A Cooperative Aggregation-based Architecturefor Vehicle-to-Grid Communications

Natalie Matta∗†, Rana Rahim-Amoud†, Leila Merghem-Boulahia∗, Akil Jrad†∗ICD/ERA (UMR CNRS 6279), Troyes University of Technology, France

Email: {natalie.matta, leila.merghem boulahia}@utt.fr†Laboratoire des Systemes electroniques, Telecommunications et Reseaux (LaSTRe), Lebanese University, Lebanon

Email: {rana.rahim, ajrad}@ul.edu.lb

Abstract—In the future Intelligent Transportation Sys-tems, vehicles will be equipped with special units givingthem the capacity to communicate wirelessly. Furthermore,electric vehicles are expected to take a bigger part ofthe market in the future. Global preoccupations to reducecarbon emissions and stimulate the use of renewable andsustainable energies have motivated the concept of vehicle-to-grid power, which integrates the electric vehicles withinthe smart grid (the electricity network of the future).The vehicle-to-grid vision aims to take advantage of thesebattery equipped vehicles, using them as a storage devicefor intermittent energy sources, and as a power resourceto provide ancillary services to the power grid. This paperfocuses on the architectural needs of such a system, specifi-cally at the communications level. It also presents an agent-based model for an aggregative system for vehicle-to-gridcommunications. The agent-based approach will allow thedistributed architecture to benefit from a variety of well-established communication and cooperation mechanismsand methods that can be applied to the vehicle-to-gridconcept.

I. INTRODUCTION

The emerging concept of Intelligent TransportationSystems (ITS) aims to “utilizing synergistic technologiesand systems engineering concepts to develop and im-prove transportation systems of all kinds” [1]. The con-cept adds a communication component to the road trans-portation networks. This will allow vehicles equippedwith an On-Board Unit (OBU) to form an ad hocnetwork and communicate with each other as well aswith other units that are typically placed along theroad, the Roadside Units (RSUs). Thus, a vehicularad hoc network (VANET) is formed [2]. Communi-cations within VANETs are called V2X communica-tions. They include Vehicle-to-Vehicle (between twoOBUs), Vehicle-to-Infrastructure (between an OBU andan RSU), and Vehicle-to-Person (between an OBU anda pedestrian) communications. A fourth type of commu-

Fig. 1. Illustrative schematic of power line and wireless controlconnections between vehicles and the electric power grid [3]

nications, called Vehicle-to-Grid (V2G), has emerged.It refers to the integration of electric vehicles with thesmart grid, in the objective of allowing electricity toflow, not only from the grid to the vehicle (G2V), butalso from the vehicle to the grid. In this paper, weare interested in V2G communications. Fig. 1 shows anillustration of the connections between vehicles and theelectric power grid which is controlled by a grid operator(the Independent System Operator ISO) [3].

In the context of V2G, the vehicles considered are bat-tery vehicles (BVs) equipped with an OBU. These vehi-cles are either plug-in hybrid electric vehicles (PHEVs),which are equipped with a power connection that allowsthem to recharge from the grid as well as from fuel,or electric drive vehicles (EDVs), which broadly use anelectric motor to drive the wheels. They communicatewith the electricity network, the smart grid, in the ob-jective of managing and controlling their power resource(the electric power). Vehicles capable of this are calledGrid Integrated Vehicles (GIVs) [4].

These communications aim to manage the use of aGIV’s battery as a power storage device and as a provider978-1-4577-1261-6/11/$26.00 c⃝ 2011 IEEE

of ancillary services to the electricity grid. First, energyproduced by intermittent renewable sources (such assolar and wind energy), as well as unused energy thatis produced at off-peak hours, can be stored for lateruse. Instead of investing in dedicated storage devices,available GIVs can be used for this purpose. In [5],MacKay considers the fluctuation of renewable energy,especially wind, and suggests the use of electric vehiclesfor its storage. According to the author, this option ispromising and scalable since it allows the power demandof EDVs’ charging to be switched on and off. Second,GIVs will be able to provide ancillary services to thesmart grid. These services can take the form of eitherregulation or spinning reserves. Regulation in the powermarket is used to keep the frequency and voltage steady;the spinning reserves are used for a quick response inthe event of failures [6]. In order to do so, GIVs needto participate in a power market where they can sell andbuy power under the regulation of a grid operator [4].However, in order to be able to provide the minimumpower capacity that is required in these markets, GIVsneed to be grouped and aggregated to provide the neededcapacity. This can be done in two ways. The firstpossibility, called fleet management, consists in usinga company’s vehicle fleet outside working hours. Thesevehicles have known and rather fixed schedules. Thisstrategy can be combined with a second one that wouldgroup personal-use vehicles [6]. In this case, we talkabout coalitions.

Therefore, it is clear that in the V2G vision, electricitydoes not only flow from the grid to the vehicle, but fromthe vehicle to the grid as well. It is expected that thisconcept will be adopted by the vehicles’ owners becausethe storage system that is purchased with the vehicle isgenerally idle 96% of the time [6]. The idea is to useGIVs, when they are not driven, as a large distributedbattery that provides power storage and ancillary servicesto the power grid. GIVs have the potential to even outthe demand for electricity by feeding electricity into thegrid from their batteries. They can do so during peakuse periods (such as midafternoon air conditioning use),while doing most of their charging at night, when thereis unused generating capacity. This V2G connection willhelp reduce the need for new power plants.

On one hand, V2G strategies and economics have beenreviewed ([3], [6], [7], [8]), along with some architec-tural frameworks ([7], [9]). On the other hand, since “thedecentralized nature and expected autonomous, intelli-gent behaviour of the smart grid have much in commonwith the Internet” [10], ICT approaches are needed tobuild and manage the grid. In particular, multi-agent

Fig. 2. A smart grid incorporates various energy sources, and iscoupled with an ICT layer. The aggregator acts as an intermediatebetween the power system and the users [15]

systems (MASs) constitute an appropriate technology forbuilding distributed systems with autonomous, and pos-sibly self-interested components [10]. They have beenapplied to the V2G context ([4], [11]) as well as thesmart grid in general ([12], [13], [14]).

This paper proposes an aggregative architecture forV2G communications that integrates MASs in the pur-pose of allowing cooperation, negotiation and tradingmechanisms within the elements of the network.

The rest of this paper is organized as follows. Somerelated work is given in section II. Our proposed systemarchitecture and scenario are exposed in sections III andIV respectively. Finally, we conclude in section V.

II. RELATED WORK

Fig. 2 shows an aggregator node as an intermediatebetween the power system and the users. Aggregatorsare assigned the role of optimizing the operation of aslarge a group as possible of buildings, industrial sites,and any power consumer in terms of their immediatepower demand, to get them involved in keeping thepower system in balance [15]. In particular, this could beapplied to vehicles in the context of V2G power. SomeV2G related work have focused on the optimization ofvehicle charging and discharging considering the settingof a constrained parking lot [16], or a setting where anaggregator provides regulation services to the grid whilemaximizing its revenue [17]. Aggregative architecturesfor V2G implementation have been considered in [7]and [9]. What seems to lack for V2G power is a proposalthat covers all the communication aspects of the system.Our paper provides a novel aggregative architecture thatensures robust V2G communications, while bringing the

added value of MASs mechanisms. It considers differentscenarios for V2G power implementation.

Quinn et al. consider two architectures for V2G ancil-lary services: the direct, deterministic architecture whereeach vehicle has a direct communication line with thegrid operator; and the aggregative architecture where anintermediate node, the aggregator, is inserted betweeneach group of vehicles and the grid operator [7]. Theystudy the effect of these communication architectures onthe availability, reliability and economics of the services.They conclude that the improvements, brought by theaggregative architecture to the system operator, have thedetrimental effect of reducing the revenue collected bythe vehicle owner; but that this architecture is morefeasible for a near-term realization.

In their proposal of a conceptual framework for V2Gimplementation [9], the authors base their architecture onaggregation. The aggregator creates groups of vehiclesand acts as an interface with the system operator. Thefocus is made on the range of services that can beprovided, and on a communication and control systembetween the different components. This paper presentsa different vision of the communications layer by mak-ing use of the existing infrastructure which guaranteescoverage and access to the vehicles (such as the GSMor WiMAX infrastructures). Multi-agent systems arealso used. They allow a wide range of techniques andmechanisms (auctions, trading, etc.) to be available forimplementation. In the literature, agents have been usedin the smart grid and V2G context. The authors of[4] model the coalition formation (grouping vehiclesaccording to specific strategies) problem for vehicles thatwish to participate in the power regulation market. Ina more general perspective, agents have been used tomodel self-interested competitive parties in the powergrid. The Continuous Double Auction1 is used as thebasis of the trading mechanism between homes andmicro-grids in [13]; and an adaptive storage strategyis proposed in [12] to account for the market pricechanges (the agents adapt over time to learn whichstorage strategies are preferable).

The next section exposes the architecture of the pro-posed system. Its aim is to propose a complete commu-nications infrastructure that is yet missing, but which isessential to the operation of V2G power.

III. SYSTEM ARCHITECTURE

In this section, a description of the proposed architec-ture’s elements is given, as well as the properties of the

1The continuous double auction is a mechanism to match buyersand sellers of a particular good, and to determine the prices at whichtrades are executed

Fig. 3. A general view of the proposed V2G architecture

agents of the system. It defines the various participatingelements, their roles and the agents that represent them.

A. The elements of the proposed architecture

Below, the proposed architecture for V2G communi-cations and its components (represented in Fig. 3) aredescribed.

• A vehicle fleet is a group of GIVs with known,rather fixed schedules. An example could be acompany’s delivery vans which are used on a dailybasis from 8 a.m. to 5 p.m. When they are not inuse, the vehicles are parked in a single location.Their collective storage capacity is an attractivesource for V2G power; the fleet can guarantee toprovide a minimum storage capacity to the grid;

• Individual, personal use GIVs, contribute to V2Gpower by forming coalitions based on their ge-ographical location at various times of the day,according to the grid’s needs;

• The grid operator advertises its need in power orexcess energy which needs to be stored, and makescontracts with service providers;

• A service provider would typically be a mobile op-erator [6] or a specialized power operator that offersthe V2G service [15] (along with other servicesand applications for VANETs). Using its coverageinfrastructure, it will be able to communicate with

the vehicles, and would have a direct link of com-munication with the grid operator;

• An aggregator could play the role of a coalitionor a fleet aggregator (or both). Information aboutthe group of vehicles is aggregated at this nodewhich acts as a sort of cluster head. It is responsiblefor optimizing scheduling (an example is given in[16]), forming coalitions based on a given strategy(an example is [4]), etc. Practically, in the case ofmobile operators for example, GSM base stations(BTSs) would be appropriate to play the role ofan aggregator given their coverage zones. Anotheroption would be to have the RSUs aggregate groupsof vehicles parked in their surroundings. In the caseof fleets, a company may choose to have its ownaggregator, or to have a contract with a certainservice provider that already has an aggregatorcovering the area.

An aggregator will handle the information transmittedby each GIV and analyze it. This information consistsof the following elements:

• ID: used to uniquely identify the vehicle. It couldbe the Vehicle Identification Number (VIN);

• The battery’s state of charge (s.o.c): defined as theratio of the energy stored in a battery to the capacityof the battery [9];

• Available hours: for fleets, fixed V2G shift hoursare known in advance. For other vehicles, prefer-ences and/ or constraints are communicated to theaggregator;

• Connection status: indicating whether the vehicle isconnected or disconnected. The vehicle could sendthe ID of the plug-in station to which it is connectedin the first case, and send some default value (nullfor example) when it is not connected;

• minMW: a threshold for the minimum power to beavailable in the battery at all times for emergencypurposes.

Note the communication links between a vehicle andan aggregator are not necessarily permanent. The vehiclemay choose to cooperate with one aggregator at somepoint and switch to another later on. This choice can bebased on various strategies (their development is outsidethe scope of this paper).

B. The agents’ propertiesMASs are appropriate in distributed settings such

as this one, and bring the added value of a rich setof techniques, algorithms and methodologies, such ascooperation mechanisms as well as the possibility ofimplementing well-known trading mechanisms that arewidely used in other applications (auctions for e.g. [13]).

Fig. 4. Scenario with three aggregators

In the proposed architecture, each vehicle is repre-sented by an agent: fleetGIV and userGIV respectivelyrepresent a vehicle in a fleet and a personal-use vehicle.The aggregators are also represented by agents: thefleetAGG and the coalitionAGG. Finally, the gridOPagent represents the grid system operator.

Agents in a fleet are cooperative, however userGIVagents are self-interested. This does not mean thatthey do not cooperate, but they individually aim atmaximizing their monetary profits, whereas fleet agentsare not pre-occupied with this issue since the benefitsgo to the company. Note that in a more complex setting,it is possible that profits be distributed among theagents of the company. Aggregators of different serviceproviders are competitive. They are utility-based; highermonetary profits will result in higher utility for theagent. In addition, userGIV agents may be predictiveand have learning abilities, which would be used topredict the owner’s next trip. These abilities will not bediscussed here as they are beyond the scope of this paper.

After defining the architecture for V2G communica-tions, a scenario of operation is presented in the nextsection.

IV. SCENARIO

This section gives the outline of a typical scenariowith respect to the different elements of the architecture,starting with coalitions, fleets, and finally the serviceproviders.

A. Coalitions

The case of a vehicle entering a given geographic zonecovered by one or more coalition aggregators that belongto different service providers is considered.

A coalition aggregator constantly broadcasts a mes-sage announcing the availability of its service. A vehiclethat wishes to cooperate will respond to this message.It communicates its information (ID, s.o.c, etc. - seesection III) to the aggregator, which initiates a ne-gotiation process between the two parties (upon thereceipt of a positive response from the aggregator). If anagreement is reached, the exchange of V2G power cantake place. Note that the vehicle needs to be physicallyconnected (plugged-in) to the grid (the details of thepower exchange are beyond the scope of this paper).

The negotiation is typically done over the price inexchange for storage space (G2V) and/or stored power(V2G). It is expected that the aggregator will have fixedprices for given conditions. However, it is possible toforesee the establishment of a customer fidelity systemthat would encourage the vehicle to cooperate with aspecific aggregator rather than another. The details ofthe negotiation algorithm are still under conception.

When the agreement is reached, the vehicle expectsa certain amount of energy (MWs) to be available inits battery at time t hours so it can pursue its trip(s).However, there might be some cases where the vehiclewould have to be plugged out earlier than expected (forunexpected trips such as emergencies, etc.). A minimumamount of energy needs to be kept in the battery so thistrip can be performed. This threshold can be fixed bythe vehicle during the negotiation process.

Fig. 4 shows a GIV in the coverage zone of aggrega-tors AGG1 and AGG2. The vehicle chooses to cooperatewith AGG1, as it has a previous agreement and a fidelityprogram with it. The second GIV is in a zone covered byaggregators AGG2 and AGG3. Having no preferences,the agent’s vehicle initiates a negotiation with bothaggregators. The agreement is done with AGG3 andthe agents cooperate. The link is temporary because thevehicle may choose to cooperate with AGG2 at anothertime.

An outline of a userGIV and a coalitionAGG algo-rithm is given in Algorithms 1 and 2 respectively.

B. Fleets

The fleet scenario is less complex given the factthat a vehicle’s schedule and its location is known inadvance. A postal services company will designate acertain parking space for its vehicles to be plugged into the grid. It will usually have a pre-defined agreement

Algorithm 1 A userGIV algorithm for V2G communi-cations{The agent sends the vehicle’s information to the ag-gregator it chose to cooperate with. structInfo containsthis information (ID, s.o.c, etc.):}sendInfo(structInfo, coalitionAGG)if (negotiation(userGIV, coalitionAGG) =agreement) then

while (isConnected = true) do{While the vehicle is connected, it will receivecharge and discharge commands to exchangeV2G power:}exchangeV2Gpower(structInfo)

end whileend if{Signal unexpected plug out to the aggregator:}if ((isConnected = false)and(currentT ime <announcedP lugOutT ime)) thenunexpectedP lugOut← truesignalUnexpectedPlugOut(unexpectedPlugOut,coalitionAGG)

end if

Algorithm 2 A coalitionAGG algorithm for V2G com-munications

handleInfo(structInfo)if (negotiation(userGIV, coalitionAGG) =agreement) then

while (isConnected = true) do{Plan a charge/ discharge schedule to optimizebattery use based on the vehicle’s information(s.o.c, available hours, minMW, etc.):}chargeDischargeOptimizer(structInfo)

end while{The vehicle is no longer connected. Check if therewas an unexpected plug-out and handle it:}if (unexpectedP lugOut = true) then

handleEmergencyPlugOut()end if

end if

with one or more aggregators (in case it does not haveits private fleet manager), or with service provider(s)to which it sells its battery capacity. Thus, the fleetagents need not perform a price negotiation. Note thatthe company will have the flexibility to re-negotiate itscontract(s) and change to another service provider. Anoutline of a fleetGIV algorithm is given in Algorithm 3.

Algorithm 3 A fleetGIV algorithm for V2G communi-cations{If there is one or more modified parameters forthe vehicle, it will send the new information to theaggregator:}if (parameterF lag = true) then

sendInfo(structInfo, coalitionAGG)end ifwhile (isConnected = true) do{While the vehicle is connected, it will receivecharge and discharge commands to exchange V2Gpower:}exchangeV2Gpower(structInfo)

end while{Signal unexpected plug out to the aggregator:}if ((isConnected = false)and(currentT ime <announcedP lugOutT ime)) then

unexpectedP lugOut← truesignalUnexpectedPlugOut(unexpectedPlugOut,coalitionAGG)

end if

C. The grid operator and service providers

It is the grid operator that manages the physicaltransmission of power over the grid. It communicatesto the service providers its storage or power needs (forthe regulation service for example, a regulation signalrequest is sent frequently, every 4 seconds for e.g.[4]). The grid operator will coordinate with the serviceproviders that satisfy its needs. It may buy storage fromone provider and power from another, etc.

V. CONCLUSION

This paper has presented an aggregation-based ar-chitecture for vehicle-to-grid communications. It hasproposed the embodiment of agents at the different net-work elements. These agents are responsible for makingcontracts and decisions with respect to the formation ofvehicle groups, as well as the charging and dischargingof the vehicles, etc. We have illustrated our approachwith an example of an expected scenario.

In the future, we plan to study in more detailsagents’ interactions in order to refine our model. Possibledecision-making strategies can be investigated and com-pared. Performance evaluation will subsequently follow.

We also plan to propose a solution for the managementof the information provided by wireless sensors placedon the vehicles. Integrated with other sensors’ data con-cerning energy measurements, a decision can be made asto whether the vehicle can take the energy it needs from aprivately-owned source (home solar panels for e.g.), and

at what times to do so. This energy could also be storedin the vehicle’s battery to be used at later times, or itcould be sold back to the grid or directly to another user.This vision will allow the electricity market to becomean open market where any user can sell power.

VI. ACKNOWLEDGMENT

This work was supported in part through grants fromthe Troyes University of Technology and the LebaneseUniversity.

REFERENCES

[1] I. I. T. S. Society, “Intelligent transportation systems,”http://www.ewh.ieee.org/tc/its/index.html.

[2] H. T. Cheng, H. Shan, and W. Zhuang, “Infotainment and roadsafety service support in vehicular networking: From a communi-cation perspective,” Mechanical Systems and Signal Processing,vol. 25, no. 6, pp. 2020 – 2038, 2011.

[3] “Vehicle-to-grid power fundamentals: Calculating capacity andnet revenue,” Journal of Power Sources, vol. 144, no. 1, pp. 268– 279, 2005.

[4] S. Kamboj, K. S. Decker, K. Trnka, N. Pearre, C. Kern, andW. Kempton, “Exploring the formation of electric vehicle coali-tions for vehicle-to-grid power regulation,” ATES@AAMAS 2010,May 2010.

[5] D. J. MacKay, “Sustainable energy: without the hot air,”http://www.withouthotair.com/.

[6] W. Kempton and J. Tomic, “Vehicle-to-grid power implementa-tion: From stabilizing the grid to supporting large-scale renewableenergy,” Journal of Power Sources, vol. 144, pp. 280–294, 2005.

[7] C. Quinn, D. Zimmerle, and T. H. Bradley, “The effect ofcommunication architecture on the availability, reliability, andeconomics of plug-in hybrid electric vehicle-to-grid ancillaryservices,” Journal of Power Sources, vol. 195, no. 5, pp. 1500 –1509, 2010.

[8] T. Yiyun, L. Can, C. Lin, and L. Lin, “Research on vehicle-to-grid technology,” in Computer Distributed Control and IntelligentEnvironmental Monitoring (CDCIEM), 2011 International Con-ference on, feb. 2011.

[9] C. Guille and G. Gross, “A conceptual framework for the vehicle-to-grid (v2g) implementation,” Energy Policy, vol. 37, no. 11, pp.4379 – 4390, 2009.

[10] A. Rogers and N. Jennings, “Intelligent agents for the smart grid,”PerAda Magazine, 2010.

[11] E. Gerding, V. Robu, S. Stein, D. Parkes, A. Rogers, and N. Jen-nings, “Online mechanism design for electric vehicle charging,”in AAMAS 2011, May 2011, pp. 811–818.

[12] P. Vytelingum, T. D. Voice, S. D. Ramchurn, A. Rogers, andN. R. Jennings, “Agent-based micro-storage management for thesmart grid,” in AAMAS 2010, May 2010, pp. 39–46.

[13] P. Vytelingum, S. D. Ramchurn, T. D. Voice, A. Rogers, andN. R. Jennings, “Trading agents for the smart electricity grid,”in AAMAS 2010, May 2010, pp. 897–904.

[14] K. Kok, G. Venekamp, and P. Macdougall, “Market-based controlin decentralized electrical power systems,” ATES@AAMAS 2010,2010.

[15] V. Environnement, “Smart grids,” Scientific Chronicles Magazine,vol. 18, Sept. 2010.

[16] A. Saber and G. Venayagamoorthy, “Optimization of vehicle-to-grid scheduling in constrained parking lots,” in Power EnergySociety General Meeting, 2009. PES ’09. IEEE, july 2009, pp. 1–8.

[17] S. Han, S. H. Han, and K. Sezaki, “Design of an optimalaggregator for vehicle-to-grid regulation service,” in InnovativeSmart Grid Technologies (ISGT), 2010, jan. 2010, pp. 1 –8.