Enhancing with EV Charging Station Functions a Residential RES
based Network
Corneliu Marinescu
1
, Luminita Barote
1
, Daniel Munteanu
1
, Vitalijs Komasilovs
2
, Aleksejs Zacepins
2
and Armands Kviesis
2
1
Department of Electrical Engineering and Applied Physics, Faculty of Electrical Engineering and Computer Science,
Transilvania University of Brasov, Brasov, Romania
2
Department of Computer Systems, Faculty of Information Technologies, Latvia University of Agriculture, Jelgava, Latvia
Keywords: Residential Electric Charging Stations, Renewable Energy Sources, Urban Electric Vehicles, Smart Cities.
Abstract: The emergence of Electric Vehicles is creating a possible congestion of the electric grid. The switch in
transportation, especially in cities (future Smart Cities are considered) is asking for the utilization of
Renewable Energy Sources, RES, to decrease pollution. To address these two demands the paper proposes a
solution based on a Residential Charging station architecture for Urban Electric Vehicles. The theoretical
structure is presented and then the practical solution, as Smart Residential MicroGrid based on RES, is
shown. In order to make an implementation more economically and technically affordable and be able to
address in the very near future the growing need of EV Charging stations, the presented solution starts from
the existing equipment used in millions of homes, mainly for solar energy.
1 INTRODUCTION: EV
EVOLUTION
The world is undergoing a transition from internal
combustion engines cars to electric vehicles, EVs.
Increased pollution, especially CO2 emissions and,
to some extent, the fossil fuel sources depletion
pushed governments and car manufacturer to
promote EVs. Therefore, since 2013 EV global sales
increased by 400% (www.navigantresearch.com,
2016).
This change is only the beginning. Due to
governments’ incentives and policies, such as zero-
emission regulations, decrease of production costs
with the scale effect consequences and arise public
interest, the global EV market is increasing rapidly.
According to IEA, (IEA, 2011):
“Assessments of country targets, original
equipment manufacturer (OEM) announcements and
scenarios .... the electric car stock will range
between 9 million and 20 million by 2020 and
between 40 million and 70 million by 2025.”
The increase in EV number will subject the
electric grid to a great strain. Transportation
consumes (uses) about 33 % of the total energy
production in EU and about 30 % at world level
(Fig.1), (Eurostat, 2017; EEA).
From this 33 %, more than half (as can be seen
from Fig. 2) is consumed by cars. That means about
17-18 % from all energy is consumed by cars, which
is oil based. To make the change and replace this
amount of fossil fuel based energy to electricity,
moreover, the useful one (letting aside the losses in
the production and distribution) is an enormous task.
Industrial
processes
21 %
Industrial
buildings
7 %
Buildings
(domestic & tertiary)
37 %
Agriculture
2 %
Transport
33 %
Figure 1: Eurostat 2017 Consumption of energy.
610
Marinescu, C., Barote, L., Munteanu, D., Komasilovs, V., Zacepins, A. and Kviesis, A.
Enhancing with EV Charging Station Functions a Residential RES based Network.
DOI: 10.5220/0006812306100616
In Proceedings of the 4th International Conference on Vehicle Technology and Intelligent Transport Systems (VEHITS 2018), pages 610-616
ISBN: 978-989-758-293-6
Copyright
c
 2019 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved

Figure 2: Transportation share (www.eea.europa.eu).
The scientific community, industry and state
entities know that and in consequence many steps
were already taken toward developing and
implementing Charging Stations for EVs.
But EVs introduction will have a positive effect
on the pollution and energy economy only if the
charging energy will come from Renewable Energy
Sources, RES.
Only the direct integration of RES with EV
charging infrastructure ensures a way to effectively
decrease the emission related to EVs, meet charging
demand, and reduce the dependence on the power
grid (IEA, 2011; Traube, 2013; Birnie, 2009; Roy,
2014; Saber, 2011). Also the total efficiency of the
EV is improved considerable if the electric energy is
produced locally using RES (Marinescu, 2017).
The literature presents results of research,
(Bhatti, 2016; Traube, 2012; Sujitha, 2017; Shareef,
2016) and industrial solutions (which can be also
found online) based on Photovoltaic, PV, (Solar
energy conversion) and considering dedicated
conversion power plants (NPTS Report, 2000)
Considering the Smart Cities Development
Context, the research proposed in this paper is
oriented toward Residential Charging Station using
RES (the most spread and available Solar and Wind)
connected in an IT driven (controlled) network.
2 THE URBAN RESIDENTIAL
CONTEXT
2.1 The Sources
In urban areas there is a lot of room available for PV
installation like homes and enterprises roofs, parking
roofs, etc. Published researches are showing a great
progress in obtaining cost-effective transparent PV
cells to replace windows or to cover facades. Also,
the already existing bifacial PV modules are
generating additional electricity from the light
reflected by the environment to the backside placed
PV cells of the modules.
In a report, Fraunhofer Institute considers that „A
typical application for bifacial solar modules would
be a tilted system on a light-coloured flat roof”
(Graichen, 2015). In cities, the authors of this paper
are taking into consideration the PV and small wind
turbines as RES being able to supply with electric
energy the residential loads including the EVs. We
are considering also small wind generators placed in
residential generating facilities. Distributed Wind
term defines a single or small number of wind
turbines serving an on-site load.
The small wind turbines can be from 0.2 kW to
several kW, more precisely 3 kW, according to
DWEA. The Distributed Wind Energy Association
(DWEA) represents the industry that manufactures,
sells, finances, installs, and supports distributed
wind energy systems. DWEA estimates that in 2030
there will be 23.7 million homes and buildings
suitable for Distributed Wind and that together
represent a potential for 1,100 GW of generating
capacity in USA (DWEA, 2015). Very appealing
small wind turbines solutions, suitable for buildings,
able to decrease the tower height, reduce noise and
vibrations and increase efficiency are presented in
(WWEA, 2015).
Even if the produced energy is not enough, there
is a considerable electric energy potential to be
created by these RES in a Smart City. Through these
means, the RES is producing energy near the
consumer, so the transport lines are not overcharged.
These Distributed RES (DRES), systems provide
generation near the point of use on utility
distribution networks. DRES systems, as the one
proposed in this paper, do not require new or
reinforced transmission lines.
Adding to the involved electronic converters
enhanced functions, DRES can improve the quality
of the utility service by providing voltage support,
harmonic compensation, PF compensation and
Enhancing with EV Charging Station Functions a Residential RES based Network
611

frequency support during faults or transients
(Munteanu, 2018).
In addition the proposed solution in this paper is
exchanging energy with the network to assist it. For
example, the residential RES based energy system
can inject energy in the grid in case of need
(contributing to peak shaving) and absorb energy
from the grid during the night, when the
consumption is low and base power capacity plants
are forced to reduce the production with negative
consequences in efficiency and pollution level.
Developing a communication system between
the grid and the residential power stations based on
economic relationships, letting the results to be
known by people, the customers are educated to
consider their energy consumption carefully.
Our solution, considering both solar and wind
resources, is exploiting the complementarities
between these RES, especially useful due to their
compensatory seasonal variation, for decreasing the
size of the storage devices and consequently
optimizing costs.
The above considerations are backed by the
forecasted evolution of prices for the considered
RES equipments. For example in (Graichen, 2015)
the PV energy cost is estimated to decrease from
9c/kWh in 2014 to 4-6 c/kWh in 2025 and 2-4
c/kWh in 2050, a conservative estimation where the
variation limits take into account the local
conditions.
For small wind turbines, WT, in (DWEA, 2015),
the cost of a kWh is previewed to drop from 28
c/kWh in 2014 to 11 c/kWh in 2030. For those
interested in the next power range, for 4-15 kW
installed power; the costs are 20 c in 2014 and 6.5
c/kWh for 2030. But such cases are not very
encountered. Even if the wind power range would be
smaller, the wind energy has a higher capacity
factor, about double: 12-18% for PV and up to 40 %
for WT. That means, in average, that a 4 kW PV will
produce the same energy as a 2 kW WT per year.
Also a shift in policies which encouraged RES
until now would work in favour of wind. Some of
the policies that have been instrumental in growing
the solar market have had as a consequence the
slowdown in growth of the distributed wind market.
As solar module prices have dropped in recent years,
many of these imbalanced solar programs have been
scaled back and an emerging effect is the reversal of
the trend in favour of wind, (DWEA, 2015):”In
Japan, the FIT (Feed in Tariff) program now pays
distributed wind up to 20 kW over twice the rate of
solar PV “to encourage technology diversity”.
2.2 The Urban Electric Vehicles
It is important to consider the type of the EV. The
future urban transportation system will imply some
changes in today’s cars structure. In crowded cities,
where it is already difficult to find parking places,
will enjoy the spread of many types of vehicles with
2, 3, 4 seats or small busses (about 8 seats). This is
because a great advantage of the EV driving system
is that variable places number vehicles can have
about the same efficiency. This is also coming from
the reduction of the size and masses, adapting the
solution to the needs. Looking at Fig. 3, it is obvious
that such variety of EVs will better ensure the
transportation needs. The variety of EVs, with
reduced number of seats, will occupy less parking
spaces, contributing to the decongestion of the future
Smart Cities.
Figure 3: Number of travellers in a car by activity purpose
(NPTS Report, 2000).
Even automated driven Electric vehicles will
ensure an optimization of the transportation. Many
studies are developed related to EVs progress. For
example on (www.navigantresearch.com) the
interested people can find many studies, such us: a)
Electric Mobility in Smart Cities: E-Buses, E-
Bikes, E-Scooters, PEVs in Shared Mobility
Services (2016); b) Light Electric Vehicles: Low
Speed/Neighbourhood EVs, Electric Motorcycles,
and Electric Scooters (2017). The studies titles are
giving a good image on the future EVs in Smart
Cities. To these lists of EVs we would like to add
three wheel EVs as an option for EVs supposed to
transport 3 people or less and some luggage in a
daily travel in a town. A very interesting research
result is presented in Table 1.
RESIST 2018 - Special Session on Resilient Smart city Transportation
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Table 1: The frequency of the daily journey length for
cars, average (%) (JRC Report, 2013).
As one can see 93% of daily journeys by car
have less than 37 km and the most frequent ones are
less than 10 km! In fact the trip length average value
is around 10 km (variations depend from country to
country).
From the above data one can conclude how
frequently an EV require recharging, considering the
battery capacity, or, in another important way of
technically considering things, how often is the car
at home to facilitate recharging.
3 RESIDENTIAL CHARGING
STATION STRUCTURE
In a previous paper, (Marinescu, 2017), is proposed
the following structure of a residential smart Micro
grid, MG, with EV charging station capabilities,
based on RES (Fig. 4).
Figure 4: RES based EV charging station diagram.
Detailed description of the theoretical and
technical reasons behind the structure components
selection can be finding in the mentioned paper.
While developing our research and reaching to
the implementation stage, some other important
reasons surfaced. The most important one was to
offer the already existing Home Renewable Energy
Systems the possibility to add EV Charging
capability. That is because there are already millions
of home MGs based on RES into operation.
Another important issue was to use the existing
electronic converters and associated equipments
dedicated to Solar and Wind RES. This is because
there are already on the market highly efficient
converters, with a remarkable flexibility of use and
at affordable prices. As the market introduction of
hundreds of millions of EVs in the next 7 to 15 years
is foreseen, the proposed solution will allow supply
this new fleet in a clean and sustainable way.
After this last part of the study, the real
implemented diagram resulted is presented in Fig. 5.
In order to study and implement optimal EV
charging solutions, a smart MG platform designed to
act as a EV charging station, too, based on RESs has
been developed at the R&D Institute of Transilvania
University of Brasov, Romania.
A schematic diagram of the developed MG is
presented in Fig. 5 and illustrations of the system’s
main components are included in Fig. 6. The MG is
supplied by an 8 kW PV power plant along with a
2 kW wind turbine emulated by means of a
laboratory test-bench, while a 48V Li-ion stationary
battery interfaced with the MG by a 6kW inverter
provides around 20 kWh storage capacity. As shown
in Fig. 5, the single-phase MG, having a rated
voltage and frequency of 230V and 50Hz
respectively, includes an EV charging port designed
for AC Level 1 and Level 2 charging modes.
The MG is connected to the utility grid and the
power bi-directional exchange is monitored by a
smart meter. Moreover, in case of grid malfunctions,
the MG can be programmed to switch into island
mode, continuing supplying the loads from the local
sources and from battery. During this state the MG
voltage is controlled by the battery inverter. The
charging and discharging processes of the Li-ion
battery pack are coordinated by a battery
management unit (BMU) providing information
regarding the battery operation to the interfacing
inverter by means of a CAN bus. The battery
Capacity choice was very important and the
selection was made considering the discussion from
Section 2.2. The selection of the battery inverter was
made in such a way as to enable the highest charging
current possible for a single-phase grid.
As Fig. 5 shows, the main MG components are
interlinked through a local communication network
based on wired and wireless technologies, while the
transferred data is collected and processed by an MG
central controller (hereafter called MG manager)
with the main purpose of managing the energy in the
system for an optimal EV charging planning.
Enhancing with EV Charging Station Functions a Residential RES based Network
613

During operation, based on data collected from
the system (e.g. available RESs, available power,
battery state of charge, EV charging status) and from
outside by means of various web services (e.g.
weather forecast, energy prices and requests for
booking of the EV charging station from customers
through the web application) the MG manager
running specific optimization algorithms will decide
the energy balance within the MG and the power
exchange with the grid.
The development of control algorithms for
optimal energy management is undergoing and they
will be integrated in the software designed for the
MG manager shown in Fig. 5. The MG will report
power statistics from the Web system every 10 or 15
minutes (depending on the available meteorological
forecasts), it will use charging demand forecast from
Web system to start energy storing earlier, etc.
The MG manager will also ensure the
connectivity with the web application for the
reservation of the charging station by EV users. The
Figure 5: Diagram of the smart Residential MG designed as an EV charging station.
Figure 6: Illustration of the smart Residential MG acting as an EV charging station.
RESIST 2018 - Special Session on Resilient Smart city Transportation
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web application structure is presented in Fig. 7.
As a result, the MG manager allows for remote
communication over the Internet with a Web
platform designed for enhancing collaboration
experience between EV drivers and EV charging
stations (the one described in the current paper).
Web platform has user friendly interface for
registering and managing owned charging stations,
provides various useful reports and allows
unmanned machine-to-machine communication
between charging hardware and hosted software.
Figure 7: Web System domain structure.
Charging station owner is represented by
authenticated user with rights to register new
charging stations (power user role). Charging station
concept in essence is geographical location with
name and other relevant attributes, like electrical
power, capability, energy source type and charging
price. In addition it has status indicator for better
management by the owner.
Charging station hardware is treated as external
unmanned party which pushes updates about its state
via provided API. Payload of data updates is
decoded and used for updating information of
charging station and its plugs. In addition data
updates are aggregated into report which provides
valuable information for charging station owner
about its facilities (e.g. usage patterns).
Moreover reservation of charging posts is
available on an established time horizon (of 24 hours
in our case). When reservations are made this
information allows MG manager to decide the best
economical use of the energy resources for the
considered time horizon ahead.
Web platform for review and testing is publicly
available at https://smart-grids.science.itf.llu.lv/.
4 CONCLUSIONS
In this paper, the impact of emerging EV-based
transportation in the future Smart Cities has been
evaluated. Enhancing the already existing millions
residential RES Microgrids with an EV Charging
function will help the utility grid, by offering to
exploit Distributing RES to avoid overcharging.
Therefore, the paper presented and analysed a
solution for an EV Charging Station starting from a
RES based home power station. The solution is
implemented with technologically mature
components, such as photovoltaic-based and wind
based systems. The selection of the main required
components is discussed and justified.
Future developments in transforming multiple
Microgrids in Smart ones are ongoing. Different
scenarios will be simulated and considered in the
MG control. As one can see following the Fig. 5 and
Fig. 6, the proposed structure was implemented with
some power reserves. Future research will establish
the optimal power values for the components taking
into account the local weather conditions for
different sites and the network energy economical
price values (which reflect the network congestion).
ACKNOWLEDGEMENTS
Scientific research, publication and presentation are
supported by the ERA Net-LAC Project Enabling
resilient urban transportation systems in smart cities
(RETRACT, ELAC2015/T10-0761). For Romania
the financing Agency is Romanian National
Authority for Research and Innovation, CCCDI
UEFISCDI, within PNCDI III programme frame.
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