IEEE Network - March / April 2017 - page 24

IEEE Network • March/April 2017
22
0890-8044/17/$25.00 © 2017 IEEE
A
bstract
Making full use of V2G services, EVs with bat-
teries may assist the smart grid in alleviating peaks
of energy consumption. Aiming to develop a sys-
tematic understanding of the interplay between
smart grid and EVs, an architecture for the V2G
networks with the EV aggregator is designed to
maintain the balance between energy suppliers
(the grid side) and consumers (the EV side). We
propose a combined control and communica-
tion approach considering distributed features
and vehicle preferences in order to ensure effi-
cient energy transfer. In our model, the integrated
communication and control unit can achieve real-
time and intelligent management with the logic
controller and collected data. On the consumers’
side, we theoretically analyze how to satisfy the
charging constraints that we incorporate in the
form of willingness to pay, and propose a distrib-
uted framework to coordinate the energy delivery
behaviors for satisfying service demands. More-
over, illustrative results indicate that the proposed
approach can yield higher revenue than the con-
ventional pricing mechanism in V2G networks.
I
ntroduction
The conventional centrally controlled electrical
grid is facing numerous challenging problems,
including aging distribution networks and instabili-
ty [1]. During peak hours, the batteries of electric
vehicles (EVs) can be used as energy providers to
meet the energy demand, at least partially. Thus,
making use of vehicle-to-grid (V2G) technology,
EVs can act as both load and distributed storage
devices that are connected to the grid [2].
Recent work focuses on the charging system
of V2G networks. Studies such as [3] have shown
that EVs can act as intelligent storage devices
and provide fast and accurate response for spare
pools. Figure 1 illustrates a general scheme of
the architecture widely used in literature, main-
ly including three entities: EVs, aggregators, and
charging/discharging devices:
• EVs are connected to a charging station now,
and then when their state-of-charge (SOC)
goes below a certain value [4].
• The aggregator is the power service that
assigns the power demand based on the
energy demand and the grid condition.
• The charging/discharging device connected
with the EVs acts as the grid-to-vehicle (G2V)
and V2G accessing point [5].
For the restriction of each EV’s capacity,
numerous EVs are grouped together to offer con-
siderable auxiliary services to the conventional
electrical grid. The aggregator is a middleware
between the power grid and the EVs for aggre-
gating power from the EVs [6]. Thereafter, the key
issue in V2G services is to design a valid coordina-
tion scheme via the aggregator to dispatch a large
EV group for fulfilling the service request.
According to previous work [7], EVs, depending
on willingness, would participate and cooperate
with each other. In practice, because of selfhood,
EVs are only interested in increasing their own
profits, and they do not care whether the service
request is wisely completed or not. Additionally,
EVs with their own interests are able to make con-
scious decisions. Therefore, it may not be realistic
to suppose that EVs unconditionally abide by the
control rules or instructions imposed by the aggre-
gator. Thus, an appropriate incentive mechanism
should be devised to motivate a selfish and rational
EV group to collaborate in order to carry through
the V2G service. However, designing effective
incentive schemes in this scenario can be a very
challenging task because there is no information
symmetry between the aggregator and the EVs. In
practical applications, many concrete constraints
exist in the V2G network, such as arrival time and
departure time, and initial and objective SOC of
EV batteries. At different times, the EVs may have
different preferences toward charging/discharging.
Besides, such preferences of EVs are not clear to
the aggregator, so it is difficult to design effective
distributed schemes.
We consider the subjective concept of each
EV toward charging/discharging battery by mod-
eling an EV’s preference as a willingness to pay
(WTP) parameter [8]. Afterward, based on this
compound pattern, we formulate the V2G adju-
vant services and solve the problem using con-
tract theory [9]. With the asymmetric information,
an action delegated to selfish agents through
an opportune offer of a contract is considered.
In other words, the aggregator is considered to
process charging/discharging for EVs at a certain
time [10]. The optimal contract is designed so
that the aggregator can not only make the EVs
coordinate to provide the adjuvant service for
the power grid, but also maximize their own rev-
enue. The optimal contract is under a simple cir-
cumstance where the EVs get two optimal unit
prices published by the aggregator, where the
two unit prices are the unit selling price and the
unit purchasing price, respectively. Such an opti-
mal scheme based on a contract is complied
effectively in a distributed manner. Compared to
the conventional pricing mechanism [11], it is a
game-theoretic model to understand the interac-
tions among EVs and aggregators in a V2G mar-
ket, where EVs participate in providing frequency
regulation service to the grid.
Distributed Energy Management for Vehicle-to-Grid Networks
Kun Wang, Liqiu Gu, Xiaoming He, Song Guo, Yanfei Sun, Alexey Vinel, and Jian Shen
Kun Wang, Liqiu Gu,
Xiaoming He and Yanfei Sun
are with Nanjing University
of Posts and Telecommuni-
cations.
Song Guo is with the Hong
Kong Polytechnic University.
Alexey Vinel is with
Halmstad University.
Jian Shen is with Nanjing
University of Information
Science and Technology.
VEHICLE-TO-GRID NETWORKS
Digital Object Identifier:
10.1109/MNET.2017.1600205NM
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