ArticlePDF Available

Hybrid Control and Energy Management of a Residential System Integrating Vehicle-to-Home Technology

MDPI
Designs
Authors:
  • ISEN Yncréa Ouest

Abstract and Figures

Electric vehicles (EV) and photovoltaic (PV) systems are increasingly becoming environmentally friendly and more affordable solutions for consumers. This article discusses the integration of PV and EV in a residential system to meet the requirements of residential loads taking into account the PV supplied power, availability and the state of charge (SOC) of EVs. A hybrid control model has been proposed to control the residential system. The combined PI-Fuzzy logic controller is employed to control the buck-boost bi-directional converter. The DC-AC grid-side converter is controlled by the ADRC controller. The effectiveness of PI-Fuzzy logic controller in reducing voltage and current ripples and ADRC controller in rejecting disturbances is demonstrated in each case. A rule-based energy management strategy has been proposed to control the flow of energy between the components of the residential system. The suggested energy management system (EMS) covers every scenario that might occur. Whether the EV is linked to the home or not, and also takes into account the owner using the EV in an emergency situation. The EV operates in two modes, Home-to-Vehicle (H2V) mode and Vehicle-to-Home (V2H) mode, depending on the power produced by the PV and the conditions related to the EV. All possible scenarios are tested and validated. The simulation results show that the proposed EMS is a reliable solution that can reduce the power grid intervention.
This content is subject to copyright.
Citation: El Harouri, K.; El Hani, S.;
Naseri, N.; Elbouchikhi, E.;
Benbouzid, M.; Skander-Mustapha, S.
Hybrid Control and Energy
Management of a Residential System
Integrating Vehicle-to-Home
Technology. Designs 2023,7, 52.
https://doi.org/10.3390/
designs7020052
Academic Editor: Joshua M. Pearce
Received: 10 February 2023
Revised: 13 March 2023
Accepted: 21 March 2023
Published: 4 April 2023
Copyright: © 2023 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).
Article
Hybrid Control and Energy Management of a Residential
System Integrating Vehicle-to-Home Technology
Khadija El Harouri 1, Soumia El Hani 1, *, Nisrine Naseri 1, Elhoussin Elbouchikhi 2, * , Mohamed Benbouzid 3,4
and Sondes Skander-Mustapha 5
1Department of Electrical Engineering, Energy Optimization Diagnosis and Control, STIS Research Center,
ENSAM, Mohammed V University in Rabat, Rabat 10100, Morocco
2ISEN Yncréa Ouest, Nantes Campus, LABISEN, 33, Avenue du Champ de Manoeuvre,
44470 Carquefou, France
3Institut de Recherche Dupuy de Lôme (UMR CNRS 6027 IRDL), University of Brest, 29238 Brest, France
4Logistics Engineering College, Shanghai Maritime University, Shanghai 201306, China
5Laboratory of Electrical Systems, Ecole Nationale d’Ingénieurs de Tunis, LR11ES15,
University of Tunis El Manar, Tunis 1002, Tunisia
*Correspondence: s.elhani@um5r.ac.ma (S.E.H.); elhoussin.elbouchikhi@isen-ouest.yncrea.fr (E.E.)
Abstract:
Electric vehicles (EV) and photovoltaic (PV) systems are increasingly becoming environ-
mentally friendly and more affordable solutions for consumers. This article discusses the integration
of PV and EV in a residential system to meet the requirements of residential loads taking into ac-
count the PV supplied power, availability and the state of charge (SOC) of EVs. A hybrid control
model has been proposed to control the residential system. The combined PI-Fuzzy logic controller
is employed to control the buck-boost bi-directional converter. The DC-AC grid-side converter is
controlled by the ADRC controller. The effectiveness of PI-Fuzzy logic controller in reducing voltage
and current ripples and ADRC controller in rejecting disturbances is demonstrated in each case. A
rule-based energy management strategy has been proposed to control the flow of energy between the
components of the residential system. The suggested energy management system (EMS) covers every
scenario that might occur. Whether the EV is linked to the home or not, and also takes into account
the owner using the EV in an emergency situation. The EV operates in two modes, Home-to-Vehicle
(H2V) mode and Vehicle-to-Home (V2H) mode, depending on the power produced by the PV and the
conditions related to the EV. All possible scenarios are tested and validated. The simulation results
show that the proposed EMS is a reliable solution that can reduce the power grid intervention.
Keywords:
Vehicle-to-Home; Home-to-Vehicle; energy management systems; residential system;
rule-based algorithm; PI-Fuzzy logic control; ADRC control
1. Introduction
Since its appearance, the automobile has become more and more a main necessity for
human beings, which allows them to make life easier and minimize travel time. However,
in recent years the automobile sector has posed certain problems, such as gas emissions,
the greenhouse effect, dependence on oil, which continues to become scarce, etc. These
circumstances impose an orientation towards new technologies to overcome these prob-
lems [
1
]. In the upcoming years, electric vehicles (EVs) powered by renewable energies,
can play a significant role in the transition to sustainable modes of transportation, reduce
CO
2
emissions, and improve local air quality. Currently, the rate of EV development is
exponentially increasing, more than 16.5 million electric automobiles were in circulation in
2021, a tripling in only three years as shown in Figure 1[
2
]. The main forces behind the
recent global growth of EVs are the tightening of CO
2
emission limits that took place in
2020 and 2021, as well as the increase in the price of fuel. The pace of sales was also aided
by the increase in tax breaks and the increase in purchase subsidies [2].
Designs 2023,7, 52. https://doi.org/10.3390/designs7020052 https://www.mdpi.com/journal/designs
Designs 2023,7, 52 2 of 15
Designs 2023, 7, x FOR PEER REVIEW 2 of 16
main forces behind the recent global growth of EVs are the tightening of CO2 emission
limits that took place in 2020 and 2021, as well as the increase in the price of fuel. The pace
of sales was also aided by the increase in tax breaks and the increase in purchase subsidies
[2].
The rapid development of EVs has led to a sharp increase in the demand for electricity
from these electric vehicles, which has led to thinking about a set of solutions to remedy
this increase in energy demand. Several research directions are addressed on this topic,
such as the integration of EV in micro-grids [3,4], the Vehicle-to-Grid (V2G) and Vehicle-
to-Vehicle (V2V) concepts [59], charging management of EVs in parking lots [10,11], the
impact of EV on the grid [12,13], the charging infrastructure [14], baery degradation [15]
and the charging economics concept [16]. The integration of EVs in residential systems is
an important aspect of addressing the problem of high energy demand, with the optimal
integration of renewable energy systems (RES) and energy storage systems (ESS). This
integration makes it possible to exploit the maximum power of renewable energy sources,
as well as to use the EV as a temporary storage system in the house and as a source of
energy production in order to decrease the budget of EV operation and home
consumption, and to relieve the public electricity grid.
Figure 1. Evolution of global EV.
Most EVs are used for a short time around 5% of the day, and for the rest of the day
around 95% of the remaining time [17], EVs are parked in workplace parking lots or at
home. Thus, we can benet from them during those times when they are parked. The EV
can use its baery to store energy from renewable energy sources or to help storage
systems to store that energy; in this case, the vehicle is used as a storage system. During
low energy production, the EV can intervene to help meet the requirements of home loads
by sending the missing energy, this is the Vehicle-to-Home (V2H) concept. During on-
peak periods when the price of electricity from the public grid is particularly high, the EV
can be used as a source of energy, and it has the ability to sell the energy stored in its
baery to the electrical grid; this is the V2G concept. To manage these operating modes,
plan the charging of the EV and manage the energy exchange in the residential system
between the sources and the loads, the EMS make it possible to meet these requirements.
Charging is said to be “normal when the car charges to full capacity as soon as it is
connected to the grid. The EMS makes it possible to modify this “normal behavior, with
the use of dierent operating modes such as V2H, and V2G to minimize the cost of the
owner’s electricity bill while meeting his personal constraints in terms of the EV’s
availability and autonomy.
0.0
2.0
4.0
6.0
8.0
10.0
12.0
14.0
16.0
18.0
2013 2014 2015 2016 2017 2018 2019 2020 2021
numbre of eletric vehicule
stock (million)
Figure 1. Evolution of global EV.
The rapid development of EVs has led to a sharp increase in the demand for electricity
from these electric vehicles, which has led to thinking about a set of solutions to remedy this
increase in energy demand. Several research directions are addressed on this topic, such as
the integration of EV in micro-grids [
3
,
4
], the Vehicle-to-Grid (V2G) and Vehicle-to-Vehicle
(V2V) concepts [
5
9
], charging management of EVs in parking lots [
10
,
11
], the impact
of EV on the grid [
12
,
13
], the charging infrastructure [
14
], battery degradation [
15
] and
the charging economics concept [
16
]. The integration of EVs in residential systems is an
important aspect of addressing the problem of high energy demand, with the optimal
integration of renewable energy systems (RES) and energy storage systems (ESS). This
integration makes it possible to exploit the maximum power of renewable energy sources,
as well as to use the EV as a temporary storage system in the house and as a source of
energy production in order to decrease the budget of EV operation and home consumption,
and to relieve the public electricity grid.
Most EVs are used for a short time around 5% of the day, and for the rest of the day
around 95% of the remaining time [
17
], EVs are parked in workplace parking lots or at
home. Thus, we can benefit from them during those times when they are parked. The
EV can use its battery to store energy from renewable energy sources or to help storage
systems to store that energy; in this case, the vehicle is used as a storage system. During
low energy production, the EV can intervene to help meet the requirements of home loads
by sending the missing energy, this is the Vehicle-to-Home (V2H) concept. During on-peak
periods when the price of electricity from the public grid is particularly high, the EV can
be used as a source of energy, and it has the ability to sell the energy stored in its battery
to the electrical grid; this is the V2G concept. To manage these operating modes, plan the
charging of the EV and manage the energy exchange in the residential system between the
sources and the loads, the EMS make it possible to meet these requirements. Charging is
said to be “normal” when the car charges to full capacity as soon as it is connected to the
grid. The EMS makes it possible to modify this “normal” behavior, with the use of different
operating modes such as V2H, and V2G to minimize the cost of the owner’s electricity bill
while meeting his personal constraints in terms of the EV’s availability and autonomy.
This work presents a residential system connected to the electrical grid. It consists of
PV as the primary source, a battery system for energy storage, an EV, and residential loads.
A hybrid control has been suggested in this article to control system components. The
charge and discharge of the EV are managed by the combined PI-Fuzzy logic control, which
combines the advantages of both conventional PI controls and fuzzy logic. The injection
and the use of the grid are controlled by the Active Disturbance Rejection Control (ADRC).
The energy management in this residential system is carried out using the rule-based
method. The electrical grid is used as a backup source, and the PV is the main source
Designs 2023,7, 52 3 of 15
that must be exploited to the maximum. In the case when the energy generated through
the PV system reached its maximum, it provides the power to the residential loads and
charges the batteries and the electrical vehicle battery, and the excess PV power is fed to the
grid. Additionally, when the PV system’s power output is low, the battery and the EV are
used to supply residential loads. The grid maintains the shortage when the power of the
components of the system is insufficient to meet the loads. Several scenarios are studied in
this work, taking into account certain conditions such as energy generated through the PV
generator, the demand for residential loads, the availability of the EV, EV SOC.
The main contributions of this paper include:
A hybrid control system that includes PI-Fuzzy logic, and ADRC.
A combined PI-Fuzzy logic control, which combines the benefits of a classical PI
controller and a fuzzy logic controller.
An energy management system that addresses all possible scenarios, considering the
emergency use of the EV by the owner.
The use of the V2H concept to relieve the electricity grid.
The outline of this article is as follows: Section 2displays a description of the residential
system. The presentation of the proposed control laws and the energy management system
are provided in Section 3. Section 4shows the simulation results and a discussion of the
main scenarios and operation modes. Finally, Section 5provides conclusions.
Motivation and Novelty
This paper proposes a hybrid control model and energy management strategy that
examines possible scenarios, for energy exchange in a residential system including PV
system, EV, and energy storage system, residential loads and electricity grid. The proposed
EMS makes it possible to exploit the maximum power of PV system. It is primarily used as
the main source, and the grid as a backup source. Furthermore, the V2H and H2V concepts
have been taken into account. Compared with the existed articles, this paper combines and
provides a complete description of all possible operational scenarios with or without an EV
connection, to relieve the power grid, considering the emergency use of the EV by the owner.
2. System Description
The proposed residential system consists of a PV system with battery storage system,
EV, residential loads, and utility grid as shown by Figure 2. The PV system is considered
as the main source of energy, the battery system acts as a method for momentary storage.
Whereas the EV is used as a load in the context of the H2V concept, or energy source
distributed as part of the possibility of income from V2H. Finally, the main grid is used as a
backup source.
At this home, the energy control system that is in charge of managing the energy
flow in the home communicates with the various elements that compose our residential
system, the load and all energy sources. This is in order to know at any time the state
of each element and information on the energy produced by the PV system, the energy
consumption, the availability of the EV, etc.
Figure 2presents the configuration of the residential system model proposed in this
work. The PV system is connected to the DC bus using the Boost converter, that achieves
the MPPT control. Additionally, the EV is coupled to the DC bus by the bi-directional
Buck-Boost converter, which carries out the charging and discharging control of the EV.
In H2V mode, the EV operates in charging mode (EV used as consumer). In V2H mode,
the EV operates in discharging mode (EV used as power source). Likewise, the storage
battery is coupled to the DC Bus by the Buck-Boost converter, which performs the charge
and discharge control of the battery. On the other side, the electrical grid is coupled to the
DC Bus using the bi-directional DC-AC converter. This converter can be operated as an
inverter, in the case of energy fed to the power grid. Or as a rectifier to supply the load, in
order to meet residential load requirements. Similarly, the residential loads are coupled to
the DC bus by the DC-AC converter.
Designs 2023,7, 52 4 of 15
Designs 2023, 7, x FOR PEER REVIEW 4 of 16
Figure 2 presents the conguration of the residential system model proposed in this
work. The PV system is connected to the DC bus using the Boost converter, that achieves
the MPPT control. Additionally, the EV is coupled to the DC bus by the bi-directional
Buck-Boost converter, which carries out the charging and discharging control of the EV.
In H2V mode, the EV operates in charging mode (EV used as consumer). In V2H mode,
the EV operates in discharging mode (EV used as power source). Likewise, the storage
baery is coupled to the DC Bus by the Buck-Boost converter, which performs the charge
and discharge control of the baery. On the other side, the electrical grid is coupled to the
DC Bus using the bi-directional DC-AC converter. This converter can be operated as an
inverter, in the case of energy fed to the power grid. Or as a rectier to supply the load, in
order to meet residential load requirements. Similarly, the residential loads are coupled
to the DC bus by the DC-AC converter.
Figure 2. The conguration of the proposed system.
Residantial System Parameters
In this house, all the components composing this residential system namely the PV
system, EV, baery storage, electrical grid and the residential loads are connected to the
DC bus, which is set to 900 V.
The model of the PV system used can generate a maximum power of 63 kW at 25 °C
and 1000 W/m2. The EV used is of the Renault ZOE type with a baery capacity of 22 kWh
[18], the storage baery is of 50 Ah capacity [19], as well as a residential load of 26 kW and
a nominal voltage electrical grid of 220/380 V, 50 Hz are considered in this work. Table 1
displays the characteristics of these components.
Figure 2. The configuration of the proposed system.
Residantial System Parameters
In this house, all the components composing this residential system namely the PV
system, EV, battery storage, electrical grid and the residential loads are connected to the
DC bus, which is set to 900 V.
The model of the PV system used can generate a maximum power of 63 kW at 25
C
and 1000 W/m
2
. The EV used is of the Renault ZOE type with a battery capacity of
22 kWh [
18
], the storage battery is of 50 Ah capacity [
19
], as well as a residential load of
26 kW and a nominal voltage electrical grid of 220/380 V, 50 Hz are considered in this work.
Table 1displays the characteristics of these components.
Table 1. Residential system component parameters.
PV System EV Battery Storage Residential Load
Module: SunPower SPR-305-WHT
Type: 305 W @ 1000 W/m2, 25 C
Number of series per string: 5
Number of parallels per string: 40
Maximum Power: 63 kW
Vehicle name: Renault ZOE
Battery capacity: 22 kWh
Rated voltage: 400 V
Capacity: 50 Ah
Single module voltage: 12 V
Number of series connected modules: 34
Rated voltage: 400 V
Type: AC
Power: 26 kW
3. Control Strategy of the Proposed Residential System
The general structure of the overall control system of the proposed residential system
is presented in Figure 3. The control system detects the power produced by the PV, the
power demanded by the residential loads, the availability of EV, the state of charge and
the charging power of the storage battery and the EV, the voltage from the DC bus to
generate the control signals for each power converter, in order to control how much energy
is exchanged in the residential system between the loads and the sources.
Designs 2023,7, 52 5 of 15
Designs 2023, 7, x FOR PEER REVIEW 5 of 16
Table 1. Residential system component parameters.
PV System EV Baery Storage Residential
Load
Module: SunPower SPR-305-
WHT
Type: 305 W @ 1000 W/m², 25 °C
Number of series per string: 5
Number of parallels per string: 40
Maximum Power: 63 kW
Vehicle name: Renault ZOE
Battery capacity: 22 kWh
Rated voltage: 400 V
Capacity: 50 Ah
Single module voltage: 12 V
Number of series connected modules:
34
Rated voltage: 400 V
Type: AC
Power: 26 kW
3. Control Strategy of the Proposed Residential System
The general structure of the overall control system of the proposed residential system
is presented in Figure 3. The control system detects the power produced by the PV, the
power demanded by the residential loads, the availability of EV, the state of charge and
the charging power of the storage baery and the EV, the voltage from the DC bus to
generate the control signals for each power converter, in order to control how much
energy is exchanged in the residential system between the loads and the sources.
Figure 3. PV system control design.
3.1. Local Controllers Design
3.1.1. PV System Control
In order to maximize the power available in PV arrays, Maximum Power Point
Tracking (MPPT) techniques based on the perturb and observe (P&O) algorithm are
employed to maintain the operating point of the PV array at its maximum power point
[20,21].
The goal of the technique is to maintain the PV output power close to the maximum
power point (MPP). To achieve this, the resulting 𝐼𝑝𝑣 current and 𝑉𝑝𝑣 voltage values of
the PV generator are recorded, then the power is determined. Subsequently, the variation
of the power 𝑃𝑝𝑣 and the voltage 𝑉𝑝𝑣 are calculated to know the location of this point
(𝑉𝑝𝑣(𝑡),𝑃𝑝𝑣 (𝑡)) in the P–V curve and the sign of the derivatives are positive or negative, a
positive power derivative means that the operating point is approaching the MPP. Then,
the sign of the voltage drift is tested to know the direction of the search.
The search direction determines whether the control signals of pulse width
modulation
(PWM) is in the increase or decrease stage of the duty cycle.
3.1.2. EV Control
A bi-directional buck-boost converter is used between the EV and the DC bus to
determine when the EV baery is charged and discharged [22,23].
Figure 3. PV system control design.
3.1. Local Controllers Design
3.1.1. PV System Control
In order to maximize the power available in PV arrays, Maximum Power Point Track-
ing (MPPT) techniques based on the perturb and observe (P&O) algorithm are employed
to maintain the operating point of the PV array at its maximum power point [20,21].
The goal of the technique is to maintain the PV output power close to the maximum
power point (MPP). To achieve this, the resulting
Ipv
current and
Vpv
voltage values of
the PV generator are recorded, then the power is determined. Subsequently, the variation
of the power
Ppv
and the voltage
Vpv
are calculated to know the location of this point
(
Vpv(t
),
Ppv(t
)) in the P–V curve and the sign of the derivatives are positive or negative, a
positive power derivative means that the operating point is approaching the MPP. Then,
the sign of the voltage drift is tested to know the direction of the search.
The search direction determines whether the control signals of pulse width modulation.
(PWM) is in the increase or decrease stage of the duty cycle.
3.1.2. EV Control
A bi-directional buck-boost converter is used between the EV and the DC bus to
determine when the EV battery is charged and discharged [22,23].
Conventional PI controllers are widely used in most of the energy systems due to their
simple design, easy implementation, affordable price and their robustness. However, for
non-linear systems and in case of parameters variation PI control is not effective. On the
other hand, the fuzzy logic control (FLC) is an effective control in case of system parameters
variations, the uncertainty of the inputs, the non-linearity of the systems and it presents a
reduced response time. Therefore, to control the converter we have developed a hybrid
PI-Fuzzy logic control, that combines the benefits of traditional PI controller with the
advantages of the fuzzy logic controller, in order to obtain a robust and flexible controller
to effectively manage EV charging and discharging.
A PI-Fuzzy logic controller controls the EV charging and discharging using a ref-
erence current that is determined by the EMS based on the operating mode of EV as
shown by Figure 4. Similarly, a bidirectional buck/boost converter based a PI-Fuzzy logic
controller is utilized to control charging and discharging of the battery energy storage, in the
same way.
Designs 2023,7, 52 6 of 15
Designs 2023, 7, x FOR PEER REVIEW 6 of 16
Conventional PI controllers are widely used in most of the energy systems due to
their simple design, easy implementation, aordable price and their robustness. However,
for non-linear systems and in case of parameters variation PI control is not eective. On
the other hand, the fuzzy logic control (FLC) is an eective control in case of system
parameters variations, the uncertainty of the inputs, the non-linearity of the systems and
it presents a reduced response time. Therefore, to control the converter we have developed
a hybrid PI-Fuzzy logic control, that combines the benets of traditional PI controller with
the advantages of the fuzzy logic controller, in order to obtain a robust and exible
controller to eectively manage EV charging and discharging.
A PI-Fuzzy logic controller controls the EV charging and discharging using a
reference current that is determined by the EMS based on the operating mode of EV as
shown by Figure 4. Similarly, a bidirectional buck/boost converter based a PI-Fuzzy logic
controller is utilized to control charging and discharging of the baery energy storage, in
the same way.
Figure 4. EV control design.
Table 2 provides the FLC rule base that was used in this research work.
The Membership functions for e and de applied in this work are:
NB: is Negative Big
NS: is Negative Small
ZZ: is Zero
PS: Positive Small
PB: is Positive Big.
The fuzzy rules used in this work are:
When E is PB and DE is NB the output is ZZ
When E is PB and DE is NS the output is PS
When E is PB and DE is ZZ the output is PS
When E is PB and DE is PS the output is PB
When E is PB and DE is PB the output is PB
Where e and de are the inputs of the FLC, they respectively present the error and the
change in error.
Figure 4. EV control design.
Table 2provides the FLC rule base that was used in this research work.
The Membership functions for e and de applied in this work are:
NB: is Negative Big
NS: is Negative Small
ZZ: is Zero
PS: Positive Small
PB: is Positive Big.
The fuzzy rules used in this work are:
When E is PB and DE is NB the output is ZZ
When E is PB and DE is NS the output is PS
When E is PB and DE is ZZ the output is PS
When E is PB and DE is PS the output is PB
When E is PB and DE is PB the output is PB
Where e and de are the inputs of the FLC, they respectively present the error and the
change in error.
Table 2. Fuzzy rule table [24].
EDE
NB NS ZZ PS PB
PB ZZ PS PS PB PB
PS NS ZZ PS PS PB
ZZ NS NS ZZ PS PS
NS NB NS NS ZZ PS
NB NB NB NS NS ZZ
3.1.3. Grid-Side Converter Control
The grid-side converter is implemented using the bi-directional DC-AC converter
located between the electrical grid and the DC bus. The control of this converter is based
on ADRC control, which controls the flow of energy from or to the electrical grid as
depicted by Figure 5. This converter operates in two ways, either rectifier or inverter
depending on the situations. When we want to use the power of the electrical grid, the
converter is therefore operated as a rectifier, the current is flowing from the converter to
the DC bus. Contrarywise, when we want to supply the excess power to the grid, the
converter is therefore operated like an inverter, the current is flowing from the DC bus to the
main grid [25].
Designs 2023,7, 52 7 of 15
ADRC is a robust control method, it is employed in this work to control the grid
currents and the DC bus voltage.
Designs 2023, 7, x FOR PEER REVIEW 7 of 16
Table 2. Fuzzy rule table [24].
E DE
NB NS ZZ PS PB
PB ZZ PS PS PB PB
PS NS ZZ PS PS PB
ZZ NS NS ZZ PS PS
NS NB NS NS ZZ PS
NB NB NB NS NS ZZ
3.1.3. Grid-Side Converter Control
The grid-side converter is implemented using the bi-directional DC-AC converter
located between the electrical grid and the DC bus. The control of this converter is based
on ADRC control, which controls the ow of energy from or to the electrical grid as
depicted by Figure 5. This converter operates in two ways, either rectier or inverter
depending on the situations. When we want to use the power of the electrical grid, the
converter is therefore operated as a rectier, the current is owing from the converter to
the DC bus. Contrarywise, when we want to supply the excess power to the grid, the
converter is therefore operated like an inverter, the current is owing from the DC bus to
the main grid [25].
ADRC is a robust control method, it is employed in this work to control the grid
currents and the DC bus voltage.
Figure 5. The grid-side converter control design.
3.2. Rule-Based Energy Management System
To manage the energy between the demand and generation in residential system, the
rule-based algorithm is used to implement an energy management system. All energy
management parameters are provided in Table 3.
Figure 5. The grid-side converter control design.
3.2. Rule-Based Energy Management System
To manage the energy between the demand and generation in residential system, the
rule-based algorithm is used to implement an energy management system. All energy
management parameters are provided in Table 3.
Table 3. Energy system parameters.
PPV Photovoltaic power
PLLoad power
PEV Electric vehicle power
Pbat Storage battery power
Pgrid Grid power
Pf g Power from the grid
Ptg Power to the grid
The proposed rule-based algorithm makes it possible to plan the use of the components
of the residential system according to the power produced, the power requested and the
availability of EV. The electric vehicle has two operating cases. The first case when the EV is
connected to the home, in this case the electric vehicle participates in the residential system
and can operate in two modes. The H2V mode, in this mode the vehicle is considered as a
load, it consumes energy. The V2H mode, in this mode the vehicle is considered as an energy
source, it provides energy. The second case of operation is when the EV is not connected at
home, in this case the electric vehicle does not participate in the residential system.
Designs 2023,7, 52 8 of 15
The PV system is used as a main source in this work and the electrical grid as a backup
source. If the PV generator produces more energy, the energy from the PV is used to satisfy
the residential loads demand, charge the home storage batteries and the EV battery (if
connected) to be used when needed, and the excess PV energy is fed to the grid. Moreover,
when the power generated by the PV generator is low, the storage battery and the EV are
used to supply the residential loads, the power grid intervenes to ensure continuity of
service if the power from the storage battery and the EV and the PV is not enough to satisfy
the demand for residential loads.
Problem formulation:
PL(t)PV(t)Pbat (t)PEV(t)Pgrid (t)=0 (1)
The proposed control system consists of several possible scenarios following two cases:
Case 1: EV is connected to home
Scenario 1: This scenario was regarded as a standard situation, PV powers residential
loads and charges the EV (H2V) and storage battery and excess PV power is fed to
the grid.
PV(t)>PL(t)+Pbat(t)+PEV (t)(2)
Ptg (t)=PV(t)PL(t)Pbat (t)PEV(t)(3)
Scenario 2: PV energy is enough to power residential loads and charges the EV (H2V)
and storage battery, no excess energy.
PV(t)=PL(t)+Pbat(t)+PEV (t)(4)
Scenario 3: the PV energy is not enough to supply the residential loads. The storage
battery is used to satisfy the demand for residential loads and charges the EV (H2V) if
it is discharged.
PV(t)+Pbat (t)=PL(t)+PEV (t)(5)
Scenario 4: PV power and storage battery power are not enough to power residential
loads, the EV intervenes to meet load demand (V2H).
PL(t)=PPV (t)+Pbat (t)+PEV(t)(6)
Scenario 5: PV power, storage battery power and EV (V2H) power are not enough to
power residential loads. In this case, the grid intervenes to ensure the load demand
is met.
Pf g(t)=PL(t)PEV(t)Pbat (t)PPV(t)(7)
Scenario 6: PV power and storage battery power are not enough to power residential
loads, and the EV is discharged. In this case, the grid intervenes to ensure load demand
is satisfied.
Pf g(t)=PL(t)+PEV(t)Pbat (t)PPV(t)(8)
Case 2: EV is not connected to home
Scenario 7: PV energy is enough to power residential loads and charges the storage
battery, and excess PV power is supplied to the grid.
Designs 2023,7, 52 9 of 15
PPV(t)>PL(t)+Pbat (t)(9)
Ptg (t)=PV(t)PL(t)Pbat (t)(10)
Scenario 8: PV energy is enough to power residential loads and charges the storage
battery, no excess energy.
PV(t)=PL(t)+Pbat(t)(11)
Scenario 9: the PV energy is not enough to supply the residential loads. The storage
battery is used to meet the demand of the loads.
Pbat (t)=PL(t)PPV (t)(12)
Scenario 10: PV power and storage battery power are not enough to power residential
loads. The grid intervenes to ensure the load demand is satisfied.
Pf g(t)=PL(t)Pbat(t)PPV (t)(13)
Figure 6depicts the algorithmic flowchart of the proposed EMS describing different
operating modes.
Designs 2023, 7, x FOR PEER REVIEW 10 of 16
Figure 6. Proposed residential EMS owchart.
4. Simulation Results and Discussion
To verify the eectiveness of the proposed residential EMS and associated control
strategies, all the derived sceanrios have been simulated and results presented and
discussed. This is in order to get a thorough investiagation that allow to demonstrate the
interest of renewables and V2H technology in the residential sector.
The EV can be operated in V2H mode only when it is available (connected to home)
and when its charge level is higher than the SOCmax, which is set in this study at 50%,
because the EV must always be available and charged with an acceptable state of charge
as soon as the user needs it, it is necessary to think of the emergency case of use of the
vehicle by the owner.
4.1. EV Connected to Home
4.1.1. Case 1
Figure 7 demonstrates the outcomes of the rst scenario. The power produced by the
PV generator is of higher value and equal to 63 kW. It is able to supply the residential
loads with 26 kW power and charge the EV with 7.5 kW charging power and charge the
storage baery with a power of 7.3 kW, and there remains about 22.2 kW it is supplied to
the electrical grid.
A negative value of the power (𝑃
,𝑃
,𝑃
) means that the element receives
energy (charging the baery/charging the EV/injecting energy into the grid).
Contrarywise, the element is providing energy (by depleting the baery, discharging the
EV, or using grid electricity) if the power value was positive.
In this case the energy produced through the PV is sucient to meet the requirements
of the household loads, to charge the EV and the storage baery, and the rest of the power
is injected into the electricity grid. Figure 8a shows EV charging and Figure 8b shows
baery charging, and the DC bus voltage is shown in Figure 9.
Figure 6. Proposed residential EMS flowchart.
4. Simulation Results and Discussion
To verify the effectiveness of the proposed residential EMS and associated control
strategies, all the derived sceanrios have been simulated and results presented and dis-
cussed. This is in order to get a thorough investiagation that allow to demonstrate the
interest of renewables and V2H technology in the residential sector.
The EV can be operated in V2H mode only when it is available (connected to home)
and when its charge level is higher than the SOCmax, which is set in this study at 50%,
because the EV must always be available and charged with an acceptable state of charge as
soon as the user needs it, it is necessary to think of the emergency case of use of the vehicle
by the owner.
Designs 2023,7, 52 10 of 15
4.1. EV Connected to Home
4.1.1. Case 1
Figure 7demonstrates the outcomes of the first scenario. The power produced by
the PV generator is of higher value and equal to 63 kW. It is able to supply the residential
loads with 26 kW power and charge the EV with 7.5 kW charging power and charge the
storage battery with a power of 7.3 kW, and there remains about 22.2 kW it is supplied to
the electrical grid.
Designs 2023, 7, x FOR PEER REVIEW 11 of 16
Figure 7. Residential system power ow with V2H.
(a) (b)
Figure 8. EV and baery charging from PV. (a) EV SOC. (b) baery SOC.
Figure 9. DC Bus voltage.
4.1.2. Case 2
In this scenario, the energy generated through the PV generator is of low value, the
PV power and the storage baery power are not enough to supply the residential loads.
Moreover, since the EV is connected to the house and its charge level is greater than 50%,
so it intervenes to meet the demand for household loads, and the electricity grid intervenes
to supply the load remaining power of around 8.5 kW as shown in Figure 10.
The EV operates in this case in V2H mode. It makes use of the energy contained in
its baery to power household loads as depicted by Figure 11.
Figure 7. Residential system power flow with V2H.
A negative value of the power (
Pgrid,Pbat,PEV
means that the element receives energy
(charging the battery/charging the EV/injecting energy into the grid). Contrarywise, the
element is providing energy (by depleting the battery, discharging the EV, or using grid
electricity) if the power value was positive.
In this case the energy produced through the PV is sufficient to meet the requirements
of the household loads, to charge the EV and the storage battery, and the rest of the power
is injected into the electricity grid. Figure 8a shows EV charging and Figure 8b shows
battery charging, and the DC bus voltage is shown in Figure 9.
Designs 2023, 7, x FOR PEER REVIEW 11 of 16
Figure 7. Residential system power ow with V2H.
(a) (b)
Figure 8. EV and baery charging from PV. (a) EV SOC. (b) baery SOC.
Figure 9. DC Bus voltage.
4.1.2. Case 2
In this scenario, the energy generated through the PV generator is of low value, the
PV power and the storage baery power are not enough to supply the residential loads.
Moreover, since the EV is connected to the house and its charge level is greater than 50%,
so it intervenes to meet the demand for household loads, and the electricity grid intervenes
to supply the load remaining power of around 8.5 kW as shown in Figure 10.
The EV operates in this case in V2H mode. It makes use of the energy contained in
its baery to power household loads as depicted by Figure 11.
Figure 8. EV and battery charging from PV. (a) EV SOC. (b) battery SOC.
Designs 2023,7, 52 11 of 15
Figure 9. DC Bus voltage.
4.1.2. Case 2
In this scenario, the energy generated through the PV generator is of low value, the
PV power and the storage battery power are not enough to supply the residential loads.
Moreover, since the EV is connected to the house and its charge level is greater than 50%,
so it intervenes to meet the demand for household loads, and the electricity grid intervenes
to supply the load remaining power of around 8.5 kW as shown in Figure 10.
Designs 2023, 7, x FOR PEER REVIEW 12 of 16
Figure 10. Power distribution in the system.
(a) (b)
Figure 11. EV and baery discharging to meet the demand of the loads. (a) EV SOC. (b) storage
baery SOC.
4.1.3. Case 3
As shown by Figure 12, the PV power and the storage baery power are not enough
to supply the residential loads, and the EV is discharged. In this case, the grid intervenes
to ensure the load requirements are satised, and the EV operates in the H2V mode.
Figure 13 represents the SOC of the EV and the storage baery. The EV operates in
H2V mode, it charges through power from storage baery, PV system, and utility grid.
Figure 12. Power distribution in the system.
Figure 10. Power distribution in the system.
The EV operates in this case in V2H mode. It makes use of the energy contained in its
battery to power household loads as depicted by Figure 11.
Designs 2023, 7, x FOR PEER REVIEW 12 of 16
Figure 10. Power distribution in the system.
(a) (b)
Figure 11. EV and baery discharging to meet the demand of the loads. (a) EV SOC. (b) storage
baery SOC.
4.1.3. Case 3
As shown by Figure 12, the PV power and the storage baery power are not enough
to supply the residential loads, and the EV is discharged. In this case, the grid intervenes
to ensure the load requirements are satised, and the EV operates in the H2V mode.
Figure 13 represents the SOC of the EV and the storage baery. The EV operates in
H2V mode, it charges through power from storage baery, PV system, and utility grid.
Figure 12. Power distribution in the system.
Figure 11.
EV and battery discharging to meet the demand of the loads. (
a
) EV SOC. (
b
) storage
battery SOC.
Designs 2023,7, 52 12 of 15
4.1.3. Case 3
As shown by Figure 12, the PV power and the storage battery power are not enough
to supply the residential loads, and the EV is discharged. In this case, the grid intervenes to
ensure the load requirements are satisfied, and the EV operates in the H2V mode.
Designs 2023, 7, x FOR PEER REVIEW 12 of 16
Figure 10. Power distribution in the system.
(a) (b)
Figure 11. EV and baery discharging to meet the demand of the loads. (a) EV SOC. (b) storage
baery SOC.
4.1.3. Case 3
As shown by Figure 12, the PV power and the storage baery power are not enough
to supply the residential loads, and the EV is discharged. In this case, the grid intervenes
to ensure the load requirements are satised, and the EV operates in the H2V mode.
Figure 13 represents the SOC of the EV and the storage baery. The EV operates in
H2V mode, it charges through power from storage baery, PV system, and utility grid.
Figure 12. Power distribution in the system.
Figure 12. Power distribution in the system.
Figure 13 represents the SOC of the EV and the storage battery. The EV operates in
H2V mode, it charges through power from storage battery, PV system, and utility grid.
Designs 2023, 7, x FOR PEER REVIEW 13 of 16
(a) (b)
Figure 13. SOC of the EV and the storage baery. (a) EV SOC. (b) Storage baery SOC.
In this case, the EV cannot operate in V2H mode because it is discharged. The grid
power used is about 22.5 kW, compared to the previous case (case 2), the grid power has
been used was just 8.5 kW. Therefore, the simulation results presented in Figure 12 show
that the use of EV in V2H mode makes it possible to relieve the electrical grid.
4.2. EV Is Not Connected to Home
PV power and storage baery power are not enough to supply residential loads, and
the EV is not connected to the house, in this case the grid intervenes to ensure the
continuity of service. In this scenario the EV is not connected to the house, it does not
intervene in the residential system.
Figure 14 shows that the power of the EV is at zero because the EV is not connected
to the home, and the power produced by the PV is low. Therefore, the storage baery,
which will be discharged to satisfy the demand of the household loads as shown by Figure
15, and the electricity grid intervenes to satisfy the load requirements. In this scenario, the
power of the grid used is approximately 16 kW. On the other hand, in the same scenario
but with the intervention of the EV, the grid uses just a power of 8.5 kW (Figure 10). This
means that the participation of the EV allows to reduce the use of the grid, which leads to
the relief of the grid.
Figure 14. Power distribution in the system.
Figure 13. SOC of the EV and the storage battery. (a) EV SOC. (b) Storage battery SOC.
In this case, the EV cannot operate in V2H mode because it is discharged. The grid
power used is about 22.5 kW, compared to the previous case (case 2), the grid power has
been used was just 8.5 kW. Therefore, the simulation results presented in Figure 12 show
that the use of EV in V2H mode makes it possible to relieve the electrical grid.
4.2. EV Is Not Connected to Home
PV power and storage battery power are not enough to supply residential loads, and
the EV is not connected to the house, in this case the grid intervenes to ensure the continuity
of service. In this scenario the EV is not connected to the house, it does not intervene in the
residential system.
Figure 14 shows that the power of the EV is at zero because the EV is not connected to
the home, and the power produced by the PV is low. Therefore, the storage battery, which
will be discharged to satisfy the demand of the household loads as shown by Figure 15,
and the electricity grid intervenes to satisfy the load requirements. In this scenario, the
power of the grid used is approximately 16 kW. On the other hand, in the same scenario
Designs 2023,7, 52 13 of 15
but with the intervention of the EV, the grid uses just a power of 8.5 kW (Figure 10). This
means that the participation of the EV allows to reduce the use of the grid, which leads to
the relief of the grid.
Designs 2023, 7, x FOR PEER REVIEW 13 of 16
(a) (b)
Figure 13. SOC of the EV and the storage baery. (a) EV SOC. (b) Storage baery SOC.
In this case, the EV cannot operate in V2H mode because it is discharged. The grid
power used is about 22.5 kW, compared to the previous case (case 2), the grid power has
been used was just 8.5 kW. Therefore, the simulation results presented in Figure 12 show
that the use of EV in V2H mode makes it possible to relieve the electrical grid.
4.2. EV Is Not Connected to Home
PV power and storage baery power are not enough to supply residential loads, and
the EV is not connected to the house, in this case the grid intervenes to ensure the
continuity of service. In this scenario the EV is not connected to the house, it does not
intervene in the residential system.
Figure 14 shows that the power of the EV is at zero because the EV is not connected
to the home, and the power produced by the PV is low. Therefore, the storage baery,
which will be discharged to satisfy the demand of the household loads as shown by Figure
15, and the electricity grid intervenes to satisfy the load requirements. In this scenario, the
power of the grid used is approximately 16 kW. On the other hand, in the same scenario
but with the intervention of the EV, the grid uses just a power of 8.5 kW (Figure 10). This
means that the participation of the EV allows to reduce the use of the grid, which leads to
the relief of the grid.
Figure 14. Power distribution in the system.
Figure 14. Power distribution in the system.
Designs 2023, 7, x FOR PEER REVIEW 14 of 16
Figure 15. State of charge of the storage baery.
5. Summary and Conclusions
The V2H concept is considered an advanced and useful technology, as it can
contribute to reducing residential electricity consumption bills and relieving the grid in
times of high electricity demand. Most EV owners can benet from this concept when the
EVs are parked at workplace or at home. This work deals with the integration of EV in a
residential system operated in a grid-connected mode, and it comprises the PV system,
baery storage system, and home loads.
A hybrid control has been suggested to control the dierent elements of the system,
the EV converter is controlled through the combined PI-Fuzzy logic control, which
combines the two controls, the conventional PI and the fuzzy logic, the bidirectional AC–
DC converter is controlled by ADRC control. The results show the eciency of the both
controllers, the PI-Fuzzy logic controller in reducing voltage and current ripples, and the
ADRC controller in rejecting disturbances.
To manage the energy exchange between residential system elements, an energy
management system based on the rule-based method is proposed. The suggested EMS
addresses all possible scenarios, considering the emergency use of the EV by the owner.
All these possible scenarios have been tested and validated.
The simulation results show that the proposed EMS with a residential system
integrating V2H technology is a reliable solution that can beer handle peak energy
demand while simultaneously reducing power grid intervention. The PV is considered as
main source, baery for storage, utility grid is used as a backup source. The EV in this
work operates in two modes, V2H mode and H2V mode depending on operating
conditions. When the energy produced through the PV generator is higher, this power is
used to meet the requirements of residential loads and charge the storage baeries and
the EV baery to be used when needed (EV operates in H2V mode), and the excess PV
energy is injected into the grid. On the contrary, when the energy generated through the
PV generator is low, the storage baery and the EV are used to supply the residential
loads (EV operates in V2H mode), the electricity grid intervenes to satisfy the load if the
power of the baery storage and EV and PV is not enough.
Author Contributions: Conceptualization, K.E.H., S.E.H., E.E.; Funding acquisition, E.E.;
Investigation, N.N.; Methodology, S.E.H., E.E. and M.B.; Software, K.E.H. and N.N.; Supervision,
S.E.H.,E.E., M.B. and S.S.-M.; Validation, S.E.H., E.E.; Writingoriginal draft, K.E.H. and N.N.;
Writing—review & editing, S.E.H., E.E., M.B. and S.S.-M. All authors have read and agreed to the
published version of the manuscript.
Funding: This research received no external funding.
Data Availability Statement: No new data were created or analyzed in this study. Data sharing is
not applicable to this article.
Conicts of Interest: The authors declare no conict of interest.
Figure 15. State of charge of the storage battery.
5. Summary and Conclusions
The V2H concept is considered an advanced and useful technology, as it can contribute
to reducing residential electricity consumption bills and relieving the grid in times of high
electricity demand. Most EV owners can benefit from this concept when the EVs are parked
at workplace or at home. This work deals with the integration of EV in a residential system
operated in a grid-connected mode, and it comprises the PV system, battery storage system,
and home loads.
A hybrid control has been suggested to control the different elements of the system, the
EV converter is controlled through the combined PI-Fuzzy logic control, which combines
the two controls, the conventional PI and the fuzzy logic, the bidirectional AC–DC converter
is controlled by ADRC control. The results show the efficiency of the both controllers, the
PI-Fuzzy logic controller in reducing voltage and current ripples, and the ADRC controller
in rejecting disturbances.
To manage the energy exchange between residential system elements, an energy
management system based on the rule-based method is proposed. The suggested EMS
addresses all possible scenarios, considering the emergency use of the EV by the owner.
All these possible scenarios have been tested and validated.
The simulation results show that the proposed EMS with a residential system inte-
grating V2H technology is a reliable solution that can better handle peak energy demand
while simultaneously reducing power grid intervention. The PV is considered as main
source, battery for storage, utility grid is used as a backup source. The EV in this work
Designs 2023,7, 52 14 of 15
operates in two modes, V2H mode and H2V mode depending on operating conditions.
When the energy produced through the PV generator is higher, this power is used to meet
the requirements of residential loads and charge the storage batteries and the EV battery to
be used when needed (EV operates in H2V mode), and the excess PV energy is injected
into the grid. On the contrary, when the energy generated through the PV generator is low,
the storage battery and the EV are used to supply the residential loads (EV operates in V2H
mode), the electricity grid intervenes to satisfy the load if the power of the battery storage
and EV and PV is not enough.
Author Contributions:
Conceptualization, K.E.H., S.E.H. and E.E.; Funding acquisition, E.E.; In-
vestigation, N.N.; Methodology, S.E.H., E.E. and M.B.; Software, K.E.H. and N.N.; Supervision,
S.E.H., E.E., M.B. and S.S.-M.; Validation, S.E.H. and E.E.; Writing—original draft, K.E.H. and N.N.;
Writing—review & editing, S.E.H., E.E., M.B. and S.S.-M. All authors have read and agreed to the
published version of the manuscript.
Funding: This research received no external funding.
Data Availability Statement:
No new data were created or analyzed in this study. Data sharing is
not applicable to this article.
Conflicts of Interest: The authors declare no conflict of interest.
References
1.
Wu, Y.; Zhang, L. Can the development of electric vehicles reduce the emission of air pollutants and greenhouse gases in
developing countries? Transp. Res. Part D Transp. Environ. 2017,51, 129–145. [CrossRef]
2.
IEA. Global EV Outlook 2022—Securing Supplies for an Electric Future. Glob. EV Outlook 2022. p. 221. Available online: https:
//iea.blob.core.windows.net/assets/ad8fb04c-4f75-42fc-973a-6e54c8a4449a/GlobalElectricVehicleOutlook2022.pdf (accessed on
9 February 2023).
3.
Liu, Z.; Chen, Y.; Zhuo, R.; Jia, H. Energy storage capacity optimization for autonomy microgrid considering CHP and EV
scheduling. Appl. Energy 2018,210, 1113–1125. [CrossRef]
4.
Bimenyimana, S.; Wang, C.; Nduwamungu, A.; Asemota, G.N.O.; Utetiwabo, W.; Ho, C.L.; Niyonteze, J.D.D.; Hagumimana, N.;
Habineza, T.; Bashir, W.; et al. Integration of Microgrids and Electric Vehicle Technologies in the National Grid as the Key Enabler
to the Sustainable Development for Rwanda. Int. J. Photoenergy 2021,2021, 1–17. [CrossRef]
5.
Chaurasiya, S.; Mishra, N.; Singh, B. A 50kW Bidirectional Fast EV Charger with G2V V2G/V2V Capability and Wide Voltage
Range. In Proceedings of the 2020 IEEE 5th International Conference on Computing Communication and Automation (ICCCA),
Greater Noida, India, 30–31 October 2020; pp. 652–657. [CrossRef]
6.
Sovacool, B.K.; Kester, J.; Noel, L.; Zarazua de Rubens, G. Actors, business models, and innovation activity systems for vehicle-to-
grid (V2G) technology: A comprehensive review. Renew. Sustain. Energy Rev. 2020,131, 109963. [CrossRef]
7.
Li, X.; Tan, Y.; Liu, X.; Liao, Q.; Sun, B.; Cao, G.; Li, C.; Yang, X.; Wang, Z. A cost-benefit analysis of V2G electric vehicles
supporting peak shaving in Shanghai. Electr. Power Syst. Res. 2020,179, 106058. [CrossRef]
8.
Taghizadeh, S.; Hossain, M.J.; Poursafar, N.; Lu, J.; Konstantinou, G. A Multifunctional Single-Phase EV On-Board Charger with a
New V2V Charging Assistance Capability. IEEE Access 2020,8, 116812–116823. [CrossRef]
9.
Koufakis, A.M.; Rigas, E.S.; Bassiliades, N.; Ramchurn, S.D. Offline and Online Electric Vehicle Charging Scheduling with V2V
Energy Transfer. IEEE Trans. Intell. Transp. Syst. 2020,21, 2128–2138. [CrossRef]
10.
Alinejad, M.; Rezaei, O.; Habibifar, R.; Azimian, M. A Charge/Discharge Plan for Electric Vehicles in an Intelligent Parking Lot
Considering Destructive Random Decisions, and V2G and V2V Energy Transfer Modes. Sustainability
2022
,14, 12816. [CrossRef]
11.
Mohammad, A.; Zamora, R.; Lie, T.T. Transactive Energy Management of PV-Based EV Integrated Parking Lots. IEEE Syst. J.
2021,15, 5674–5682. [CrossRef]
12.
Wang, L.; Qin, Z.; Slangen, T.; Bauer, P.; Van Wijk, T. Grid Impact of Electric Vehicle Fast Charging Stations: Trends, Standards,
Issues and Mitigation Measures-An Overview. IEEE Open J. Power Electron. 2021,2, 56–74. [CrossRef]
13.
Stiasny, J.; Zufferey, T.; Pareschi, G.; Toffanin, D.; Hug, G.; Boulouchos, K. Sensitivity analysis of electric vehicle impact on
low-voltage distribution grids. Electr. Power Syst. Res. 2021,191, 106696. [CrossRef]
14.
Das, H.S.; Rahman, M.M.; Li, S.; Tan, C.W. Electric vehicles standards, charging infrastructure, and impact on grid integration: A
technological review. Renew. Sustain. Energy Rev. 2020,120, 109618. [CrossRef]
15.
Liu, K.; Hu, X.; Yang, Z.; Xie, Y.; Feng, S. Lithium-ion battery charging management considering economic costs of electrical
energy loss and battery degradation. Energy Convers. Manag. 2019,195, 167–179. [CrossRef]
16.
Patil, H.; Kalkhambkar, V.N. Grid Integration of Electric Vehicles for Economic Benefits: A Review. J. Mod. Power Syst. Clean
Energy 2021,9, 13–26. [CrossRef]
17.
Ouramdane, O.; Elbouchikhi, E.; Amirat, Y.; Le Gall, F.; Gooya, E.S. Home Energy Management Considering Renewable Resources,
Energy Storage, and an Electric Vehicle as a Backup. Energies 2022,15, 2830. [CrossRef]
Designs 2023,7, 52 15 of 15
18.
Berjoza, D.; Jurgena, I. Effects of change in the weight of electric vehicles on their performance characteristics. Agron. Res.
2017
,
15, 952–963.
19.
Kamal, T.; Karabacak, M.; Hassan, S.Z.; Fernández-Ramírez, L.M.; Riaz, M.H.; Riaz, M.T.; Khan, M.A.; Khan, L. Energy
management and switching control of PHEV charging stations in a hybrid smart micro-grid system. Electronics
2018
,7, 156.
[CrossRef]
20.
Thakran, S.; Singh, J.; Garg, R.; Mahajan, P. Implementation of P&O Algorithm for MPPT in SPV System. In Proceedings of the
2018 International Conference on Power Energy, Environment and Intelligent Control (PEEIC), Greater Noida, India, 13–14 April
2018; pp. 242–245. [CrossRef]
21.
Bhattacharyya, S.; Kumar P, D.S.; Samanta, S.; Mishra, S. Steady output and fast tracking MPPT (SOFT-MPPT) for P&O and InC
algorithms. IEEE Trans. Sustain. Energy 2021,12, 293–302. [CrossRef]
22.
El Harouri, K.; El Hani, S.; Elbouchikhi, E.; Benbouzid, M.; Mediouni, H. Grid-connected plug-in electric vehicles charging
stations energy management and control. Int. J. Energy Convers. 2019,7, 49–57. [CrossRef]
23.
Aboudrar, I.; El Hani, S.; El Harouri, K.; Martins, J.; Goncalves, R.J. Reactive Power Compensation by ADRC in Vehicle to Grid
Application during Grid Fault Conditions. In Proceedings of the 2020 International Conference on Electrical and Information
Technologies (ICEIT), Rabat, Morocco, 4–7 March 2020; pp. 1–6. [CrossRef]
24.
Sahin, M.E.; Okumus, H.I. Fuzzy logic controlled buck-boost DC-DC converter for solar energy-battery system. In Proceedings of
the 2011 International Symposium on Innovations in Intelligent Systems and Applications, Istanbul, Turkey, 15–18 June 2011;
pp. 394–397. [CrossRef]
25.
Liu, Y.; Wen, J.; Xu, D.; Huang, Z.; Zhou, H. The decoupled vector-control of PMSM based on nonlinear multi-input multi-output
decoupling ADRC. Adv. Mech. Eng. 2020,12, 1–12. [CrossRef]
Disclaimer/Publisher’s Note:
The statements, opinions and data contained in all publications are solely those of the individual
author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to
people or property resulting from any ideas, methods, instructions or products referred to in the content.
... El Harouri et al. [1] focused on the large-scale integration of more affordable and environmentally friendly energy demands for residential consumers by considering the state of charge (SoC) of EVs and the amount of power supplied by the PV system. In this work, a combination of fuzzy logic controller (FLC) and proportional integral (PI) controller is developed to control the residential system with the help of a bi-directional buck-boost converter; this controller is effective in minimizing the current and voltage ripples in the system. ...
Article
Full-text available
The collection series presents various emerging approaches for designing growing renewable energy (RE), energy storage (ES), and smart transportation with electric vehicles (EVs) in the power and automobile industries [...]
... Researchers have suggested various strategies to find the best answer to this scheduling issue for EV charging and discharging [5][6][7][8]. For instance, the authors of [9] formulated a rule-based energy management mechanism to control the flow of energy of the EV where they considered 10 different scenarios. The authors of [10] presented a comprehensive review of the scheduling of EVs where different methodologies used for this application were discussed. ...
Article
Full-text available
As the adoption of electric vehicles (EVs) continues to rise, efficient scheduling methods that minimize operational costs are critical. This paper introduces a novel EV scheduling method utilizing a heuristic graph-search algorithm for cost minimization due to its admissible nature. The approach optimizes EV charging and discharging schedules by considering real-time energy prices and battery degradation costs. The method is tested on systems with solar generation, electric loads, and EVs featuring vehicle-to-grid (V2G) connections. Various charging rates, such as standard, fast, and supercharging, along with uncertainties in EV arrival and departure times, are factored into the analysis. Results from various case studies demonstrate that the proposed method outperforms popular heuristic optimization techniques, such as particle swarm optimization and genetic algorithms, by 3–5% for different real-time energy prices. Additionally, the method’s effectiveness in reducing operational costs for workplace EVs is confirmed through extensive case studies under varying uncertain conditions. Finally, the system is implemented on a digital real-time simulator with DNP3 communication, where real-time results align closely with offline simulations, confirming the algorithm’s efficacy for real-world applications.
... With the aim of introducing 12 EVs by 2023, Nissan, Renault, and Mitsubishi are developing pure EVs. Hybrid and electric variants of 300 Volkswagen Group vehicles are anticipated to be available by 2030 [10][11][12][13] [17]. ...
... In particular, the control of vehicles and their integration into the smart home environment can offer added value to users who charge at home, as many studies suggest [31], [32]. A pressing issue for the future is sustainability and the reduction of the carbon footprint, which home charging stations will be able to address to a greater extent in the future. ...
Conference Paper
Full-text available
The rapid growth of e-mobility has led to an increased demand for charging infrastructure, requiring innovative solutions to optimize and adapt the existing charging infrastructure. The current charging infrastructure faces challenges such as limited availability, insufficient charging capacity, and inflexibility when using renewable energy sources for mobility. To overcome these challenges, an Internet of Things (IoT) platform with intelligent service applications is expected to enable an adaptive charging infrastructure for efficient and sustainable resource use. As the attractiveness of the platform depends on the relevant service applications for users, which are the basis for an efficient, flexible, and user-friendly charging infrastructure, this research aims to identify and specify the digital affordances of the existing e-mobility applications in the Apple App Store. Based on a qualitative analysis of application descriptions, this study identifies several digital affordances that are beneficial for the seamless integration of charging stations into the IoT platform with a service store. This finding advances our understanding of which affordances should be promoted by an IoT platform provider at the application level. The study results help IoT platform providers in the e-mobility context identify the relevant service application providers and convince them to migrate their applications so that the user base of e-mobilists can also migrate. Furthermore, this paper lays the groundwork for the development and implementation of an IoT platform with service applications for charging stations, which can be utilized independently from the major mobile app store providers, thereby contributing to the advancement of e-mobility and the sovereignty of the charging infrastructure.
... These systems must be capable of dynamically balancing energy needs between home consumption and EV charging demands. Moreover, the integration of V2H technology requires the development of smart grid infrastructures that can accommodate the additional load and energy flow from EVs to homes, while maintaining grid stability and efficiency [88], [89]. ...
Article
Full-text available
The transportation industry is one of the greatest contributors to a growing carbon footprint. As the world increasingly prioritizes a greener and cleaner future, the transition from ICEs (Internal Combustion Engine) to EVs (Electric Vehicle) offers significant benefits as EVs are zero-emission vehicles. However, considering the production and disposal stages of EVs, then they cannot be considered entirely emission-free. But in comparison to ICEs, they still have a much smaller carbon footprint. Moreover, studying EV technology is essential to bringing this to the lowest possible extent. However, these advancements come with challenges. In this review article, we have taken an in-depth look into the various challenges, namely the economic challenges, technological challenges, and environmental challenges, faced by EV technology today and the various solutions proposed by researchers, their limitations, and future potential in EV technology advancement Within this context, we discuss the influence of governmental policies, accessibility and affordability of EVs to the masses, natural and man-made disasters, economic instability, technological limitations posed by the vehicle technology, and limitations posed by battery technology with batteries being the central element for EVs, limitations associated with the existing infrastructure and its ability to support widescale use of EVs, and environmental factors like carbon footprint, among other factors, on EVs technology and the opportunities this technology can make use of in the future. This paper discusses these concerns in detail and gives extensive insight into the challenges, solutions, and future trends pertaining to economic impacts, vehicle technology, battery technology, power grids and charging infrastructure, and environmental impact, of EVs and EV technology. The novelty of our paper lies in its comprehensive scrutiny of these topics, while also critically evaluating the perspectives and findings from another research in this domain.
Article
The global push toward sustainable transportation has driven significant advancements in green vehicle technologies. This paper presents a critical examination of the environmental impacts associated with various GVs throughout their lifecycle, addressing the escalating concerns over environmental degradation and the rising demand for eco-friendly alternatives to conventional fossil fuel-powered vehicles. We evaluate technologies such as biofuel vehicles, hybrid electric vehicles, plug-in hybrid electric vehicles, fuel cell electric vehicles, battery electric vehicles, and solar-powered electric vehicles, highlighting their potential to mitigate the transportation sector’s carbon footprint. This research extends the current literature by providing an innovative, holistic lifecycle assessment of green vehicles, including a thorough analysis of solar vehicles, which are often overlooked in existing studies. Our approach encompasses the environmental impacts of these vehicles from raw material extraction through to end-of-life disposal, considering critical factors such as energy sources, vehicle efficiency, and sustainable practices in battery manufacturing, a crucial yet frequently underexplored aspect in previous research.
Article
Full-text available
The random decisions of electric vehicle (EV) drivers, together with the vehicle-to-vehicle (V2V) and vehicle-to-grid (V2G) energy transfer modes, make scheduling for an intelligent parking lot (IPL) more complex; thus, they have not been considered simultaneously during IPL planning in other studies. To fill this gap, this paper presents a complete optimal schedule for an IPL in which all the above-mentioned items are considered simultaneously. Additionally, using a complete objective function—including charging/discharging rates and prices, together with penalties, discounts, and reward sets—increases the profits of IPL and EV owners. In addition, during peak times, the demand for energy from the distribution system is decreased. The performance of the proposed schedule is validated by comparing three different scenarios during numerical simulations. The results confirm that the proposed algorithm can improve the IPL’s benefits up to USD 1000 and USD 2500 compared to the cases that do not consider the V2V and V2G energy transfer modes, respectively.
Article
Full-text available
The vehicle-to-grid concept emerged very quickly after the integration of renewable energy resources because of their intermittency and to support the grid during on-peak periods, consequently preventing congestion and any subsequent grid instability. Renewable energies offer a large source of clean energy, but they are not controllable, as they depend on weather conditions. This problem is solved by adding energy storage elements, implementing a demand response through shiftable loads, and the vehicle-to-grid/vehicle-to-home technologies. Indeed, an electric vehicle is equipped with a high-capacity battery, which can be used to store a certain amount of energy and give it back again later when required to fulfill the electricity demand and prevent an energy shortage when the main-grid power is limited for security reasons. In this context, this paper presents a comparative study between two home microgrids, in one of which the concept of vehicle-to-home is integrated to provide a case study to demonstrate the interest of this technology at the home level. The considered microgrid is composed of renewable energy resources, battery energy storage, and is connected to the main grid. As the vehicle is not available all day, in order to have consistent results, its intervention is considered in the evening, night, and early morning hours. Two case studies are carried out. In the first one, the vehicle-to-home concept is not taken into account. In this case, the system depends only on renewable resources and the energy storage system. Subsequently, the electric vehicle is considered as an additional energy storage device over a few hours. Electric vehicle integration brings an economic contribution by reducing the cost, supporting the other MG components, and relieving the main grid. Simulation results using real weather data for two cities in France, namely Brest and Toulon, show the effectiveness of the vehicle-to-home concept in terms of cost, energy self-sufficiency, and continuity of electrical service.
Article
Full-text available
Rwanda is an East African Community (EAC) nation with rapid and remarkable past development in different sectors and still with the ambitious targets and plans to be achieved in the coming years ahead. The government plans universal electricity access by 2024 with 52% grid connection and 48% off-grid connections. In the transport sector, the concept of electric vehicles has been initiated and started in order to contribute to the UN Paris agreement and decrease the reliance of the transport sector on gaseous fuels which are one source of air pollutants leading to climate change, premature deaths, and morbidity associated with poor air quality. With higher electricity demand than the generation of the Rwandan power grid, different energy strategies are being developed with the overall objective to achieve the targeted universal energy access. In order to overcome the aforementioned issue, this paper proposes an integration of solar PV microgrids for the satisfaction of electric vehicle (EV) technology in Rwanda. Using HOMER Grid software, a managed EV charging station is simulated to a grid connected solar PV microgrid with storage in order to assess the economic impact. The results show that the proposed technology can lower the levelized cost (LCOE) of electricity by 139.7%. This study can contribute to further research developments in either different perspectives related to the integration of distributed energy resources (DERs) with electric vehicles or studies related to affordable and environment-energy systems.
Article
Full-text available
With growing concern on climate change, widespread adoption of electric vehicles (EVs) is important. One of the main barriers to EV acceptance is range anxiety, which can be alleviated by fast charging (FC). The main technology constraints for enabling FC consist of high-charging-rate batteries, high-power-charging infrastructure, and grid impacts. Although these technical aspects have been studied in literature individually, there is no comprehensive review on FC involving all the perspectives. Moreover, the power quality (PQ) problems of fast charging stations (FCSs) and the mitigation of these problems are not clearly summarized in the literature. In this paper, the state-of-the-art technology, standards for FC (CHAdeMO, GB/T, CCS, and Tesla), power quality issues, IEEE and IEC PQ standards, and mitigation measures of FCSs are systematically reviewed.
Article
Full-text available
Emissions from the Internal Combustion Engine (ICE) Vehicles are one of the primary cause of air pollution and climate change. In recent years, Electric Vehicles (EVs) are becoming a more sensible alternative to these ICE vehicles. With the recent breakthroughs in battery technology and large scale production, EVs are becoming cheaper. In the near future, mass deployment of EVs will put severe stress on the existing Electrical Power System (EPS). Optimal scheduling of EV can reduce the stress on the existing network while accommodating large scale integration of EV. Integration of these EVs can provide several economic benefits to different players in the energy market. In this paper, recent works related to the integration of EV with electrical power system are classified based on their relevance to different players in the electricity market. This classification considers four players-Generation Company (GENCO), Distribution System Operator (DSO), EV Aggregator and End User. Further classification is done based on scheduling or charging strategies used for the grid integration of EVs. This paper provides a comprehensive review of technical challenges in grid integration of EVs along with their solution based on optimal scheduling and controlled charging strategies.
Article
Full-text available
The Permanent Magnet Synchronous Motor (PMSM) is widely used in many fields. Aiming at nonlinearity, strong coupling and uncertainty of the PMSM, this paper proposes a nonlinear multi-input multi-output (MIMO) decoupling PMSM algorithm based on Active Disturbance Rejection Control (ADRC). A Lower-Upper matrix factorization approach is introduced to solve a general inverse of the measured time-varying matrix in real-time decoupling ADRC. This PMSM is based on the vector control. First, the PMSM model and vector control are simulated. Then, a first-order ADRC is introduced and used to replace the PID controller in the d and q axis of PMSM respectively. The simulation shows that the replaced system has a smaller fluctuation, faster response and better stability. Finally, the nonlinear MIMO decoupling ADRC and its inverse matrix method are deduced. Then, the decoupling PMSM control based on ADRC is verified. The simulation shows that this system has a better static and dynamic performance, and it conforms to the PMSM characteristics better. All this shows that the nonlinear MIMO decoupling ADRC is a better strategy for the PMSM. The presented algorithm also has advantage in method compared with some recent results of decoupling PMSM control.
Article
Full-text available
Electric vehicles (EVs) are a viable alternative for a sustainable mode of transportation. A huge penetration of EVs in future will lead to increasing demand in commercial parking lots equipped with charging systems. Hence, this research area is being pursued meaningfully in recent years. This article proposes a transactive energy management system (EMS) for commercial parking lots equipped with EV charging system and rooftop PV system. Initially, the EMS is optimized with the objective of balancing charging demand with supply. To make the EMS more realistic, factors such as battery degradation cost and photovoltaic levelized cost of energy are considered. At the later stage, the EMS of each parking lot communicates the energy requirement and excess energy to the local trading agent to initiate the proposed transactive energy transaction mechanism. The double-sided auction bidding mechanism is price flexible and based on the valuation of the energy requirement of parking lots. For a system with six parking lots and 25 EVs in each parking lot, the proposed scheme results in cost savings in the range of 2% to 7% for different cases of fixed and variable feed-in tariff. The uncertainty analysis shows that the cost savings vary from a minimum of 2.41% to maximum 12.09% with an average of 6.11%. The case studies demonstrate the potential economic benefit of the proposed scheme.
Conference Paper
The presented work identifies the dominating influencing factors in electric vehicle (EV) modelling on low-voltage distribution grids to establish guidance for reliable impact assessments of increasing EV penetration. Seven aspects are distinguished with respect to the modelling of the load of EVs that influence the flows and voltages in the grid. For each of these aspects sensitivity analyses are carried out by running power flow simulations in a Monte-Carlo fashion to account for the stochasticity in the model parameters. The impacts are analysed using a variety of metrics including transformer and line loadings. The highest sensitivities are observed for the number of vehicles in the grid, the used charger power rating and the modelling of driving patterns. The grid configuration as well as locally higher EV shares gain significance for line loading assessments. Car modelling and people’s charging behaviour play minor roles.
Article
This study is motivated by the prospect of needing to harness significant flows of investment and finance, along with private sector commitment, towards decarbonizing passenger transport in Europe. It asks: what types of actors and stakeholder groups, business models, and resulting innovation activity systems might vehicle-to-grid (V2G) technology create or accelerate? Based primarily on qualitative research interviews and focus groups in five countries—Denmark, Finland, Iceland, Norway and Sweden, and a comprehensive literature review, the study assess stakeholder perceptions of primary and secondary business models for V2G. It identifies at least twelve meaningful stakeholder types and corresponding business markets: automotive manufacturers, battery manufacturers, vehicle owners, energy suppliers, transmission and distribution system operators, fleets, aggregators, mobility-as-a-service providers, renewable electricity independent power providers, public transit operators, secondhand markets and secondary markets. These business models fall into the five clusters of equipment, grid services, aggregation, bundling, and secondary markets. We then examine how these business models differ by innovation activity systems—that is, by content, structure, and governance. We lastly translate these findings into policy recommendations of relevance for all types of countries.