
Electric vehicles
Electricity grid
Electricity tariffs
15/$ - see front matter & 2012 Elsevier Ltd. A
x.doi.org/10.1016/j.enpol.2012.06.039
esponding author. Tel.: þ1 765 494 7901; fax
ail addresses: [email protected] (S. Huang
@purdue.edu (H. Safiullah), [email protected]
purdue.edu (B.-M. Hodge), ray.hoffman@aes
[email protected] (J. Soller), [email protected] (
[email protected] (D. Dininger), wtyner@purd
[email protected] (A. Liu), [email protected] (
a b s t r a c t
There is an increasing impetus to transform the U.S transportation sector and transition away from the
uncertainties of oil supply. One of the most viable current solutions is the adoption of electric vehicles
(EVs). These vehicles allow for a transportation system that would be flexible in its fuel demands.
However, utilities may need to address questions such as distribution constraints, electricity tariffs and
incentives and public charging locations before large scale electric vehicle adoption can be realized. In
this study, the effect of electric vehicles on households in Indianapolis is examined. A four-step traffic
flow model is used to characterize the usage characteristics of vehicles in the Indianapolis metropolitan
area. This data is then used to simulate EV usage patterns which can be used to determine household
electricity usage characteristics. These results are differentiated by the zones with which the house-
holds are associated. Economic costs are then calculated for the individual households. Finally, possible
public charging locations are examined.
& 2012 Elsevier Ltd. All rights reserved.
1. Introduction
Energy security is one of the biggest concerns in the worldpolitical landscape. Instability in oil producing nations has furtherfueled the need to be less reliant on foreign sources of energy. Thetransportation sector, which imports two thirds of its dailyconsumption, is one sector that is heavily dependent on foreignsources of energy (EIA, 2010). The ability to move even a part ofthe sector from petroleum products to electricity is of greatinterest as it mitigates this risk of crude oil dependence.
ll rights reserved.
: þ1 765 494 0805.
),
(J. Xiao),
.com (R. Hoffman),
D. Jones),
ue.edu (W.E. Tyner),
J.F. Pekny).
In recent times, there have been tremendous developments inelectric vehicle (EV) and plug-in hybrid electric vehicle (PHEV)technologies. EVs and PHEVs use electricity stored in the battery asthe primary fuel for propulsion. The significant difference betweenthe two technologies is that PHEVs can utilize a secondary fuel sourcefor propulsion when the battery is depleted. Current examples of EVsinclude the Nissan Leaf, Think City and the Tesla Roadster while thedominant model for PHEVs is the Chevrolet Volt. When compared toother alternative fuel vehicle technologies, these vehicles have anadvantage because of the readily available power grid infrastruc-ture. However, this shifting of the energy requirement of thetransportation sector to the power grid might increase the strainon the grid. Battery charging during peak hours might increasethe peak load and would require energy from peaking powerplants which is relatively more expensive than energy from non-peaking plants. On the other hand, off-peak charging couldpotentially be very beneficial to both the electric utilities andconsumers.
S. Huang et al. / Energy Policy 49 (2012) 442–455 443
There have been several studies conducted about the impact ofhow electric vehicles would affect the electrical grid. Most studieshave focused on PHEVs. However, since both EVs and PHEVs aretechnologically very similar, the results of these studies can beapproximated to EVs. One of the more extensive studies hasestablished the upper bound of PHEV adoption using existing U.Selectricity generation assets (Kintner-Meyer et al., 2007). Thisstudy provided a theoretical upper bound based on optimisticassumptions that allowed for perfect control of vehicle chargingto utilize excess supply capacity. In another analysis, Parks et al.used four idealized charging patterns combined with a unitcommitment model to determine PHEV penetration effects forthe Colorado service territory (Parks et al., 2007). PHEVs or EVscould potentially act as agents that help in integrating higherpenetrations of renewable energy (Hodge et al., 2010, 2011b;Kempton and Tomic, 2005).
Some of the more recent studies of PHEVs have includedempirical data for driving patterns to better mimic realisticcharging demand on the system. Sioshansi et al. used empiricaldriving data from the St. Louis metropolitan area in Missouri tomodel PHEV charging patterns and coupled it with a unitcommitment model to determine grid level impacts on the Ohiopower system (Sioshansi et al., 2010). In another study, drivingcycle data obtained through GPS data loggers was used todetermine the optimal battery size needed for light duty PHEVs(Smith et al., 2011). Another option that researchers haveturned to is the extensive data that is available through theNational Household Travel Survey (NHTS). Zhang et al. con-verted data from the 2009 NHTS survey into a Matlab modeland used it to analyze different charging scenarios in the SouthCoast Air Basin of California (Zhang et al., 2011). The effects ofPHEVs on the Illinois power system with both wind power anddemand response penetration has also been examined usingthis database (Wang et al., 2011). It has also been used toestimate detailed power consumption information for thesevehicles (Wu et al., 2011).
The main limitation of the approaches listed above is thatusage data for EVs is limited to average or aggregated approx-imations. Average usage characteristics can be very useful foranalysis that deal with macro level effects such as system widebenefits or costs, but they can be inadequate for determining localeffects for planning purposes. Although empirical data collectionwould also provide the same level of granularity, the effort tocollect new data for all the different zones may prove to beprohibitive. An alternate approach would be to adapt traffic flowmodels used by local planning agencies to more accuratelypredict local flow patterns, and thus provide localized chargingpatterns. A general framework has been proposed in a previousstudy for the city of Alexandria, Virginia. In that study, realisticdriving and charging patterns were studied to determine thesystem wide benefits of PHEV penetration (Hodge et al., 2011a).
Although both EVs and PHEVs have the potential to be adoptedand would impact the electricity grid, only EVs are considered inthis study since they present a bigger potential strain on thesystem and analyzing them would provide a base case for allelectricity propelled vehicles, PHEVs included. EV adopters couldface problems of range limitations due to the fixed capacity of onboard batteries and relatively long recharge times, however, inmost situations, the problem of range anxiety is more psycholo-gical than physical (Franke et al., 2011). The provision of publiccharging locations can also significantly reduce the problem ofrange anxiety (Botsford and Szczepanek, 2009). In this study, weexamine the effects that the widespread introduction of EVs willhave on the electricity demand profile and evaluate publiccharging locations. The geographical region chosen for the studyis Indianapolis, Indiana. This region is a sufficiently large and
diverse area so that it is expected that there could be pocketswhere EVs would have higher penetration and local effects wouldbe significant. Therefore, the significant contribution of this studyis the integration of realistic zonal characteristics with a detailedresidential model such that local distribution level effects can beanticipated. This would allow for utilities to better anticipate EVeffects on local electricity demand. Since studies have also shownthat the attractiveness of PHEVs and EVs are hugely dependent onthe structure of electricity tariffs (Huang et al., 2011; Lidickeret al., 2010); electricity rate tariffs proposed by the local utility,Indianapolis Power & Light Company, are also examined todetermine how attractive EVs are to the local populace. Anexamination of the undervaluation of gasoline savings by house-holds is also examined. To give a comprehensive assessment ofIndianapolis, proposed charging stations are analyzed with thetraffic flow results.
2. Methodology
A multi-paradigm modeling approach has been used to exam-ine the effects of the introduction of EVs on the electricitydemand sector. The multi-paradigm approach enables differentsub-systems to be simulated with the most representative mod-eling approaches, levels of data, and model granularity that reflectthe subsystems most accurately. This paper considers two sub-systems of the electricity system: an electrified personal trans-portation system and the residential electricity demand sector.
A four step transportation model has been adapted for theIndianapolis metropolitan area and used to determine the travelcharacteristics of vehicles in the system. The zonal transportationdata is then fed into an agent-based residential demand modelthat determines the electricity consumption profile of residentialhouseholds. The economic costs and benefits of an electric vehicleare then calculated for the household. The fact that there will berange anxiety problems attached to electric vehicle usage isrecognized in this study. As a comprehensive solution to facilitateelectric vehicle usage, several preselected locations for EVcharging stations are evaluated for their vehicle flow influence.Fig. 1 shows a simplified flowchart of the aggregated system.
2.1. Transportation characteristics
A four-step model has been used to simulate the usagecharacteristics of vehicles in Indianapolis (FHWA, 1977; Hensherand Button, 2000). For this model, the area under analysis isdivided into zones called Traffic Analysis Zones (TAZs). The TAZsare used to group areas with similar attributes (residential,commercial, educational and industrial). The size of each TAZmay vary from a single building to several miles in radius. Thetraffic analysis zones are classified at the discretion of metropolitanplanning organizations. In our study, the Indianapolis MetropolitanPlanning Organization (IndyMPO) had split the city of Indianapolisinto 2573 zones. The IndyMPO uses surveys to obtain socio-economic data. The socio-economic characteristics of locations indifferent states are different. Among all, the most critical censusdata are automobiles per household, income and employment.
The steps involved in the modeling are as shown in Fig. 2. Thetransportation system represents the transportation infrastruc-ture and related services. The internal activity system representsthe economic activity, demographic, and land use data defined forthe TAZs. As the first step, in trip generation, the census data isused to generate the number of trips that are produced (depar-ture) and attracted (arrival) by each zone. Next, the trips depart-ing from a zone is matched with trip arrivals of other zones in thesystem. Travel impedance ( travel distance) is used in forming
Fig. 2. Flowchart of transportation model.
Fig. 1. Flowchart of model used in the study.
S. Huang et al. / Energy Policy 49 (2012) 442–455444
complete trips. This step is known as trip distribution. Modechoice is used to classify the complete trips by mode of trans-portation. Finally, the route choice step is used to model the flowof traffic on each transportation infrastructure. There could beinstances where traffic flow violation occurs and the process isreiterated by using the feedback from traffic flows. The travelimpedances may be adjusted to obtain a more adherent solution;but convergence is not guaranteed (FHWA, 1977; Hensher andButton, 2000).
The trips produced and attracted are obtained from the tripdistribution step. This information is converted to origin-destina-tion (OD) matrices (in transportation analysis software), whereeach element of the matrix represents the number of travelers
moving from the origin to the destination. The OD matrix is usedwith the road network information (transportation system) in thefour-step model to calculate the flow on each road/link. The modechoice and route choice steps are not used extensively in thecurrent scope of the study since the study looks only at arrivaland departure patterns of personal vehicles, not traffic flows ofvehicles on the system. The transportation planning softwareTransCAD is used in trip distribution and route choice step. Asnoted above, the zone level socio-economic data and the roadnetwork information were obtained from the Indianapolis Metro-politan Planning Organization (IndyMPO). The data is for the ninecounties in Indianapolis: Boone, Hamilton, Hancock, Hendricks,Johnson, Madison, Marion, Morgan and Shelby.
S. Huang et al. / Energy Policy 49 (2012) 442–455 445
2.1.1. Trip generation
The trips are classified into three categories. Home-based work(HBW) trips, home-based other (HBO) trips and non-home based(NHB) trips. HBW trips start from home and end in the workplace. HBO trips originate at home and are undertaken forpurposes other than work; for example: trips to the shoppingmall from home, trips to the grocery store from home etc. NHBare all other trips which do not originate from home; for example:trips from office to a restaurant, etc.
The procedure makes use of certain available informationabout the zone to estimate the number of trips that wouldoriginate or end in the zone. The socio-economic and geographicaldata of each zone contains valuable indicators that could be usedto estimate the trips. For instance, a zone closer to the downtownarea, with lot of shopping activities, will make shorter trips than azone in the outer suburbs. In the study, the IndyMPO carried outthe trip generation process (NuStats, 2011). The metropolitanplanning organization employed established guidelines, such asthe Institute of Transportation Engineers’ Trip Generation manual(Institute of Transportation Engineers, 2008), to generate vehicletrips. The manual provides standard factors and procedures togenerate vehicle trips from socioeconomic data. For example, thenumber of HBW trips could be generated by multiplying 1.46 bythe total employment in the zone (Martin and McGuckin, 1998).Socio-economic data of the zone, like population, number ofhouseholds and employment information, are used to estimatethe trips between zones. Household data gives an indication ofthe number of people residing in the zone and is used forestimating the home-based trip productions. The employmentin the zone relates to the work trip attraction. The other inter-esting data is the retail employment that is used for calculatingshopping based trips.
Table 1Parameters used in the gamma function.
Trip purpose Alpha Beta Gamma
HBW 28507 �0.02 �0.123
HBO 139173 �1.285 �0.094
NHB 219113 �1.332 �0.01
2.1.2. Trip distribution
This step is used to match the trip production and attraction ofeach zone based on geographical factors to form complete trips.For example, the trips that are produced in a zone in Carmel,Indiana, will be distributed to other zones in Indianapolis down-town, shopping districts, etc. based on their geographical proxi-mity, and thereby forming complete trips. The process is repeatedfor every zone in the system.
The general assumption is that the farther the distance of thedestination, the less the trip attraction. The effect of travel timealso varies depending on the trip type. Travel times have apronounced effect on non-home-based trips as it is discouragingto travel very long distances for personal chores. On the otherhand, travel times have very little effect on work-based trips as thetravel destination cannot be substituted. For this study, the‘‘Gravity Model’’ is used (Martin and McGuckin, 1998), with thetrip lengths or travel times between zones represented by ‘‘frictionfactors’’. The Gravity Model formulation is expressed as follows:
Tij ¼ Pi �AjFijKij
Pnj ¼ 1ðAjFijKijÞ
where, Tij is the number of trips from zone i to zone j, Pi is thenumber of trip productions in zone i, Aj is the number of tripattractions in zone j, Fij is the friction factor for interchangei, j (basedon travel time between i and j), Kij is the optional adjustment factor.
Friction factors are used to account for the travel time betweentwo zones. The friction factors are different for each of the triptypes. For our model, friction factors were developed using agamma function. The gamma functions used to develop thesefunctions used the following equation (LSA, 2011):
Fij ¼p� Ibij � eIij�g
where is, Fij: the friction factor between zones i and j, a, b and g:model coefficients; b and g should be negative; a is the scalingfactor, Iij: the impedance factor (travel time) between zonesi and j, and e: the base of natual logarithm.
I is the impedance matrix. The trip length (in minutes) is usedas impedance in our study. I is represented as a matrix and eachcell Iij represents the time it takes to travel from zone i to zone j
without traffic. The I matrix is obtained by processing the roadnetwork Geographic Information System (GIS) of Indianapolis.The GIS road network of Indianapolis is as shown in a later sectionin Fig. 6. The aim is to select an impedance function and itscorresponding parameters such that the gravity model repro-duces the trip length distribution of the study area. There areseveral ways to arrive at the parameters. We have used para-meters suggested by Travel Estimation Techniques for UrbanPlanning (Martin and McGuckin, 1998). The work suggests thatthe gamma function be used with the parameters in Table 1. Thefinal output of this process is the production–attraction matrix.Each element in the matrix represents the number of trips madefrom one zone to another.
2.1.3. Mode choice and route choice
Mode choice accounts for the different means of transportationavailable. A particular mode of transport is chosen based on itsrelative accessibility and convenience of the mode. Travel time,travel cost and automobile ownership constitute the convenienceattribute. And, parking availability and mass transit availability arefactored in the accessibility attribute. Usually, the traveler woulduse the public transport only when it is easily accessible fromorigin and destination. Otherwise, the traveler would use personaltransportation. According to the national household survey con-ducted by the U.S. Bureau of Transportation statistics, 87% of dailytrips take place in personal vehicles and 91% of people commutingto work use personal vehicles (Bureau of Transportation Statistics,2012). Furthermore, the public transit system functions mostly inurban Indianapolis with limited or no service to suburban areas.Due to these facts, less emphasis is given to mass transit and themode choice step is not used. The analysis is focused solely onpersonal vehicles and the related trips.
The final step of the modeling is the route assignment process.This step is used to estimate vehicle flows on each of the roadsegments. In this process, the model initially chooses the shortestroute between two zones. It then iterates based on the congestionpattern to achieve equilibrium on the flow. As this study is notconcerned with traffic flows through roads and highways, theroute assignment process is not used.
2.1.4. Data feed into electricity demand model
The time of charging and the quantity of charge are the twocharacteristic data that define the EV behavior in the electricitydemand model. The production–attraction matrix, from the tripdistribution process, is matched with the trip length matrix (I) toobtain the average trip length and trip length frequency distribu-tion for each zone under consideration. The quantity of chargerequired from the grid directly relates to the trip length. There-fore, the trip length frequency distribution obtained will be used
Table 2Characteristics of various charging configurations.
Charging
level
Voltage/current requirement
Level 1 120 V/16 A
Level 2 208–240 V/12 A to 80 A
Level 3 No specific limits; very high voltages (300–600 V DC), very high
currents (or 480 V AC)
S. Huang et al. / Energy Policy 49 (2012) 442–455446
in electricity demand model simulation to model the energyrequired to charge the battery.
The ability of EVs to charge is dependent on the locations ofthe EVs, which are obtained from the hourly vehicle flow for thezone. For this process, it is essential to convert the production–attraction matrix to origin-destination matrix because the cell-value in the production–attraction matrix is non-directional. Thecell values of an origin-destination matrix have directional mean-ing, indicating the number of trips going from an origin to adestination. This is essential to deduce the number of vehiclesarriving at home as destination at each hour. The production–attraction matrix is converted to origin-destination matrix in thetransportation analysis software using the hourly production/attraction probabilities shown in Table A2. The hourly origin-destination matrix is thus used as an input to the electricitydemand model to characterize the time of charging.
2.2. Residential household model
The residential model used in this study is in essence a multi-paradigm model, a discrete event household appliance modelcoupled with an agent based EV model. The detailed frameworkfor the household appliance model and the agent based model canbe found in previous studies (Hodge et al., 2011a, 2011b., 2011c;Huang et al., 2011) and is not presented in detail in thispublication. A general flowchart for the agent based EV model ispresented in Fig. 3 below.
2.2.1. Baseline appliances
The basic framework of the household model has beendiscussed in a previous publication (Huang et al., 2011). The mostsignificant difference is the treatment of temperature sensitiveappliances with respect to temperature. This will be discussed ina separate section below. Appliance saturation rate figures were
Fig. 3. Flowchart for EV
obtained from the 2005 RECS micro data (EIA, 2008). The data wassorted first by census region and subsequently for heating degreedays. The data that was most similar to geographical data fromIndianapolis was then used to create the appropriate appliancetables. Power consumption characteristics of individual appli-ances were built using similar approaches to the previous studyand fitted to historical data. The collection of appliances is givenin Table A1 in the appendix.
2.2.2. Electric vehicle
As the authors mentioned above, details on the exact formula-tion for EV agents have been published in previous works, theinterested reader is directed to the mentioned references for moredetail. The general modeling framework is described in Hodgeet al. (2011c), while the detailed EV agents can be found in Hodgeet al. (2011a). EVs require electrical energy from the power grid tocharge the on-board batteries. The charging scheme for thesevehicles can be classified into different levels, namely level 1,level 2 and level 3. Table 2 describes each of the charging types(Morrow et al., 2008). Level 1 and 2 chargers are expected to bethe most abundant chargers installed by consumers due to theirlower costs, practicality of providing service, and use of standar-dized equipment. Level 2 chargers are the preferred and recom-mended scheme for EVs due to their large battery capacities.
agent framework.
Table 3Summary of IPL electricity rates.
Residential rate (RS)Customer charge
0–325 kW h/month $6.70 per month
4325 kW h/month $11.00 per month
Energy charge
0–500 kW h $0.067 per kW h
4500 kW h $0.044 per kW h
With electric heating and/or water heating
41000 kW h $0.0318 per kW h
EV TOU rate (EVX)Summer (June–September)
Non holiday weekdays
Off-peak (midnight–10am, 10pm–midnight) $0.02331 per kW h
Mid–peak (10am–2pm, 7pm–10pm) $0.05507 per kW h
Peak (2pm–7pm) $0.12150 per kW h
Weekends and holidays (independence day and labor day)
Off-peak (midnight–10am, 10pm–midnight) $0.02331 per kW h
Mid-peak (10am–10pm) $0.05507 per kW h
Non-summer
All days
Off-peak (midnight–8am, 8pm–midnight) $0.02764 per kW h
Peak (8am–8pm) $0.06910 per kW h
Public charging rate (EVP)
Usage charge $2.50 per use
For more details, please refer to the tariffs filing available on the IPL website
(www.IPLPower.com). For billing purposes, the RS and EVX rates above will be
added to IPL’s current rider adjustments (fuel cost adjustment, environmental
adjustment, and demand side management adjustment).
S. Huang et al. / Energy Policy 49 (2012) 442–455 447
A 24 kW h EV battery would take approximately 8 h to fullycharge on a level 2 charger. In this study, a level 2 charging stationis assumed to be installed for both residential consumers and atpublic charging stations. A 3.3 kW output is assumed for thecharger (Morrow et al., 2008).
Vehicle usage characteristics are obtained from the vehicleflow model. Important information generated from TransCadinclude, the diurnal distribution of generated trips from differentzones, distribution of trip lengths by trip purpose and frequencyof trips by trip purpose. The trips are categorized into three typesas discussed in a previous section. Each HBW trip has a corre-sponding return trip attached, while the duration of HBO and NHBtrips are determined from the 2009 NHTS survey (FHWA, 2009).
A typical usage pattern for the electric vehicle can be describedas follows. The EV makes a decision at every time step in themodel. If the EV is not in use, there is a probability of the EVmaking a trip. The probability distributions of the trips aredetermined through the steps described in the previous sections.If a trip is scheduled, a corresponding distance and duration forthe trip is also determined probabilistically. At its destination, theEV then makes a decision on whether public charging is needed.If the vehicle is not able to complete the round trip with theenergy left in the battery or if the battery level drops below 20%,the EV plugs into a public charging location and charges. At theend of the trip duration, the EV either returns home, to its startinglocation or proceeds on another scheduled trip.
As the goal of the study is to determine the effects of EVs ondifferent households in Indianapolis, no penetration levels forvehicles are assumed, instead EV costs are calculated for a typicalhousehold residing in the zone in question using characteristicsassociated with each zone.
2.2.3. Temperature effects
One of the most significant factors that determine how muchelectricity a household uses is the weather, best exemplified bythe outside temperature. System operators in most of the UnitedStates manage their grid operations around peak events thatoccur during summer, when high temperatures drive up air-conditioning loads and system resources are the most strained(CAISO, 2011). In this study, household appliances such asrefrigerators, water heaters, air-conditioning units and spaceheaters are assumed to be sensitive to weather effects. Tempera-ture factors affecting usage probabilities and power consumptionwere derived from previous statistical studies done by Hart andde Dear (2004). Their study focused on various householdappliances in the Sydney metropolitan area. These temperaturefactors have been adapted for Indianapolis weather patterns.
Weather also plays a significant factor when determining the all-electric range for EVs. Temperature is especially critical as energyfrom the battery would be diverted to climate control to keep theinterior of the vehicle comfortable for passengers. The end result isthat the driving range for EVs can vary greatly under differentconditions (Christensen et al., 2011; THINKUSA, 2011). For example,the range of the Nissan Leaf can go from 138 miles to 62 milesdepending on both temperature and traffic conditions (NissanUSA,2011). In this study, the EV is expected to travel in city traffic withrange dependent on temperature based on figures released by Nissan.
2.3. Electricity cost
The Indianapolis metropolitan area is serviced by IndianapolisPower & Light Company (IPL). Residential households are servedunder its residential rate RS, which has a decreasing tier structure.This essentially translates into cheaper electricity rates as the house-hold consumes more energy (IPL, 2011). Compared to an increasing
tier rate, this provides a positive incentive for the adoption of EVs(Huang et al., 2011). IPL also has a residential Time Of Use (TOU)electricity tariff for EVs which is separately metered under rate EVX. Itis designed to encourage consumers to charge during off-peakperiods. IPL also provides public charging locations for EVs underrate EVP. A charge of $2.50 per charging session is imposed for usageat these locations. A summary of the rates can be found in Table 3.
2.4. EV charging behavior
It is assumed that EVs would need to be charged daily in orderto remain viable transportation options. In this study, two char-ging behaviors for EVs are looked at. The first pattern assumesthat EV owners are not sensitive to electricity prices at home.However, consumers recognize the higher cost of public charging,and as such, public charging would be used primarily to eliminaterange anxiety and extend EV functionality. In this scenario, the EVis plugged in for charging whenever the vehicle is at home and atpublic charging locations when the battery charge level is below apredetermined minimum. This type of charging pattern is definedas an unresponsive charging pattern.
For the other charging behavior, the EV owner is assumed tobe price sensitive and would seek to minimize expenses onelectricity charging. The EV would only be charged if the vehicleis at home and the price of electricity is at its lowest; here definedas a TOU charging profile. However, it is assumed that a minimumlevel of charge is required in the vehicle at all times and thevehicle would either charge at home or at public charginglocations when the battery level falls below this minimum. Fromthese two behaviors, it is possible to estimate the potentialsavings of a behavioral change with regards to vehicle charging.
2.5. Economic analysis
The economic costs of owning an EV were calculated and abenefits-cost analysis performed with comparisons to similarlysized vehicles. The EV was modeled after the Nissan Leaf, the first
Table 4Parameters for vehicles.
Nissan leaf Toyota corolla Toyota prius
MSRP $32,780.00 $17,300.00 $23,050.00
Miles per unit fuel 4.17 mile/kW h 30 mpg 50 mpg
Tax credit $7500 – –
Loan interest rate 6%
Loan tenure 5 Years
Salvage value 15%
Fig. 4. Regression analysis between U.S composite refiner acquisition cost and
Midwestern retail gasoline price.
S. Huang et al. / Energy Policy 49 (2012) 442–455448
total battery Electric Vehicle produced by a major car manufac-turer. The vehicles selected for comparison were a Toyota Corolla,the best-selling compact in the United States for 2010 and theToyota Prius, the best-selling conventional hybrid. The economicparameters used for the three vehicles are included in Table 4.
For all three vehicles, a nominal loan interest rate of 6% over5 years was assumed. The vehicle life was assumed to be 10 years,with a 15% resale value at the end of 10 years (in real terms).Maintenance and insurance were assumed to be the same for allthree vehicles and battery lifespan is assumed to be 100,000 miles.Since the average miles driven per day in a city is expected to beless than 100,000 miles over ten years, it was assumed that thebattery would not be replaced for this study (Peterson et al., 2010).
Two petroleum price scenarios were considered: a base caseand a high oil price scenario. These two scenarios follow theassumptions given in the latest Annual Energy Outlook (AEO) thatwas released in April (EIA, 2011). In the reference case, real oilprices increase 2.75% per year, and 7.6% per year in the high pricecase, between 2010 and 2020. In terms of crude oil, the pricesrange from $75 to $99 in the reference case and $75 to $160 in thehigh oil projection. The Midwestern retail gasoline price was usedto approximate Indiana gasoline retail price. To convert the AEOcrude oil price projection to Midwestern retail gasoline price, weused the Department of Energy (DOE) historic monthly data onthe Midwestern gasoline price and U.S. composite refiner acquisi-tion cost of crude oil (Fig. 4). The R2 for this regression was 0.944.
The starting gasoline price for both the reference case and highoil price case was $2.78/gal. The price of electricity to consumersin real terms was assumed to remain constant based on tariffs.The assumed general inflation rate is 3%. A real discount rate of 6%was used for baseline net present value calculations.
2.6. Evaluation of commercial charging locations
It is proposed to evaluate the potential usage levels of thecharging stations based on the number of vehicles that could beinfluenced. Proposed charging station locations were mapped in
TransCAD and the corresponding zones identified. Public chargingstations will attract not only the visitors to a particular zone butalso the visitors to the neighboring zones. Based on this idea, theanalysis was performed for all the zones covered by a 0.25 mile,as well as a 0.5 mile, radius. Fig. 5 shows an example of zonesbeing covered by a charging station at Denison Merchants garage,where public chargers have been installed. The inner green circlerepresents the 0.25 mile radius area of influence and the red circlerepresents the 0.5 mile radius area of influence.
All the zones, covered by the area of influence for eachlocation, are included in the vehicle flow modeling procedure.The hourly vehicle flow is obtained from the hourly origin-destination matrix. The vehicle flows during each hour as wellas the total number of vehicles influenced are then computed.
3. Results and discussion
This study focuses on the impact of EVs on specific zones inIndianapolis. From an earlier study, it is expected that the earlyadopters of EVs have an average household income of above $114,000per annum and reside in an urban or suburban neighborhood(Giffi et al., 2010). With this consideration in mind, zones that haveaverage incomes of above $100,000 were shortlisted and of thesezones, the following 5 zones (Fig. 6), labeled A to E, were picked to bestudied in greater detail. Care was taken in this selection to try toobtain a diverse group of zones, given the constraints.
3.1. Traffic flows
The flow data from the Indianapolis traffic simulation wascompiled into the respective matrices and from those flowsrelevant tables were produced and fed into the residential demandmodel. They are available for all the different TAZs analyzed forIndianapolis. The data for a single zone is given in the Appendix forillustration purposes. Table A2 shows trip generation probabilitiesover the course of a day. The trips are divided into the threedistinct categories, HBW, HBO and NHB. Both production andattraction tables are available for work trips, while only theproduction tables are utilized in non-work trips. The duration ofthese trips are determined from another time distribution obtainedthrough the NHTS and shown here in Table A3. After a trip isdetermined, the distance travelled by the vehicle is determinedfrom another set of distributions. Each trip type has its uniquedistribution and these distances are given in Table A4.
3.2. Residential electricity demand
In order to obtain credible results from the simulation model,it must first be validated against historical data. Historical loaddata for a representative sample of residential customers in thecity of Indianapolis was obtained from Indianapolis Power & LightCompany (IPL). Summer season for Indianapolis is considered asthe months from June to September. In Fig. 7, the averagehousehold summer load profile is given for both the historicalload for 2009 and the simulated model. The error bars given arefor one standard deviation for hourly electricity demand. Thehistorical average for a residential household in Indianapolis isabout 34.5 kW h per day while the model over predicts the loadslightly higher at 35 kW h per day.
3.3. Peak electricity demand
The effect of EVs on peak daily demand is of great interest toutilities. Since the effective range of EVs is significantly affectedby weather, the combined electricity demand could present a
Fig. 6. GIS road network of Indianapolis, in particular Marion County. Five zones labeled A to E have been selected for analysis.
Fig. 5. Possible area of influence of apublic charging location on EVs.
S. Huang et al. / Energy Policy 49 (2012) 442–455 449
Fig. 7. The figure on the left represents an average historical household profile for Indianapolis while the figure on the right represents simulation results.
Fig. 8. Charging profile of a household during a peak period.
Table 5Representative monthly bills for households in Indianapolis.
Home use
base case
Incremental EVX costs by zone
A B C D E
Unresponsive $69.51 $17.01 $15.07 $13.68 $10.99 $14.94
EVX $11.28 $9.74 $8.26 $6.61 $8.82
Average miles – 32.94 30.60 27.29 22.75 28.03
S. Huang et al. / Energy Policy 49 (2012) 442–455450
potential strain on local distribution equipment, especially inregions where spare capacity is at a premium. Summer peakstypically occur when temperatures are the highest, due toincreased air conditioning usage. During peak periods, electricitypeak demand increases significantly, with a household hourlypeak potentially exceeding 5 kW. Fig. 8 shows a household duringa peak summer day with two possible EV charging schedules. Itcan be seen that with an added EV charging at peak times, thehousehold peak could potentially get pushed to 9 kW. This is fourtimes the average system hourly peak for Indianapolis perhousehold.
At the local distribution level, this could present a potentiallysignificant capacity problem for regions where EVs could have ahigh penetration. A few of the ways that utilities may wish toaddress these potential challenges could include identifyingpotential problem locations, planning for distribution capacityupgrades, providing incentives to consumers to shift electricityconsumption off-peak, and understanding where customers whoare interested in EVs are in the service territory.
3.4. Electricity cost
The costs of EV ownership are calculated based on simulationresults. Electricity costs are calculated based on electricity tariffsprovided by IPL including an electricity tariff rate for EV owners.Consumers using this tariff have a separate meter installed for theelectric vehicle. The electricity costs to the consumer are then
split into two components, the household electricity cost and theEV charging cost. Household electricity consumption is calculatedbased on the standard household electricity rate, which is adecreasing tier schedule and the EV electricity cost is calculatedon the EVX TOU electricity schedule available to EV owners.
The costs given in Table 5 represent an estimated monthly costfor consumers in the IPL service territory. The base case repre-sents a household bill with no EV, while the values for thedifferent zones represent the incremental monthly electricity costfor an EV. Two electricity costs are calculated, one assuming thathouseholds do not adjust charging habits based on electricityprice, in other words, unresponsive to price signals; and the otherwhere residents only charge their EVs when the electricity price isat its lowest (EVX). In both situations, households have access tothe same set of electricity tariffs. It can be seen that even withoutadjusting consumption for electricity price (unresponsive), thecosts of owning an EV in Indianapolis are very low, ranging from$11.00 to slightly over $17.00 per month. This represents coststhat are significantly less than a full tank of petrol for conven-tional vehicles, especially in today’s climate. When consumersconsciously adjust EV charging patterns with respect to electricityprice (EVX), the benefits obtained averaged $5.40 per month, orabout an average of 38% in savings from an unresponsive chargingscenario. The electricity costs obtained from this simulationexercise represent a realistic upper and lower bound of averageelectric vehicle cost for the city of Indianapolis.
The daily electricity demand profiles of an EV are given for thedifferent zones in Fig. 9 below. It can be seen that the zones varyslightly with respect to charging patterns. The bulk of vehiclecharging still occurs early in the evening, leaving a significantportion of the off-peak period untouched. This clearly indicates ahuge potential for load shifting of EV charging or applianceelectricity demand.
When compared to the electricity demand profiles for anunresponsive charging scenario, we can see that the EVX chargingpeak gets shifted later in the day (Fig. 10). However, as before,there remains significant leeway for charging to be shifted further
Fig. 9. Daily electricity profiles for EVs under unresponsive charging.
Fig. 10. Daily electricity profiles for EVs under EVX charging.
Table 7Tax rebate values needed for breakeven of NPV of Leaf when compared to similar
alternatives.
Zone Oil price TOU Unresponsive
Prius Corolla Prius Corolla
A Base: See Table 6 $4969 $6530 $5584 $7,144
D $6717 $9820 $7198 $10,301
A High: See Table 6 $2708 $2761 $3323 $3,376
D $5155 $7217 $5636 $7,699
Table 6Incremental net present values of Nissan Leaf when compared to similar alter-
natives under different oil price scenarios.
Zone Oil price TOU Unresponsive
Prius Corolla Prius Corolla
A Base: 2.75% increase in
oil price per annum
$1917 $735 $1451 $269
B $1658 $207 $1218 �$232
C $1226 �$604 $772 �$1058
D $593 �$1757 $229 �$2121
E $1300 �$445 $789 �$956
A High: 7.6% increase in
oil price per annum
$3629 $3589 $3164 $3123
B $3248 $2858 $2809 $2419
C $2644 $1760 $2191 $1306
D $1776 $214 $1411 �$150
E $2757 $1983 $2246 $1472
S. Huang et al. / Energy Policy 49 (2012) 442–455 451
later into the night. Strategies to implement this will be discussedin a future study.
3.5. Economic competitiveness of EVs in Indianapolis
In this study, the EV was modeled after the Nissan Leaf andwas compared with alternative vehicles that are available toconsumers. Two general scenarios are considered for this study,a base case where the price of oil would follow historicaltrajectories and another where price of oil increases at a muchfaster rate. The economic parameters have been discussed inSection 3.5 and would not be presented here again. The values inTable 6 represent the incremental net present value (NPV)between the Nissan Leaf and the other alternatives. A positivevalue means that the Leaf is more attractive than the optionconsidered and a negative value means that it is less attractive.
It can be seen that the differences in NPVs between anunresponsive charging profile and an EVX profile are generallyfairly significant. Once again this represents a realistic lower andupper bound on the benefits of EVs in Indianapolis, and theamount of savings that a consumer can achieve depends greatlyon the consumer’s behavior. Another observation is that theattractiveness of EVs seems to be correlated to the averagedistance travelled by consumers; the higher the average milestravelled in a day, the more attractive the EV is to consumers.In other words, the more a consumer substitutes gasoline mileswith electric miles, the higher the potential savings. However,since EVs do have a limited range, this condition needs to beconsidered together with increasing range anxiety with moreelectric miles consumed, especially when the distance travelled
nears the range limit of the EV. The presence of public chargingstations would help reduce the concern over range anxiety(Franke et al., 2011), and since the average miles travelled inIndianapolis is around 30 miles per day, there remains a reason-able buffer to the range limit of the EV.
It can be observed that NPV analysis indicates that EVs aremore attractive than conventional hybrids in Indianapolis underbase case assumptions. At the higher end of the spectrum, avehicle travels only about 33 miles a day, significantly less thanthe available range of an EV. This observation, coupled with theavailability of charging at public locations, enable EVs to be a veryviable alternative to consumers looking for options other thanconventional hybrids. Even when compared to conventionalvehicles, EVs are competitive. For vehicle owners staying in zoneA and B, EVs are even economically more attractive than aconventional internal combustion vehicle. Even for zones C andE, an EV charging following a TOU profile is just $500 lessattractive than a Toyota Corolla. If non-tangible factors such asenvironmental issues, reduction of carbon footprint, and sustain-ability are considered the EV becomes very attractive.
Under high oil price assumptions, following EIA scenarios,where oil reaches almost $6.00 a gallon by 2020, EVs are veryattractive when compared to their alternatives. Even in Zone Dwhere the Nissan Leaf was over $1700 less attractive than itsnearest competitor in the base case, the Nissan Leaf becomeseconomically competitive under these assumptions. At the higherend of the spectrum, an EV vehicle owner in zone A can expectover $3000 in savings over a conventional hybrid.
It must be noted that EVs enjoy a significant tax rebateprovided by the federal government of $7500. This is an impor-tant factor in giving the Leaf an advantage over its alternatives. Aninteresting piece of further analysis is the sensitivity of the NPVsto the value of this tax rebate. Table 7 shows the NPV compar-isons for Zones A and D. These zones represent the upper andlower bound for NPVs in this analysis respectively. Since the Leafstarts off as more competitive against conventional hybrids, the
Table 9Incremental net present values of Nissan Leaf when compared to similar alter-
natives under different oil price scenarios with a discount rate of 16%.
Zone Oil price TOU Unresponsive
Prius Corolla Prius Corolla
A Base: 2.75% increase in
oil price per annum
$300 �$1467 �$6 �$1773
B $135 �$1805 �$154 �$2094
C �$142 �$2326 �$439 �$2624
D �$547 �$3066 �$786 �$3306
E �$94 �$2224 �$430 �$2560
A High: 7.6% increase in
oil price per annum
$1237 $95 $931 �$211
B $1005 �$354 $717 �$643
C $635 �$1032 $337 �$1330
D $100 �$1988 �$139 �$2227
E $703 �$895 $367 �$1231
S. Huang et al. / Energy Policy 49 (2012) 442–455452
value of the tax rebate can actually decrease for the NPV of a Leafto achieve parity with the Prius. However, the tax rebates need tobe increased to achieve breakeven when compared with theCorolla. In Zone D, an increase of over $2000 in tax rebates makesthe Leaf comparable to the Corolla. Under assumptions of a highoil price scenario, the tax rebate can be reduced by almost $5000from $7500 for the most competitive zones in Indianapolis. Whilethe value of tax rebate needed for NPV to break even will varyfrom one person to another, Table 6 shows that $7500 is areasonable value.
Battery costs constitute a major component of the retail priceof the EV. It is interesting to determine the sensitivity of the NPVof the Leaf with respect to its battery cost. Some estimates havepegged the value of the Leaf battery at $750/kW h (Hidrue et al.,2011), through the incremental cost of the Leaf over a comparablevehicle. In this study, we take a slightly different approach andestimate the cost of the battery to be half the retail cost of the carprior to the tax rebate, estimating the current price of the batteryat about $675/kW h. It is assumed that the oil price follows thebase case. We also examined the cost at which EVs would be ableto compete with its competitors without any need for subsidies.Table 8 shows the battery cost in terms of dollars per kW h thatthe battery would have to achieve in order for the Leaf to achievecost parity to similarly sized vehicles. At current rebate levels, thecost of the battery would have to drop only slightly for the EV tobe cost competitive in Zone D. Without the tax rebates, the costsof the batteries would have to drop by more than $200/kW h forcompetitiveness.
3.5.1. Economic competitiveness of EVs under fuel price myopia
Some studies have shown that consumers generally undervaluecosts that accrue in the future, resulting in consumers under-estimating the benefits of better fuel economies and efficiencies.Turrentine and Kurani (2007) showed that consumers generally donot think effectively about future gasoline consumption. Allcottand Wozny (2011) concluded an implied discount rate of 16% thatrationalizes the discrepancy between future gas savings and netpresent value of vehicles. Gallagher and Muehlegger (2011) con-cluded that the discount rate was about 14.6%.
An electric vehicle could be categorized as a high-MPG vehicleand would suffer from this undervaluation of gasoline consump-tion savings. If households are assumed to have a high discountrate when considering a high cost EV, the NPVs for EVs inIndianapolis could be similar to those in Table 9. An implicitdiscount rate of 16% was assumed for each household.
It can be seen that a household would still determine that anEV would be comparable to a conventional hybrid vehicle inIndianapolis. However, under base case assumptions, EVs do notcompare favorably with conventional internal combustionengines. Even under high oil price situations, conventional inter-nal combustion engines are generally more attractive.
The discrepancy in NPVs highlights the importance of informingconsumers about the potential fuel savings of EVs. This misallocationof resources reduces the overall welfare of society and represents a
Table 8Per kW h battery costs needed for breakeven of NPV of Leaf when compared to
similar alternatives.
Zone Current federal tax rebate value TOU Unresponsive
Prius Corolla Prius Corolla
A $7500 $768 $711 $745 $688
D $704 $590 $686 $572
A – $493 $436 $471 $414
D $429 $316 $412 $298
waste of resources. It also indicates opportunities to create appro-priate corrective policies that can reduce this gap in perceived andactual value. Another explanation of the apparent lack of considera-tion of future costs is capital constraints. Some consumers, especiallylower income consumers, might prefer a vehicle with lower futurecosts, but they may not be able to afford or obtain credit for thehigher capital costs of these vehicles. This explanation results in thesame appearance of high discount rate but has a different origin.However, the comparable attractiveness of EVs to conventionalhybrids does suggest that EVs can certainly achieve a certainproportion of the ‘‘green car’’ market enjoyed by hybrids presently.
Similarly, EVs are further differentiated from conventionalvehicles in other non-economic factors such as drive quality andnoise level. Although these factors could factor in consumerconsiderations of vehicles, they are not considered in this analysisas they are not easily quantifiable.
3.6. Evaluation of public charging locations
Due to the existence of range anxiety, commercial chargingstations are required to support the large-scale adoption ofelectric vehicles. Utility companies would then need to evaluatepotential locations for charging stations. Fig. 11 shows potentialcharging station locations that have been put forth by IPL andtheir corresponding vehicle influence figures. Locations such asdowntown parking garages show particularly good vehicle influ-ence figures and could potentially be excellent commercial char-ging sites. It must be noted however, since EV charging requires asignificant time investment, vehicle flow figures should not be theonly criterion that is used to evaluate public charging locations.Other factors such as proximity of public attractions, worklocations, activity durations would need to be examined too.A good location may be one with a high vehicle flow influencethat is close to a popular recreation center. A combination of allthese evaluation matrices should be used to determine attractiveand effective locations.
4. Conclusion
A traffic flow model was combined with a residential electri-city demand model to determine the effect of EVs in a cityenvironment. The region analyzed is the city of Indianapolis. Thiscombined model allows for detailed analysis of specific zoneswithin the city that would not have been possible with aggre-gated or averaged vehicle usage patterns. It was found that theusage of vehicles varied greatly from zone to zone, which wouldcreate different charging profiles unique to each of the zones.
Fig. 11. Traffic flow analysis for potential charging locations in Indianapolis. * Uses 1 mile radius because of the vast non-commercialized area surrounding the base location.
Table A1Saturation data for appliances in Indianapolis.
Appliance name Saturation
Stove and oven 0.63
Microwave oven 0.92
Coffee maker 0.65
Refrigerator 1.00
2nd refrigerator 0.28
Freezer 0.39
Dishwasher 0.51
Clothes washer 0.85
Electric dryer 0.70
Television 1.00
2nd television 0.78
3rd television 0.45
Set top box 0.78
Video recorder 0.80
DVD 0.81
Radio/player 0.73
Personal computer 0.68
Printer 0.61
Lighting 1.00
Other occasional Loads 1.00
Central air conditioning 0.65
Room airconditioning 0.31
Water heater 0.32
Telephone 0.77
Answering machine 0.62
Electric space heating 0.33
Pool pump 0.07
Other equipment 1.00
Table A2Normalized hourly production and attraction probabilities for Zone E.
Hour HBW production HBW attraction HBO production NHB production
1 0.72 0.08 0.25 0.60
2 0.36 0.04 0.10 0.20
3 0.36 0.00 0.00 0.00
4 0.50 0.04 0.03 0.00
5 0.72 0.08 0.00 0.10
6 4.86 0.51 0.15 0.40
7 14.22 1.51 0.66 1.50
8 34.57 3.65 2.24 6.60
9 16.57 1.75 1.56 4.00
10 5.40 0.57 1.60 3.60
11 1.26 0.13 2.69 5.60
12 1.08 0.12 3.09 6.30
13 1.53 2.67 3.04 10.20
14 1.35 2.64 4.37 7.20
15 1.68 5.90 4.58 6.90
16 2.16 10.43 7.83 8.00
17 3.56 23.81 12.50 8.00
18 3.32 21.47 14.34 6.20
19 1.67 5.73 14.31 4.70
20 1.40 3.19 12.44 6.30
21 1.27 1.92 6.86 5.80
22 0.55 5.25 4.02 3.90
23 0.53 5.07 2.15 2.40
24 0.36 3.44 1.20 1.50
S. Huang et al. / Energy Policy 49 (2012) 442–455 453
The impact on local distribution capacity for zones that have highpenetration of EVs could be significant, potentially increasing thesummer peak electricity demand in those zones.
On the other hand, it appears that the decreasing tier pricestructure coupled with the availability of a TOU price schedulemakes the EV a viable economic mode of transportation inIndianapolis. In the base case scenarios the EV is economicallymore attractive than conventional hybrids, while only slightly lesscompetitive than conventional IC vehicles. However, this attrac-tiveness greatly depends on the rebates available to EVs at themoment, and on consumers internalizing future operating costsavings in the purchase decision. If oil prices trend high or EVbattery costs decrease, the rebates could potentially be reduced
significantly or eliminated, and the EV would still be attractive.The availability of public charging locations in Indianapolis willhelp alleviate range anxiety. However the relative higher usagecharge of the public charging locations encourages charging athome at off peak times. With these system considerations the EVis becoming an economically attractive vehicle in Indianapolis.Indeed the analysis in this paper shows that EVs are at the cusp ofbecoming competitive with mature internal combustion enginevehicles. Further advances in EV technology and cost reductionswill increase the number of people for which EVs will provide aneconomic advantage. EV specific considerations, such as locationsfor public chargers are an important consideration. Using resultsfrom the transportation flow simulation, it is possible to provide abenchmark against which proposed charging locations can beevaluated. The electric vehicle represents a viable and attractiveoption to address problems such as energy independence,
S. Huang et al. / Energy Policy 49 (2012) 442–455454
increasing petroleum cost, and environmental impacts. This studyhas shown that the adoption of EVs in Indianapolis is alreadyattractive to some consumers and the conditions to be attractiveto most or all consumers could conceivably evolve over the nextfew years under free market conditions. The current conditionsanalyzed in this paper show a great potential for Indianapolis tobe a model city for demonstrating and developing EVs as apractical and effective transportation solution.
However, there still exist certain limitations to the currentstudy which would provide interesting research studies in thenear future. As mentioned above, the evaluation of chargingstation locations in this study could serve as a useful benchmarkfor optimal charging locations, but a more comprehensive studycould be conducted where optimality of locations could be moresubstantially examined. Complementary policies such as vehicleto grid technologies which would benefit EV adoption have not
Table A3Normalized frequency table for trip duration.
Time (min) Frequency
1 0.16
5 1.00
15 0.47
25 0.36
35 0.25
45 0.27
55 0.27
90 0.92
150 0.47
210 0.27
270 0.16
300 0.28
Table A4Distance distribution for Zone E.
Distance(Miles) HBW HBO NHB
1 0.00 0.00 0.02
2 0.14 0.03 0.08
3 0.53 0.16 0.21
4 0.23 0.26 0.39
5 0.66 0.48 0.61
6 1.00 0.87 0.85
7 0.96 1.00 0.91
8 0.72 0.88 1.00
9 0.44 0.60 0.95
10 0.31 0.26 0.84
11 0.28 0.29 0.81
12 0.18 0.18 0.74
13 0.21 0.26 0.64
14 0.14 0.12 0.60
15 0.05 0.08 0.58
16 0.09 0.11 0.52
17 0.17 0.15 0.47
18 0.16 0.23 0.45
19 0.17 0.26 0.44
20 0.07 0.09 0.38
21 0.10 0.17 0.34
22 0.04 0.07 0.28
23 0.01 0.01 0.23
24 0.00 0.00 0.19
25 0.02 0.05 0.15
26 0.00 0.00 0.11
27 0.00 0.00 0.09
28 0.00 0.01 0.07
29 0.00 0.00 0.05
30 0.00 0.00 0.04
31 0.00 0.00 0.03
32 0.00 0.00 0.02
33 0.00 0.00 0.07
been examined and could be examined in finer detail. EVs areassumed to be a substitution for conventional vehicles in thisstudy, however, EVs may be utilized in behavioral patterns thatare different from conventional vehicles and would warrant an indepth study.
Appendix
The following tables give parameters of the model employed inthe study. Data related to appliances have been obtained from the2005 Residential Energy Consumption Survey. Trip data has beengenerated from TransCAD through parameters obtained from theIndianapolis Metropolitan Planning Organization. (Table A1)
References
Allcott, H., Wozny, N., 2011. Gasoline Prices, Fuel Economy, and the EnergyParadox. MIT.
Botsford, C., Szczepanek, A., 2009. Fast Charging vs. Slow Charging: Pros and Consfor the New Age of Electric Vehicles, EVS24 International Battery, Hybrid andFuel Cell Electric Vehicle Symposium, Stavanger, Norway.
Bureau of Transportation Statistics, 2012. National Household TravelSurvey—Daily Travel Quick Facts. From: /http://www.bts.gov/programs/national_household_travel_survey/daily_travel.htmlS.
CAISO, 2011. 2011 Summer Loads and Resources Assessment. California Indepen-dent System Operator.
Christensen, N., Patten, J., Srivastava, S., Nola, G.P., 2011. The impact of drivingconditions on PHEV battery performance. Green Manufacturing ResearchJournal, 2.
EIA, 2008. Residential Energy Consumption Survey 2005. From: /http://www.eia.gov/consumption/residential/data/2005/microdata.cfmS.
EIA, 2010. Annual Energy Review 2009. Energy Information Administration.EIA, 2011. Annual Energy Outlook 2011. Energy Information Administration.FHWA, 1977. An Introduction to Urban Travel Demand Forecasting—a Self
Instructional Text. U.S. Department of Transportation, Washington DC.FHWA, 2009. 2009 National Household Travel Survey. Federal Highway
Administration.Franke, T., Neumann, I., Buhler, F., Cocron, P., Krems, J.F., 2011. Experiencing range
in an electric vehicle: understanding psychological barriers. AppliedPsychology.
Gallagher, K.S., Muehlegger, E., 2011. Giving green to get green? Incentives andconsumer adoption of hybrid vehicle technology. Journal of EnvironmentalEconomics and Management 61, 1–15.
Giffi, C., Hill, R., Gardner, M., Hasegawa, M., 2010. Gaining traction: a customerview of electric vehicle mass adoption in the U.S automotive market. DeloitteConsulting LLP.
Hart, M., de Dear, R., 2004. Weather sensitivity in household appliance energyend-use. Energy and Buildings 36, 161–174.
Hensher, D.A., Button, K.J. (Eds.), 2000. Pergamon.Hidrue, M.K., Parsons, G.R., Kempton, W., Gardner, M.P., 2011. Willingness to Pay
for electric vehicles and their attributes. Resource and Energy Economics 33,686–705.
Hodge, B.-M., Shukla, A., Huang, S., Reklaitis, G., Venkatasubramanian, V., Pekny, J.,2011a. Multi-paradigm modeling of the effects of PHEV adoption on electricutility usage levels and emissions. Industrial & Engineering ChemistryResearch.
Hodge, B.-M.S., Huang, S., Shukla, A., Pekny, J.F., Reklaitis, G.V., 2010. The effects ofvehicle-to-grid systems on wind power integration in California. In: Ferraris,S.P.a.G.B. (Ed.), Computer Aided Chemical Engineering. Elsevier,pp. 1039–1044.
Hodge, B.-M.S., Huang, S., Shukla, A., Pekny, J.F., Venkatasubramanian, V., Reklaitis,G.V., 2011b. The effects of vehicle-to-grid systems on wind power integration.Wind Energy.
Hodge, B.-M.S., Huang, S., Siirola, J.D., Pekny, J.F., Reklaitis, G.V., 2011c. A multi-paradigm modeling framework for energy systems simulation and analysis.Computers and Chemical Engineering 35, 1725–1737.
Huang, S., Hodge, B.-M.S., Taheripour, F., Pekny, J.F., Reklaitis, G.V., Tyner, W.E.,2011. The effects of electricity pricing on phev competitiveness. Energy Policy39, 1552–1561.
Institute of Transportation Engineers, 2008. Trip Generation, 8th Edition.: An IteInformational Report, eighth ed., p. 1919.
IPL, 2011. Indianapolis Power & Light Company Tariffs. From: /http://www.iplpower.com/ipl/index?page=IPLGeneral&Menu=01090000&DocID=0205016c163f01078f72b731006ec5S.
Kempton, W., Tomic, J., 2005. Vehicle-to-grid power implementation: fromstabilizing the grid to supporting large-scale renewable energy. Journal ofPower Sources 144, 280–294.
Kintner-Meyer, M., Schneider, K., Pratt, R., 2007. Impacts Assessment of Plug-inHybrid Vehicles on Electric Utilities and Regional U.S Power Grids Part 1.Technical Analysis. Pacific Northwest National Laboratory.
S. Huang et al. / Energy Policy 49 (2012) 442–455 455
Lidicker, J.R., Lipman, T.E., Shaheen, S.A., 2010. Economic Assessment of Electric-Drive Vehicle Operation in California and the United States. UC Davis:Institute of Transportation Studies.
LSA, 2011. Travel Demand Model: Model Development and Validation Report.Lincoln Metropolitan Plannning Organization, Lincoln, Nebraska.
Martin, W.A., McGuckin, N.A., 1998. Nchrp Report 365: Travel Estimation Techni-ques for Urban Planning. Transportation Research Board.
Morrow, K., Karner, D., Francfort, J., 2008. Plug-in Hybrid Electric Vehicle ChargingInfrastructure Review. Idaho National Laboratory.
NissanUSA, 2011. Nissan Leaf Electric Car: Range- the Basics. From: /http://www.nissanusa.com/leaf-electric-car/index#/leaf-electric-car/theBasicsRange/indexS.
NuStats, 2011. Central Indiana Travel Survey — Final Report. The IndianapolisMetropolitan Planning Organization.
Parks, K., Denholm, P., Markel, T., 2007. Costs and Emissions Associated with Plug-in Hybrid Electric Vehicle Charging in the Xcel Energy Colorado ServiceTerritory. NREL.
Peterson, S.B., Apt, J., Whitacre, J.F., 2010. Lithium-ion battery cell degradationresulting from realistic vehicle and vehicle-to-grid utilization. Journal ofPower Sources 195, 2385–2392.
Sioshansi, R., Fagiani, R., Marano, V., 2010. Cost and emissions impacts of plug-inhybrid vehicles on the ohio power system. Energy Policy 38, 6703–6712.
Smith, R., Shahidinejad, S., Blair, D., Bibeau, E.L., 2011. Characterization of urbancommuter driving profiles to optimize battery size in light-duty plug-inelectric vehicles. Transportation Research Part D: Transport and Environment16, 218–224.
THINKUSA, 2011. Why Think City: Range. From: /http://www.thinkev-usa.com/why-think-city/range/S.
Turrentine, T.S., Kurani, K.S., 2007. Car buyers and fuel economy? Energy Policy 35,1213–1223.
Wang, J., Liu, C., Ton, D., Zhou, Y., Kim, J., Vyas, A., 2011. Impact of plug-in hybridelectric vehicles on power systems with demand response and wind power.Energy Policy 39, 4016–4021.
Wu, D., Aliprantis, D.C., Gkritza, K., 2011. Electric energy and power consumptionby light-duty plug-in electric vehicles. Power systems. IEEE Transactions on26, 738–746.
Zhang, L., Brown, T., Samuelsen, G.S., 2011. Fuel reduction and electricityconsumption impact of different charging scenarios for plug-in hybrid electricvehicles. Journal of Power Sources 196, 6559–6566.