ArticlePDF Available

Electric vehicles and smart grid interaction: A review on vehicle to grid and renewable energy sources integration

Electric vehicles and smart grid interaction: A review on vehicle to grid
and renewable energy sources integration
Francis Mwasilu, Jackson John Justo, Eun-Kyung Kim, Ton Duc Do, Jin-Woo Jung
Division of Electronics and Electrical Engineering, Dongguk University-Seoul, 26, Pil-dong 3-ga, Jung-gu, Seoul 100-715, South Korea
article info
Article history:
Received 10 July 2013
Received in revised form
29 October 2013
Accepted 13 March 2014
Vehicle to grid (V2G)
Electric vehicle (EV)
Smart grid
Advanced metering infrastructure
Smart charging
Renewable energy sources
This paper presents a comprehensive review and assessment of the latest research and advancement of
electric vehicles (EVs) interaction with smart grid portraying the future electric power system model.
The concept goal of the smart grid along with the future deployment of the EVs puts forward various
challenges in terms of electric grid infrastructure, communication and control. Following an intensive review
on advanced smart metering and communication infrastructures, the strategy for integrating the EVs into
the electric grid is presented. Various EV smart charging technologies are also extensively examined with the
perspective of their potential, impacts and limitations under the vehicle-to-grid (V2G) phenomenon.
Moreover, the high penetration of renewable energy sources (wind and photovoltaic solar) is soaring up
into the power system. However, their intermittent power output poses different challenges on the planning,
operation and control of the power system networks. On the other hand, the deployment of EVs in the
energy market can compensate for the uctuations of the electric grid. In this context, a literature review on
the integration of the renewable energy and the latest feasible solution using EVs with the insight of the
promising research gap to be covered up are investigated. Furthermore, the feasibility of the smart V2G
system is thoroughly discussed. In this paper, the EVs interactions with the smart grid as the future energy
system model are extensively discussed and research gap is revealed for the possible solutions.
&2014 Elsevier Ltd. All rights reserved.
1. Introduction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 501
2. EVs integration into electric grid . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 502
2.1. EV charging and electric grid interaction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 503
2.2. EVs with V2G system architecture . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 506
3. EVs and smart grid infrastructure. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 506
3.1. EV smart charging . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 507
3.2. Advanced metering infrastructure with EVs. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 507
3.3. Advanced communication and control network infrastructure with EVs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 508
4. Renewable energy sources integration with EVs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 509
4.1. PV solar energy with EVs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 511
4.2. Wind energy with EVs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 513
5. Feasibility of smart V2G system . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 513
5.1. Intelligent EV scheduling. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 513
5.2. Renewable energy sources integration using EVs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 514
5.3. V2G impacts, potential and limitation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 514
6. Conclusion and future trends . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 515
Acknowledgments. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 515
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 515
Contents lists available at ScienceDirect
journal homepage:
Renewable and Sustainable Energy Reviews
1364-0321/&2014 Elsevier Ltd. All rights reserved.
Corresponding author. Tel.: þ82 2 2260 3348; fax: þ82 2 2275 0162.
E-mail address: (J.-W. Jung).
Renewable and Sustainable Energy Reviews 34 (2014) 501516
1. Introduction
In the world today fossil fuels are the dominant energy sources
for both transportation sector and power generation industry.
Depletion of fossil fuel reserves gives a wakeup call for nding
the alternative energy sources for these sectors. In fact, the future
of oil economy which is considered to be highly dependable by
vehicle eets in the world is not only unsustainable but also very
limited. Besides, burning fossil fuels produces greenhouse gases
(GHGs) which highly inuence the world climate change. Accord-
ing to the report [1], the oil consumption in transport sector will
raise by 54% until the year 2035. Also the projection by Energy
Information Agency (EIA) reveals that the oil prices will substan-
tially rise in the next two decades. In this context, various efforts
related to reducing oil consumption have emerged. In the trans-
portation sector, electric vehicles (EVs) are the promising solution
and they are taking a remarkable pace in the vehicle market. In the
future, the economic studies predict a replacement of the internal
combustion engine vehicles (ICEVs) with the EVs. The Australian
Energy Market Commission (AEMC) projection shows up that by
the year 2020 the growth in the EV's share of new vehicle sales
will increasingly account for less than 10%, and it will further
account for 15% to 40% increase of the new light vehicle sales after
the year 2020 [2]. Much effort has to be devoted to reach the
future EV market projections as they feature high initial cost
compared to the ICEVs.
On the other hand, the electrication of transportation sector
appears to be one of the feasible solutions to the challenges such
as global climate change, energy security and geopolitical concerns
on the availability of fossil fuels. The EVs are potential on serving
the electric grid as independent distributed energy source. It has
been revealed by some studies that most vehicles are parked
almost 95% of their time. In this case, they can remain connected
to grid and be ready to deliver the energy stored in their batteries
under the concept of vehicle to grid (V2G) introduced earlier by
Kempton [3].
To this end, the EV technology can provide the grid support
by delivering the ancillary services such as peak power
shaving, spinning reserve, voltage and frequency regulations [4]
whenever needed. Besides, the integration of large renewable
energy sources (RES) like wind and photovoltaic (PV) solar
energies into the power system has grown up recently. These
RES are intermittent in nature and their forecast is quite unpre-
dictable. The penetration of the RES into the power market is
enormously increased to meet the stringent energy policies and
energy security issues.
China for the year 2020 has set a goal to install 150180 GW o f
wind power and 20 GW of PV solar power. This huge penetration
of the RES into power system will require large energy storage
systems (ESS) to smoothly support electric grids so that the
electrical power demand and operating standards are met at all
the times [5]. In this case, the EV eets are the possible candidate
to play a major role as the dynamic energy storage systems using
the V2G context. To this point, the EVs can be aggregated and
controlled under the virtual power plant (VPP) concept model [6].
While the EVs are providing these opportunities through charging
and discharging of their battery packs, a number of challenges are
imposed to the power system grid. These challenges compel the
changes on the planning, operation and control of the electric grid
[7]. To the utility, the EVs are both the dynamic loads which are
difcult to schedule but also a potential back up for the electric
grid. Similarly, the vehicle owners have some notion that posses-
sing an EV will substantially increase an extra operating cost when
compared to owning an ICEV. Hence, an attractive scenario is
needed to merge them so that a sharing of load can be realized
between the two parties.
However, as the majority of the people witness and become
aware of the contemporary penetration of the EVs, they would
require knowing how much it costs for recharging their vehicles
and nd a way to minimize charging costs similar to their usual
ICEV refueling practice. On the other hand, a cost for selling power
to the grid should instantaneously be known by the vehicle
owners or EVs eet operator/aggregator in the case of providing
V2G services. Furthermore, the aggregator has to know in real-
time the characteristic parameters (i.e. driving patterns, state of
charge, total capacity, etc.) of the aggregated EVs for the network
management response such as demand side management issues,
frequency regulation and other ancillary services [8].Denitely,
this demonstrates how the EVs would change the way we daily
understand and interact with the electric grid. The cost of
electricity will be sensitive and determinant factor for the EV
owners or energy market players to interact with the grid while
the load prole will dictate on the grid operator (GO) side.
With the deregulated power market, the real-time-pricing
scenario is quite intuitive but it requires advanced metering,
information and communication control systems. This is shifting
the existing grid to the future electric grid network mostly
referred to as smart grid where the EVs as dynamic loads and
potential energy buffer (i.e. dynamic ESS) can be accommodated.
In the smart grid infrastructure, the real-time pricing and com-
munication are conceivable through smart metering and advanced
information and communication technology (ICT) [9]. Intelligent
scheduling of the EV charging is also attainable to relieve the
stresses on the power distribution system facility. These mutual
relationships between the EVs and smart grid make a perfect
match for a modern power system model.
To further identify and potentially utilize these aforementioned
opportunities a clear understanding of an integrated framework of
the EV niche market, distributed RES and electric power grid is
vital and indispensable. This paper extensively reviews and
assesses the EVs interactions with the smart grid infrastructure
as the future energy system model. A research gap is discussed to
unveil possible solutions and the EV-V2G future research trends
are uncovered. The integration of the renewable energy sources
especially wind and PV solar using the EVs is evaluated in the light
of the latest research works. We also examine the feasibilities of
the V2G transactions under the recent pilot projects and demon-
strations. For the purpose of this study, the battery electric vehicle
(BEV) and plug-in hybrid electric vehicle (PHEV) can be referred to
as electric vehicles (EVs).
The paper is organized as follows: the integration of the EVs
into the power system under the V2G concept and its realization
within the VPP phenomenon are reviewed and discussed in
Section 2. On the other hand, Section 3 extensively assesses the
interaction of the EVs and smart grid with the focus on the smart
charging and advanced metering and communication infrastruc-
tures. An intensive review on the integration of the RES using EVs
is presented in Section 4. Moreover, Section 5 evaluates the
feasibility of the EV integration in the smart grid infrastructure
with an insight of the relevant current and future projects. Also
the general EV and V2G future trends are unveiled in this section.
At last, the conclusion is drawn in Section 6.
2. EVs integration into electric grid
Integration of a large number of EVs into the electric power
system is a major challenge which requires an intensive assess-
ment and observation in terms of economic impacts, operation
and control benets at optimal conditions. Many existing litera-
tures analyzed the impact of the EVs on the distribution
power system [10] while others dig-down the different application
F. Mwasilu et al. / Renewable and Sustainable Energy Reviews 34 (2014) 501516502
models on how to realize this EVs adoption into the power system
[11]. Based on the recent studies, the majority of the EV charging
systems are conceived to be undertaken at home. On the other
hand, the EV charging is also foreseen to be mainly taking place in
the public, commercial or working place charging stations [12].
Therefore, the consequences of the EV charging are expected to
directly affect the electric power distribution system. These effects
range from overheating power transformers to incurring new
investments of power distribution facilities. However, the adop-
tion of the EVs can be able to extensively add value in the electric
grid in terms of performance, efciency and power quality
improvements. This is possible if at all the large number of the
EVs integration is well planned and technically reorganized to
conform to the power system operational standards [13].
To realize the actual benets of integrating large EV eet into
the electric grid, different approaches have been proposed in the
literatures. The backbone of this scenario is of two folds, that is,
the electric vehicle owner and utility entity. More importantly,
both parties can enjoy the system interaction at the expense of
advanced control, ICTs and operation compromises. The most
common architecture explicitly involves the EV aggregator and it
has gained interests to the researchers in the recent years [14]. The
aggregator is considered to be a central in-charge who coordinates
all the required operational activities like communicating with the
distribution system operator (DSO), transmission system operator
(TSO) and energy service providers. In most cases, the aggregator
maintains the link between energy market players and the EV
owners. Besides, the realization of this EV integration can be
conceived within the virtual power plant (VPP) concept in which
the electric vehicles are clustered and controlled as a single
distributed energy source [15]. Within the VPP architecture, the
EVs are visible to the DSO, TSO or grid operator (GO) through the
aggregator and can easily participate in the energy market. On the
other hand, another possible solution is to integrate large EV eet
in the sense that individual vehicle owners play a central role to
participate in the energy market [7,16]. This means that the EV
owner is dedicated to manage the queries from the DSO, TSO and/
or energy market players with the help of the two-way commu-
nication and control systems. Recent literatures have presented
this model of the EV integration by optimizing charging price so
that the EV owner can minimize the charging cost at all times
while reducing the stresses on the power grid [16]. With this
integration scheme, the aggregator as a third party is not com-
pletely isolated but rather indirectly involved. This can be mani-
fested with the price responsive bids in the energy market.
However, to some extent this integration scheme might not be
reliable because dealing with each individual EV owner increases
the complexity in energy planning, security and control. To be
more precise, the optimization function becomes complex with
It is noted that the battery technology dictates the EV penetra-
tion into energy market. The battery technology involves numer-
ous chemistries such as lithium-ion (Li-ion), lead acid and nickel
metal hydride (NiMH). The center for the massive penetration of
the EVs into the world power market and transportation industry
relies mainly on the intensive research in the battery technology. It
is well known that this poses more challenges towards the initial
cost reduction, vehicle performance (e.g. driving range) and high
prot margin in the power market. In the V2G application, the
battery lifetime is highly affected due to imposing frequent
charging and discharging cycles. This effect has become prominent
and gained interests by researchers recently [17]. The interesting
study by Peterson et al. [18], investigated the capacity fade
characteristics of the lithium-iron-phosphate (LiFePO
) battery
cells when deployed in both V2G and normal driving modes. The
battery loss capacity was quantied as the function of the driving
days, combined energy usage and battery capacity. The study
revealed that this type of battery sustains the frequent charging
discharging cycles with very minimal capacity loss. Guenther et al.
[19] conducted a study to examine the battery degradation
characteristics of the Li-ion battery based on the aging model.
The loading behavior took into account various combinations of
driving scenarios, charging schemes and peak shaving (V2G
transaction). The results show that V2G transactions reduce the
battery lifetime for nearly 3 years because of the prolonged
discharging cycles and greater cycle depths. However, the battery
life can be extended by adopting intelligent charging schemes.
More studies are required to unveil the other battery life span
behavior under these promising EV application scenarios espe-
cially the V2G transactions. The realistic battery model for these
studies should consider calendar aging, self-discharging and aging
cycles as whole. The future expectation is to have the batteries
with high energy and power capacities, small size and affordable
purchasing cost. Table 1 presents the current battery technologies
used by various automotive manufacturers.
To this end, a real time-advanced communication is a vital
ingredient for the information exchange especially pricing, energy
forecast and EV-driving characteristics among parties. Hence to
successfully operate this scenario, the smart grid platform is
indispensable. In smart grid implementation, an advanced com-
munication infrastructure can be easily accessed and make it a
potential moving target for the EV penetration into the energy
market. The subsequent sections will give a detailed review on
these electric vehicles to smart grid interaction scenarios.
2.1. EV charging and electric grid interaction
EV charging is one of the fundamental schemes in the electric
vehicles' applications. There are several charging levels for EVs
that reect the power capability and charging duration. These
Table 1
Battery capacity and technologies by various EV manufacturers.
S/N Car model/EV type Company Battery chemistry Capacity [kWh]
1 Chevrolet Volt/PHEV GM Lithium manganese oxide spinel Polymer (LMO spinel) 16.5
2 Prius Alpha/PHEV Toyota NiMH 1.3
3 Prius (ZVW35)/PHEV Toyota Lithium nickel cobalt aluminum oxide (NCA) 4.4
4 Leaf/BEV Nissan Lithium manganese oxide (LMO) 24
5 iMiEV/BEV Mitsubishi Lithium manganese oxide (LMO) 16
6 E6/BEV BYD Lithium iron phosphate (LFP) 75
7 Tesla model S/BEV Tesla Lithium manganese oxide (LMO) 85
8 Chevrolet spark/BEV GM Nano lithium iron phosphate (LFP) 21.3
9 Fiat 500e/BEV Chrysler Lithium iron phosphate (LFP) 24
10 Honda Accord/PHEV Honda Lithium manganese oxide (LMO-NMC) 6.7
F. Mwasilu et al. / Renewable and Sustainable Energy Reviews 34 (2014) 501516 503
levels have been standardized to reveal the EV slow or fast
charging scenarios. The slow charging (typically up to 8 h-PHEV
or 20 -BEV) can be experienced at home or ofce areas whereas
the fast charging (typically 15 min to 1 h) at dedicated charging
stations in commercial or public places. As shown in Table 2 [20],
the AC Level 1 is practically realized at home environment while
the AC Level 2 is suitable for public and commercial areas like
workplace, movie theaters, shopping malls etc. However, the DC-
fast charging (DC Level 13) is envisioned to cover the public,
private or commercial charging stations [20,21].
The charging power delivered is usually determined by the
nominal ratings of the battery charger and in most recent studies
the EV battery voltage is typically limited to a less or equal to
400VDC (DC bus voltage). Also, the charging time that the EV can
spend fully charging its battery pack may vary depending on the
battery storage capacity and charging level characteristics (voltage
and current ratings). There is a great debate on how to standardize
the fast charging portfolio. However, fast charging is essential for
charging the EV battery within few minutes. The recent develop-
ment of a universal charging facility accomplished by the global
automakers in collaboration with the Society of Automotive
Engineers (SAE) incorporates both the AC charging and DC-fast
charging solutions. It combines AC single-phase charging, AC
three-phase charging (AC-fast charging) and utra-fast DC charging
in a single unit connector (SAE combo standard). Besides, the fast
charging standard known as CHAdeMO, which was developed by
the Tokyo Electric Power Company (TEPCO), is also attaining a
remarkable acceptance in the EV market [21]. This will attract the
adoption of the EVs as a reliable transport facility as it will mimic
the fast ICEV refueling phenomenon. The recent study by
Chaundhry and Bohn [22] proposes an overview of the V2G
application using DC fast charging Level 1 supporting up to
36 kW, Level 2 up to 90 kW and DC fast charging with the
CHAdeMo standard capable of delivering power up to 62.5 kW.
This is one of the attempts to unveil the feasibility of the V2G
using the DC fast charging infrastructure. In this study the AC Level
1 and 2 are also investigated.
However, the current power system is supplying the AC
voltages to the loads. In order to supply power to the EV battery
pack, a rectier power circuit is mandatory. But the cost and
thermal issues limit the power capability of the rectier circuit.
Noting that, the DC-fast charging infrastructure requires high
power capability (in terms of current and voltage ratings) as can
be observed in Table 2. In this case the size and volume of the
rectier circuit have a great impact on the DC-fast charging
infrastructure as it reects the circuit dimensions to be used for
the EV application. There are very few literatures that account for
the feasibility, impacts and economics of the DC fast charging
solution. In the decade to come this type of EV charging will be the
most promising charging solution and the stations will be visua-
lized as existing gasoline relling stations. The challenge remains,
being high power demand from these stations which would
require a dedicated power supply, power conversion interface
modeling and battery life span. And it is posing a great challenge
on the deployment of the V2G services. Feasibility studies are
required to unveil the features and performance of the DC fast
charging infrastructures for the V2G services.
The revised version of the SAE standard J1772 released in
October 2012 [20] introduced more exibility to accommodate
the EVs particularly for the V2G and charging solutions in the
smart grid environment. This includes the DC fast charging levels,
electric vehicle supply equipment (EVSE) requirements and
reverse energy ow communication portfolios for the PHEVs.
And the National Electrical Code (NEC) in article 625 (NEC 625)
and IEC 62196 cover other details on the EV charging systems. The
advances in bidirectional power converters for electric chargers
with low electromagnetic interference (EMI) to support the V2G
will now may be standard for the EVs. Figs. 1 and 2 illustrate the
EV charging congurations for the AC Level 1 & 2 requirements
(EV includes an on-board charger) and DC Level 1 & 2 (electric
vehicle supply equipment (EVSE) includes an off-board charger),
respectively. The two gures illustrate the setup of the facilities at
the charging point and embedded EV kits for charging scenarios by
considering the AC and DC charging levels as depicted in Table 2.
With the AC Level 1 and 2 congurations in Fig. 1, the electric
vehicle supply equipment (EVSE) is provided at the charging point
by supplying the AC power to an on-board charger. However, with
the DC Level 1 and 2 congurations in Fig. 2, the charging point
supplies the DC current to the EV battery pack.
With the current EV battery technology such as 24 kWh battery
pack for Nissan Leaf, to recharge an EV will consume power almost
the same as a single household in Europe or US per day. When two
or three EVs are connected for charging purposes, there is a
proportional growth of the energy usage. Hence, it reects the
increase in the consumption capacity to the existing grid infra-
structure. The authors in [23] surveyed various issues regarding
the electrication of the transport sector. They include the policies
to foster the EV adoption, charging infrastructures and standards.
The study reported that the 3.3 kW charger used at 220 V/15 A
would increase the current demand of the household by 1725%.
Different charging schemes have been discussed recently regard-
ing the driving patterns of the vehicle owner and existing grid
Table 2
AC/DC charging levels characteristics as per SAE J1772 standard.
Power level type Voltage level [V] Current capacity [A] Power capacity [kW] Remark(s)
AC Level 1 120VAC 12 1.4 1-phase supply (EV contains an on-board charger)
16 1.9 Charging time PHEV: 7 h
BEV: 17 h
AC Level 2 240VAC Up to 80 19.2 1 or 3-phase supply (EV contains an on-board charger)
3.3 kW charger PHEV: 3 h
BEV: 7 h
7 kW charger PHEV: 1.5 h
BEV: 3.5 h
AC Level 3 –– 420 Under development
DC Level 1 200500VDC o80 Up to 40 3-phase supply (EVSE contains an off-board charger)
20 kW charger PHEV: 22 min
BEV: 1.2 h
DC Level 2 200500VDC o200 Up to 100 3-phase supply (EVSE contains an off-board charger)
45 kW charger PHEV: 10 min
BEV: 20 min
DC Level 3 200600VDC o400 Up to 240 Under development
F. Mwasilu et al. / Renewable and Sustainable Energy Reviews 34 (2014) 501516504
model. These schemes include uncontrolled (dumb) charging, dual
tariff charging and smart or intelligent charging [24]. In uncon-
trolled charging scheme, an EV starts charging immediately when
connected to the electric power. Numerous studies have been
conducted to assess the impact of this type of charging approach
on the power system networks [25]. Almost all studies concluded
that this kind of charging increases the overloading and invest-
ment cost of the power distribution system.
As mentioned in the previous subsections, the impacts of the
EV loading are substantially realized on the power distribution
system level. The EV charging increases an additional burden on
the existing power distribution grid. If this additional load is not
appropriately controlled, it can result to further aging of the power
system equipment and tripping of the relays under rigorous
overload conditions. It is reported in [26] that up to 6070%
of the required incremental investment cost in the power
Fig. 1. EV charging conguration at AC level 1 and 2 setup (i.e. onboard charger).
Fig. 2. EV charging conguration at DC level 1 and 2 framework (i.e. off-board charger).
F. Mwasilu et al. / Renewable and Sustainable Energy Reviews 34 (2014) 501516 505
distribution system facility can be circumvented if the EV smart
charging schemes are adopted. Similarly, one of the mitigations
used to safely operate the distribution system while accommodat-
ing the large size of the EVs penetration is by shifting this extra
load to a valley period or to optimize the available power using the
coordinated charging schemes. In this case up to 535% of the
essential investment cost have been reported to be avoided by
load shifting practice with the energy losses up to 40% of the
actual values [26]. The authors have devoted much time to study-
ing the loading behavior of the power distribution system follow-
ing the EVs charging consequences while considering a large scale
distribution networks.
2.2. EVs with V2G system architecture
Electric vehicles can be integrated into power systems and
operate with different objectives such as the dynamic loads by
drawing power from the grid (during charging) or dynamic ESS by
feeding power to the electric grid. It is worth mentioning that the
latter is referred to as vehicle to grid (V2G). The limited EVs as
resources, their spatial-location and low individual storage capa-
city make them unrealizable for the V2G services. In this case, a
large number of EVs are aggregated in different ways depending
on the control schemes and objectives to realize the V2G concept
[27]. The aggregation of the EVs as a single controllable distributed
energy source can participate in energy market for supporting
electric grid in regulation and system management.
The interaction of EV with smart grid can realize V2G services
through bidirectional power ow or unidirectional power ow.
The former means the electric power can ow from the vehicle to
grid (V2G) and the grid can send power back to the EV at the time
of charging. Most of the literatures have investigated the econom-
ics and feasibility of this mutual interaction between the grid and
aggregated EVs [28]. Extensive safety protection measures such as
anti-islanding and system cost are among the demerits reported to
reduce full benets of this system architecture. On the other side,
the unidirectional conguration offers power ow in only one
direction, from the grid to EV (only to charge the battery but not to
discharge it) [29]. Studies have shown that in this conguration,
the EVs can participate in the energy market and provide ancillary
services like frequency and voltage regulation. Fsugba and Krein
[30] examine the costbenet analysis of the V2G transaction
when the EV supplies regulation services to the grid using both
aforementioned power ow scenarios. In this study, a comparison
of maximum hourly regulation capacity for the EV systems that
employed a unidirectional charger (UC) and bidirectional charger
(BC) was made. It was revealed that the battery with UC has to
double its capacity to match the same demand that the battery
with BC could supply for grid support. To be more precise, the case
study involved the battery with 20 kWh stored energy using the
BC to support the regulation capacity of 6.6 kW and the battery
with 20 kWh energy request employing the UC managed to
support a maximum regulation capacity of 3 kW. Furthermore,
the annual revenue acquired by the bidirectional charger (battery
capacity fade taken into account) is 12.3% more as compared to the
unidirectional one. With issues like protection and metering
systems, the extra revenue earned by the bidirectional power ow
architecture can be nullied to negative. It is concluded that
almost all the V2G benets acquired by using bidirectional power
ow can also be achieved by adopting unidirectional power ow.
Further studies are however required to demonstrate the viability
of the unidirectional power ow over its counterpart in the areas
such as lower power capacity for the V2G transactions.
Meanwhile, the conceptual framework of the VPP offers an
aggregation scenario that eases control and information exchange
between the utility entity (control center) and the EV eet to
facilitate the V2G realization. Different schemes of the VPP frame-
works in the V2G context can be modeled depending on the
control philosophy and aggregation type to meet the grid and EVs
integration challenges. The control approach in the VPP can be
centralized, hierarchical or distributed. In centralized control
scheme the decision making and data exchange are based on the
VPP central control center (VPPC) while in the distributed control
scheme the decisions and ow of information are fully achieved in
the distributed manner. On the other hand, the hierarchical
scheme includes some decision making and information exchange
levels within the spatial VPP model [31]. The VPPC makes
decisions and provides some modications of its requests to the
VPP resources in real time by utilizing the measured data collected
with the smart meters and the updated information from the
energy market. The aggregated EV batteries under the VPP
architecture can be used to balance the demand and consumption
forecast deviations of the electric power grid. Fig. 3 illustrates the
VPP control and implementation in the V2G context. Within the
electric grid and energy market players, the EV aggregator will
operate as a virtual power plant. As depicted in Fig. 3, the clustered
EV eet at charging station provides status like available
SOC/available power to the charging management system (CMS)
that communicates with the aggregator control center (local
VPP control). At the VPP control center the aggregated battery
power can be dispatched to provide ancillary services whenever
requested by the DSO or TSO. The VPP control center is set to
centralize the energy and communication ow management
between energy market players (i.e., power customers and pro-
ducers) and grid operators.
In the Ref. [32] the authors conceptualize the operation of the
VPP as an optimized problem to minimize operating cost. It is
observed that by operating the EV eet as both demand side
management unit, dynamic load and ESS (through V2G concept)
reduces the operating cost by 26.5%. The study considered char-
ging and discharging cycles of the battery pack together with the
EV purchasing costs and other various assumed costs.
3. EVs and smart grid infrastructure
Penetration of more distributed energy resources (DERs) into the
energy market is shifting the power generation and distribution
industries. The DERs feature variability of time and space of the
power production and consumption. This makes the energy manage-
ging. Smart grid comes along as a means to enhance power
generation and distribution, which is more exible, efcient, reliable
andsecured.Thesmartgridencompasses advanced technologies in
communication, smart energy metering and advanced control. And it
energy sources a exible and optimized deployment in the power
industry [33]. Studies have been conducted to assess and realize the
smart grid infrastructure for fostering the EVs penetration in the
energy market. Standardization of technologies and protocols in
electric power distribution communication are the key motives to
the implementation of interactive smart grid. Standards and speci-
cations towards interoperability and seamless integration of the EVs
into the electric grid have also been released [34].
Besides, an EV through the electric vehicle management system
(EVM) receives and sends information to the GO or aggregator and
vice versa. The EVM may embed smart meter (SM) as one of its key
components to facilitate real time energy measurement, commu-
nication and control. Based on the impact of the EV charging, a
smart scheduling can be implemented to optimize the available
grid power through the advanced bidirectional data exchange in
the smart grid context [35].
F. Mwasilu et al. / Renewable and Sustainable Energy Reviews 34 (2014) 501516506
3.1. EV smart charging
In the previous subsections, the potential undesirable impacts
of uncontrolled EV charging have been discussed, such as the
overloading of the power system facility and increased power
demand leading to a less efcient electricity supply. To this end, an
important part of the literature has been devoted to intelligent
charging schemes (i.e. smart charging) [36]. Smart-charging
schemes can pursue various objectives. Some studies focus on
the minimization of system or charging costs in the electricity
market [37] which in most cases leads to a valley-lling type of
charging. Other studies do not model the supply side explicitly but
rather try to nd some intelligent ways to avoid undesirable
impacts on the electric grid network [38]. It has been observed
that an optimized algorithm is very crucial to effectively schedule
and utilize in an intelligent manner the benets of the EV niche
market. With the large EV penetration into the power systems,
many constraints coexist in the real world implementation sce-
narios which have to be optimized for the better solutions. The
constraints are not constant but they do vary depending on the
objectives of the deployed EV system, such as minimization of the
charging cost, GHG emissions or losses in the power system, a few
to mention. The authors in [39] presented a day-ahead energy
resource scheduling for smart grid by considering participation of
the DERs and V2G. A modied particle swarm optimization
approach is used for intelligent optimal scheduling. Besides, the
EVs are controlled to respond to the demand response programs.
The overall operating cost reduction demonstrates the efcacy of
the smart EVs scheduling in the smart grid environment.
An optimized price algorithm pertaining the scheduled EV
charging and V2G operation is proposed in [40]. To facilitate this
intelligent charging, the Radio Frequency Identication (RFID) tag
technology is also used. The authors involve EV owner via web
mobile application to acquire information and to have control over
the EV charging by using parameters like the desired state of
charge (SOC), arrival and departure times or the option for the V2G
services to maximize prot. The scheduled charging scheme
reported to be cost effective. It resulted in 10% and 7% savings
for drivers with exible charging scheme and enterprise commu-
ters, respectively. In addition, a 56% reduction of the peak power
demand is attained with the driver variable charging scheme.
As per [41] a real time approach is proposed to minimize power
losses and enhance voltage prole in the smart grid power
distribution. The uncontrolled and controlled charging behaviors
of the plug-in electric vehicle (PEV) with different penetration are
investigated to reveal their impacts on the electric grid. With the
modied IEEE 23 kV distribution system, it is observed that high
(63%) or low (16%) penetration of the PEV with the uncontrolled
charging results in severe voltage deviations of up to 0.83 p.u.
(below 0.9 p.u. margin), high power losses and cost in generation.
However, with coordinated charging schemes the voltage prole is
improved up to 0.9 p.u. and the losses are reduced. Likewise,
Ferreira et al. [42] proposed a conceptual smart charging system
which relies on the consumption historical statistics with data
mining approaches. The charging facility and EV system are
interfaced by the web applications capable of running on the
mobile devices like smart phones. A mobile device with the GPS-
assisted functionality is used to determine the driving character-
istics of the EV from which the battery SOC is captured. Never-
theless, there is a slow communication response in this
architecture. It would be better all the process information to be
handled automatically at the machine level without much invol-
ving a third party (i.e. driver) for efcient and reliable operation.
3.2. Advanced metering infrastructure with EVs
Energy management system (EMS) in smart grid is accom-
plished by measuring, analyzing and reporting the energy use or
demand in near-real time phenomenon. Smart metering is a core
component in the effort to realize online EMS functionalities in the
Fig. 3. VPP realization and control in V2G context.
F. Mwasilu et al. / Renewable and Sustainable Energy Reviews 34 (2014) 501516 507
smart grid. In the integration of the EVs into the power grid, a
smart meter (SM) plays a major role in obtaining the near-real
time information of the power demanded or consumed. Hence,
the SMs make the process of energy forecast such as day-ahead or
intraday forecast and energy pricing more feasible [9,34]. These
are the fundamental roles of the SMs in the smart grid operation.
To this end, the advanced technologies in the smart metering are
necessary to accommodate the dynamic EV loads. Hence, the
advanced metering infrastructure (AMI) is a framework that
embraces the real time smart metering and communication as a
single unit.
In [43], the EV and AMI are listed among the eight priorities to
implement an effective smart grid. The AMI system encapsulates
various technologies and applications that are integrated as a
single functional unit. They include meter data management
system (MDMS), home area network (HAN), SMs, computer hard-
ware, software, advanced sensor networks and different commu-
nication technologies. The communication technologies in the
AMI framework can be wireless or broadband over power line
(BPL)/power line communication (PLC) that provides a two-way
communication link between the utility network, smart meters,
various sensors, computer network facilities and EV management
system (EVMS) [44]. The information gathered by the AMI can be
used to implement intelligent decision and control system. To this
end, the electric vehicle's intelligent scheduling is possible in the
smart grid using an effective AMI. In [44], the AMI solution is
adopted as a platform for the EV charging system under dynamic
pricing and charging schedule scenarios. It is concluded that the
deployment of EVs using AMI platform can manage to reduce the
peak energy consumption by 36%. It shifts 54% of the energy
demand to the off-peak period. Hence, it releases the stresses of
the power system during peak demand.
Fig. 4 depicts an overview of the AMI solutions for the EV inter-
actions with the smart grid. It represents collection of information
of the energy usage or demanded using SMs. The SMs commu-
nicate data collected through the communication technologies like
BPLC or WiMAX in a particular Field Area Network (FAN), Local
Area Network (LAN) or HAN. In fact, these data are received at
the AMI head-end system (i.e. where it performs data validation
before making them available) prior to the MDMS which is
responsible for the data management, storage and analysis. The
EV aggregator or utility can access the energy information through
the MDMS. By using consumer web portal; the human machine
interface can be realized between the EVMS, MDMS, utility service
provider and energy market.
Different functionalities of the AMI such as seamless connec-
tivity, extended data storage and bidirectional power measure-
ment and communication for the EV applications in the energy
industry have been discussed in [45]. The authors presented
various functionalities for the AMI deployment in the EV charging,
V2G services and vehicle to home (V2H) application cases. The
AMI solutions in the V2G context will be an effective gateway to
deliver universal functions which include both measurement and
communication so as to achieve a high intelligent level of energy
3.3. Advanced communication and control network infrastructure
with EVs
A two-way communication network of the smart grid infra-
structure enables many demand response technologies, which
control a number of distributed energy resources over enormous
dispersed geographical areas. In this case, wireless communication
is the ambitious solution for the V2G applications. It features
low cost and wide area coverage. In the EVs interaction with the
smart grid, we anticipate frequent request and acknowledgment
modes of communication with various system devices like SMs for
successful operation. Depending on the EV integration scheme
into the smart grid, communication solutions can be envisioned in
two different scenarios. First, the communication link from the
advanced sensors and EVMS to the SMs. The second one is being
between the SMs and grid operators/aggregators data centers. The
former can be accomplished by using PLC or wireless communica-
tion technologies while the latter by employing advanced mobile
network solutions like 3G, WiMAX and 4G LTE.
However, with the EVs deployment to the power industry,
new challenges are brought up on monitoring, communication
and control architecture due to its dynamic mobility nature.
Fig. 4. Overview of the AMI architecture in V2G framework.
F. Mwasilu et al. / Renewable and Sustainable Energy Reviews 34 (2014) 501516508
For instance, an advanced SM should be able to allow the EV to be
connected to a different aggregator, energy supplier or visiting
network when it's away from its HAN or LAN. To properly handle
these routines an effective communication with wide area cover-
age should be reliable. As per [46] advanced development in
wireless communication appears to favor smart metering facilities.
This is an attractive case for the EV applications as most of the EVs
are spatially dispersed in the real world. For successful operation
of the EVs, they have to be able to connect at any time (wherever
charging point is available) for recharging their batteries or
supplying power to the grid (i.e. V2G). In this case, the GO or EV
aggregator has to be able to identify a particular electric vehicle in
the near-real time environment for billing the demanded power.
On the other hand, the EV has to obtain the time of use or real time
pricing trends from the energy market to deliver power to the grid.
Moreover, wireless sensor network (WSN) is an emerging
control network which has gained popularity in smart grid.
Recently, some researches have shown promising applications of
the WSN in the DG and microgrid (MG) operation. By using the
same concept, the wireless sensor network can be adopted to
enhance the EV penetration. The challenges are still high in
adopting WNSs for the EV applications especially in the V2G
services. These challenges include shorter ranges as compared to
other wireless technologies, which result in packet delays and
decreasing success ratio as the number of hops is increased. In [47]
the information system for the V2G application based on the WSN
is proposed. The vehicle-grid operator communication is distrib-
uted wirelessly to improve the grid demand prole, EV reliability
and data delivery with minimum number message broadcast. This
study is one of the attempts to realize the advanced EV system
with the WSN architecture for supporting V2G transactions. Apart
from that, ZigBee technology has been investigated and tested
by various researchers, particularly for the EV applications [48].
The ZigBee technology is simple and requires low bandwidth for
its implementation. However, the issues like communication
interference with other devices sharing the same transmission
line, small memory and communication delays need to be
addressed to allow ZigBee technology to be reliable and effective
for the V2G applications. Table 3 shows the characteristics of some
various wireless technologies that can be used for the EV applica-
tions such as V2G services.
On the other hand, the cyber-security for the communication
network between EV and utility or power market should be
assured in order to prevent the smart grid from the cyber-
attacks such as price tampering and system congestions by
malicious software. These are critical issues as the deployed EV
to the grid network is vulnerable as it can easily open doors for
cyber-attacks. And it is also necessary to provide secured EV
services at the visiting networks. If these aforementioned issues
are not taken care they would reduce the effective benets and
reliability of the EVs in the energy market [49].Fig. 5 shows
communication network architecture and functionalities for the
EV interactions with the smart grid. The wireless communication
technology to be employed depends on the distance between
communicating hot spots and the amount of data to be trans-
mitted. In this gure, the smart mobile phone is used as an
interface between the EVMS, charging point and aggregator via
GPS or/and Bluetooth-enabled functionalities. All the statuses from
the EV are communicated to the outside environment through the
CAN gateway. The WiMAX protocol represents a long distance
communication scenario that covers communication between the
aggregator, energy market and utility (TSO/DSO). To increase
reliability in the smart grid environment, the Near Field Commu-
nication (NFC) protocol can be used to automatically support
Bluetooth pairing and intuitively reduce more than eight user
interaction to establish Bluetooth connection [50].
4. Renewable energy sources integration with EVs
The increase in penetration of renewable energy sources (RES)
into the electric power system is quite appealing. The existing
power grid suffers from unpredictable and intermittent supply of
the electricity from these sources especially wind and PV solar
energies [5]. The electric power production from these RES can be
very high (more than the power demand) or very low (less than
the power demand) depending on the available energy sources, i.e.
wind speed and sun radiation. In short, these RES are variable with
time, nondispatchable with limited control and have low capacity
credit especially on the power system planning. Most of the
studies revealed that the integration of wind energy conversion
systems (WECS) and PV solar systems into the electric power grid
is pretty mature and practically viable [51]. However, the promis-
ing solution to balance the electricity generation from these
RES on the grid can be accomplished by adopting the stationary
energy storage systems (ESS) or controllable dispatch loads [52].
The stationary energy storage systems absorb or supply electricity
in the case of excess and low power generation, respectively. As
this solution involves high investment cost, it delays the high
penetration of the RES into the power system or even increases the
overall investment cost.
As pinpointed earlier the electrication of the transportation
sector is envisioned by the numerous researchers to populate the
sector in a decade to come. Then, the EV batteries can be
aggregated and act as the ESS that will pivot the integration of
the RES into the power market as dynamic energy storage devices.
The EVs can absorb the surplus power generated by the RES
through different charging schemes or can deliver power to the
grid in the low power generation scenarios and level the grid
operations through the V2G schemes [53]. To this end, the EVs will
be acting like energy buffer for the grid regulations and ancillary
services. In [54], it is stated that a possible solution to maintain
energy security while reducing GHG emissions can be achieved by
integrating the distributed RES (PV solar and wind in this study)
and adopting EVs with capability to deliver the V2G services.
To simultaneously achieve the GHG emissions and cost reduction,
a strategy to optimize maximum utilization of both EVs and RES is
required. The authors propose a dynamic optimization approach
based on particle swarm optimization. The ndings from this
study show that for the random charging, the load increases by
10% every year in the power grid but an intelligent scheduling of
the EVs (without RES) can solve the problem at the expense of
increased cost per day by 1.7% and emissions by 3%. On the other
hand, in smart grid mode with both EV-V2G enabled cars and RES,
the cost is reduced by 0.9% and emission by 4.3% per day. These
results give a glimpse of the perfect match for the interactions
Table 3
Wireless communication technologies for V2G applications.
S/N Technology Operating frequency Covered
1 ZigBee 868 MHz (Europe) 10100 m
915 MHz (North
2.4 GHz (Worldwide)
2 Near Field Communication
13.56 MHz 510 cm
3 Bluetooth 2.4 GHz 1100 m
4 IEEE 802.11p 5.855.925 GHz 5001000 m
5 WiMAX 26 GHz 25km
F. Mwasilu et al. / Renewable and Sustainable Energy Reviews 34 (2014) 501516 509
between the EVs and RESs in the smart grid infrastructure.
The subsequent subchapters assess the integration of the PV solar
and wind energies using EVs.
Fig. 6 illustrates the integration of wind and PV solar energy
sources into the power grid with EVs. The electric vehicles are
aggregated at the charging station located at public area or ofce
Fig. 5. EV communication network architecture with smart grid.
F. Mwasilu et al. / Renewable and Sustainable Energy Reviews 34 (2014) 501516510
and can be used to suppress power uctuations from these RES in
the V2G mode. In this gure we assume all necessary commu-
nication and control schemes are available as described in details
in the previous section for the V2G and charging scenarios. In this
gure and other subsequent gures T
stands for the power
transformer in the electric grid, where i¼1,2,3,,n.
4.1. PV solar energy with EVs
Electricity production from PV solar energy has already shown
a promising feasibility. The PV solar arrays are usually clustered to
cumulatively provide power to the electric grid. With the EVs
penetration getting large share, the PV solar power is more likely
to be deployed for charging purposes and grid support. A number
of analyses have been presented to show that the deployment of
the PV solar on the roof of parking lots for charging EVs is quite
appealing [55,56]. Besides, the V2G transactions are also feasible in
these PV solar systems [57] and an optimal generation scheduling
is possible to reduce operating cost and enhance grid operation as
reported in [58]. Tulpule et al. [55] perform the energy economics
and emission analysis of the workplace charging station based on
the PV solar system by comparing optimal charging schemes with
uncontrolled ones. A day-time workplace EV charging behavior
under this study considers various data including vehicle parking
charges and different parking locations to account for the solar
insolation variations. Observations from this study reveal that one
vehicle would save 0.6 ton of CO
emissions per year by using solar
charging at the workplace which amounts up to 55% savings in
emissions when employing home charging (night charging at
home) scheme. And it reduces 0.36 ton of CO
emissions when
an optimal charging scheme is implemented, which amounts up to
85% savings in emissions if the home charging scheme is adopted.
The SMs and communication infrastructure appear to increase cost
for the home charging case and make the PV based workplace
charging station a better choice. Fig. 7 depicts the conguration of
the standalone solar carport charging station at working place or
public area.
In [56] the impact of solar PV arrays built over the rooftop at
workplace parking lot to offer charging services for the commuter
during day-time is investigated. The study reveals that during
Fig. 6. Wind and PV solar energy sources integration into the electric grid with EVs.
F. Mwasilu et al. / Renewable and Sustainable Energy Reviews 34 (2014) 501516 511
summertime the solar electricity production (up to12.6 kWh) is
high and most of the power can be sold back to the grid (V2G) or
used at the workplace. This can compensate for some of the
investment costs with a bit prolonged payback. On the other hand,
during wintertime season, the production (up to 3.78 kWh) is
reported to be enough for recharging. The analysis on how to
offset the extra cost in the winter is not given though it is
important to potentially justify the feasibility. Furthermore, a
bidirectional DC charger is modeled in [57] to realize the EV
interaction with the power system fed by the PV solar system. The
demonstration of the ramp rate compensation for the PV inverter
output is also presented. Three scenarios are analyzed in this
study: to only charge EV without including any other services (e.g.
V2G), EV to provide grid support while charging and EV to offer
grid support without involving charging scenarios. The results
reveal that the EV charger can offset the huge sudden uctuations
of the PV power due to clouding condition which amounts up to
22.5% of the DC bus voltage per second for the 1.2 kW PV array.
In [58], a generation scheduling scheme that is coordinated
with the dynamic PEV charging is investigated in the industrial
microgrid (IMG). This scheme encapsulates the distributed RES
(with PV solar) and combined heat and power generation.
The dynamic optimal power ow (DOPF) approach is proposed
to achieve a low operating cost. It is observed that the generation
scheduling of the IMG with the PV and PEV signicantly reduces
the overall operating and charging costs. Nevertheless, the varia-
bility of the PV output power can be compensated easily at less
expense of communication and control complexity. Fig. 8 depicts a
solar carport charging station connected to the electric grid
through a bidirectional DC/AC power converter. The two charging
stations 1&2 in this gure represent a possibility of having a
number of charging points connected to the power distribution.
The aggregated EVs at the charging stations 1&2 can support the
grid as ESS and provide ancillary services through their bidirec-
tional DC/AC power converters. However, the EVs incorporating
the bidirectional DC charger [57] are connected directly at the PV
controller and can absorb excess power generated. The DC power
system is envisioned to be feasible and attractive solution in the
future electric grid model as reported in [59]. In this case, the
bidirectional DC charger can be easily accommodated in this
electric model. And it can feed back the stored battery power
during high demand period when the PV power generation is low.
The investigation on the large penetration of rooftop PV solar
and EVs is reported in [60]. The study is centered on the impacts of
the synergy between EV charging and large distributed rooftop PV
installations especially with the voltage mitigation support. This
mutual relationship works to complement each other; hence the
EVs can enhance large integration of the PV solar by providing
voltage support and can reduce the stresses on the power
distribution system through V2G services. It is noted that a specic
integration of PV solar and EVs presented a reduction of about 15%
of the voltage uctuations. The IEEE 123-node feeder was used to
characterize a distribution system in a region. However, its stiff
condition with relative short distance may not represent the real
large power ow scenarios in the existing power system. This
gives some perception that a more detailed analysis is required to
represent the impact and limitation of the EVs charging and V2G
transaction with the large PV solar power to support the grid.
Besides, in [61] a potential analysis to deploy PV solar on car
parking lots in the Swiss city of Frauenfeld is extensively explored.
Results reveal that the installation of the PV solar on parking lots
can cover between 15% and 40% of the energy demand of the EVs
in the future. The methodology used is simple but it excluded a
detailed transportation demand and system analysis. All these
Fig. 7. EV charging station deploying standalone PV solar on rooftop at the
parking lot.
Fig. 8. EV charging station based on grid connected PV solar at the parking lot.
F. Mwasilu et al. / Renewable and Sustainable Energy Reviews 34 (2014) 501516512
studies have shown a potential mutual relationship on the
penetration of both EV and solar PV systems.
4.2. Wind energy with EVs
The concept of using wind energy conversion systems (WECSs)
for electricity generation is a prevalent and feasible alternative
solution to produce power as discussed previously. The synergy
between the WECSs and EVs has been widely investigated by
various researchers in different scenarios to deduce their impact
and viability onto the electric grid [62,63]. The early study by Lund
and Kempton [62] assesses the use of the EVs to provide ancillary
services and regulation based on the grid interaction with the
WECSs in the US power market. The authors in [63] estimate the
amount of wind that can securely be integrated into an isolated
electric grid with the vicinity of the EVs. In this study the EVs are
considered to participate in the primary frequency regulation and
their interactions during smart charging mode are also assessed.
The EVs through V2G services support the increase of wind
penetration from 41% to 59% in the isolated grid. The study
assumes that all the available EVs have intermediate charge and
are ready for balancing the grid.
Pillai et al. [64] presented a comparative analysis of an isolated
power grid (Danish island, Bornholm) capability to integrate large
share of the wind energy system by using hourly-EnergyPLAN
model and short duration-dynamic simulation scenarios.
The aggregated EV batteries were used under the V2G context
for the frequency regulation support. The frequency instability was
due to the uctuations of the large wind power penetration. From
this analysis, an aggregated EV battery storage of 16 MW capacity
supported (through V2G) 42 MW of wind power penetration into
the power grid while without using EV to provide V2G services
only a wind integration of 20 MW into the electric grid would be
possible. With an aggregated V2G capacity of 16 MW, it was
possible to integrate 82% and 70% of wind energy power of the
total installed capacity for hourly and short time dynamic simula-
tion, respectively. And in both scenarios, the V2G managed to
efciently support frequency stability. An interesting study on
large integration of the RES (especially wind) into the North-
eastern Brazil power system using PHEVs was conducted in [65]
for the years 2015 and 2030 projections. The authors used
governmental PHEV eet which was assumed to be highly con-
trollable in their driving patterns by the eet personnel. As
reported from this study, the option possesses two faces. First,
the charging behavior can be easily managed to reduce the
stresses on the power system and second, the incorporation of
the smart grid technology can also be avoided to reduce initial
cost. In year 2020, with 500 thousand PHEVs there could be an
increase of the wind power capacity by 4%. Although the smart
technologies were not considered in this study, a clear comment
made is that smart metering and other communication technol-
ogies are inevitable for the efcient EV and WECS integration in
the electric power grid.
A study on large integration of wind energy systems into the
microgrid (MG) using PHEVs was conducted in [66]. The energy
dispatching strategy is proposed to meet the dynamic power
demands. The approach features a coordinated wind-PEV scheme
that optimizes the effective utilization of these energy sources. It is
observed that the power produced from the wind energy systems
and corresponding consumption in the MG without taking into
account PEVs indicates a big mismatch between the forecasted
power and consumption over a day. This is due to the fact that the
surplus power generated is not consumed by available loads.
However, with the PEVs in place, the matching performance is
highly enhanced. In this case, the PEV charging and discharging
(V2G) process balances the power prole. Similarly, an interesting
study by Liu et al. [67] adopted a two-stage stochastic unit
commitment model that considers the interactions of the thermal
generating units, PHEVs and large scale wind power systems.
The study revealed that intelligent scheduling of the PHEVs
signicantly reduces the operating costs of the power systems
and balances the uctuations of the wind power generators. It is
important to mention that with these studies the battery life
cycles and PHEV with different capacities and driving patterns
have to be considered in order to increase reliability in the real
world scenarios.
5. Feasibility of smart V2G system
The interaction of electric vehicles with an electric grid is an
attractive research area which has drawn attention to a number of
people in the academia, industrial, public and private research
institutes. As presented in this paper, we have detailed various
application schemes of the EV technology in the power market
under the smart grid context. However, there are few practical
projects or studies to cover the actual implementation of the inter-
action of the EVs with the smart grid to realize the V2G schemes
in the real world. Numerous smart grid and V2G technologies
necessary to integrate electric vehicles into the smart grid in an
efcient manner are yet under development stages. This includes
battery technology, communication and power interfaces as a few
to mention [68]. Besides, the intensive research and development
are required to enhance the efciency and lower the cost of
various technologies like the EV charging infrastructures. Research
activities and pilot projects initiatives are already in place to
escalate the V2G concept into reality. Early pilot project was
pioneered by Kempton et al. [69] for the EVs to feed the grid
(V2G) in order to provide a real-time frequency regulation. The
project demonstrated various possibilities of the V2G deployment
to support the grid. However, it involves a single EV; in this case it
is very tricky to conclude the results for a large EV eet scenario.
5.1. Intelligent EV scheduling
The emergence of the EVs poses a great challenge on rechar-
ging their batteries as the EVs impact the grid by increasing load
demand. It can be summed up that if charging of the EVs in some
way is intelligently coordinated then a big shift of the load can be
distributed. However, this requires a great deal of advanced
control and communication incorporated for both parties; the
grid side management system, market operators and EV manage-
ment system.
One of the options proposed to alleviate the overloading of the
distribution system due to EV charging is by introducing smart
charging schemes discussed in the previous chapters. The concept
is conceivable recently as the smart grid technologies penetrate
into the existing power system together with the adoption of the
smart grid test-beds.
The availability of wireless communication,
GPS facility and smart metering infrastructures in the smart grid
framework is becoming more apparent. Wireless connectivity in
vehicles is extensively becoming a prevalent experience. The
European Union (EU) enforcement regulation on the automatic
crash notication (ACN) by the year 2015 for road safety and
quick emergency response is one of the sailing boats to enhance
communication between vehicle and infrastructure (V2I).
the smart charging technology and communication facilities
will be envisioned as an extended service to this wireless
Korea Smart Grid Institute (KSGI).
F. Mwasilu et al. / Renewable and Sustainable Energy Reviews 34 (2014) 501516 513
infrastructure in place. The smart meter can be congured as
rmware rather than hardware while encapsulating roaming
services to cope with the EV mobility nature for the dynamic
pricing and other data exchange purposes to enable intelligent EV
A body of research institutes and organizations is embarking on
the programmes to integrate EVs into the smart grid. An interna-
tional company called Better Place pursues a number of projects
that demonstrates the electrication of transport sector from
vision to reality; such projects include battery switching stations
(BSS). In this case, the EV battery pack can be readily swapped
with the fully charged battery packs from the BSS and the EV can
continue with the daily activities. Hence, the process at the battery
switching station increases the reliability of the EVs eet. It is
noted that in just 5 min one can swap the batteries and continue
with his normal business. These stations have been opened
recently in Israel, China, Netherlands and Denmark in addition to
that in Tokyo city, Japan.
Another pilot project named e-mobility
is operating in three cities in Italy (Pisa, Rome and Milan) and
pioneered by the Daimier and Enel companies. It involves around
100 smart EVs and 400 smart charging stations to be completed in
December 2013.
The smart charging system in this project
encapsulates smart meters from Enel Company with GPRS com-
munication technology, RFID and PLC link between the EV and
control center. These demonstration projects reveal the insights
and possibility of the smart electrication of the transport sector.
5.2. Renewable energy sources integration using EVs
Using electric vehicles to support the integration of the renew-
able energy sources (RES) especially wind and PV solar energies is
becoming a major research topic. Introducing the EVs to this role
will highly support and enhance more penetration of the RES into
the grid. However, this concept is cross-cutting which prompts for
a more detailed analysis in both technical and costbenet
justication. There are already some demonstration projects to
assess the impacts and feasibility of the EV interaction with the
RES. The Zem2All e-mobility pilot project inaugurated recently
(April 2013) in Malaga city, Spain will be the largest V2G pilot
project. It features 23 CHAdeMO DC fast charging points including
6 bidirectional chargers capable of providing V2G functionalities.
The project comprises 200 EVs (Nissan Leafs & Mitsubishi iMiEV)
compatible with the CHAdeMO DC-fast charging standard. To be
precise, it makes up 229 EV charging points in total [70]. More
importantly, the EVs will support the integration of the intermit-
tent renewable sources by absorbing the excess power produced
by the RES and supply back to the grid at the times of peak
demand (i.e. V2G). This will demonstrate the real life scenario for
the interaction of the EVs with electric power system incorporat-
ing the RES and fast charging for the V2G services. Fig. 9 [70]
shows a detailed view of one of the charging stations in this
Zem2All project in Malaga city.
5.3. V2G impacts, potential and limitation
The electric vehicles are usually aggregated and treated as
dynamic distributed energy sources in the V2G schemes to
support the electric grid by providing ancillary services. A number
of studies have shown the superiority of this concept and proved
to be a better choice for future power system model as discussed
previously. The deployment of the ESSs to balance the electric grid
is not a new concept, and the energy sources like dedicated battery
storage systems, pumped hydroelectric storage, y wheel and
concentrating solar power (CSP) are among the technologies used.
These are competing with the V2G penetration in the energy
market. For instance, the pumped hydroelectric storage is con-
sidered to be much cheaper option than the V2G. The CPS as an
energy storage system has higher efciency up to 99% and can
store energy for quite long time as compared to the EV battery
pack [71,72].
Studies show that the CPS plant as energy storage unit for
supporting the peak demand and regulation is quite appealing as
the technology keeps on maturing. The world's largest CSP plant of
100 MW capacity has been recently inaugurated in Abu Dhabi,
UAE. The penetration of the CPS and other energy sources into the
power system grid is expected to increase as projected by the
International Energy Agency (IEA). This prompts for an intensive
research to justify the economic feasibility of the EVs for the V2G
transactions as compared to these potential energy storage units.
Furthermore, the V2G schemes have shown an auspicious
solution to the energy market. The primary goal for the EV
adoption in the world is to replace fossil fuels from powering
the normal internal combustion vehicles. Introducing the V2G
transactions prompts for the upgrading of the EV technology to
accommodate this extended application in the power market. The
technology upgrade includes but not limited to bidirectional
power converters, advanced communication, smart meters and
new market players. Besides, the EV manufacturers have not yet
introduced much of the EV-enabled cars for the V2G services
because the EV owners will also have to decide to enter into those
contracts or refrain from the new market opportunity (i.e. V2G).
The question is, manufacturers should produce the EV with two
variants (normal EV and EV-enabled for V2G) or single variant
with two options, in the latter option it is obvious that an extra
cost will be incurred (i.e. the technology remains redundant to the
owner who will be reluctant to join the energy market). This is
tricky and can divide the market share. Studies and researches are
required to merge this gap of uncertainties while providing a
feasible option to both parties; that is manufactures and/or
customers before energy market players.
Most of the recent researches on the V2G deployment have
centered their focus and analysis under the consideration of the
deregulated electric market, in which the price tag of electric
demand varies depending on the electricity producers (e.g. Gen-
eration Companies) or market players (e.g. energy brokers). These
price variations (bidding) are optimized in the literatures to reduce
the charging cost or even investment cost of the V2G or power
distribution infrastructures [73]. In this case, the V2G transactions
have been proved to be economically viable and technically
Fig. 9. CHAdeMO chargers including bidirectional charger technology for V2G in
Zem2All project, Malaga city [70].
F. Mwasilu et al. / Renewable and Sustainable Energy Reviews 34 (2014) 501516514
feasible in terms of the EV scheduling. Studies on different energy
markets are becoming important to globally adopt the EV technol-
ogy and fully utilize its potential. For example, Foley et al. [74]
assesses and presents the impacts of the EV charging in the single
wholesale electricity market operation with a case study of the
Republic of Ireland. It is worth noting that an EV eet has different
impacts on the different electricity markets such as deregulated or
regulated (monopoly) one. We anticipate that between the years
2020 and 2030, there will be a signicant penetration of the EVs
into the vehicle market. With the same projection, the V2G
technology will also be matured. Many countries will adopt this
technology but they already have different electricity market
operation. For instance, electricity market in the Republic of Korea
adopts the regulated power market while the US partly adopts the
deregulated energy market. The comparative studies to assess the
impact and feasibility of the EVs interaction with the grid at
different energy market are imperative but have not yet drawn
attention to a large extent recently.
As analyzed in the previous sections, it is clear that EV adoption
into the power system will be visualized in the same way as
dynamic distributed energy sources within the context of the
virtual power plant (VPP). And the other functionalities like virtual
STATCOM (STATCOM) will be possible [75]. In this case, they will
support the integration of renewable energy sources especially
wind and PV solar energies. Researches on these areas are critical
to explore the benets and enhance the mutual relation with the
smart grid. The V2G services on the grid will widen the shift of the
power system to the efcient virtual power grid.
6. Conclusion and future trends
This paper has presented an intensive review of the interaction
of electric vehicles in the smart grid infrastructure. The integration
of the renewable energy sources using EVs has also been dis-
cussed. It has been observed that electric vehicles can provide
ancillary services to the grid such as voltage and frequency
regulation, peak power leveraging and reactive power support to
enhance the operational efciency, secure the electric grid and
reduce power system operating cost. The study has shown that the
deployment of the EVs into the smart grid system would be
possible with the advanced communication, control and metering
technologies. In this case the smart grid will foster the interoper-
ability of the EVs for the grid support. Following that note, a
correlation between the smart grid and the EVs has been exten-
sively investigated in this paper. However, more research and
analysis are required to justify the adoption of the V2G framework
over other energy storage systems. To realize a near-real time
communication and power measurement, an advanced technology
in these areas has to be enforced to identify the challenges and
limitations. Few researches have been reported but the issues like
communication delays, routing protocols and cyber security are
very critical for the reliable and efcient adoption of the V2G
transactions framework in the smart grid context.
Moreover, the feasibility of the smart grid with the V2G
schemes has been explored with the insight of the recent projects.
The low penetration of the electric vehicles embedded with the
V2G functionalities is one of the challenges which hinder to a large
extent the EVs adoption in the energy market. The side effects of
the EV technologies like low cost and high efcient power
converters (for EV charger) are among the other factors manifested
at the automotive manufacturers' perspectives. For the effective
V2G operation with the current battery technology, the challenge
still remains to be battery wearing under frequent charging and
discharging cycles. The researches have shown some promising
results for lithium ion (LFP) battery. Nevertheless, to guarantee
high penetration of the EVs, further detailed studies are required
that would take into account various research areas like the
strategies to enhance battery lifetime extension and costbenet
analysis for their (batteries) deployment in the V2G services.
Besides, the same studies on the other battery technologies like
NiMH and various other lithium ion chemistries are yet to be
revealed. This is an important topic as it involves the core
technology for the EV applications especially the V2G services.
However, in the V2G applications, the dynamics of the power
system (e.g. voltage dips) are inevitable. Studies on the V2G that
consider weak grid dynamics are quite important but very few
have been reported. Likewise, the integration of the RES into the
power system such as wind and solar energy sources using EVs is
one of the good representation models that require weak grid
scenarios to be considered. To foresee the effective and reliable
electric grid operation with the V2G support, a clear understand-
ing of the dynamic behaviors of the electric grid is indispensable.
This work was supported by the National Research Foundation of
Korea (NRF) grant funded by the Korea government (MSIP, Ministry
of Science, ICT & Future Planning) (No. 2012R1A2A2A01045312).
[1] International energy outlook 2011: Energy Information Administration (EIA),
Ofce of Integrated Analysis and Forecasting, U.S. Department of Energy,
Washington, DC; 2011.
[2] Australian Energy Market Commission (AEMC). Market arrangements for
electric and natural gas vehicles. Approach paper, Sydney; 2011.
[3] Kempton W, Letendre S. Electric vehicles as a new power source for electric
utilities. J Transp Res Part D 1997;2(3):15775.
[4] Ehsani M, Falahi M, Lotfard S. Vehicle to grid services: potential and
applications. Energies 2012;5:407690.
[5] International Electrotechnical Commission (IEC). Grid integration of large-
capacity renewable energy sources and use of large-capacity electrical energy
storage. White paper 3; 2012.
[6] Vasirani M, Kota R, Cavalcante RLG, Ossowski S, Jennings NR. An agent-based
approach to virtual power plants of wind power generators and electric
vehicles. IEEE Trans Smart Grid 2013;4(3):131422.
[7] Galus MD, Vaya MG, Karuse T, Andersson G. The role of electric vehicles in
smart grids. Wiley Interdiscip Rev.: Energy Environ. 2012;00:117.
[8] Ortega-Vazquez MA, Bouffard F, Silva V. Electric vehicle aggregator/system
operator coordination for charging scheduling and services procurement. IEEE
Trans Power Syst 2013;28(2):180615.
[9] Gungor VC, et al. A survey on smart grid potential applications and
communication requirements. IEEE IEEE Trans Ind Inform. 2013;9(1):2842.
[10] Green II RC, Wang L, Alam M. The impact of plug-in hybrid electric vehicles on
distribution networks: a review and outlook. Renew Sustain Energy Rev
[11] Shaaban MF, Atwa MY, El-Saadany EF. PEVs modeling and impacts mitigation
in distribution networks. IEEE Trans Power Syst 2013;28(2):112231.
[12] Su W. Smart grid operations integrated with plug-in electric vehicles and
renewable energy resources [Ph.D. dissertation]. North Carolina: Department
of Electrical and Computer Engineering, North Carolina State University; 2013.
[13] Pecas LJA, Soares FJ, Almeida PMR. Integration of electric vehicles in the
electric power system. Proc IEEE 2011;99(1):16883.
[14] Bessa RJ, Matos MA. Economic and technical management of an aggregation
agent for electric vehicles: a literature survey. Eurn Trans Electr Power
[15] Pillai JR, Bak-Jensen B. Integration of vehicle to grid in the western Danish
power system. IEEE Trans Sustain Energy 2011;2(1):129.
[16] Ma Z, Callaway DS, Hiskens IA. Decentralized charging control of large
populations of plug-in electric vehicles. IEEE Trans Control Syst Technol
[17] Bishop JDK, et al. Evaluating the impact of V2G services on the degradation of
batteries in PHEV and EV. Appl Energy 2013;111:20618.
[18] Peterson SB, Apt J, Whitacre JF. Lithium-ion battery cell degradation resulting
from realistic vehicle and vehicle-to-grid utilization. J Power Sources 2010;195
[19] Guenther C, Schott B, Hennings W, Waldowski P, Danzer MA. Model-based
investigation of electric vehicle battery aging by means of vehicle-to-grid
scenario simulations. J Power Sources 2013;239:60410 .
[20] SAE Electric vehicle and plug-in hybrid electric vehicle conductive charge
coupler. SAE standard J1772; 2012.
F. Mwasilu et al. / Renewable and Sustainable Energy Reviews 34 (2014) 501516 515
[21] Yilmaz M, Krein PT. Review of battery charger topologies, charging power
levels, and infrastructure for plug-in electric and hybrid vehicles. IEEE Trans
Power Electron 2013;28(5):215169.
[22] Chaundhry H, Bohn T. A V2G application using fast charging and its impact on
the grid. In: IEEE Transportation electrication conference and expo (ITEC),
[23] Boulanger AG, Chu AC, Maxx S, Waltz D. Vehicle electrication: status and
issues. Proc IEEE 2011;99(6):111638.
[24] Waraich R, et al. Plug-in hybrid electric vehicles and smart grids: investiga-
tions based on a microsimulation. Transp Res Part C 2013;28:7486.
[25] Darabi Z, Ferdowsi M. Aggregated impact of plug-in hybrid electric vehicles on
electricity demand prole. IEEE Trans Sustain Energy 2011;2(4):5018.
[26] Fernandez LP, Roman TGS, Cossent R, Domingo CM, Frias P. Assessment of the
impact of plug-in electric vehicles on distribution networks. IEEE Trans Power
Syst 2011;26(1):20613 .
[27] Sortomme E, El-Sharkawi MA. Optimal scheduling of vehicle-to-grid energy
and ancillary services. IEEE Trans Smart Grid 2012;3(1):3519.
[28] Dashora Y, Barnes JW, Pillai RS, Combs T, Hilliard M. Optimized energy
management for large organizations utilizing an on-site PHEV eet, storage
devices and renewable electricity generation. Energy Syst 2012;3:13351.
[29] Sortomme E, El-Sharkawi MA. Optimal charging strategies for unidirectional
vehicle-to-grid. IEEE Trans Smart Grid 2011;2(1):1318.
[30] Fasugba MA, Krein PT. Cost benets and vehicle-to-grid regulation services of
unidirectional charging of electric vehicles. In: IEEE Energy Conversion
Congress and Exposition (ECCE); 2011. p. 82734.
[31] Raab AF, et al. Virtual power plant control concepts with electric vehicles. In:
Procedings of the 16th International conference on intelligent systems
application to power system (ISAP); 2011.
[32] Musio M, Lombardi P, Damiano A. Vehicles to grid (V2G) concept applied to a
virtual power plant structure. In: Proceedings of the XIX International
Conference on Electrical Machines ICEM; 2010.
[33] Su W, Rahimi-Eichi H, Zeng W, Chow M-Y. A survey on the electrication of
transportation in a smart grid environment. IEEE Trans Ind Inform 2012;8
[34] Gungor VC, et al. Smart grid technologies: communication technologies and
standards. IEEE Trans Ind Inform 2011;7(4):52939.
[35] Guo Q, et al. Factor Analysis of the aggregated electric vehicle load based on
data mining. Energies 2012;5:205370.
[36] Amoroso FA, Cappuccino G. Advantages of efciency-aware smart charging
strategies for PEVs. Energy Convers Manag 2012;54:16.
[37] Al-Awami AT, Sortomme E. Coordinating vehicle-to-grid services with energy
trading. IEEE Trans Smart Grid 2012;3(1):45362.
[38] Sousa T, Morais H, Vale Z, Faria P, Soares J. Intelligent energy resource
management considering vehicle-to-grid: a simulated annealing approach.
IEEE Trans Smart Grid 2012;3(1):53542.
[39] Soares J, Morais H, Sousa T, Vale Z, Faria P. Day-ahead scheduling including
demand response for electric vehicles. IEEE Trans Smart Grid 2013;4(1):
[40] Mal S, Chatttopadhyay A, Yang A. Electric vehicle smart charging and vehicle
to grid operation. Int J Parallel Emergent Distrib Syst 2013;28(3):24965.
[41] Deilami S, Masoum AS, Moses PS, Masoum MAS. Real-time coordination of
plug-In electric vehicle charging in smart grids to minimize power losses and
improve voltage prole. IEEE Trans Smart Grid 2011;2(3):45667.
[42] FerreiraJC, Monteiro V, Afonso JL, Silva A.Smart electric vehicle chargingsystem.
In: Proceedings of IEEE intelligent vehicles symposium (IV); 2011. p. 75863.
[43] NIST framework and roadmap for smart grid interoperability standards,
release 2.0. Ofce of the National Coordinator for Smart Grid Interoperability;
U.S. Department of Commerce, Report; 2012.
[44] Lam LK, et al. Advanced metering infrastructure for electric vehicle charging.
Smart Grid Renew Energy 2011;2:31223.
[45] Rua D., et al. Advanced metering infrastructure functionalities for electric
mobility. In: Proceedings of IEEE PES international conference on innovative
smart grid technologies (ISGT Europe); 2010.
[46] Li H, Lai Lifeng, Qiu RC. Scheduling of wireless metering for power market
pricing in smart grid. IEEE Trans Smart Grid 2012;3(4):161120.
[47] Lim Y, Kim HM, Kang S. Information systems for electric vehicle in wireless
sensor networks. In: Kim TH, et al., editors. Communication and networking.
Berlin, Heidelberg: Springer; 2010. p. 199206.
[48] Lam KL., et al. ZigBee electric vehicle charging system. In: Proceedings of IEEE
international conference on consumer electronics (ICCE); 2011. p. 5078.
[49] Liu H, Ning H, Zhang Y, Yang LT, Guizani M. Battery status-aware authentica-
tion scheme for V2G networks in smart grid. IEEE Trans Smart Grid 2013;4
(1):99110 .
[50] Steffen R, Preißinger J, Schöllermann T, Müller A, Schnabel I. Near eld
communication (NFC) in an automotive environment. In: Proceedings of the
second international workshop on near eld communication (NFC); 2010. p.
[51] Dallinger D, Gerda S, Wietschel M. Integration of intermittent renewable
power supply using grid-connected vehicles a 2030 case study for California
and Germany. Appl Energy 2013;104:66682.
[52] Battke B, Schmidt TS, Grosspietsch D, Hoffmann VH. A review and probalistic
model of lifecycle costs of stationary batteries in multiple applications. Renew
Sustain Energy Rev 2013;25:24050.
[53] Richardson DB. Electric vehicles and electric grid: a review of modeling
approaches, impacts and renewable energy integration. Renew Sustain Energy
Rev 2013;19:24754.
[54] Saber AY, Venayagamoorthy GK. Plug-in vehicles and renewable energy
sources for cost and emission reductions. IEEE Trans Ind Electron 2011;58
[55] Tulpule P, Marano V, Yurkovich S, Rizzoni G. Economic and environmental
impacts of a PV powered workplace parking garage charging station. Appl
Energy 2013;108:32332.
[56] Birnnie III DP. Solar-to-vehicle (S2V) systems for powering commuters of the
future. J Power Sources 2009;186:53942.
[57] Traube J, Lu F, Maksimovic D. Photovoltaic power System with integrated
electric vehicle DC charger and enhanced grid support. In: Proceedings of the
15th International power electronics and motion control conference (EPE/
PEMC); 2012.
[58] Derakhshandeh SY, et al. Coordination of generation scheduling with PEVs
charging in industrial microgrids. IEEE Trans Power Syst 2013;28(3):345161.
[59] Justo JJ, Mwasilu F, Lee J, Jung JW. AC-microgrids versus DC-microgrids with
distributed energy resources: a review. Renew Sustain Energy Rev 2013;24:
[60] Tuffner FK, Chassin FS, Kintner-Meyer MCW, Gowri K. Utilizing electric
vehicles to assist integration of large penetrations of distributed photovoltaic
generation. Pacic Northwest National Laboratory; Report; 2012.
[61] Neumann HM, Schär D, Baumgartner F. The potential of photovoltaic carports
to cover the energy demand of road passenger transport. Prog Photovolt: Res
Appl 2012;20:63949.
[62] Lund H, Kempton W. Integration of renewable energy into the transport and
electricity sectors through V2G. Energy Policy 2008;36:357887.
[63] Lopes JAP, Almeida PMR, Soares FJ. Using vehicle-to-grid to maximize the
integration of intermittent renewable energy resources in islanded electric
grids. In: Proceedings of the international conference on clean electrical
power; 2009. p. 2905.
[64] Pillai RJ, Heussen K, Østergaard PA. Comparative analysis of hourly and
dynamic power balancing models for validating future energy scenarios.
Energy 2011;36:323343.
[65] Borba BSM, Szklo A, Schaeffer R. Plug-in hybrid electric vehicles as a way to
maximize the integration of variable renewable energy in power systems: the
case of wind generation in northeastern Brazil. Energy 2012;37:46981.
[66] Wu T, Yang Q, Bao Z, Yan W. Coordinated energy dispatching in microgrid
with wind power generation and plug-in electric vehicles. IEEE Trans Smart
Grid 2013;4(3):145363.
[67] Liu C, Wang J, Botterud A, Zhou Y, Vyas A. Assessment of impacts of PHEV
charging patterns on wind-thermal scheduling by stochastic unit commit-
ment. IEEE Trans Smart Grid 2012;3(2):67583.
[68] Yilmaz M, Krein PT. Review of the impact of vehicle-to-grid technologies on
distribution systems and utility interfaces. IEEE Trans Power Electron 2013;28
[69] Kempton W, et al. A test of vehicle-to-grid (V2G) for energy storage and
frequency regulation in the PJM System. Results from an IndustryUniversity
Research partnership, University of Delware; Report; 2009.
[70] Smart city project in Malaga. [Online]. Available: http:/
[71] Harnandez Moro J, Martinez-Duart JM. CSP electricity cost evolution and grid
parities based on the IEA roadmaps. Energy Policy 2012;41:18492.
[72] Mullan J, Harries D, Braunl T, Whitely S. The technical, economical and
commercial viability of the vehicle to grid concept. Energy Policy 2012;48:
[73] Rotering N, Ilic M. Optimal charge control of plug-in hybrid electric vehicles in
deregulated electricity markets. IEEE Trans Power Syst 2011;26(3):10219.
[74] Foley A, Tyther B, Calnan P, Gallachóir BÓ. Impacts of electric vehicle charging
under electricity market operations. Appl Energy 2013;101:93102.
[75] Mitra P, Venayagamoorthy GK, Corzine KA. Smartpark as a virtual STATCOM.
IEEE Trans Smart Grid 2011;2(3):44555.
F. Mwasilu et al. / Renewable and Sustainable Energy Reviews 34 (2014) 501516516
... The energy used to charge an EV is comparable to the daily use of a home in Europe or the US. Therefore, the appeal of EVs can only be assured if RER penetration is substantial [109]. There will be a lack of green energy if EV integration is not accompanied by significant RER penetration. ...
Full-text available
By increasing environmental problems including air pollution, rising greenhouse gases, and global warming, the need to use clean energy resources seems essential. Therefore, the usage of clean and renewable energy sources is suggested as a suitable solution to overcome the mentioned concerns. The transportation section has a huge percentage of energy consumption. Hybrid electric vehicles (HEVs), by combining several energy resources, are accounted as a crucial solution to decrease fossil fuel consumption and improve the environmental challenges. The existence of an alternative energy resource and the internal combustion engine (ICE) together provides optimal power distribution among them to maximize power usage and minimize fuel consumption. Energy management strategy (EMS) is an essential challenge in HEV's design procedure to deal with the power distribution in multiple power source systems to improve the performance of the HEVs. A review of various EMSs for HEVs, followed by an analysis of each type including its benefits and drawbacks, is presented in this paper. In addition to this, the major challenges in EMSs for HEVs are described and a comprehensive review is presented for strategies addressing these issues.
... Third, and most importantly, we propose a solution to the aggregation problem based on the concept of a virtual energy storage unit. In energy system modeling, the concept of virtual power plants as a generation technology is widely used, but the virtual energy storage unit as we use it in this article remains a novel concept (Mwasilu et al., 2014;Taljegard et al., 2019). The main idea behind the virtual energy storage unit approach is that the EV's flexibility potential is modeled as the deviation from an uncontrolled charging strategy while respecting its primary purpose: mobility. ...
... Additionally, EVs produce zero tailpipe emissions, meaning they don't release any Carbon Dioxide (CO2) or other harmful pollutants while driving. Several studies have been conducted in the state-of-the-art for the utilization of solar energy and its impacts on the environment, economic, and technological advancement [5]. Based on the fact that 10 Photovoltaics (PVs) should provide roughly enough electricity to power 21 km of electric driving each year [6]. ...
Full-text available
Solar Energy (SE) plays a crucial role in charging Electric Vehicles (EVs), providing a sustainable and renewable source of power. This explores the significance of solar energy in charging EVs, highlighting its environmental impact, economic benefits, and technological advancements. However, solar energy could face a limitation of intermittency due to the changeable weather throughout the year and the speed of charging the EVs. The integrated technology in charging EVs represents a crucial step toward sustainable transportation. By reducing emissions, offering economic benefits, and leveraging technological advancements, solar-powered EV charging contributes to a cleaner and more energy-efficient future.
The estimation of the state of charge (SoC) of lithium-ion batteries is crucial for battery management systems. SoC is one of the most critical parameters that must be determined in real-time to ensure the reliable and safe operation of Li-ion batteries. SoC is a non-measurable parameter, but its value can be derived from other measurable parameters, such as current, voltage, and temperature. Unlike most studies available in the literature, this paper presents a comparative study between two machine learning methods: the Random Forest Regressor (RFR) and the Multi-layer Perceptron (MLP) to accurately estimate the SoC of lithium-ion batteries from data collected under Matlab/Simulink software from a \(LiCoO_2\) battery cell, taking into account the effect of the operating temperature on the battery, and under different current charge/discharge profiles. The results indicate that the Random Forest regressor model is reliable in estimating the SoC with a coefficient of determination of 0.99, a mean error value of less than 0.5%, and a maximum error value of less than 1.83%. In contrast, the MLP yields a mean error value of less than 0.8%, and a maximum error value of less than 1.87%, demonstrating the accuracy and robustness of the Random Forest regressor model for SoC estimation.
Full-text available
Electric Vehicle (EV) technology is expected to take a major share in the light-vehicle market in the coming decades. Charging of EVs will put an extra burden on the distribution grid and in some cases adjustments will need to be made. On the other hand, EVs have the potential to support the grid under various conditions. This paper studies possible potential and applications of Vehicle to Grid (V2G) services, including active power services, which discharge the EV batteries, and power quality services, which do not engage the battery or require only small amounts of battery charge. The advantages and disadvantages of each service and the likelihood that a given service will be effective and beneficial for the grid in the future are discussed. Further, the infrastructure cost, duration, and value of V2G services are compared qualitatively.
Full-text available
The widely adoption of Electric Vehicle (EV) has been identified as a major challenge for future development of smart grids. The ever increasing electric vehicle charging further increases the energy demand. This paper reports the development of an Advanced Metering Infrastructure (AMI) as an effective tool to reshape the load profile of EV charging by adopting appropriate demand side management strategy. This paper presents a total solution for EV charging service platform (EVAMI) based on power line and internet communication. It must be stressed that the development of Third Party Customer Service Platform in this investigation facilitates a single bill to be issued to EV owners. Hence, EV owners understand their energy usage and thus may perform energy saving activity efficiently. Experiment and evaluation of the proposed system show that the throughput achieved is about 5 Mbps at 10 ms end to end delay in Power line Communication. By introducing two dimensional dynamic pricing and charging schedule, the proposed EVAMI successfully reduces 36% peak consumption and increases the “off peak” consumption by 54%. Therefore the EVAMI does not only reduce the peak consumption but also relocates the energy demand effectively.
Full-text available
This paper develops a strategy to coordinate the charging of autonomous plug-in electric vehicles (PEVs) using concepts from non-cooperative games. The foundation of the paper is a model that assumes PEVs are cost-minimizing and weakly coupled via a common electricity price. At a Nash equilibrium, each PEV reacts optimally with respect to a commonly observed charging trajectory that is the average of all PEV strategies. This average is given by the solution of a fixed point problem in the limit of infinite population size. The ideal solution minimizes electricity generation costs by scheduling PEV demand to fill the overnight non-PEV demand "valley". The paper's central theoretical result is a proof of the existence of a unique Nash equilibrium that almost satisfies that ideal. This result is accompanied by a decentralized computational algorithm and a proof that the algorithm converges to the Nash equilibrium in the infinite system limit. Several numerical examples are used to illustrate the performance of the solution strategy for finite populations. The examples demonstrate that convergence to the Nash equilibrium occurs very quickly over a broad range of parameters, and suggest this method could be useful in situations where frequent communication with PEVs is not possible. The method is useful in applications where fully centralized control is not possible, but where optimal or near-optimal charging patterns are essential to system operation.
Full-text available
This paper presents the latest comprehensive literature review of AC and DC microgrid (MG) systems in connection with distributed generation (DG) units using renewable energy sources (RESs), energy storage systems (ESS) and loads. A survey on the alternative DG units' configurations in the low voltage AC (LVAC) and DC (LVDC) distribution networks with several applications of microgrid systems in the viewpoint of the current and the future consumer equipments energy market is extensively discussed. Based on the economical, technical and environmental benefits of the renewable energy related DG units, a thorough comparison between the two types of microgrid systems is provided. The paper also investigates the feasibility, control and energy management strategies of the two microgrid systems relying on the most current research works. Finally, the generalized relay tripping currents are derived and the protection strategies in microgrid systems are addressed in detail. From this literature survey, it can be revealed that the AC and DC microgrid systems with multiconverter devices are intrinsically potential for the future energy systems to achieve reliability, efficiency and quality power supply.
Full-text available
Electric vehicles (EVs) and the related infrastructure are being developed rapidly. In order to evaluate the impact of factors on the aggregated EV load and to coordinate charging, a model is established to capture the relationship between the charging load and important factors based on data mining. The factors can be categorized as internal and external. The internal factors include the EV battery size, charging rate at different places, penetration of the charging infrastructure, and charging habits. The external factor is the time-of-use pricing (TOU) policy. As a massive input data is necessary for data mining, an algorithm is implemented to generate a massive sample as input data which considers real-world travel patterns based on a historical travel dataset. With the input data, linear regression was used to build a linear model whose inputs were the internal factors. The impact of the internal factors on the EV load can be quantified by analyzing the sign, value, and temporal distribution of the model coefficients. The results showed that when no TOU policy is implemented, the rate of charging at home and range anxiety exerts the greatest influence on EV load. For the external factor, a support vector regression technique was used to build a relationship between the TOU policy and EV load. Then, an optimization model based on the relationship was proposed to devise a TOU policy that levels the load. The results suggest that implementing a TOU policy reduces the difference between the peak and valley loads remarkably.
Plug-in vehicles can behave either as loads or as a distributed energy and power resource in a concept known as vehicle-to-grid (V2G) connection. This paper reviews the current status and implementation impact of V2G/grid-to-vehicle (G2V) technologies on distributed systems, requirements, benefits, challenges, and strategies for V2G interfaces of both individual vehicles and fleets. The V2G concept can improve the performance of the electricity grid in areas such as efficiency, stability, and reliability. A V2G-capable vehicle offers reactive power support, active power regulation, tracking of variable renewable energy sources, load balancing, and current harmonic filtering. These technologies can enable ancillary services, such as voltage and frequency control and spinning reserve. Costs of V2G include battery degradation, the need for intensive communication between the vehicles and the grid, effects on grid distribution equipment, infrastructure changes, and social, political, cultural, and technical obstacles. Although V2G operation can reduce the lifetime of vehicle batteries, it is projected to become economical for vehicle owners and grid operators. Components and unidirectional/bidirectional power flow technologies of V2G systems, individual and aggregated structures, and charging/recharging frequency and strategies (uncoordinated/coordinated smart) are addressed. Three elements are required for successful V2G operation: power connection to the grid, control and communication between vehicles and the grid operator, and on-board/off-board intelligent metering. Success of the V2G concept depends on standardization of requirements and infrastructure decisions, battery technology, and efficient and smart scheduling of limited fast-charge infrastructure. A charging/discharging infrastructure must be deployed. Economic benefits of V2G technologies depend on vehicle aggregation and charging/recharging frequency and strategies. The benefits will receive increased attention from grid operators and vehicle owners in the future.