Conference Paper

NREL/CP-540-46224

August 2009

Communication and Control of 

Electric Vehicles Supporting 

Renewables 

Preprint 

T. Markel, M. Kuss, and P. Denholm 

To be presented at the 2009 IEEE Vehicle Power and Propulsion 

Systems Conference

Dearborn, Michigan

September 7-10, 2009
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Communication and Control of 

Electric Drive Vehicles Supporting Renewables 

Tony Markel, Michael Kuss, and Paul Denholm

Center for Transportation Technologies and Systems

National Renewable Energy Laboratory

Golden, CO 80401

Tony.markel@nrel.gov, Michael.kuss@nrel.gov, Paul.denholm@nrel.gov

Abstract— The coming intersection between a growing electrified 

vehicle fleet and desired growth in renewable electricity 

generation presents an opportunity for synergistic value. Some 

types of renewable electricity generation are variable and 

intermittent and are rarely coincident with utility load patterns. 

Vehicles are typically parked 90% of the time, and the batteries 

are a significant capital investment. In this paper we discuss the 

intersection of these two growth areas, the technology needed for 

integration, and several potential scenarios highlighting the 

limitations and opportunities for renewable energy resources to 

fuel electrified vehicles of the future. 

Keywords- Electric vehicle, EV, PEV, PHEV, plug-in hybrid, 

PHEV; vehicle to grid, V2G; renewable energy integration; 

renewable energy variability.

I. INTRODUCTION

Renewable energy (RE) generation capacity is growing 

rapidly – the United States increased wind power generating 

capacity by 5.24 GW in 2007 alone, a 45% increase over wind 

power generating capacity in 2006 [1]. Currently, 24 states 

have renewable portfolio standards (RPS), which are legally 

binding RE generation targets that utilities must achieve by a 

specific date [2]. While these renewable energy targets are an 

important step in reducing fossil fuel consumption and 

greenhouse gas emissions, some renewable resources, such as 

wind and solar, have an integration challenge: they are nondispatchable, unlike hydro or concentrating solar power. This 

means that wind and solar units cannot be commanded to 

increase or decrease output power, despite constantly changing 

consumer demand. This nondispatchable subset of RE 

generation is the focus of this paper.

Several solutions to RE dispatchability challenges are 

currently being employed, including increasing operational

reserves (usually fossil fuel units), storing renewable energy 

using a variety of emerging methods, and demand-side 

management programs [3, 4]. 

Demand-side management programs include energy 

efficiency and conservation, peak-shaving by shifting bulk 

loads, and demand response [5]. Shifting bulk loads and 

demand response programs require communication between 

consumers and producers of energy and will be more widely 

enabled with the expansion of grid communication networks

across the country [6]. Plug-in electric vehicles (PEVs) could 

support RE expansions through any of the three options listed: 

reserves, storage, and demand-side management. 

Charging schemes based on grid communication of realtime load, price, and RE generation are developed and 

discussed. The first half of the analysis describes the modeling 

tool that was developed in MATLABTM. The second half of 

the analysis describes physical-layer grid-networking 

technologies and evaluates each technology based on its 

suitability for grid communication purposes. Finally, 

simulation results are presented for several cases in the Los 

Angeles area.

 

II. VEHICLE FLEET-GRID INTERACTION MODEL

The interaction between plug-in vehicles and the power 

grid is modeled using several real-world data sets to simulate 

driver behavior, vehicle systems, and the power grid. The 

simulator takes these inputs and models the data and power 

transactions that occur between the vehicles and the power 

utility, calculating the fuel consumed, battery wear, data 

throughput, load and ramping characteristics of the PEV fleet 

and renewable generation, and overall operating cost for the 

various system architectures. Each subsystem included in the 

model is described in detail below.

A. GPS Travel Survey Data and Vehicle Model

GPS travel survey data were collected by the Southern 

California Association of Governments as part of its

transportation planning process. A total of 621 unique 

vehicles were instrumented with GPS data loggers between 

October 2001 and February 2002, and they recorded 1144 24-

hour driving profiles. Each data file includes a 1 Hz record of 

time, speed, and location. Plug-in hybrid electric vehicle 

(PHEV) operation was simulated over each driving profile.

The vehicle model is a parallel hybrid that operates in a 

charge-depleting (CD) mode, then reverts to a chargesustaining (CS) mode when the battery state-of-charge (SOC) 

falls below 35%. In CD mode, the model assumes a battery 

power consumption rate of 0.18 kWh/km (0.29 kWh/mi) and a 

fuel consumption rate of 1.5 L/100 km (157 mpg). In CS 

1
mode, the model assumes a fuel consumption rate of 5.2 L/100 

km (45 mpg). The battery pack holds 9 kWh of energy, of 

which 5.85 kWh are usable, giving the vehicle an electric 

range of 32 kilometers (20 miles). The battery wear model is 

based on the real-time (RT) lithium-ion battery modeled in [7], 

and assumes a battery cost of $900, $600, and $300/kWh for 

the three scenarios, as shown in Table 1. The $300/kWh 

scenario is below the current market price for lithium ion 

batteries and was chosen to represent the scenario in which the 

U.S. Department of Energy (DOE) battery cost target is met. 

The three scenarios represent near-, mid-, and long-term RE 

and PHEV adoption goals. The vehicle’s battery charger and 

power electronics operate at 90% efficiency. Power and fuel 

consumption are calculated by integrating the second-bysecond distance traveled. The battery wear is based on the 

daily maximum discharge, and calculated according to the 

corresponding lifetime cycles using the battery life model. 

The instantaneous vehicle-to-grid (V2G) offering price 

calculation is described in (1):

where is the real-time offering price for the energy in 

the vehicle’s battery pack, is the cost of the battery pack, 

is the number of lifetime discharge cycles at the current 

state of charge, is the RT energy available in the battery 

pack, and is the base price of electricity, representing 

the cost originally paid to get the energy into the battery.

All vehicles plug into a 240 V/30 A outlet, operating at a 

maximum of 20 A, for a maximum charging rate of 4.8 kW. 

At this rate, vehicles completely charge within two hours.

B. Utility Load & Renewable Energy Generation Data

The utility load and price data used for this analysis come

from the 2005 FERC filings for Los Angeles Department of 

Water and Power [8]. The hourly data are interpolated to fit 

the 10-second time step of the simulation. In the V2G-enabled 

scenarios, vehicles are paid for spinning reserve services based 

on the available kilowatt-hours in the battery pack. The wind 

energy data (Fig. 1) are based on NREL’s Western Wind 

Resources Dataset [9]. The wind turbines surrounding 

the Los Angeles area were chosen to simulate the wind power

tied to the local grid; 10-minute wind data were aggregated 

from the wind farms located between 33.67oN and 36.06oN, 

and 116.16oW and 119.17oW, as shown in Fig. 1. To simulate 

a range of wind energy penetration levels, the data were

integrated over the full-year data set to match the full-year load 

data, then scaled to simulate the desired penetration 

percentage. For the solar power profile, data from [10] were

used and averaged over 10-minute intervals to represent the 

power output shape of an agglomeration of PV plants. Load 

and ramp rates were calculated to evaluate the impacts of the 

plug-in fleet and RE generation.

III. CONTROL ALGORITHMS

A variety of fleet control methods have been suggested in 

the literature [11]. Of the proposed methods, three promising 

fleet charge-control methods are demonstrated here: pricesignal-based charging, load-signal-based charging, and REbased charging. The three scenarios will be compared with a 

baseline case of 4.8 kW opportunity charging, meaning that the 

vehicles are assumed to be plugged in and charged as soon as 

they are parked. The PHEV adoption cases were chosen to 

emulate the adoption of traditional hybrid vehicles. Scenario 1 

represents a near-term PHEV adoption scenario with a 

conservatively chosen battery price. By contrast, scenario 3 

represents a longer-term scenario with more ambitious PHEV 

adoption and RE penetration rates, as well as a low battery 

cost. All scenarios are summarized in Table 1.

TABLE I. SIMULATION SCENARIOS

Simulation Scenarios –

Opportunity, Price, Load, and RE-based Charge-Control

Scenario # PHEV 

Adoption %

Annual % Energy 

from Wind and Solar Battery Price

1 1% 15% $900/kWh

2 5% 20% $600/kWh

3 20% 40% $300/kWh

A. Opportunity Charging

The opportunity charging scenario assumes a pervasive EV 

charging infrastructure. All vehicles charge at the 4.8 kW rate 

and 90% efficiency as soon as they are parked and continue 

charging until the battery pack is fully charged. No 

communication or V2G services are offered.

B. Price-Signal Charging

Price-signal-based charging presumes a one- or two-way 

communication network and is based on the RT price of 

electricity. In this scheme, the PHEVs passively listen to the 

utility’s broadcast of the charging rate. The vehicle charging 

rate is calculated using (2),

where is the real-time charging rate in kW, is the 

maximum physically allowable charge rate in kW, is the 

real-time marginal price of electricity from the utility in 

Figure 1. Map of the area from which wind farm data were aggregated to

simulate wind power generation (from NREL’s Western Wind Data Set)

(1)

(2)

2
$/MW, and and are the daily minimum and 

maximum RT marginal prices for the day in $/MW. 

The daily minimum and maximum prices correspond to the 

bounds of the charging rate (0-4.8 kW). Therefore, if the RT 

price of electricity is equal to the maximum 24-hour price of 

electricity, the charge rate goes to zero. If the RT price of 

electricity is equal to the minimum 24-hour price of electricity, 

the charge rate reaches the maximum value of 4.8 kW. When 

the RT price of electricity is below a PHEV’s offering price, 

power is supplied to the grid from the vehicle’s battery. 

C. Load-Signal Charging

The load-signal-based charging scheme is identical to the 

price-signal-based algorithm, except that the maximum and 

minimum charge rates are defined by the RT load rather than 

the price. The vehicle charging rate is calculated using (3), 

 

where is the RT utility load in megawatts, and and 

are maximum and minimum daily loads in megawatts. 

As in the price-signal algorithm, a communication network 

is presumed, and V2G energy is supplied when the RT price of 

electricity is lower than the PHEV offering price. The power 

supplied to the grid is proportional to the load and calculated 

using (3). 

D. Renewable Energy-Signal Charging

Renewable-energy-signal charging is based on the premise 

that the plug-in fleet can be charged “exclusively” using 

renewable energy, with plug-in vehicles acting as an energy

sink. During windy or sunny conditions, the PHEV fleet 

absorbs bulk power generated by wind and solar farms. 

During low-renewable-power conditions, the vehicles charge 

at a slower rate. No wind or solar generation would normally

result in no vehicle charging; however, if RE generation is 

zero, a base charging rate is set such that the vehicles will be 

able to fully charge within six hours. To accomplish this, a 

two-way communication network is needed. The utility 

receives a 1024-bit charging request from each vehicle at 

regular 10-second intervals. In a real-world implementation of 

this system, the data packets would include encoded vehicle 

identification information, charging requests, grid state and 

location information, and the battery pack SOC. The utility 

calculates a charging rate and broadcasts the information over 

the network. The charge rate is calculated in (4):

 

where is the power output of wind and solar in MW, and 

is the number of plugged-in vehicles requesting to charge.

Note that if the power output from RE sources is 

sufficiently high, the calculated charge rate may be higher than 

the rate physically allowed by the charging circuit, and the 

charge rate is clipped at the maximum 4.8 kW value. In an 

actual implementation of this scheme, communication 

dropouts are likely to occur. In this case, the vehicles would 

limit excessive transients from switching on and off by 

maintaining a constant charge rate until communication with 

the utility is reestablished. The V2G services are not offered in 

this scenario, since the purpose of the algorithm is to test 

whether a plug-in fleet can maintain the original load profile 

using down-regulation only. 

IV. GRID COMMUNICATION NETWORKS

Plug-in vehicle charging coincident with the existing peak

load requires capacity expansion. Communication with 

PHEVs and infrastructure could shift charging to off-peak 

times. Many utilities across the United States have already 

installed grid communication networks to enable load shifting 

[6]. However, a variety of methods for accomplishing grid 

communication exist, and a survey of grid-networking 

protocols is needed. This section attempts to summarize the 

technologies being deployed for grid communication purposes. 

Each technology is evaluated based on how well it provides 

low-power, low-throughput, high-security communication

applicable to plug-in vehicles. Two-way data transfer between 

the customer and electric utility is assumed to be accomplished 

via internet protocol (IP) on the utility side. We consider three

wireless technologies as well as broadband over power line 

(BPL). Interference issues, power consumption, and security 

merits of each protocol are discussed.

A. Overview

1) Broadband Over Powerline and HomePlugTM 

Broadband communication over power line, or more 

generally, power line communication, is a technology that 

utilizes existing power line conductors as data transmission 

lines. High-frequency data signals are superimposed on top of 

the distribution voltage. BPL can also be transmitted across 

medium-voltage lines, providing last-mile service that can then 

be tied to the nearest wide-area network. BPL has been 

deployed in a number of U.S. cities since 2005; however, radio 

interference can be an issue [13]. New installations transmit 

data only over residential-side power lines leading up to 

neighborhoods, reducing the antennae effect from mediumvoltage power lines, with successful results [14]. Utility data 

would then have to continue its path to wide-area networks 

through traditional internet data carriers, such as cable or fiber 

optic. 

2) IEEE 802.15.4 (ZigbeeTM) 

Zigbee is a specialized protocol for small, self-programming 

mesh network devices based on the IEEE 802.15.4 wireless 

standard. Zigbee networks are ad hoc self-associating mesh or 

star networks [15]. Zigbee operates in 

Figure 2. Anatomy of the simulated 1024-bit Zigbee data packets, based on [12]

(3)

(4)

3
both the 2.4 GHz and 900 GHz bands, giving Zigbee the 

flexibility to choose a different frequency band in noisy radio 

environments. Zigbee is specifically designed for sensing and 

automation applications, so a number of power-reducing 

functions are written into the 802.15.4 standard. The 

throughput of Zigbee networks can reach 250 kbit/s [16].

3) ZWaveTM 

The sole designed purpose of the ZWave protocol is home 

automation – HVAC, lighting, security, garage access, etc. 

[17]. ZWave devices automatically set up an ad hoc mesh

network upon start-up [18]. Of the four protocols discussed, 

ZWave operates at the lowest data rate, 9.6 kbit/s, and is the 

only protocol operating exclusively in the 900 MHz frequency

band. 

4) Cellular Network

The cellular network is a widely available long-range

wireless data network, making it a good option for highly

mobile devices such as PEVs. Both the Global System for 

Mobile Communications (GSM) and Code Division Multiple 

Access (CDMA) network handle individual user data rates 

above 100 kbits/s [19].

B. Power Consumption

In general, power consumption is an important

consideration for grid networking devices, since the power 

supplying the device may be a battery. However, networking 

devices in PEVs would likely be powered from the wall outlet; 

therefore, power consumption considerations will be discussed 

only briefly.

BPL/HomePlug products do not require batteries, because 

the power supplies to these devices are provided through the 

electrical network connection itself.

Zigbee devices are designed for low power consumption 

(<1 mW) and have found a niche in the digital electric 

metering device market, making Zigbee a likely option for 

plug-in vehicle communications. Electric meters spaced less 

than 100 m apart (line of sight) are within the radio range of 

Zigbee devices, making it a suitable protocol for urban and 

suburban environments, though not particularly for rural areas. 

While the IEEE 802.15.4 standard does not explicitly define a 

maximum power draw for Zigbee devices, much of the 

language in the standard inherently reduces power 

consumption, such as small interframe spacing times and low 

duty cycle transmit/sleep periods [20].

ZWave is another purpose-built standard for connecting 

battery-powered devices wirelessly and was first released in 

2004. ZWave power-saving features are similar to Zigbee’s, 

and include a duty-cycled power save/nontransmitting mode, 

in which the chip is powered down to just the internal clock. 

Cellular devices consume more power than Zigbee and 

ZWave devices because of the larger range required of the 

transmitter; however, like any of the networking devices 

discussed, the power supply would likely come from the 

vehicle’s connection to the wall outlet.

C. Interference

Because many different wireless protocols utilize the same 

industrial, medical, and scientific (ISM) bands, frequency 

congestion and interference can become an issue in networkpopulous areas. The 2.4 GHz band alone supports 

IEEE802.11b/g/n (WifiTM), IEEE802.15.4 (Zigbee), 

BluetoothTM, and cordless phone service. Interference between 

Wifi, Zigbee, and Bluetooth has been well documented in the 

literature [21, 22, 23]. Common coexistence mechanisms 

between the three standards on the 2.4 GHz band have been 

shown by Zeghdoud to greatly decrease packet collisions 

between Zigbee and WiFi networks operating together [21]. 

No explicit coexistence mechanisms or standards for WiFiZigbee are currently defined, so interference must be 

minimized by careful channel selection and thoughtful network 

management when in the presence of multiple standards 

operating on the 2.4 GHz band [21]. Cellular communication 

uses dedicated frequency bands, and hence there are fewer 

documented issues related to network traffic collisions.

D. Security

Perhaps most important of all is the need for grid networks 

to be secure from hackers seeking to gain access to the power 

grid. Parties with any level of data access to the power grid 

could potentially disrupt the normal flow of power. Unlike 

wired networks, the data transmission media in wireless 

networks (the air) is inherently public, which presents a unique 

security threat at the physical (PHY) layer.

Advanced Encryption Standard (AES)

HomePlug, Zigbee, and ZWave all use 128-bit AES 

encryption to secure data transmitted across the physical

network. AES encryption is a 128-bit fixed-length block 

cipher, standardized in 2002 by the National Institute of

Standards and Technology [24]. Successful attacks have not 

been documented, and the U.S. government has approved 192- 

and 256-bit keys for use in encrypting top-secret information 

[24].

Since HomePlug utilizes home electrical wires instead of 

shielded coaxial cable, BPL transmissions may be 

unintentionally transmitted into the air at radio frequencies 

[25]. Thus, HomePlug uses the same PHY layer encryption as 

Zigbee and ZWave, to prevent RF transmission from being 

intercepted by unwanted parties.

Cellular security has improved with the introduction of 

data-transfer via cellular telephone; however, the security may 

not be suitable enough for grid communication purposes. The 

current security protocol is an A5/3 stream cipher called 

KASUMI, and it is currently used in 3G networks. Alternative 

proposals for more secure cellular data encoding schemes have 

been proposed, such as the one developed by Soyjaudah, 

which is similar to AES [26]. 

V. SIMULATION RESULTS

The simulation program was run through 12 different subscenarios using data from April 11, 2005. April 11 was chosen 

because it represents a typical utility load profile found in the 

2005 Los Angeles (LA) data set, and the magnitude of the 

4
peak load is between the largest and smallest peak throughout 

the 2005 LA data set.

The daily operating cost of the PHEV fleet was compared 

with two other simulated fleets – a 7.84 L/100 km (30 mpg) 

vehicle fleet, which represents a typical light duty sedan, and a 

4.7 L/100 km (50 mpg) vehicle fleet, to represent a traditional 

hybrid electric vehicle (HEV) fleet. The additional PHEV load 

was large enough to limit the charge rate only during peak 

hours. In the RE-based charging scenario, the RE generation 

was significantly greater than PHEV load, so the charge rate 

was never limited. Thus, the charging profiles for all scenarios 

are very similar. 

Fig. 3 shows that the operating cost per vehicle remains 

relatively constant, regardless of PHEV fleet size. The PHEV 

fleet used 29% as much fuel as the conventional fleet and 49% 

as much fuel as the HEV fleet. The PHEV fleet’s operating 

costs were between 50% and 62% less than those of the 

conventional vehicle fleet and between 16% and 27% less than 

those of the HEV fleet.

Fig. 4 shows the wear cost associated with large SOC 

charge/discharge cycling characteristic of PHEVs. The priceand load-based charging schemes incur a slightly higher 

battery wear penalty than the other schemes. This is because 

the battery is not topped off as quickly as the other algorithms, 

allowing the battery to reach a lower state of charge. Because 

nearly all of the trips in the LA GPS drive cycle data were less 

than 32 km (20 miles), and charging takes place throughout the 

day between trips, the vehicles operated in CD mode about 

80% of the time. The larger battery packs of the PHEVs 

allowed the vehicle to operate as an electric vehicle a majority 

of the time. 

From a utility perspective, the opportunity-charged PHEV 

fleet added an additional load during peak hours roughly equal 

to the PHEV adoption percentage. Therefore, adoption of 

plug-in vehicles will necessitate either additional generating 

capacity or grid communication to shift the load to off-peak 

hours. The peak load increases caused by the opportunitycharged (no communication) fleet are summarized in Table 2. 

Because the simulation was run on a spring day, the resulting 

summer peak load increase was adjusted using data from the 

highest peak of the year on July 22. Finally, because per 

capita energy use in Los Angeles is about 38% lower than the 

national average [27], the data were additionally scaled to 

represent the change in summer peak load for a typical 

American city. 

Based on simulation, a load- or RE-based charging scheme 

appears to maintain the peak load at its original value at a 20% 

PHEV adoption level. A load-based charge scenario reduces 

the need for capacity expansion without substantially 

increasing vehicle fuel consumption. The inclusion of RE 

supports vehicle charging while simultaneously reducing the

peak load.

In the load- and price-based scenarios, V2G services are 

offered when the RT price of electricity is higher than the 

PHEV offering price. The RT utility price is plotted against

the average fleet price for all three scenarios (Fig. 5). Utility 

price data from December 9 was added for reference because it 

had the highest peak electricity price of the year, at 

$137/MWh. Notice that when the battery cost reaches 

$600/kWh in scenario 2, V2G energy prices appear to match 

the utility prices, including battery wear.

Assuming that a one-way communication infrastructure is 

available, the price- and load-based charging algorithms are 

possible. The utility broadcasts the proportional charge rate 

based on the real-time price of electricity or load. The PHEV 

fleet load is plotted on top of the existing utility load for 

comparison in fig. 6, assuming a 5% PHEV adoption rate. 

In the load-based case, the PHEV load is zero during the 

original peak, while the maximum power demand increases for

TABLE II. PEAK LOAD INCREASE – OPPORTUNITY CHARGING

 Figure 4. Battery wear cost for 12 scenarios 

Figure 3. Daily fleet operating cost for all 12 scenarios at $0.53/liter 

($2/gallon) and $1.06/liter ($4/gallon) fuel cost for April 11, 2005

5
both the opportunity charging and the price-based charging 

cases. Only the load-based charging algorithm maintains the 

daily maximum load at its original magnitude. Thus, the 

preliminary results suggest that a load-based charging scheme 

is an effective way to offset the need for additional generating 

capacity. 

Finally, the ability of the PHEV fleet to serve as an energy 

sink for renewable power generation was tested. Fig. 7 

illustrates the variability of RE generation at a 15% wind-solar 

integration level. 

Fig. 8 demonstrates the interaction of PHEVs with RE

generation under three scenarios. As shown in Fig. 9, the

network traffic of all 1144 vehicles never exceeded 17 kbit/s,

meaning that each vehicle is transmitting data at no more than 

150 bit/s on average. This data rate is far below the maximum 

throughput capabilities of the networking options available.

The final simulations were based on the RE-charging 

scheme. Based on the PHEV adoption rate and RE 

deployment rate assumed here, the RE resources were more 

than enough to supply the PHEV fleet demands. PHEV

Figure 9. Throughput for renewables-based charging scenarios 

Figure 8. Scenario 3: Utility load curve, including renewable power generation 

and PHEV loads.

Figure 7. Utility load curve for April 11, 2005, assuming a 5% PHEV 

adoption rate. Opportunity, load-, and LMP-based charging load profiles are 

plotted on top of the existing load.

Figure 6. Utility load curve for April 11, 2005, assuming a 5% PHEV adoption 

rate. Opportunity, load-, and LMP-based charging load profiles are plotted on 

top of the existing load. 

Figure 5. Real-time utility prices and PHEV fleet V2G offering prices, using 

the price-based algorithm 

6
charging in the RE scenario reduced the utility 10-minute ramp 

rates by 5%, in comparison to the case without PHEVs. 

Charge control schemes that help reduce RE ramp rates could 

provide value to the utility. 

VI. CONCLUSION & FUTURE WORK

A MATLAB model for simulating the interaction between 

a plug-in hybrid fleet and the power grid was developed using 

GPS drive cycle data and utility load data as data inputs. 

Based on the simulation results, several observations were 

made: 

• Based on the assumptions stated, PHEVs appear to be

an economically competitive substitute for gasolineonly vehicles, including the cost of battery wear

• Grid communication networks have been shown to 

offset the need for additional generating capacity

required to charge the simulated plug-in vehicle fleet

• Real-time price payments are sufficient to offset 

battery wear impacts when battery costs fall below 

$600/kWh, to support V2G

• Renewable energy generation is more than sufficient 

to charge the PHEV fleet, based on the growth rates 

assumed in the analysis

• BPL, Zigbee, ZWave, and cellular networks have 

sufficient throughput to relay IP data between PHEVs 

and the power utility to realize grid communication

Based on the results from the initial simulations, several 

aspects of PEV integration warrant further investigation:

• Higher time-resolution load and price data should be 

used to simulate regulation services and quantify the 

value to consumers and utilities

• The battery model should be expanded to include 

temperature effects on wear and efficiency, by linking 

to a meteorological database. Microcycle degradation

should also be included

• A RE ramp rate-limiting algorithm should be explored

• Seasonal differences in load and generation should be 

simulated further

• A vehicle test fleet should be used to validate 

predictions regarding communication architecture and 

fleet charging control

The conclusions of this paper apply to an aggregate service 

area; distribution- and feeder-level effects have been excluded. 

Localized effects may limit the charge rate of vehicles 

concentrated on the same feeder; for an analysis of PEV 

impacts on distribution-level systems, refer to [28, 29, 30].

PHEVs were also shown to provide value to customers and 

the environment by reducing daily driving costs by 50% to 

62% and lowering petroleum consumption by 71%, when 

compared with a standard 30 mpg vehicle fleet. Although the 

anticipated PHEV adoption rate implies that the energy sink 

would be too small to absorb bulk power from renewable

energy generation, incentives that encourage more aggressive 

plug-in adoption rates might improve the grid-buffering 

capabilities of the PHEV fleet. By implementing a relatively 

low-throughput communication system, utilities may be able to 

reduce the extra capacity needed to serve PEVs. Grid 

communication networks enable load management and 

consumer participation toward RE expansion.

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8
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Communication and Control of Electric Vehicles Supporting 

Renewables: Preprint

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DE-AC36-08-GO28308

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T. Markel, M. Kuss, and P. Denholm

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14. ABSTRACT (Maximum 200 Words)

The coming intersection between a growing electrified vehicle fleet and desired growth in renewable electricity 

generation presents an opportunity for synergistic value. Some types of renewable electricity generation are variable 

and intermittent, and rarely coincident with a utility's load pattern. Vehicles are typically parked 90% of the time, and 

the batteries are a significant capital investment. This paper discusses the intersection of these two growth areas, the 

technology needed for integration, and several potential scenarios highlighting the limitations and opportunities for 

renewable energy resources to fuel the electric vehicles of the future. 

15. SUBJECT TERMS

hybrid electric vehicles; plug-in hybrid electric vehicles; electric vehicles; grid integration; renewable energy

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