Role of PHEVs and BEVs in Coupled Power and

Transportation Systems

Mladen Kezunovic

Department of Electrical and Computer Engineering

Texas A&M University

College Station, USA

abphjb@r.postjobfree.com

S. Travis Waller

Department of Civil Engineering

University of Texas

Austin, USA

abphjb@r.postjobfree.com

Table of Contents

DEFINITION 5

INTRODUCTION 6

POLICY ISSUES 11

MODELING OF COMPLEX SYSTEMS 13

BENEFITS 18

CONCLUSIONS AND FUTURE RESEARCH 26

4

Glossary of Terms

PHEV: Plug-in Hybrid Electric Vehicle

BEV: Battery Electric Vehicle

HEV: Hybrid Electric Vehicle

G2V: Grid-to-vehicle; using the electrical grid to charge the battery of a

vehicle.

V2G: Vehicle-to-grid; exporting electrical power from a vehicle battery to

the electrical grid.

V2B: Vehicle-to-building; exporting electrical power from a vehicle

battery into a building

OM: Outage Management; set of manual and/or automated procedures

used by operators of electric distribution systems to assist in restoration of

power.

DSM: Demand-side Management; Utility-sponsored programs to influence

the time of use and amount of energy use by select customers.

5

DEFINITION

With the price of oil peaking in the recent past close to the once

unimaginable $150 per barrel and the threat of global climate change

increasingly acknowledged, the transportation sector is employing a

number of new technologies that will enhance energy security by reducing

the current dependency on oil-based fuels. Should the gasoline cost

increase in the future, Plug-in Hybrid Electric Vehicles (PHEVs) and

Battery Electric Vehicles (BEVs) will become the economical choice for

transportation. Widespread adoption of PHEVs/BEVs will also improve air

quality and carbon footprint, since point source pollution is easier to control

than mobile source pollution. This level of control is essential for effective

implementation of carbon cap-and-trade markets, which should spur further

innovation. U.S. sales of Hybrid Electric Vehicles (HEVs) have grown 80%

each year since 2000, proving that PHEVs/BEVs are likely an eventual

reality that must be dealt with 0. The implications of this reality will be

highly dependent on the policies in place to use PHEVs/BEVs to the benefit

of the transportation and power systems, as well as the drivers, industry and

public at large.

Beyond fuel costs and sustainability, the primary concern of the

transportation sector is congestion. In 2005, congestion was estimated to

cost the U.S. economy $78.2 billion in wasted time and fuel 0. If

PHEV/BEV drivers are given appropriate incentives (e.g., strategically

placed energy exchange stations) traveler behavior (e.g., choice of routing,

departure time, destination) impacting congestion may be affected.

In addition, the power industry is currently challenged to maintain

reliability of operation while expanding the grid to meet growing demand.

Large blackout such as the northeastern one in 2003 may create loses in

billions of dollars 0. Introducing the renewable resources to meet growing

demand requires energy storage to deal with interfacing 0. If proper policy

is in place PHEVs/BEVs can provide a promising solution acting as mobile

decentralized storage (MDS) of electrical energy. In this capacity,

PHEVs/BEVs can serve in two modes: grid-to-vehicle (G2V) and vehicle-

to-grid (V2G), each providing benefits to the power system operation. The

G2V mode can be used to charge PHEVs/BEVs at reduced cost when the

power system load is reduced and generation capacity is abundant, such as

during night time. The V2G mode may be used when demand is high or

supply is accidentally lost since the stored electric energy can be released

from PHEVs/BEVs in an aggregated way, which will offer major

contributions to regulation service and spinning reserves, as well as load-

shedding prevention. The mobility of the energy storage in PHEVs/BEVs

allows for strategic placement of the distributed generation source to

optimize power system needs.

6

Figure 1 illustrates the spatial and temporal coupling of the power and

transportation systems through showing an example of a PHEV/BEV

driver s route, highlighting destinations where the driver could potentially

engage in G2V or V2G activity. Options for meeting selected criteria for

electricity and transportation networks simultaneously are numerous.

Developing policy strategy requires understanding of trade-offs involved in

pursuing certain solutions at the expense of others. The all-encompassing

theoretical framework for such studies to the best of our knowledge is not

available.

Fig. 1 Temporal and spatial dimensions of plug-in opportunities

Traditionally, scientists have adopted a divide and conquer approach to

understanding complex phenomena. Unfortunately, systems with emergent

dynamics that are dominated by contextual interactions are not well suited

to this classical approach (e.g.,[5-7]). In such cases, directly addressing the

couplings of system components may actually hasten progress. While this

linkage presents new opportunities to improve the functioning and capacity

utilization of each system, it also raises the spectrum of increasing dynamic

complexity and cascading failures across systems.

In this chapter, several open policies and research goals will be

discussed, which facilitate optimizing the integration of the transportation

systems and the behavior of its travelers with the electricity systems and

behavior of its end-customers. PHEVs/BEVs based demand side

management (DSM) and outage management (OM) are also presented as an

application of PHEVs/BEVs using in the coupled power and transport

system.

INTRODUCTION

The impacts PHEVs/BEVs will have on transportation systems, power

systems, and air quality are very complex. Studies conducted to date on this

7

topic make many assumptions to simplify the problem. As stated in the

definition, the problem space must be treated as one large complex system

in order to capture emergent behavior.

The complexity of the issues involved in studying PHEVs/BEVs and

their interaction with electricity and transportation networks is shown in

Figure 2 where several disciplines that need to be involved in researching

this multidisciplinary problem are shown.

Fig. 2 Illustration of multidisciplinary nature of problem

Recent analyses confirm the feasibility of the grid-to-vehicle (G2V) and

vehicle-to-grid (V2G) concepts [8-13]. The Electric Power Research

Institute speculates that V2G could reduce the requirement for global,

central-station generation capacity by up to 20% by the year 2050 0.

Several studies omit any consideration of vehicle locations and desired

activity patterns and assume a percentage of vehicles are plugged in and

available when estimating the benefits to the grid and to drivers [8, 10, 11].

Many researchers have investigated the various potential benefits and

implementation issues of the V2G concept. Kempton and Tomi studied the

fundamentals of using PHEVs/BEVs for load leveling, regulation, reserve,

and other purposes [15, 16]. Hadley and Tsvetkova analyzed the potential

impacts of PHEVs on electricity demand, supply, generation, structure,

prices, and associated emission levels in 2020 and 2030 in 13 regions

specified by the North American Electric Reliability Corporation (NERC)

0. Meliopoulos, et al. considered the impacts of PHEVs/BEVs on electric

power network components 0. Anderson, et al. performed the case studies

of PHEVs/BEVs as regulating power providers in Sweden and Germany 0.

Guille and Gross presented a proposed framework to effectively integrate

the aggregated battery electric vehicles into the grid as distributed energy

resources 0. The combined impact PHEVs/BEVs make on both electric

power system and transportation network has not been explored as much.

When considering the role of PHEVs/BEVs as dynamically configurable

(mobile) energy storage, the potential impacts on both electricity and

transportation networks may become quite diverse. The flow of traffic is an

8

important factor in deciding the flow of electric power that could be utilized

from PHEVs/BEVs. Correlating the movement of people to the movement

of the power load offers new opportunities in the smart grid.

One of the major advantages of PHEVs/BEVs is their usefulness as an

MDS. MDS is a revolutionary concept because currently the power grid has

no storage except for 2.2% of its capacity in pumped storage 0. Without

significant and reliable storage of energy, maintaining grid stability and

reliability under the growing electricity demand is a complex problem.

Utilities may contract with others to provide power in any one of the four

types of markets: base-load power, peak power, spinning reserves, and

regulation services. Several studies have shown that PHEVs/BEVs can

provide ancillary services (spinning and regulation) at a profit [8, 10, 11].

Spinning reserves receive payment for providing continuous capacity

regardless of whether energy is provided, and receive further payment if

called on to feed energy into the grid. Regulation services feed a nominal

amount of energy into the grid, and receive payment for reducing or

increasing their energy consumption as needed. In the case of

PHEVs/BEVs, being plugged-in in a predictable way means that capacity is

available to feed into the system if called upon. PHEVs/BEVs are

particularly well-suited for regulation services since the impact on vehicle s

energy resources may be zero..

The pricing of V2G and G2V services is expected to cause a

fundamental shift in the behavior of PHEVs/BEVs drivers. Further research

is needed to investigate the exact nature of this shift; however, if the pricing

schemes are developed with both the power system and transportation

system in mind, then PHEVs/BEVs could help solve problems plaguing the

traffic network, particularly congestion. The pricing scheme should also

consider air quality impacts caused by charging at different times in the

day. As mentioned earlier, MDS will allow for renewable energy to be used

more efficiently. There will however remain times of the day more

dominated by dirty fuels than others.

As observed, the body of research literature related to the

multidimensional impact of PHEVs/BEVs is quite small. The remainder of

this section will focus separately on the dual problems of improving the

stability and reliability of the electrical grid and improving the efficiency of

the roadway network.

Stability and reliability of the electric grid

Stability and reliability of the U.S. electric grid have become issues of

increasing concern since the occurrence of several blackouts in the 1990s

(Western Interconnect in 1994 and 1996, and the Eastern Interconnect in

1999) and system deregulation. The devastating impact of the northeast

blackout from August 14, 2003 reminded that the situation with the grid is

9

only worsening and not improving. Here, a stable system is defined as one

in which the phase and frequency of power generation units are constant.

Ability of the system to maintain the state of equilibrium during normal and

abnormal conditions is a measure of stability. Reliability is defined as the

ability of the system to meet unexpected demand and respond to failures.

Ability of the system to deliver electricity to customers within the accepted

standards, which may be affected by the failure rate, repair rate, or duration

of loss, is a measure of reliability. Figure 3 illustrates the worsening

stability problem. Order 888 in Figure 3 relates to the Open Access to

Transmission issued by Federal Regulatory Commission in 1996, which is

the result of an authorization passed by the Congress as a part of the Energy

Policy Act of 1992.

Fig. 3 Illustration of multidisciplinary nature of problem [21]

A major challenge in achieving these goals (stability and reliability) is

the lack of energy storage. Figure 4 depicts the peaking structure of an

example power load over the course of one day. In this example demand

grows rapidly starting at 6am and begins to decline after hitting a peak

around 3pm. This peaking phenomenon is especially important to consider

given that different energy sources are available at different times of day.

For example, wind energy is most widely available at night when the

demand for power is the lowest. While it may seem intuitive that a flat

demand curve is the ideal, this is not necessarily true. More research is

required to determine if parts of the system (e.g., transformers) require time

to cool down. The large scale use of energy storage would significantly

help meeting the stability and reliability needs, including managing the load

variations shown in Figure 4.

10

Fig. 4 Illustrative peaking of electricity load 0

Efficiency of the roadway network

Congestion is a problem not only in the electricity grid network, but also

in the roadway network. Vehicle miles traveled (VMT) has risen

consistently since the advent of the automobile, with dips when gasoline

prices rise quickly (See Figure 5 for the VMT trend since 1992). If the

transportation sector is shifted to an alternative fuel source (i.e., electricity)

with greater price stability, and especially if the source of the fuel is

renewable, then VMT is expected to continue to increase into the

foreseeable future. While mobility is an indicator of economic success, the

expansion of a roadway system is limited by available space and finances.

Roadway network efficiency is further constrained by the individual

autonomy of drivers who act in their self-interest instead of the interest of

the system (see 0 for a theoretical description of traveler behavior).

Fig. 5 U.S. highway VMT 0

Extensive research has been conducted on improving the efficiency of

the transportation system via methods such as pricing and technology, but

few solutions proposed offer a case even close to being as comprehensive

as PHEVs/BEVs.

11

POLICY ISSUES

The policy issues presented here are centered on incentives to help

industry develop and bring new value to end users of electricity and

transportation networks, and society at large, while encouraging

competition and development of new business opportunities.

Improve electric grid performance

Widespread deployment of PHEVs/BEVs will allow for increased

energy storage, and improved reliability and stability of the electric grid.

Linking the transportation and power systems through PHEVs/BEVs will

allow for electrical energy storage on a scale much larger than is currently

feasible. The additional energy capacity will be directly proportional to the

penetration of PHEVs/BEVs into the automobile market, and modeling (see

the modeling section) is needed to determine the exact increase in capacity

across the space and time dimensions.

The new mobile storage can only benefit the electric grid if it is

available at the right time and place to service the grid when needed. To

determine PHEVs/BEVs demand for electric energy across space and time,

travel patterns must be considered. Figure 1 shows an example of such a

pattern, highlighting several destinations where a driver could potentially

engage in G2V or V2G.

Stability and reliability issues were mentioned earlier. V2G is poised to

greatly aid the grid in becoming more reliable and stable because vehicles

are only in use for a small portion of each day (average daily travel time

person in 2001 was 82.3 minutes 0). During the remainder of the day, the

vehicles can be plugged in and provide services (e.g., ancillary or

regulatory).

This approach requires a policy shift to allow use of the MDS for energy

to maintain stability and reliability. Also, policy that encourages utilities to

cooperate with the PHEV/BEV owners or aggregators and provide tariff

incentives for their participation in programs aimed at demand and

distributed generation management and optimization is missing at the

moment.

Enhance penetration of renewable energy sources to improve

energy security

Increasing energy capacity by using PHEVs/BEVs as MDS will allow

for increased investment in renewable energies by alleviating concerns

related to the temporaly highly variable nature of solar (daytime) and wind

(primarily nighttime). Using renewable energy has benefits not only for the

environment and air quality, but also for energy security by reducing

reliance on the supply from oil producing countries.

12

This approach requires a policy shift to allow and encourage large scale

use of the MDS for energy to support interfacing of renewable generation.

Reduce and redistribute pollution in the electric grid and

transportation network

By shifting the source of pollution away from vehicles, PHEVs/BEVs

will change the transportation-based air pollution problem from a mobile

source issue to a point source issue. This redistribution of pollution will

likely have the effect of reducing pollution because point sources are much

easier to control and some already have emission caps in place. Hadley [25]

conducted initial research into the potential air quality impacts of

PHEVs/BEVs, describing the impacts of G2V charging on air quality, and

considering the types of power generation that are typically used at

different times of day (e.g., coal-fired generation is prevalent at night in

some regions).

Some policies are already in place to ensure that the redistribution of

pollution that will occur with widespread deployment of PHEVs/BEVs will

actually lead to a reduction in pollution. Further policy analysis is needed to

ensure that V2G and G2V services are incentivized to occur at times when

it will result in the maximum improvement in air quality.

Create new markets and further deregulate existing markets

PHEVs/BEVs are poised to open new markets and increase

opportunities in existing ones. Carbon-trade markets should be aided

because they facilitate the change of the transportation-based air pollution

problem from a mobile source issue to a point source issue (as described

earlier). While point sources of pollution are much easier to control, if they

are non-renewable, they will likely need to trade carbon credits to counter

the increased emissions.

PHEVs/BEVs will also create new modes for participation in the

electricity markets. There will be opportunities for businesses to act as

Qualified Scheduling Entities (QSE) to the electric utility by facilitating

V2G/G2V interactions. Such a QSE that aggregates across vehicles is

necessary because any one vehicle s contribution will be too small to allow

it to participate directly in the market. PHEVs/BEVs will likely function

akin to small generators as a distributed energy resource.

Policy that enhances market development and deregulation allowing a

new type of QSE to bid in a variety of markets is needed to facilitate the

aggregated use of PHEVs/BEVs in transportation-energy markets.

Plan and develop energy exchange stations

Energy exchange stations (for G2V and V2G) could take one of at least

two forms. The first, the way considered by most electric vehicle research

13

to date, assumes that individual drivers plug in and charge their vehicle

over a period of several hours. Some examples of potential charging station

locations are shopping malls, recreational areas, schools, and of course

homes.

Further, rather than requiring drivers to plug into the grid and wait

several hours to charge their batteries, battery exchange locations could be

as ubiquitous as gas stations and automatically exchange discharged

batteries with fully charged batteries. Charging PHEVs in this way has

benefits for drivers because the process takes only a few minutes as

opposed to several hours. Also, this system would require a leasing system

for batteries similar to the system in place for leasing cell phones,

alleviating driver concerns about battery life. The benefit for utilities is that

control over charging and servicing the grid is centralized.

In reality, charging (G2V) and discharging (V2G) services will likely be

based on a hybrid of the two methods mentioned above (individual drivers

plugging into the grid and stations designed to exchange batteries).

Depending on the pricing structure in place, it may make sense for drivers

to exchange batteries during long drives and plug-in to a household plug at

night. Incentive structures will need to be developed that consider the

different players energy exchange stations and individual drivers.

The temporal and spatial aspects of the activity patterns travelers choose

(see, e.g., Figure 1) adds a layer of complexity to the problem of locating

charging stations to link the transportation and energy systems. This

requires both micro (neighborhood, city, and metropolitan area) and macro

(region, state, and nation) driver behavioral dynamics to be studied in

detail. If appropriate incentives are developed, drivers could be encouraged

not only to act in a way that best serves the grid, but also to act in a way

that best serves the transportation system. The incentives could be passive

such as pricing electricity for planned contribution at the location of

charging facilities (either stations or induction charging embedded in the

roadway), or active such as pricing electricity based on congestion in both

the power grid and local transportation system. Cognitive and behavioral

research is needed to determine the appropriate incentives.

Policy that addresses the planning requirements for charging stations

and regulates emerging energy exchange markets is needed.

Comprehensive policy that develops joint electricity and transportation

programs for incentivizing drivers to participate in the transportation and

electricity grid optimization are not yet proposed or even clearly defined.

MODELING OF COMPLEX SYSTEMS

To develop policy strategies that allow for faster and more significant

penetration of PHEVs/BEVs, research is needed to model the interactive

14

performance of two complex systems, power and transportation, linked

through the behavior of individual vehicle operators, where this linkage is

determined by the location of interface infrastructure. The behavior of

travelers defines the required inputs into power modeling since time-

dependent PHEVs/BEVs locations are critical. Every aspect of this meta-

system enterprise (power, transport, consumer choice, infrastructure

development) is inter-linked, therefore fully understanding policy issues is

quite challenging. This section explores each aspect of the modeling

approach beginning with transportation modeling, and then power systems

modeling, then modeling the role of human agents, and finally determining

economic feasibility (see Figure 2 for illustration).

T ransportation modeling

Travel models typically contain demand and supply components. While

most demand models used in practice are static and consider each leg of a

trip separately, activity-based models are gaining momentum. Lemoine et

al. 0 illustrate the problems that PHEVs/BEVs could pose if proper

incentives are not given to ensure that energy exchange occurs at times

beneficial to the grid. Activity-based travel models are better suited for

PHEVs/BEVs modeling because they recognize that travel arises from a

fundamental need to participate in activities, and thus the models capture

trip-chaining behavior (e.g., home to work to grocery to home). Other

benefits of activity-based models are the incorporation of intra-household

interactions, inter-personal and intra-personal consistency measures,

consideration of space-time constraints on activities and travel, and

emphasis on individual level travel patterns (as opposed to monitoring

aggregate travel demands). A number of micro-simulation platforms that

employ the activity-based paradigm of transportation demand forecasting

have been developed in the last five years (e.g.,[26-28]).

On the supply side, conventional techniques of trip assignment are static

in nature, and consider vehicle flows aggregated over one or several hour

time periods. The limitations of the static assignment procedures and the

increase in computing capacity have allowed the field to move toward more

behaviorally realistic dynamic traffic assignment (DTA) models. DTA

techniques offer a number of advantages including capturing the spatial and

temporal evolution of traffic dynamics across the transportation network,

superior capability to capture traffic congestion build-up and dissipation,

and explicitly representing the route-choice effect of external dynamic

prices and other costs and incentives. A number of simulation-based DTA

modules have been developed in recent years [29-32]. The above

mentioned features of DTA make it an ideal choice for modeling the

network congestion patterns induced by PHEVs/BEVs usage and their

impact on other vehicles.

15

Travel models produce numerous outputs, metrics, and system

properties. Of critical importance for connecting the transportation and

energy models are predictions regarding time-dependent vehicle locations.

This inference directly relates to the number of PHEVs/BEVs present at a

specific power grid node, which will be related to the node s self-

admittance described in the next sub-section on power systems modeling.

Consideration of multiple classes of travelers, PHEVs/BEVs and non-

PHEVs/BEVs, will be critical until PHEVs/BEVs reach high % penetration.

It has been long understood that through pricing-based incentives, the

system-level performance of transportation networks can be greatly

improved. The entire field of congestion pricing (e.g., [33, 34]) addresses

this fact. For instance, PHEVs/BEVs provide a novel opportunity to

achieve gains in controlling and managing congestion in transportation

systems through an incentive based approach that persuades users to act in

an altruistic manner. Further, such incentives provide a unique opportunity

(and complexity) in that dual objectives must be balanced: improving the

efficiency of the transportation as well as that of the power system. For the

transportation system, incentives influence route, departure, as well as

destination choice. Incentives change the fundamental costs traveler s

associate with their choices and a new general cost dynamic equilibrium

emerges (for normal operating states). This requires a further broadening of

the previously mentioned integrated modeling approach to include

generalized costs as well as heterogeneous values of time.

Clearly, there will be significant uncertainty in the model inputs that

must be built-in to ensure that the policy recommendations work well for a

wide range of potential future outcomes. A vast amount of research has

already been performed on stochastic transportation modeling both on the

demand and supply side [35-40].

Power systems modeling

The planning, design and operation of modern power systems call for

extensive and detailed simulation. Models used to simulate power system

behavior depend on the purpose and uses. When considering the need of

studying PHEVs/BEVs impact on power system, different levels of

modeling are required.

At the macro level, the power system planning related to the uses of

PHEVs/BEVs requires understanding of the generation, storage and load

characteristics, as well as power flow projections impacted by the

anticipated use of PHEVs/BEVs. A stochastic nature of PHEV/BEV use in

the multiple possible roles will require advanced probabilistic methods for

power flow analysis, as well as stochastic optimization related to operation

and investment planning of dispersed generation [41, 42]. Enhanced

modeling techniques must be developed for PHEV/BEV behavior as a load

16

to assess dynamic stability of the power system operating in G2V mode 0.

Hadley 0 used the Oak Ridge Competitive Electricity Dispatch (ORCED)

model to simulate PHEV/BEV electricity demand. It did not directly

include transmission and distribution impacts, but discussed the issues of

increased continuous transmission. Also, power system contingency

analysis must be improved to account for the dynamic nature of both

temporal and spatial properties of PHEVs/BEVs. In the V2G mode,

PHEVs/BEVs may impact power grid operation in many different roles,

both as energy storage used to improve performance of renewable such as

wind and solar 0, as well as a market participant through aggregated

distributed generation 0] . While it has been recognized that PHEVs/BEVs

can be used for regulation services [10, 11], some studies also suggested the

PHEVs/BEVs use for peak power shaving services 0. A customized

modeling tool that allows examining the potential impacts of large scale

deployment of PHEVs/BEVs on a given electricity system, such as the

PHEV-load tool developed by the National Renewable Energy

Laboratory (NREL) may be needed 0

At the micro level, the PHEV/BEV powertrain system itself, which is a

very complex dynamic electro-mechanical system, may be studied.

Specialized modeling and simulation tools, such as Argon National

Laboratory s (ANL s) Powertrain System Analysis Toolkit (PSAT) are well

suited for such an analysis 0. This toolkit allows detailed modeling of

charging and discharging dynamics of PHEVs/BEVs, which is crucial when

defining properties of PHEVs/BEVs as loads, energy storage or generation,

as discussed above, Other ANL s tools such as GCtool, GREET and

AirCred may also be needed to asses other impacts 0.

The impact of PHEVs/BEVs ranges from the macro to micro scales,

both in size and time. Different power system states (steady state, dynamic,

transient) may need to be represented in a framework using different types

of mathematical formulations (waveforms, phasor, algebraic). This leads to

a new requirement for developing a method for linking different modeling

techniques for accurate and efficient simulation when representing large

scale penetration of PHEVs/BEVs as generators, storage elements or loads

0.

Human behavior modeling

The widespread adoption of PHEVs/BEVs will place human vehicle

operators at the intersection of power and transportation systems. Thus, it is

critical to understand human decision making in the context of PHEV/BEV

usage and how behavior can be shaped by incentive structures and training

interventions. The large disparate group of decision makers includes not

only drivers, but also utilities, battery exchange location coordinators, and

fleet managers. Cognitive research will be critical to not only to understand

17

and optimize human decision making involving PHEVs/BEVs, but to also

increase the rate of PHEV/BEV adoption.

Route planning for any type of vehicle is an example of a dynamic

decision task 0. Choosing a route requires a series of inter-related decisions

that occur in a changing and uncertain environment. PHEVs/BEVs

introduce a number of additional decision elements, such as whether to

draw energy from the grid or deliver energy to the grid at destinations with

facilities allowing such interfaces. Complicating this decision process, G2V

costs and V2G credits vary through time and are not perfectly predictable

from the driver s perspective.

One successful framework for modeling human performance in dynamic

decision making tasks is reinforcement learning (RL) [51, 52]. The theory

of RL comprises an array of techniques for learning temporally extended

tasks in dynamic environments. An agent is assumed to be immersed in its

environment, with some number of actions available to be taken at any

given time. The chosen action has an effect, depending on the current state

of the environment, the immediate reward (or punishment) the agent

receives, and the future state of the environment. Thus actions can influence

situations and rewards arbitrarily far into the future, and successful

performance hinges on effective planning and coordination of extended

sequences of actions 0.

Previous research demonstrates that RL agents and humans are more

likely to discover the underlying structure of a task when state cues are

present that allow for generalization 0. A state cue in the context of

PHEV/BEV decision making would include observable properties - such as

time of day, weather conditions, and congestion - that enable prediction of

G2V credits and V2G costs. State cues play a critical role in shaping

learning and it has been shown that variability in state predictors disrupts

performance more than equivalent variability in the reward structure 0.

Further research is needed to examine how variability in state cues and

reward structure affects PHEV/BEV route selection. Establishing how

PHEV/BEV driver performance (with respect to improving conditions on

the grid and transportation system) declines with variability in state cues is

important because transient changes in incentives could have negative,

unintended consequences, making it difficult for people to acquire the basic

pricing contingencies. Research is also needed to find the best methods for

PHEV /BEV operators or aggregators to learn about incentive structures.

Various types of feedback are available (e.g., Reward Only, First Error) and

the optimal approach should be determined via experiment.

Determining economic feasibility

To take advantage of the proposed transportation-energy markets,

interface infrastructure - the facilities that will bridge the two systems and

18

serve as energy transfer points - must be developed and planned. While

prevalence of PHEVs/BEVs in the future is unknown, their ultimate value

can only come if the interfaces are in place. This leads to a situation where

the demand for PHEVs/BEVs depends on the infrastructure supply, which

in turn is defined by the demand. The traditional project valuation models

fall short of accounting for this feedback loop.

Developing interface infrastructure is a uniquely challenging problem

because the equipment must not only adjust energy flow over time, but the

location of transfer points must be determined to maximize long-term value

and minimize risks. Technology adoption, incentives, and systems

interdependencies all play a role.

To maximize the value of developing interface infrastructure in a

particular location, two aspects of the problem must be considered: 1) the

value created to the grid by using PHEVs/BEVs for regulation services, and

2) the value of the activity-based travel patterns that could include a visit to

the interface infrastructure. The former value can be explicitly determined,

but unless the latter is considered and travelers are enticed to use the new

infrastructure, the value to the grid will not be achieved. Typically, the

traveling public selects route and activity patterns without considering

energy exchange opportunities. New methodologies and modeling

techniques must be developed for valuing interface infrastructure given its

dependence on traveler behavior.

Unlike most past research into making investment decisions for

infrastructure projects that focus on a single system (e.g., [56, 57]), the

problem posed here must consider the interdependencies between several

systems as well as the rate of technology adoption (availability of

PHEVs/BEVs to use this facility and generate value). In fact, this problem

exhibits both spatial network effects and strategic bandwagon network

externalities (see seminal contributions in this area by Rohlfs 0, Farrell and

Saloner 0, and David and Greenstein 0).

It is clear that in the face of this bandwagon effect, the value of deferral

flexibility is marginal. Hence, the project developer action space should

consider actions that promote early adoption without fully committing to

irreversible capital expenditures. Stochastic modeling approaches could be

useful here to consider that the outcome and uncertainty space of the

valuation problem is decision dependent (see, e.g., 0).

BENEFITS

This section aims at demonstrate the potential benefits of PHEVs/BEVs

that may be used to feed power back to home or office building, which is

known as Vehicle-to-Building (V2B) operation. The new parking facility

called smart garage is introduced and its eclectic power capacity is

19

discussed. Based on the availability analysis of smart garages, a strategy to

adopting PHEV/BEV uses in the V2B mode under peak load and outage

condition is proposed. V2B approach considers PHEVs/BEVs as a

generation resource for the buildings at certain periods of time via

bidirectional power transfers, which could increase the flexibility of the

electrical distribution system operation. It is expected that V2B operation

will improve the reliability of the distribution system, provide extra

economic benefits to the vehicle owners, and reduce the home or building

electricity purchase cost based on the demand side management and outage

management programs with customer incentives.

Demand Side Management (DSM)

For electric utility, DSM is defined as Utility-sponsored programs to

influence the time of use and amount of energy use by select customers.,

which includes peak clipping, valley filling, load shifting, strategic

conservation, strategic load growth, and flexible load shape 0. However,

for utility end-user (customer), DSM is often understood to include two

components: energy efficiency (EE) and demand response (DR). EE is

designed to reduce electricity consumption during all hours of the year;

DR is designed to change on-site demand for energy in intervals and

associated timing of electric demand by transmitting changes in prices,

load control signals or other incentives to end-users to reflect existing

production and delivery costs 0. By cooperative activities between the

utility and its customers to utilize DSM, it will provide the benefits to the

customer, utility, and society as a whole, which is summarized in Table I

0.

TABLE I

DSM BENEFITS TO C USTOMER, UTILITY AND SOCIETY 0

C ustomer benefits Societal benefits Utility benefits

Satisfy electricity demands Reduce environmental impacts Lower cost of service

Improved operating

Reduce / stabilize costs Conserve resources

efficiency,

Improve value of service Protect global environment Flexibility of operation

M aintain/improve lifestyle Maximize customer welfare Reduced capital needs

In the V2B option, the owners will plug in their vehicles during the day

at their final destination for a given time frame. As an example, this may

be either at their workplace (central business district) or at the place of

their study (university). The destinations, either parking lots or parking

garages next to the buildings, are assumed to be equipped with a bi-

20

directional charger and controller. The parking facility should allow either

charge or discharge mode for the car batteries when necessary. The idea is

that the parking facility can offer an aggregation service for charging the

batteries when the building demand is lower than its peak load and

discharge the batteries to partially supply the building to reduce the peak

demand during a high demand. This mode will be considered as DSM by

V2B. Considering the electricity rate when the vehicle batteries were

charged is lower than when the batteries are discharged, the battery storage

may be used to offset high cost during the peak demand.

Outage Management (OM)

Another important benefit of V2B is using the battery energy storage in

PHEVs/BEVs as an emergency back-up power for the commercial

facility/building, which increases the reliability of the power supply for

that load.

An outage is typically caused by several unplanned events and a timely

detection and mitigation of such situations is a real concern for the utility.

Outage management system helps the operators to locate an outage, repair

the damage and restore the service. Outage management must be

performed very quickly to reduce outage time. Recently completed project

proposes an optimal fault location scheme which will help the operator to

find the faulted section very quickly 0. In this chapter, the restoration

strategy under an outage will be mainly discussed.

The following types of outages and study the impact of PHEVs/BEVs

adoption are considered:

a) Outage beyond the distribution system: These may be caused by

generator failure, fault in transmission line or substation busbar. Usually

spinning reserves are kept for these circumstances. From the previous

studies it is concluded that PHEVs/BEVs can be a candidate solution for

spinning reserves (as the traditional fastest acting spinning reserve

generators are highly costly while PHEVs/BEVs qualify for fast response

with lesser cost). One may consider using a real-time security constrained

optimal power flow under the contingencies to calculate amount of

PHEV/BEV battery capacity required for a certain location at a specific

instance.

b) Outage in distribution system: These may be caused by fault inside

the distribution system and can be mitigated by precise spatial adjustment

of PHEV/BEV battery generation that may be used to feed elctricty locally

during and after outage.

To propose the restoration strategy where PHEV/BEV batteries are used

21

to mitigate an outage condition, the information about events (where the

fault is located and how the impact will propagate) and the location of the

battery storage need to be correlated. Thus a spatial as well as temporal

analysis should be performed.

The restoration strategy can be executed in the following steps:

1) Detect a fault;

2) Estimate the location of the fault;

3) Analyze the amount of battery generation required and the

availability of PHEVs/BEVs that can provide an alternative

generation support in the vicinity of the faulted area until the

faulted section is repaired. This will also consider the generation

duration requirement (i.e. time to repair the faulted section).

4) Schedule the aggregated PHEVs/BEVs generation optimally. This is

a multi-objective optimization problem which can be formulated to

minimize the distance between location of the fault and available

PHEVs/BEVs battery generation locations as well as minimize the

operating cost under system operation and security constraints.

Garage Location and Charging/Discharging Infrastructure

Commercial and public parking garages in a central business district

(CBD) provide thousands of parking spaces for commuters and visitors.

After penetrating the conventional vehicle market, owners of

PHEVs/BEVs will be using these parking garages, which may provide an

aggregated service to act as an electric power source or storage.

Figure 6 shows a simple transportation network with smart garage

building. As a smart garage is constructed, PHEV/BEV drivers have two

options: proceed to final destination directly or park at the smart garage

and walk to the destination along walking links. Drivers in transportation

network select parking garage based on the location and financial

incentives (less parking fee), which can be modeled as traffic assignment

problem. Demand of smart garage (number of parked PHEVs/BEVs)

calculated from the traffic assignment problem would vary by the location

and incentive of the smart garage.

22

Fig. 6. Simple transportation network with smart garage

Electric power capacity of smart garage is estimated based on demand

of smart garage. Demand of smart garage building is not constant.

Generally, the demand of smart garage building during the day would be

higher than during the night, similar to the demand structure for a

conventional garage as shown in Figure 7. Due to the versatility, electric

power capacity needs to be defined in two parts: for periodic service and

for continuous service as in Figure 7. The available electric power

estimated based on the demand of smart garage can be used for

determining the support service that can be provided during outage

management and demand side management in vehicle-to-building (V2B)

mode.

Fig. 7. Demand of smart garage for a day

23

Case Study

Test cases for two scenarios are studied: demand side management

using V2B mode during peak power demand and outage management

using V2B mode during faults.

Demand Side Management during Peak Power Demand

In this case, a large commercial building is analyzed to demonstrate the

potential savings using demand side management based on V2B operation.

Itron, Inc. prepared a technical survey for the California Energy

Commission (CEC), which modeled difference commercial sectors,

including large office building 0. The load shapes include typical day, hot

day, cold day, and weekend for each of four seasons. According to the

definition used in this report, large office buildings are defined as premises

with total floor area equal or larger than 30,000 square feet. The largest

electric end-uses in this building type are interior lighting, cooling, office

equipment, and ventilation 0.

The summer typical load shape for a large office building is selected for

our case study. The single building demand is obtained from the results

reported in the literature 0. The following assumptions are taken:

The studied building is 450,000 sq ft;

There are up to eighty PHEVs/BEVs that arrive at 8 AM and are

available for the entire day;

Maximum capacity of each vehicle is 10 kWh (very conservative

for BEVs);

The batteries in PHEVs/BEVs are drained by an average of 4.0

kWh by the driving cycle used.

When PHEVs/BEVs are on site, the building can charge the batteries

during the morning hours (lower electricity price) and drain the batteries

by an equal amount during afternoon hours (higher electricity price). Thus

the owner of PHEV/BEV will have the required energy in his/her battery

to make sure the driving cycle to return home can be met. Figure 8 shows

the impacts of charging PHEVs by faster charging methods (AC Level 3 or

DC charging). It will elevate the peak demand to 1.86 MW of the office

building since the faster charging method will cause a large load in a short

period (10-15 minutes), which is not recommend for either utilities or

customers.

24

Fig. 8. Impacts of faster charging PHEVs/BEVs on load demand

Figure 9 shows the change in the load shape for the typical summer day

by using the AC Level 1 charging method defined by the Society for

Automotive Engineers (SAE) J1772 [67]. The load curve was changed by

shifting the afternoon peak load to the morning off-peak load when

charging and discharging PHEVs/BEVs. Considering the rate structures

for peak and off-peak load in commercial buildings, peak load shifting

using V2B mode may provide the electricity bill saving. Further study

could be conducted to show the total saving expressed in dollars.

Fig. 9. Peak load shifting with PHEVs/BEVs for a typical summer daily load

Outage Management during Faults

The proposed restoration scheme was tested on a small distribution

25

system (IEEE 37 node radial test feeder 0).

This is an actual feeder located in California, which consists of several

unbalanced spot loads. The nominal voltage is 4.8kV.

Figure 10 shows the test feeder with smart garages at some nodes.

Fig. 10. Diagram of test feeder with smart garages

The following assumptions are taken:

Three nodes are specified as smart garages (nodes 718,735 and

740);

Maximum capacity of each vehicle is 10 kWh;

Discharge vehicles with state of charge (soc)>70%;

PHEV/BEV tariff for charging is 5c/kWh and for discharging is

(15-40) c/kWh (depending on different garages). Discharging tariff for

node 718 is 40 c/kWh, for node 735 is 30c/kWh, for node 740 is 25

c/kWh.

Under normal operating condition, node no. 799 acts as an infinite bus

and all the loads are fed through it. Two different outage cases are studied:

1) Case 1: Fault on or beyond node 799: PHEVs/BEVs at nodes

718,735 and 740 were scheduled to satisfy all the loads on the feeder.

26

Table II shows the case results.

TABLE II

CASE STUDY 1: RESULTS FOR P HEV/BEV GENERATION SCHEDULING

Node 718 N ode 735 N ode 740

Ph-1 Ph-2 Ph-3 Ph-1 Ph-2 Ph-3 Ph-1 Ph-2 Ph-3

(kW) (kW) (kW) (kW) (kW) (kW) (kW) (kW) (kW)

0 0-411-***-***-*** 427 339 380

Total cost for three phases is 733.2$/hr.

2) Case 2: Fault on line segment 703-730: Node 799 will supply all the

loads beyond this line segment. PHEVs/EBVs at nodes735 and 740 will be

scheduled to satisfy the island created by a fault on line 703-730. Table III

shows the case results.

TABLE III

CASE STUDY 2: RESULTS FOR P HEV/BEV GENERATION SCHEDULING

Node 735 Node 740

Ph-1 Ph-2 Ph-3 Ph-1 Ph-2 Ph-3

(kW) (kW) (kW) (kW) (kW) (kW)

300-***-***-** 0 81

Total cost for three phases is 221.35$/hr.

CONCLUSIONS AND FUTURE RESEARCH

The policy implications of widespread PHEV/BEV deployment in the

energy and transportation systems are explored. Previous research has

approached the problem from selected angles, making many simplifying

assumptions. Some thoughts on how the problem may be approached from

a non-myopic perspective are provided.

In summary, numerous policy shifts are needed to realize the full

potential of PHEVs/BEVs, and the cooperation of the transportation and

energy sectors is vital. If policies such as the ones outlined in this paper are

adopted, PHEVs/BEVs can provide many benefits to the electric grid in

terms of reliability and stability by acting as mobile decentralized storage

and allowing for vehicle-to-grid and grid-to-vehicle services. PHEVs/BEVs

will also allow for enhanced penetration of renewable energy resources

such as wind and solar, which will also aid with energy security by

reducing dependence on foreign sources of oil. Important benefits can be

made to air quality through transferring pollution from numerous mobile

27

sources to fewer point sources that are easier to control and may participate

in cap-and-trade markets. In addition to the carbon market, new markets

will be created in power systems due to the potential for PHEVs/BEVs (or

aggregators of PHEVs/BEVs) to participate, particularly with ancillary and

regulation services. Lastly, charging stations must be planned and

developed carefully to allow for flexibility in driver options and optimal

performance of the transportation and electricity networks.

The proposedmulti-layered modeling framework considers the spatial

and temporal nature of the system interactions. PHEV/BEV time-dependent

travel patterns are outputs of a transportation model and inputs to power

systems model. The framework also includes cognitive behavior modeling

for the purposes of developing appropriate incentives to encourage drivers

to behave in a way that improves the efficiency of the transportation and

energy systems.

The potential benefits of using PHEVs/BEVs as dynamically

configurable dispersed energy storage that can serve as load or generation

in a power system as needed is discussed. If serving in G2V as well as V2B

mode and if aggregated, PHEVs/BEVs may play a major role in both the

electricity the transportation networks. Selecting garage location and

charging/discharging infrastructure needs special attention from the

transportation system demand point of view. For demand side management

in electricity networks, the use of PHEVs/BEVs to create a peak load

shifting strategy can reduce the electricity purchase cost for the customer

and vehicle owner. For outage management in electricity networks, the use

of PHEVs/BEVs to generate power during outage restoration stage is

envisioned by solving a multi-objective optimization problem of merit-

order scheduling of PHEVs/BEVs under operating constraints.

In recent years, Smart Grid revolution has begun with the sponsorship

and involvement from government, businesses, utilities, and other

stakeholders, especially with the development and integration of renewable

energy resources. Envisioning the longer-term impact, if there is enough

aggregated PHEV/BEV vehicles, such as a fleet, they can serve as backup

generation and storage for renewable energy in smart grid applications.

Many other functions of the future electricity network may be affected

when PHEVs/BEVs acting as dynamically configurable energy storage,

which may have profound impact on the transportation networks as well

Better understanding the role of PHEVs/BEVs in coupled power and

transportation systems will be beneficial to transform existing power grid

into the Smart Grid, a power system that is more efficient, reliable, resilient

and responsive.

28

ACKNOWLEDGEMENTS

The author wishes to acknowledge numerous colleagues and graduate

students that contributed to the findings of this chapter: Dr. Bradley Love,

and Dr. Jennifer Duthie from The University of Texas at Austin, as well as

Dr. Ivan Damnjanovic and graduate students Mr. Chengzong Pang, Ms.

Papiya Dutta, and Mr. Seok Kim from Texas A&M University. Funding

for this study came from the National Science Foundation through I/UCRC

grant for the Center for PHEVs/BEVs: Transportation and Electricity

Convergence, and another NSF I/UCRC grant for the Power Systems

Engineering Research Center .

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