The Effects of Electric Vehicles on Residential Households in the City of Indianapolis

Energy Policy 49 (2012) 442–455

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Energy Policy

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The effects of electric vehicles on residential households in 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, USA b School of Industrial Engineering,Purdue University, 315N. Grant Street, West Lafayette, IN 47907, USA c Indianapolis Power and Light Company, One Monument Circle, Indianapolis, IN 46204, USA d Department of Agricultural Economics, Purdue University, 403W State Street, West Lafayette, IN 47907, USA

HIGHLIGHTS 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. article info abstract

Article history: There is an increasing impetus to transform the U.S transportation sector and transition away from the Received 11 December 2011 uncertainties of oil supply. One of the most viable current solutions is the adoption of electric vehicles Accepted 21 June 2012 (EVs). These vehicles allow for a transportation system that would be flexible in its fuel demands. Available online 2 August 2012 However, utilities may need to address questions such as distribution constraints, electricity tariffs and Keywords: incentives and public charging locations before large scale electric vehicle adoption can be realized. In Electric vehicles this study, the effect of electric vehicles on households in Indianapolis is examined. A four-step traffic Electricity grid flow model is used to characterize the usage characteristics of vehicles in the Indianapolis metropolitan Electricity tariffs 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 households 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 In recent times, there have been tremendous developments in electric vehicle (EV) and plug-in hybrid electric vehicle (PHEV) Energy security is one of the biggest concerns in the world technologies. EVs and PHEVs use electricity stored in the battery as political landscape. Instability in oil producing nations has further the primary fuel for propulsion. The significant difference between fueled the need to be less reliant on foreign sources of energy. The the two technologies is that PHEVs can utilize a secondary fuel source transportation sector, which imports two thirds of its daily for propulsion when the battery is depleted. Current examples of EVs consumption, is one sector that is heavily dependent on foreign include the Nissan Leaf, Think City and the Tesla Roadster while the sources of energy (EIA, 2010). The ability to move even a part of dominant model for PHEVs is the Chevrolet Volt. When compared to the sector from petroleum products to electricity is of great other alternative fuel vehicle technologies, these vehicles have an interest as it mitigates this risk of crude oil dependence. advantage because of the readily available power grid infrastructure. However, this shifting of the energy requirement of the transportation sector to the power grid might increase the strain n Corresponding author. Tel.: þ1 765 494 7901; fax: þ1 765 494 0805. on the grid. Battery charging during peak hours might increase E-mail addresses: [email protected] (S. Huang), the peak load and would require energy from peaking power hsafi[email protected] (H. Safiullah), [email protected] (J. Xiao), plants which is relatively more expensive than energy from non- [email protected] (B.-M. Hodge), [email protected] (R. Hoffman), peaking plants. On the other hand, off-peak charging could [email protected] (J. Soller), [email protected] (D. Jones), [email protected] (D. Dininger), [email protected] (W.E. Tyner), potentially be very beneficial to both the electric utilities and [email protected] (A. Liu), [email protected] (J.F. Pekny). consumers.

There have been several studies conducted about the impact of diverse area so that it is expected that there could be pockets how electric vehicles would affect the electrical grid. Most studies where EVs would have higher penetration and local effects would have focused on PHEVs. However, since both EVs and PHEVs are be significant. Therefore, the significant contribution of this study technologically very similar, the results of these studies can be is the integration of realistic zonal characteristics with a detailed approximated to EVs. One of the more extensive studies has residential model such that local distribution level effects can be established the upper bound of PHEV adoption using existing U.S anticipated. This would allow for utilities to better anticipate EV electricity generation assets (Kintner-Meyer et al., 2007). This effects on local electricity demand. Since studies have also shown study provided a theoretical upper bound based on optimistic that the attractiveness of PHEVs and EVs are hugely dependent on assumptions that allowed for perfect control of vehicle charging the structure of electricity tariffs (Huang et al., 2011; Lidicker to utilize excess supply capacity. In another analysis, Parks et al. et al., 2010); electricity rate tariffs proposed by the local utility, used four idealized charging patterns combined with a unit Indianapolis Power & Light Company, are also examined to commitment model to determine PHEV penetration effects for determine how attractive EVs are to the local populace. An the Colorado service territory (Parks et al., 2007). PHEVs or EVs examination of the undervaluation of gasoline savings by house- could potentially act as agents that help in integrating higher holds is also examined. To give a comprehensive assessment of penetrations of renewable energy (Hodge et al., 2010, 2011b; Indianapolis, proposed charging stations are analyzed with the Kempton and Tomic, 2005). traffic flow results. Some of the more recent studies of PHEVs have included empirical data for driving patterns to better mimic realistic charging demand on the system. Sioshansi et al. used empirical 2. Methodology driving data from the St. Louis metropolitan area in Missouri to model PHEV charging patterns and coupled it with a unit A multi-paradigm modeling approach has been used to exam- commitment model to determine grid level impacts on the Ohio ine the effects of the introduction of EVs on the electricity power system (Sioshansi et al., 2010). In another study, driving demand sector. The multi-paradigm approach enables different cycledataobtainedthroughGPSdataloggerswasusedto sub-systems to be simulated with the most representative mod- determine the optimal battery size needed for light duty PHEVs eling approaches, levels of data, and model granularity that reflect (Smith et al., 2011). Another option that researchers have the subsystems most accurately. This paper considers two sub- turned to is the extensive data that is available through the systems of the electricity system: an electrified personal trans- National Household Travel Survey (NHTS). Zhang et al. con- portation system and the residential electricity demand sector. verteddatafromthe2009NHTSsurveyintoaMatlabmodel A four step transportation model has been adapted for the and used it to analyze different charging scenarios in the South Indianapolis metropolitan area and used to determine the travel Coast Air Basin of California (Zhang et al., 2011). The effects of characteristics of vehicles in the system. The zonal transportation PHEVs on the Illinois power system with both wind power and data is then fed into an agent-based residential demand model demand response penetration has also been examined using that determines the electricity consumption profile of residential this database (Wang et al., 2011). It has also been used to households. The economic costs and benefits of an electric vehicle estimate detailed power consumption information for these are then calculated for the household. The fact that there will be vehicles (Wu et al., 2011). range anxiety problems attached to electric vehicle usage is The main limitation of the approaches listed above is that recognized in this study. As a comprehensive solution to facilitate usage data for EVs is limited to average or aggregated approx- electric vehicle usage, several preselected locations for EV imations. Average usage characteristics can be very useful for charging stations are evaluated for their vehicle flow influence. analysis that deal with macro level effects such as system wide Fig. 1 shows a simplified flowchart of the aggregated system. benefits or costs, but they can be inadequate for determining local effects for planning purposes. Although empirical data collection 2.1. Transportation characteristics would also provide the same level of granularity, the effort to collect new data for all the different zones may prove to be A four-step model has been used to simulate the usage prohibitive. An alternate approach would be to adapt traffic flow characteristics of vehicles in Indianapolis (FHWA, 1977; Hensher models used by local planning agencies to more accurately and Button, 2000). For this model, the area under analysis is predict local flow patterns, and thus provide localized charging divided into zones called Traffic Analysis Zones (TAZs). The TAZs patterns. A general framework has been proposed in a previous are used to group areas with similar attributes (residential, study for the city of Alexandria, Virginia. In that study, realistic commercial, educational and industrial). The size of each TAZ driving and charging patterns were studied to determine the may vary from a single building to several miles in radius. The system wide benefits of PHEV penetration (Hodge et al., 2011a). traffic analysis zones are classified at the discretion of metropolitan Although both EVs and PHEVs have the potential to be adopted planning organizations. In our study, the Indianapolis Metropolitan and would impact the electricity grid, only EVs are considered in Planning Organization (IndyMPO) had split the city of Indianapolis this study since they present a bigger potential strain on the into 2573 zones. The IndyMPO uses surveys to obtain socio- system and analyzing them would provide a base case for all economic data. The socio-economic characteristics of locations in electricity propelled vehicles, PHEVs included. EV adopters could different states are different. Among all, the most critical census face problems of range limitations due to the fixed capacity of on data are automobiles per household, income and employment. board batteries and relatively long recharge times, however, in The steps involved in the modeling are as shown in Fig. 2. The most situations, the problem of range anxiety is more psycholo- transportation system represents the transportation infrastruc- gical than physical (Franke et al., 2011). The provision of public ture and related services. The internal activity system represents charging locations can also significantly reduce the problem of the economic activity, demographic, and land use data defined for range anxiety (Botsford and Szczepanek, 2009). In this study, we the TAZs. As the first step, in trip generation, the census data is examine the effects that the widespread introduction of EVs will used to generate the number of trips that are produced (depar- have on the electricity demand profile and evaluate public ture) and attracted (arrival) by each zone. Next, the trips depart- charging locations. The geographical region chosen for the study ing from a zone is matched with trip arrivals of other zones in the is Indianapolis, Indiana. This region is a sufficiently large and system. Travel impedance ( travel distance) is used in forming 444 S. Huang et al. / Energy Policy 49 (2012) 442–455

Fig. 1. Flowchart of model used in the study.

Fig. 2. Flowchart of transportation model. complete trips. This step is known as trip distribution. Mode moving from the origin to the destination. The OD matrix is used choice is used to classify the complete trips by mode of trans- with the road network information (transportation system) in the portation. Finally, the route choice step is used to model the flow four-step model to calculate the flow on each road/link. The mode of traffic on each transportation infrastructure. There could be choice and route choice steps are not used extensively in the instances where traffic flow violation occurs and the process is current scope of the study since the study looks only at arrival reiterated by using the feedback from traffic flows. The travel and departure patterns of personal vehicles, not traffic flows of impedances may be adjusted to obtain a more adherent solution; vehicles on the system. The transportation planning software but convergence is not guaranteed (FHWA, 1977; Hensher and TransCAD is used in trip distribution and route choice step. As Button, 2000). noted above, the zone level socio-economic data and the road The trips produced and attracted are obtained from the trip network information were obtained from the Indianapolis Metro- distribution step. This information is converted to origin-destina- politan Planning Organization (IndyMPO). The data is for the nine tion (OD) matrices (in transportation analysis software), where counties in Indianapolis: Boone, Hamilton, Hancock, Hendricks, each element of the matrix represents the number of travelers Johnson, Madison, Marion, Morgan and Shelby. S. Huang et al. / Energy Policy 49 (2012) 442–455 445

2.1.1. Trip generation where is, Fij: the friction factor between zones i and j, a, b and g: The trips are classiﬁed into three categories. Home-based work model coefﬁcients; b and g should be negative; a is the scaling

(HBW) trips, home-based other (HBO) trips and non-home based factor, Iij: the impedance factor (travel time) between zones (NHB) trips. HBW trips start from home and end in the work i and j, and e: the base of natual logarithm. place. HBO trips originate at home and are undertaken for I is the impedance matrix. The trip length (in minutes) is used purposes other than work; for example: trips to the shopping as impedance in our study. I is represented as a matrix and each mall from home, trips to the grocery store from home etc. NHB cell Iij represents the time it takes to travel from zone i to zone j are all other trips which do not originate from home; for example: without traffic. The I matrix is obtained by processing the road trips from office to a restaurant, etc. network Geographic Information System (GIS) of Indianapolis. The procedure makes use of certain available information The GIS road network of Indianapolis is as shown in a later section about the zone to estimate the number of trips that would in Fig. 6. The aim is to select an impedance function and its originate or end in the zone. The socio-economic and geographical corresponding parameters such that the gravity model repro- data of each zone contains valuable indicators that could be used duces the trip length distribution of the study area. There are to estimate the trips. For instance, a zone closer to the downtown several ways to arrive at the parameters. We have used para- area, with lot of shopping activities, will make shorter trips than a meters suggested by Travel Estimation Techniques for Urban zone in the outer suburbs. In the study, the IndyMPO carried out Planning (Martin and McGuckin, 1998). The work suggests that the trip generation process (NuStats, 2011). The metropolitan the gamma function be used with the parameters in Table 1. The planning organization employed established guidelines, such as final output of this process is the production–attraction matrix. the Institute of Transportation Engineers’ Trip Generation manual Each element in the matrix represents the number of trips made (Institute of Transportation Engineers, 2008), to generate vehicle from one zone to another. trips. The manual provides standard factors and procedures to generate vehicle trips from socioeconomic data. For example, the number of HBW trips could be generated by multiplying 1.46 by 2.1.3. Mode choice and route choice the total employment in the zone (Martin and McGuckin, 1998). Mode choice accounts for the different means of transportation Socio-economic data of the zone, like population, number of available. A particular mode of transport is chosen based on its households and employment information, are used to estimate relative accessibility and convenience of the mode. Travel time, the trips between zones. Household data gives an indication of travel cost and automobile ownership constitute the convenience the number of people residing in the zone and is used for attribute. And, parking availability and mass transit availability are estimating the home-based trip productions. The employment factored in the accessibility attribute. Usually, the traveler would in the zone relates to the work trip attraction. The other inter- use the public transport only when it is easily accessible from esting data is the retail employment that is used for calculating origin and destination. Otherwise, the traveler would use personal shopping based trips. transportation. According to the national household survey conducted by the U.S. Bureau of Transportation statistics, 87% of daily trips take place in personal vehicles and 91% of people commuting 2.1.2. Trip distribution to work use personal vehicles (Bureau of Transportation Statistics, This step is used to match the trip production and attraction of 2012). Furthermore, the public transit system functions mostly in each zone based on geographical factors to form complete trips. urban Indianapolis with limited or no service to suburban areas. For example, the trips that are produced in a zone in Carmel, Due to these facts, less emphasis is given to mass transit and the Indiana, will be distributed to other zones in Indianapolis down- mode choice step is not used. The analysis is focused solely on town, shopping districts, etc. based on their geographical proxi- personal vehicles and the related trips. mity, and thereby forming complete trips. The process is repeated The final step of the modeling is the route assignment process. for every zone in the system. This step is used to estimate vehicle flows on each of the road The general assumption is that the farther the distance of the segments. In this process, the model initially chooses the shortest destination, the less the trip attraction. The effect of travel time route between two zones. It then iterates based on the congestion also varies depending on the trip type. Travel times have a pattern to achieve equilibrium on the flow. As this study is not pronounced effect on non-home-based trips as it is discouraging concerned with traffic flows through roads and highways, the to travel very long distances for personal chores. On the other route assignment process is not used. hand, travel times have very little effect on work-based trips as the travel destination cannot be substituted. For this study, the ‘‘Gravity Model’’ is used (Martin and McGuckin, 1998), with the 2.1.4. Data feed into electricity demand model trip lengths or travel times between zones represented by ‘‘friction The time of charging and the quantity of charge are the two factors’’. The Gravity Model formulation is expressed as follows: characteristic data that define the EV behavior in the electricity demand model. The production–attraction matrix, from the trip A F K T ¼ P P j ij ij distribution process, is matched with the trip length matrix (I)to ij i n ðA F K Þ j ¼ 1 j ij ij obtain the average trip length and trip length frequency distribution for each zone under consideration. The quantity of charge where, Tij is the number of trips from zone i to zone j, Pi is the required from the grid directly relates to the trip length. There- number of trip productions in zone i, Aj is the number of trip fore, the trip length frequency distribution obtained will be used attractions in zone j, Fij is the friction factor for interchangei, j (based on travel time between i and j), Kij is the optional adjustment factor. Friction factors are used to account for the travel time between Table 1 Parameters used in the gamma function. two zones. The friction factors are different for each of the trip types. For our model, friction factors were developed using a Trip purpose Alpha Beta Gamma gamma function. The gamma functions used to develop these functions used the following equation (LSA, 2011): HBW 28507 0.02 0.123 HBO 139173 1.285 0.094 b p Iijg NHB 219113 1.332 0.01 Fij ¼ Iij e 446 S. Huang et al. / Energy Policy 49 (2012) 442–455 in electricity demand model simulation to model the energy obtained from the 2005 RECS micro data (EIA, 2008). The data was required to charge the battery. sorted first by census region and subsequently for heating degree The ability of EVs to charge is dependent on the locations of days. The data that was most similar to geographical data from the EVs, which are obtained from the hourly vehicle flow for the Indianapolis was then used to create the appropriate appliance zone. For this process, it is essential to convert the production– tables. Power consumption characteristics of individual appli- attraction matrix to origin-destination matrix because the cell- ances were built using similar approaches to the previous study value in the production–attraction matrix is non-directional. The and fitted to historical data. The collection of appliances is given cell values of an origin-destination matrix have directional mean- in Table A1 in the appendix. ing, indicating the number of trips going from an origin to a destination. This is essential to deduce the number of vehicles arriving at home as destination at each hour. The production– 2.2.2. Electric vehicle attraction matrix is converted to origin-destination matrix in the As the authors mentioned above, details on the exact formula- transportation analysis software using the hourly production/ tion for EV agents have been published in previous works, the attraction probabilities shown in Table A2. The hourly origin- interested reader is directed to the mentioned references for more destination matrix is thus used as an input to the electricity detail. The general modeling framework is described in Hodge demand model to characterize the time of charging. et al. (2011c), while the detailed EV agents can be found in Hodge et al. (2011a). EVs require electrical energy from the power grid to charge the on-board batteries. The charging scheme for these 2.2. Residential household model vehicles can be classified into different levels, namely level 1, level 2 and level 3. Table 2 describes each of the charging types The residential model used in this study is in essence a multi- (Morrow et al., 2008). Level 1 and 2 chargers are expected to be paradigm model, a discrete event household appliance model the most abundant chargers installed by consumers due to their coupled with an agent based EV model. The detailed framework lower costs, practicality of providing service, and use of standar- for the household appliance model and the agent based model can dized equipment. Level 2 chargers are the preferred and recom- be found in previous studies (Hodge et al., 2011a, 2011b., 2011c; mended scheme for EVs due to their large battery capacities. Huang et al., 2011) and is not presented in detail in this publication. A general flowchart for the agent based EV model is Table 2 presented in Fig. 3 below. Characteristics of various charging configurations.

2.2.1. Baseline appliances Charging Voltage/current requirement level The basic framework of the household model has been discussed in a previous publication (Huang et al., 2011). The most Level 1 120 V/16 A significant difference is the treatment of temperature sensitive Level 2 208–240 V/12 A to 80 A appliances with respect to temperature. This will be discussed in Level 3 No specific limits; very high voltages (300–600 V DC), very high currents (or 480 V AC) a separate section below. Appliance saturation rate figures were

Fig. 3. Flowchart for EV agent framework. S. Huang et al. / Energy Policy 49 (2012) 442–455 447

A 24 kW h EV battery would take approximately 8 h to fully Table 3 charge on a level 2 charger. In this study, a level 2 charging station Summary of IPL electricity rates. is assumed to be installed for both residential consumers and at Residential rate (RS) public charging stations. A 3.3 kW output is assumed for the Customer charge charger (Morrow et al., 2008). 0–325 kW h/month $6.70 per month Vehicle usage characteristics are obtained from the vehicle 4325 kW h/month $11.00 per month flow model. Important information generated from TransCad Energy charge 0–500 kW h $0.067 per kW h include, the diurnal distribution of generated trips from different 4500 kW h $0.044 per kW h zones, distribution of trip lengths by trip purpose and frequency With electric heating and/or water heating of trips by trip purpose. The trips are categorized into three types 41000 kW h $0.0318 per kW h as discussed in a previous section. Each HBW trip has a corre- EV TOU rate (EVX) sponding return trip attached, while the duration of HBO and NHB Summer (June–September) trips are determined from the 2009 NHTS survey (FHWA, 2009). Non holiday weekdays A typical usage pattern for the electric vehicle can be described Off-peak (midnight–10am, 10pm–midnight) $0.02331 per kW h Mid–peak (10am–2pm, 7pm–10pm) $0.05507 per kW h as follows. The EV makes a decision at every time step in the Peak (2pm–7pm) $0.12150 per kW h model. If the EV is not in use, there is a probability of the EV Weekends and holidays (independence day and labor day) making a trip. The probability distributions of the trips are Off-peak (midnight–10am, 10pm–midnight) $0.02331 per kW h determined through the steps described in the previous sections. Mid-peak (10am–10pm) $0.05507 per kW h If a trip is scheduled, a corresponding distance and duration for Non-summer All days the trip is also determined probabilistically. At its destination, the Off-peak (midnight–8am, 8pm–midnight) $0.02764 per kW h EV then makes a decision on whether public charging is needed. Peak (8am–8pm) $0.06910 per kW h If the vehicle is not able to complete the round trip with the Public charging rate (EVP) energy left in the battery or if the battery level drops below 20%, Usage charge $2.50 per use the EV plugs into a public charging location and charges. At the For more details, please refer to the tariffs filing available on the IPL website end of the trip duration, the EV either returns home, to its starting (www.IPLPower.com). For billing purposes, the RS and EVX rates above will be location or proceeds on another scheduled trip. added to IPL’s current rider adjustments (fuel cost adjustment, environmental As the goal of the study is to determine the effects of EVs on adjustment, and demand side management adjustment). different households in Indianapolis, no penetration levels for vehicles are assumed, instead EV costs are calculated for a typical household residing in the zone in question using characteristics tier rate, this provides a positive incentive for the adoption of EVs associated with each zone. (Huang et al., 2011). IPL also has a residential Time Of Use (TOU) electricity tariff for EVs which is separately metered under rate EVX. It is designed to encourage consumers to charge during off-peak 2.2.3. Temperature effects periods. IPL also provides public charging locations for EVs under One of the most significant factors that determine how much rate EVP. A charge of $2.50 per charging session is imposed for usage electricity a household uses is the weather, best exemplified by at these locations. A summary of the rates can be found in Table 3. the outside temperature. System operators in most of the United States manage their grid operations around peak events that 2.4. EV charging behavior occur during summer, when high temperatures drive up airconditioning loads and system resources are the most strained It is assumed that EVs would need to be charged daily in order (CAISO, 2011). In this study, household appliances such as to remain viable transportation options. In this study, two char- refrigerators, water heaters, air-conditioning units and space ging behaviors for EVs are looked at. The first pattern assumes heaters are assumed to be sensitive to weather effects. Tempera- that EV owners are not sensitive to electricity prices at home. ture factors affecting usage probabilities and power consumption However, consumers recognize the higher cost of public charging, were derived from previous statistical studies done by Hart and and as such, public charging would be used primarily to eliminate de Dear (2004). Their study focused on various household range anxiety and extend EV functionality. In this scenario, the EV appliances in the Sydney metropolitan area. These temperature is plugged in for charging whenever the vehicle is at home and at factors have been adapted for Indianapolis weather patterns. public charging locations when the battery charge level is below a Weather also plays a significant factor when determining the all- predetermined minimum. This type of charging pattern is defined electric range for EVs. Temperature is especially critical as energy as an unresponsive charging pattern. from the battery would be diverted to climate control to keep the For the other charging behavior, the EV owner is assumed to interior of the vehicle comfortable for passengers. The end result is be price sensitive and would seek to minimize expenses on that the driving range for EVs can vary greatly under different electricity charging. The EV would only be charged if the vehicle conditions (Christensen et al., 2011; THINKUSA, 2011). For example, is at home and the price of electricity is at its lowest; here defined the range of the Nissan Leaf can go from 138 miles to 62 miles as a TOU charging profile. However, it is assumed that a minimum depending on both temperature and traffic conditions (NissanUSA, level of charge is required in the vehicle at all times and the 2011). In this study, the EV is expected to travel in city traffic with vehicle would either charge at home or at public charging range dependent on temperature based on figures released by Nissan. locations when the battery level falls below this minimum. From these two behaviors, it is possible to estimate the potential 2.3. Electricity cost savings of a behavioral change with regards to vehicle charging.

The Indianapolis metropolitan area is serviced by Indianapolis 2.5. Economic analysis Power & Light Company (IPL). Residential households are served under its residential rate RS, which has a decreasing tier structure. The economic costs of owning an EV were calculated and a This essentially translates into cheaper electricity rates as the house- beneﬁts-cost analysis performed with comparisons to similarly hold consumes more energy (IPL, 2011). Compared to an increasing sized vehicles. The EV was modeled after the Nissan Leaf, the ﬁrst 448 S. Huang et al. / Energy Policy 49 (2012) 442–455

Table 4 TransCAD and the corresponding zones identified. Public charging Parameters for vehicles. stations will attract not only the visitors to a particular zone but also the visitors to the neighboring zones. Based on this idea, the Nissan leaf Toyota corolla Toyota prius analysis was performed for all the zones covered by a 0.25 mile, MSRP $32,780.00 $17,300.00 $23,050.00 as well as a 0.5 mile, radius. Fig. 5 shows an example of zones Miles per unit fuel 4.17 mile/kW h 30 mpg 50 mpg being covered by a charging station at Denison Merchants garage, Tax credit $7500 – – where public chargers have been installed. The inner green circle Loan interest rate 6% represents the 0.25 mile radius area of influence and the red circle Loan tenure 5 Years Salvage value 15% represents the 0.5 mile radius area of influence. All the zones, covered by the area of influence for each location, 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 well as the total number of vehicles influenced are then computed.

3. Results and discussion

This study focuses on the impact of EVs on specific zones in Indianapolis. From an earlier study, it is expected that the early adopters of EVs have an average household income of above $114,000 per annum and reside in an urban or suburban neighborhood (Giffi et al., 2010). With this consideration in mind, zones that have average incomes of above $100,000 were shortlisted and of these zones, the following 5 zones (Fig. 6), labeled A to E, were picked to be studied in greater detail. Care was taken in this selection to try to Fig. 4. Regression analysis between U.S composite refiner acquisition cost and obtain a diverse group of zones, given the constraints. Midwestern retail gasoline price.

3.1. Traffic flows total battery Electric Vehicle produced by a major car manufac- turer. The vehicles selected for comparison were a Toyota Corolla, The flow data from the Indianapolis traffic simulation was the best-selling compact in the United States for 2010 and the compiled into the respective matrices and from those flows Toyota Prius, the best-selling conventional hybrid. The economic relevant tables were produced and fed into the residential demand parameters used for the three vehicles are included in Table 4. model. They are available for all the different TAZs analyzed for For all three vehicles, a nominal loan interest rate of 6% over Indianapolis. The data for a single zone is given in the Appendix for 5 years was assumed. The vehicle life was assumed to be 10 years, illustration purposes. Table A2 shows trip generation probabilities with a 15% resale value at the end of 10 years (in real terms). over the course of a day. The trips are divided into the three Maintenance and insurance were assumed to be the same for all distinct categories, HBW, HBO and NHB. Both production and three vehicles and battery lifespan is assumed to be 100,000 miles. attraction tables are available for work trips, while only the Since the average miles driven per day in a city is expected to be production tables are utilized in non-work trips. The duration of less than 100,000 miles over ten years, it was assumed that the these trips are determined from another time distribution obtained battery would not be replaced for this study (Peterson et al., 2010). through the NHTS and shown here in Table A3.Afteratripis Two petroleum price scenarios were considered: a base case determined, the distance travelled by the vehicle is determined and a high oil price scenario. These two scenarios follow the from another set of distributions. Each trip type has its unique assumptions given in the latest Annual Energy Outlook (AEO) that distribution and these distances are given in Table A4. was released in April (EIA, 2011). In the reference case, real oil prices increase 2.75% per year, and 7.6% per year in the high price 3.2. Residential electricity demand case, between 2010 and 2020. In terms of crude oil, the prices range from $75 to $99 in the reference case and $75 to $160 in the In order to obtain credible results from the simulation model, high oil projection. The Midwestern retail gasoline price was used it must first be validated against historical data. Historical load to approximate Indiana gasoline retail price. To convert the AEO data for a representative sample of residential customers in the crude oil price projection to Midwestern retail gasoline price, we city of Indianapolis was obtained from Indianapolis Power & Light used the Department of Energy (DOE) historic monthly data on Company (IPL). Summer season for Indianapolis is considered as the Midwestern gasoline price and U.S. composite refiner acquisi- the months from June to September. In Fig. 7, the average tion cost of crude oil (Fig. 4). The R2 for this regression was 0.944. household summer load profile is given for both the historical The starting gasoline price for both the reference case and high load for 2009 and the simulated model. The error bars given are oil price case was $2.78/gal. The price of electricity to consumers for one standard deviation for hourly electricity demand. The in real terms was assumed to remain constant based on tariffs. historical average for a residential household in Indianapolis is The assumed general inflation rate is 3%. A real discount rate of 6% about 34.5 kW h per day while the model over predicts the load was used for baseline net present value calculations. slightly higher at 35 kW h per day.

2.6. Evaluation of commercial charging locations 3.3. Peak electricity demand

It is proposed to evaluate the potential usage levels of the The effect of EVs on peak daily demand is of great interest to charging stations based on the number of vehicles that could be utilities. Since the effective range of EVs is signiﬁcantly affected inﬂuenced. Proposed charging station locations were mapped in by weather, the combined electricity demand could present a S. Huang et al. / Energy Policy 49 (2012) 442–455 449

Fig. 5. Possible area of inﬂuence of apublic charging location on EVs.

Fig. 6. GIS road network of Indianapolis, in particular Marion County. Five zones labeled A to E have been selected for analysis. 450 S. Huang et al. / Energy Policy 49 (2012) 442–455

Fig. 7. The figure on the left represents an average historical household profile for Indianapolis while the figure on the right represents simulation results.

Table 5 Representative monthly bills for households in Indianapolis.

Home use Incremental EVX costs by zone base case ABCDE

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

split into two components, the household electricity cost and the EV charging cost. Household electricity consumption is calculated based on the standard household electricity rate, which is a decreasing tier schedule and the EV electricity cost is calculated on the EVX TOU electricity schedule available to EV owners. The costs given in Table 5 represent an estimated monthly cost for consumers in the IPL service territory. The base case represents a household bill with no EV, while the values for the Fig. 8. Charging profile of a household during a peak period. different zones represent the incremental monthly electricity cost for an EV. Two electricity costs are calculated, one assuming that potential strain on local distribution equipment, especially in households do not adjust charging habits based on electricity regions where spare capacity is at a premium. Summer peaks price, in other words, unresponsive to price signals; and the other typically occur when temperatures are the highest, due to where residents only charge their EVs when the electricity price is increased air conditioning usage. During peak periods, electricity at its lowest (EVX). In both situations, households have access to peak demand increases significantly, with a household hourly the same set of electricity tariffs. It can be seen that even without peak potentially exceeding 5 kW. Fig. 8 shows a household during adjusting consumption for electricity price (unresponsive), the a peak summer day with two possible EV charging schedules. It costs of owning an EV in Indianapolis are very low, ranging from can be seen that with an added EV charging at peak times, the $11.00 to slightly over $17.00 per month. This represents costs household peak could potentially get pushed to 9 kW. This is four that are significantly less than a full tank of petrol for conven- times the average system hourly peak for Indianapolis per tional vehicles, especially in today’s climate. When consumers household. consciously adjust EV charging patterns with respect to electricity At the local distribution level, this could present a potentially price (EVX), the benefits obtained averaged $5.40 per month, or significant capacity problem for regions where EVs could have a about an average of 38% in savings from an unresponsive charging high penetration. A few of the ways that utilities may wish to scenario. The electricity costs obtained from this simulation address these potential challenges could include identifying exercise represent a realistic upper and lower bound of average potential problem locations, planning for distribution capacity electric vehicle cost for the city of Indianapolis. upgrades, providing incentives to consumers to shift electricity The daily electricity demand profiles of an EV are given for the consumption off-peak, and understanding where customers who different zones in Fig. 9 below. It can be seen that the zones vary are interested in EVs are in the service territory. slightly with respect to charging patterns. The bulk of vehicle charging still occurs early in the evening, leaving a significant 3.4. Electricity cost portion of the off-peak period untouched. This clearly indicates a huge potential for load shifting of EV charging or appliance The costs of EV ownership are calculated based on simulation electricity demand. results. Electricity costs are calculated based on electricity tariffs When compared to the electricity demand profiles for an provided by IPL including an electricity tariff rate for EV owners. unresponsive charging scenario, we can see that the EVX charging Consumers using this tariff have a separate meter installed for the peak gets shifted later in the day (Fig. 10). However, as before, electric vehicle. The electricity costs to the consumer are then there remains significant leeway for charging to be shifted further S. Huang et al. / Energy Policy 49 (2012) 442–455 451

Table 6 Incremental net present values of Nissan Leaf when compared to similar alternatives under different oil price scenarios.

Zone Oil price TOU Unresponsive

Prius Corolla Prius Corolla

A Base: 2.75% increase in $1917 $735 $1451 $269 Boil price per annum $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 $3629 $3589 $3164 $3123 Boil price per annum $3248 $2858 $2809 $2419 C $2644 $1760 $2191 $1306 D $1776 $214 $1411 $150 E $2757 $1983 $2246 $1472

Fig. 9. Daily electricity proﬁles for EVs under unresponsive charging. Table 7 Tax 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

nears the range limit of the EV. The presence of public charging stations would help reduce the concern over range anxiety (Franke et al., 2011), and since the average miles travelled in Indianapolis is around 30 miles per day, there remains a reason- Fig. 10. Daily electricity profiles for EVs under EVX charging. able buffer to the range limit of the EV. It can be observed that NPV analysis indicates that EVs are later into the night. Strategies to implement this will be discussed more attractive than conventional hybrids in Indianapolis under in a future study. base case assumptions. At the higher end of the spectrum, a vehicle travels only about 33 miles a day, significantly less than 3.5. Economic competitiveness of EVs in Indianapolis the available range of an EV. This observation, coupled with the availability of charging at public locations, enable EVs to be a very In this study, the EV was modeled after the Nissan Leaf and viable alternative to consumers looking for options other than was compared with alternative vehicles that are available to conventional hybrids. Even when compared to conventional consumers. Two general scenarios are considered for this study, vehicles, EVs are competitive. For vehicle owners staying in zone a base case where the price of oil would follow historical A and B, EVs are even economically more attractive than a trajectories and another where price of oil increases at a much conventional internal combustion vehicle. Even for zones C and faster rate. The economic parameters have been discussed in E, an EV charging following a TOU profile is just $500 less Section 3.5 and would not be presented here again. The values in attractive than a Toyota Corolla. If non-tangible factors such as Table 6 represent the incremental net present value (NPV) environmental issues, reduction of carbon footprint, and sustain- between the Nissan Leaf and the other alternatives. A positive ability are considered the EV becomes very attractive. value means that the Leaf is more attractive than the option Under high oil price assumptions, following EIA scenarios, considered and a negative value means that it is less attractive. where oil reaches almost $6.00 a gallon by 2020, EVs are very It can be seen that the differences in NPVs between an attractive when compared to their alternatives. Even in Zone D unresponsive charging profile and an EVX profile are generally where the Nissan Leaf was over $1700 less attractive than its fairly significant. Once again this represents a realistic lower and nearest competitor in the base case, the Nissan Leaf becomes upper bound on the benefits of EVs in Indianapolis, and the economically competitive under these assumptions. At the higher amount of savings that a consumer can achieve depends greatly end of the spectrum, an EV vehicle owner in zone A can expect on the consumer’s behavior. Another observation is that the over $3000 in savings over a conventional hybrid. attractiveness of EVs seems to be correlated to the average It must be noted that EVs enjoy a significant tax rebate distance travelled by consumers; the higher the average miles provided by the federal government of $7500. This is an impor- travelled in a day, the more attractive the EV is to consumers. tant factor in giving the Leaf an advantage over its alternatives. An In other words, the more a consumer substitutes gasoline miles interesting piece of further analysis is the sensitivity of the NPVs with electric miles, the higher the potential savings. However, to the value of this tax rebate. Table 7 shows the NPV compar- since EVs do have a limited range, this condition needs to be isons for Zones A and D. These zones represent the upper and considered together with increasing range anxiety with more lower bound for NPVs in this analysis respectively. Since the Leaf electric miles consumed, especially when the distance travelled starts off as more competitive against conventional hybrids, the 452 S. Huang et al. / Energy Policy 49 (2012) 442–455 value of the tax rebate can actually decrease for the NPV of a Leaf Table 9 to achieve parity with the Prius. However, the tax rebates need to Incremental net present values of Nissan Leaf when compared to similar alter- be increased to achieve breakeven when compared with the natives under different oil price scenarios with a discount rate of 16%.

Corolla. In Zone D, an increase of over $2000 in tax rebates makes Zone Oil price TOU Unresponsive the Leaf comparable to the Corolla. Under assumptions of a high oil price scenario, the tax rebate can be reduced by almost $5000 Prius Corolla Prius Corolla from $7500 for the most competitive zones in Indianapolis. While A Base: 2.75% increase in $300 $1467 $6 $1773 the value of tax rebate needed for NPV to break even will vary Boil price per annum $135 $1805 $154 $2094 from one person to another, Table 6 shows that $7500 is a C $142 $2326 $439 $2624 reasonable value. D $547 $3066 $786 $3306 Battery costs constitute a major component of the retail price E $94 $2224 $430 $2560 of the EV. It is interesting to determine the sensitivity of the NPV A High: 7.6% increase in $1237 $95 $931 $211 of the Leaf with respect to its battery cost. Some estimates have Boil price per annum $1005 $354 $717 $643 pegged the value of the Leaf battery at $750/kW h (Hidrue et al., C $635 $1032 $337 $1330 D $100 $1988 $139 $2227 2011), through the incremental cost of the Leaf over a comparable E $703 $895 $367 $1231 vehicle. In this study, we take a slightly different approach and estimate the cost of the battery to be half the retail cost of the car prior to the tax rebate, estimating the current price of the battery at about $675/kW h. It is assumed that the oil price follows the waste of resources. It also indicates opportunities to create appro- base case. We also examined the cost at which EVs would be able priate corrective policies that can reduce this gap in perceived and to compete with its competitors without any need for subsidies. actual value. Another explanation of the apparent lack of considera- Table 8 shows the battery cost in terms of dollars per kW h that tion of future costs is capital constraints. Some consumers, especially the battery would have to achieve in order for the Leaf to achieve lower income consumers, might prefer a vehicle with lower future cost parity to similarly sized vehicles. At current rebate levels, the costs, but they may not be able to afford or obtain credit for the cost of the battery would have to drop only slightly for the EV to higher capital costs of these vehicles. This explanation results in the be cost competitive in Zone D. Without the tax rebates, the costs same appearance of high discount rate but has a different origin. of the batteries would have to drop by more than $200/kW h for However, the comparable attractiveness of EVs to conventional competitiveness. hybridsdoessuggestthatEVscancertainlyachieveacertain proportion of the ‘‘green car’’ market enjoyed by hybrids presently. Similarly, EVs are further differentiated from conventional 3.5.1. Economic competitiveness of EVs under fuel price myopia vehicles in other non-economic factors such as drive quality and Some studies have shown that consumers generally undervalue noise level. Although these factors could factor in consumer costs that accrue in the future, resulting in consumers under- considerations of vehicles, they are not considered in this analysis estimating the benefits of better fuel economies and efficiencies. as they are not easily quantifiable. Turrentine and Kurani (2007) showed that consumers generally do not think effectively about future gasoline consumption. Allcott and Wozny (2011) concluded an implied discount rate of 16% that 3.6. Evaluation of public charging locations rationalizes the discrepancy between future gas savings and net present value of vehicles. Gallagher and Muehlegger (2011) con- Due to the existence of range anxiety, commercial charging cluded that the discount rate was about 14.6%. stations are required to support the large-scale adoption of An electric vehicle could be categorized as a high-MPG vehicle electric vehicles. Utility companies would then need to evaluate and would suffer from this undervaluation of gasoline consump- potential locations for charging stations. Fig. 11 shows potential tion savings. If households are assumed to have a high discount charging station locations that have been put forth by IPL and rate when considering a high cost EV, the NPVs for EVs in their corresponding vehicle influence figures. Locations such as Indianapolis could be similar to those in Table 9. An implicit downtown parking garages show particularly good vehicle influ- discount rate of 16% was assumed for each household. ence figures and could potentially be excellent commercial char- It can be seen that a household would still determine that an ging sites. It must be noted however, since EV charging requires a EV would be comparable to a conventional hybrid vehicle in significant time investment, vehicle flow figures should not be the Indianapolis. However, under base case assumptions, EVs do not only criterion that is used to evaluate public charging locations. compare favorably with conventional internal combustion Other factors such as proximity of public attractions, work engines. Even under high oil price situations, conventional inter- locations, activity durations would need to be examined too. nal combustion engines are generally more attractive. A good location may be one with a high vehicle flow influence The discrepancy in NPVs highlights the importance of informing that is close to a popular recreation center. A combination of all consumers about the potential fuel savings of EVs. This misallocation these evaluation matrices should be used to determine attractive of resources reduces the overall welfare of society and represents a and effective locations.

Table 8 Per kW h battery costs needed for breakeven of NPV of Leaf when compared to 4. Conclusion similar alternatives. A trafﬁc ﬂow model was combined with a residential electri- Zone Current federal tax rebate value TOU Unresponsive city demand model to determine the effect of EVs in a city

Prius Corolla Prius Corolla environment. The region analyzed is the city of Indianapolis. This combined model allows for detailed analysis of speciﬁc zones A $7500 $768 $711 $745 $688 within the city that would not have been possible with aggre- D $704 $590 $686 $572 gated or averaged vehicle usage patterns. It was found that the A – $493 $436 $471 $414 usage of vehicles varied greatly from zone to zone, which would D $429 $316 $412 $298 create different charging proﬁles unique to each of the zones. S. Huang et al. / Energy Policy 49 (2012) 442–455 453

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 A1 Table A2 Saturation data for appliances in Indianapolis. Normalized hourly production and attraction probabilities for Zone E.

Appliance name Saturation Hour HBW production HBW attraction HBO production NHB production

Stove and oven 0.63 1 0.72 0.08 0.25 0.60 Microwave oven 0.92 2 0.36 0.04 0.10 0.20 Coffee maker 0.65 3 0.36 0.00 0.00 0.00 Refrigerator 1.00 4 0.50 0.04 0.03 0.00 2nd refrigerator 0.28 5 0.72 0.08 0.00 0.10 Freezer 0.39 6 4.86 0.51 0.15 0.40 Dishwasher 0.51 7 14.22 1.51 0.66 1.50 Clothes washer 0.85 8 34.57 3.65 2.24 6.60 Electric dryer 0.70 9 16.57 1.75 1.56 4.00 Television 1.00 10 5.40 0.57 1.60 3.60 2nd television 0.78 11 1.26 0.13 2.69 5.60 3rd television 0.45 12 1.08 0.12 3.09 6.30 Set top box 0.78 13 1.53 2.67 3.04 10.20 Video recorder 0.80 14 1.35 2.64 4.37 7.20 DVD 0.81 15 1.68 5.90 4.58 6.90 Radio/player 0.73 16 2.16 10.43 7.83 8.00 Personal computer 0.68 17 3.56 23.81 12.50 8.00 Printer 0.61 18 3.32 21.47 14.34 6.20 Lighting 1.00 19 1.67 5.73 14.31 4.70 Other occasional Loads 1.00 20 1.40 3.19 12.44 6.30 Central air conditioning 0.65 21 1.27 1.92 6.86 5.80 Room airconditioning 0.31 22 0.55 5.25 4.02 3.90 Water heater 0.32 23 0.53 5.07 2.15 2.40 Telephone 0.77 24 0.36 3.44 1.20 1.50 Answering machine 0.62 Electric space heating 0.33 Pool pump 0.07 significantly or eliminated, and the EV would still be attractive. Other equipment 1.00 The availability of public charging locations in Indianapolis will help alleviate range anxiety. However the relative higher usage The impact on local distribution capacity for zones that have high charge of the public charging locations encourages charging at penetration of EVs could be significant, potentially increasing the home at off peak times. With these system considerations the EV summer peak electricity demand in those zones. is becoming an economically attractive vehicle in Indianapolis. On the other hand, it appears that the decreasing tier price Indeed the analysis in this paper shows that EVs are at the cusp of structure coupled with the availability of a TOU price schedule becoming competitive with mature internal combustion engine makes the EV a viable economic mode of transportation in vehicles. Further advances in EV technology and cost reductions Indianapolis. In the base case scenarios the EV is economically will increase the number of people for which EVs will provide an more attractive than conventional hybrids, while only slightly less economic advantage. EV specific considerations, such as locations competitive than conventional IC vehicles. However, this attrac- for public chargers are an important consideration. Using results tiveness greatly depends on the rebates available to EVs at the from the transportation flow simulation, it is possible to provide a moment, and on consumers internalizing future operating cost benchmark against which proposed charging locations can be savings in the purchase decision. If oil prices trend high or EV evaluated. The electric vehicle represents a viable and attractive battery costs decrease, the rebates could potentially be reduced option to address problems such as energy independence, 454 S. Huang et al. / Energy Policy 49 (2012) 442–455 increasing petroleum cost, and environmental impacts. This study been examined and could be examined in finer detail. EVs are has shown that the adoption of EVs in Indianapolis is already assumed to be a substitution for conventional vehicles in this attractive to some consumers and the conditions to be attractive study, however, EVs may be utilized in behavioral patterns that to most or all consumers could conceivably evolve over the next are different from conventional vehicles and would warrant an in few years under free market conditions. The current conditions depth study. analyzed in this paper show a great potential for Indianapolis to be a model city for demonstrating and developing EVs as a practical and effective transportation solution. Appendix However, there still exist certain limitations to the current study which would provide interesting research studies in the The following tables give parameters of the model employed in near future. As mentioned above, the evaluation of charging the study. Data related to appliances have been obtained from the station locations in this study could serve as a useful benchmark 2005 Residential Energy Consumption Survey. Trip data has been for optimal charging locations, but a more comprehensive study generated from TransCAD through parameters obtained from the could be conducted where optimality of locations could be more Indianapolis Metropolitan Planning Organization. (Table A1) substantially examined. Complementary policies such as vehicle to grid technologies which would benefit EV adoption have not References

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