Optimum Resilient Operation and Control DC Microgrid Based Electric Vehicles Charging Station Powered by Renewable Energy Sources

Abstract

This paper introduces an energy management and control method for DC microgrid supplying electric vehicles (EV) charging station. An Energy Management System (EMS) is developed to manage and control power flow from renewable energy sources to EVs through DC microgrid. An integrated approach for controlling DC microgrid based charging station powered by intermittent renewable energies. A wind turbine (WT) and solar photovoltaic (PV) arrays are integrated into the studied DC microgrid to replace energy from fossil fuel and decrease pollution from carbon emissions. Due to the intermittency of solar and wind generation, the output powers of PV and WT are not guaranteed. For this reason, the capacities of WT, solar PV panels, and the battery system are considered decision parameters to be optimized. The optimized design of the renewable energy system is done to ensure sufficient electricity supply to the EV charging station. Moreover, various renewable energy technologies for supplying EV charging stations to improve their performance are investigated. To evaluate the performance of the used control strategies, simulation is carried out in MATLAB/SIMULINK.

Full text

energies
Article
Optimum Resilient Operation and Control DC
Microgrid Based Electric Vehicles Charging Station
Powered by Renewable Energy Sources
Khairy Sayed 1, Ahmed G. Abo-Khalil 2, 3, * and Ali S. Alghamdi 2
1Faculty of Engineering, Sohag University, Sohag 82524, Egypt; khairy_fathy@yahoo.ca
2Department of Electrical Engineering, College of Engineering, Majmaah University,
Almajmaah 11952, Saudi Arabia; aalghamdi@mu.edu.sa
3Department of Electrical Engineering, College of Engineering, Assuit University, Assuit 71515, Egypt
*Correspondence: a.abokhalil@mu.edu.sa
Received: 25 September 2019; Accepted: 30 October 2019; Published: 7 November 2019


Abstract:
This paper introduces an energy management and control method for DC microgrid
supplying electric vehicles (EV) charging station. An Energy Management System (EMS) is developed
to manage and control power flow from renewable energy sources to EVs through DC microgrid.
An integrated approach for controlling DC microgrid based charging station powered by intermittent
renewable energies. A wind turbine (WT) and solar photovoltaic (PV) arrays are integrated into
the studied DC microgrid to replace energy from fossil fuel and decrease pollution from carbon
emissions. Due to the intermittency of solar and wind generation, the output powers of PV and WT
are not guaranteed. For this reason, the capacities of WT, solar PV panels, and the battery system
are considered decision parameters to be optimized. The optimized design of the renewable energy
system is done to ensure sufficient electricity supply to the EV charging station. Moreover, various
renewable energy technologies for supplying EV charging stations to improve their performance are
investigated. To evaluate the performance of the used control strategies, simulation is carried out in
MATLAB/SIMULINK.
Keywords:
DC microgrid; electric vehicles; resilient microgrid; solar PV; wind turbine; charging
station; control
1. Introduction
Recently, electric vehicles (EVs) became more widespread, and thus, installing EV charger stations
is substantial to satisfy the electrical energy demand of a large number of EVs [
1
]. However, due to the
extended electrical grids, fast EV charger stations, parking lots, and residential areas can supply the
electrical energy desired to charge EVs. Energy management control strategies are required for the
charger stations for designing and calculating an optimal contracted ability to promote performance and
operation [
2
–
4
]. Effective battery chargers represent a substantial role in the evolution of modern EVs.
The characteristics of the battery charger affect the battery life and charging energy efficiency, as well as
the charging time. EV battery chargers should have the key advantages of higher power density, higher
efficiency, better reliability, smaller size, lighter weight, and cheaper cost. The operation of the charger
circuit relies fundamentally on power circuit topology, power circuit passive and active devices, soft
switching techniques and control schemes [
5
]. Mostly, the EV-charger control techniques can be carried
out by using analog/digital controllers, digital signal processors, microcontrollers, and some particular
integrated circuits. However, this depends upon the complexity of the power circuit topology, cost,
and the power rating of converters. Although plug-in electric vehicles (PEVs) are being promoted in
the market with the objective of reducing the pollution from conventional automobiles, the energy
Energies 2019,12, 4240; doi:10.3390/en12224240 www.mdpi.com/journal/energies

Energies 2019,12, 4240 2 of 23
demands for charging the EV batteries are still supplied by power generated by conventional fossil
fuel sources. For this reason, many researchers have proposed the solution of charging PEVs using
renewable energy sources like photovoltaic (PV) and wind. Numerous pilot projects are also carried
out to charge PEVs from solar PV and wind energy systems [
4
–
9
]. These projects are still in the
development stage [
10
]. Moreover, due to the economic and social benefits, research work on charging
stations powered by the PV system has engaged researchers worldwide. Generally, the use of solar
energy charger is a dependable source for charging small scale electric vehicles, such as scooters, golf
carts, and airport utility carriages [
11
]. The use of photovoltaic powered chargers in a parking lot
is analyzed in [
12
]. A photovoltaic PV-based charging station that is connected with the utility grid
is described in [
13
,
14
]. Solar PV parking lot chargers and other application models to supply PEVs
with solar energy are explored in [
15
]. Economic studies of PV powered charging stations have been
done by [
16
,
17
]. Reference [
18
] depicts how intelligent control algorithms can support PEVs and PV to
integrate with the existing electrical power systems. PV system provides a potential source for PEV of
median generation capacity, while PEVs represent a dispatchable load for low and extra PV generation
during periods of light load demand.
For the stand-alone microgrid, energy management system (EMS) can control demand/supply
balance and maximize the environmental or economic benefits. EMS is a key technology for stable
microgrid operation. For the stand-alone wind-diesel microgrid in [
19
], an optimal EMS strategy is
proposed, which optimizes the charging/discharging cycles of storage system and system operation
cost according to the prediction of wind turbine (WT) output and load demand. A novel EMS-based
on a rolling horizon strategy for a renewable-based microgrid, which includes PV, WT generator, diesel
generator, and energy storage system (ESS), is proposed in [
20
]. In [
21
], a control strategy to reduce
power fluctuations is proposed, which utilizes the ESS to smooth the output power of the wind farm.
DC microgrids have a less complex control strategy which only adopts P-V droop, mitigates the need
for reactive power compensation, and reduce the circulating reactive power. Furthermore, elimination
of frequency and phase angle would ease the resynchronization to the utility grid. Without reactive
power and harmonics, DC microgrids could also offer a better quality of power [
22
]. They can feed the
DC loads directly by avoiding the conversion losses.
Renewable energy-based charging stations (wind and solar) are friendly EV charging that reduces
fossil fuel exhaustion, optimizes investment cost and accommodates fluctuations of generated power
by renewable sources. The evaluation objectives of charging stations include operational performance
and customer acceptance of charging equipment; pricing criteria to encourage off-peak charging;
and grid impacts [23]. Hence, adopting a DC microgrid is presented for enhancing the resilience and
optimum operation of microgrid, including distributed generation [
24
]. The broad problem considered
in this research is the optimization of energy flows in the DC microgrid. For this reason, a stable,
robust and optimal supervisory control algorithm is substantial for the large scale hybrid dynamical
system of PEV charging station. Since the system is subjected to random variations in solar power
and the connected vehicles in the parking lot, the system operation must be robust against these
disturbances. In a DC-microgrid, buses can be classified into four types: Generation bus, DC load bus,
batteries energy storage system (BESS) bus, and connection bus to AC-microgrid using voltage-source
converters (VSCs). Moreover, these types of buses can be divided into two groups according to their
contribution to microgrid operation and control, which are power bus and slack bus. The power bus
absorbs power from/to the microgrid on its own. Typical examples are variable DC-loads and variable
(non-dispatchable) generation, such as photovoltaic and wind turbines generation systems. In contrast,
slack buses are responsible for balancing the power surplus/deficit resulting from power buses and
maintaining stable operation of the microgrid.
Generally, in the largely inhibited parking lot, interventions are focused on removing fully charged
EVs, to give non-charged EVs a chance to be an effective to realize powerful utilization of the charging
infrastructure [
25
]. Specifically, two resilience measures are considered, the resilience related to the
amount of energy delivered to EVs and the resilience related to the average charging time, are provided

Energies 2019,12, 4240 3 of 23
for the EV charging station. Resilience can be defined as the ability of the studied system to withstand
disturbance state and return to a regular state quickly. It has become a new challenge facing the EV
charging station design [
26
]. A related key performance indicator (KPI) is the percent of sessions
with a low charging time ratio to the total charging time divided by the amount of total connection
time [
27
,
28
]. However, these sessions are often a burden during peak hours and daytime. To increase
availability for other EV users, the fully charged EVs should be removed.
References [
29
,
30
] propose power management strategies for an autonomous DC-microgrid
based on a PV source, a supercapacitor, electrochemical storage, and a diesel generator. However,
these papers have difficulties in achieving power balance, while accounting for the slow start-up
characteristic of the diesel generator, the self-discharge of an SC. Moreover, the economic operating
mode of the diesel generator can increase the total energy cost of the DC-microgrid. References [
31
,
32
]
has studied a test bed to investigate the dynamic response of a DC-microgrid to major disturbances,
but it did not calculate the resilience of the studied DC-microgrid. References [
33
,
34
] have investigated
the dynamic response of microgrids powered by renewable energy sources, but they did not define
resilience of the studied systems.
This paper presents the control strategy of an isolated standalone EV chargers station incorporated
in a DC microgrid. This control strategy is investigated using proportional-integral controllers (PI).
This controller will regulate the charging of EVs. The proposed EMS is considered promising, due to
its robustness and simplicity that makes this suitable for applications in the future smart DC microgrid.
A new resilience measurement is defined as the ratio of the normalized system, integrated within its
maximum permissible recovery time after the disturbance to the performance integral in the ordinary
state. This measure enables the resilience of various systems to be compared on the same comparative
scale. To estimate the resilience of DC microgrid, a resilience measurement scheme is developed.
2. System Description
A DC microgrid is a low-voltage network that consists of several energy components, such as
controllable loads and distributed energy resources (DERs). The standalone system can decrease the
carbon footprint and reduce the losses of power transmission [
35
]. Figure 1shows a standalone DC
microgrid supplying EVs charging station. The studied system is composed of WT, photovoltaic (PV),
and energy storage systems (ESS), such as battery bank. In this system, controllable loads include
electric vehicles (EVs). The EVs are charged from DC microgrid through DC-DC converters controlled
by a charging regulation control scheme. The battery bank has a dual power flow in the whole system
that acts as the energy provider and consumer according to the condition of wind turbine and PV
panels’ production. The configuration of the standalone charging station is shown in Figure 1. The PV
connected to the DC microgrid through a DC-DC converter controlled by the maximum power point
tracker (MPPT) scheme. The wind generator connected to the DC microgrid through an AC-DC and
DC-DC converters. At the same time, the battery bank charged and discharged from DC microgrid
using a bidirectional DC-DC converter.

Energies 2019,12, 4240 4 of 23
Energies 2016, 9, x 4 of 5
Figure 1. Configuration of the proposed DC microgrid based charging station.
2.1. Configuration of Electric Vehicle
The PEV has three major subsystems: Electric propulsion subsystem, Power source, and
Auxiliary system. The electrical propulsion subsystem is composed of the electronic controller; DC-
AC power converter; electric traction motor; driving wheels, and mechanical transmission. Based on
the control input signals from the accelerator and brake pedals, the electronic or digital controller
provides the required control commands to switch on or off the power converters which in turn
coordinate the power flow between the electric motor and the EV battery source. However, the
backward power flow is due to regenerative braking of the EV, and consequent regenerative energy
can be maintained to charge EV Battery. The energy management unit (EMU) cooperates with the
electronic controller to deal with regenerative braking and its recovered energy. Generally, the
auxiliary power system supplies the necessary energy at various voltage levels to auxiliaries in EV,
particularly the power steering units and temperature control. The successful utilization of EVs over
the next decade is promoted according to international standards and regulations. Safety codes and
standards define a wide range of issues related to EVs. For example, article 625-18 of the national
electrical code [36], requires that cables and connectors for levels 2 and 3 be de-energized unless
connected to an EV for charging. Typically, there are various types of charging systems for EVs, and
generally, they are categorized as level 1, level 2 charging, and level 3 charging, as shown in Figure
2. All commercial EVs have the capability to charge using level 1 or level 2 charging systems. Level 1
and 2 charging are ranged from 2 to 20 kW single or three-phase to supply AC charging current of
up to 80 A. Level 2 chargers are equipped with SAE J1772 AC charging port to charge the EV batteries.
Level 3 charging, delivers a rapid DC charging method to charge the vehicle batteries at installed
stations. This charging technique practices a 3-phase source with a DC output to the vehicle.
Figure 1. Configuration of the proposed DC microgrid based charging station.
2.1. Configuration of Electric Vehicle
The PEV has three major subsystems: Electric propulsion subsystem, Power source, and Auxiliary
system. The electrical propulsion subsystem is composed of the electronic controller; DC-AC power
converter; electric traction motor; driving wheels, and mechanical transmission. Based on the control
input signals from the accelerator and brake pedals, the electronic or digital controller provides the
required control commands to switch on or offthe power converters which in turn coordinate the
power flow between the electric motor and the EV battery source. However, the backward power
flow is due to regenerative braking of the EV, and consequent regenerative energy can be maintained
to charge EV Battery. The energy management unit (EMU) cooperates with the electronic controller
to deal with regenerative braking and its recovered energy. Generally, the auxiliary power system
supplies the necessary energy at various voltage levels to auxiliaries in EV, particularly the power
steering units and temperature control. The successful utilization of EVs over the next decade is
promoted according to international standards and regulations. Safety codes and standards define a
wide range of issues related to EVs. For example, article 625-18 of the national electrical code [
36
],
requires that cables and connectors for levels 2 and 3 be de-energized unless connected to an EV
for charging. Typically, there are various types of charging systems for EVs, and generally, they are
categorized as level 1, level 2 charging, and level 3 charging, as shown in Figure 2. All commercial
EVs have the capability to charge using level 1 or level 2 charging systems. Level 1 and 2 charging are
ranged from 2 to 20 kW single or three-phase to supply AC charging current of up to 80 A. Level 2
chargers are equipped with SAE J1772 AC charging port to charge the EV batteries. Level 3 charging,
delivers a rapid DC charging method to charge the vehicle batteries at installed stations. This charging
technique practices a 3-phase source with a DC output to the vehicle.

Energies 2019,12, 4240 5 of 23
Energies 2016, 9, x 5 of 5
Figure 2. Block diagram of different types of charging systems.
2.2. Capacity Sizing of the Charging Station
In this paper, the charging station is considered far away from a community. The charging point
in the station provides four typical DC chargers and two fast DC chargers (the fast charger typically
requires 20 min to complete charging 80%, while the standard charger requires about 11 h to complete
80% charging). For example, the Nissan leaf car has about 24 kWh lithium-ion battery banks to store
and supply power for EV motor [37–40]. Hence, within 30 min it reaches about 80% of its capacity at
level 3 charging condition.
In this paper, the maximum capacity of the charging station output power was assumed. The
load demand is treated as a changeable parameter and pursues normal allocation. For a public
charging station, the chargers should endorse standard chargers, in order to be utilized for various
types of EVs. Home charging usually uses AC charging and it includes two types of charging; level
1 charging (120 V) and level 2 charging (240). On the other hand, DC faster chargers are largely used
in the commercial chargers stations. The capital cost of the charging station will be cut-price as the
prices of wind and solar PV generator apparatus come down. Any overabundant energy from WT
and PV can be accumulated in the energy storage batteries. The optimal sizing of WT, PV and battery
capacities could depend on the variance of wind velocities and solar irradiance. Table 1 shows the
renewable sources-based specifications of the charging station.
Table 1. The output of the charging station.
Output Qty Working Hours
Faster DC charger 50 kW 2 24
DC standard charger 10 kW 4 24
Lights and other loads 10 kW 12
The total output 150 kW
The charging station is designed, such as the maximal output power is 150 kW. The daily
operation time is 24 h and seven days a week. The typical battery bank capacity is 24 kWh (Nissan
Leaf, 2014). The calculated maximal demand for the charging station is 4728 kWh every day. The
most popular charging technology, based on Japan’s EV association standard [36,37], can deliver 50
kW output. However, the power rating of the charger is 90 kW at Tesla’s supercharger station. Then
the car can travel about 240 km after charging for 30 min. In this paper, it is assumed that the DC fast
charger rates 50 kW output.
3. Control Strategy for PV/Wind/Storage Hybrid System
Figure 2. Block diagram of different types of charging systems.
2.2. Capacity Sizing of the Charging Station
In this paper, the charging station is considered far away from a community. The charging point
in the station provides four typical DC chargers and two fast DC chargers (the fast charger typically
requires 20 min to complete charging 80%, while the standard charger requires about 11 h to complete
80% charging). For example, the Nissan leaf car has about 24 kWh lithium-ion battery banks to store
and supply power for EV motor [
37
–
40
]. Hence, within 30 min it reaches about 80% of its capacity at
level 3 charging condition.
In this paper, the maximum capacity of the charging station output power was assumed. The load
demand is treated as a changeable parameter and pursues normal allocation. For a public charging
station, the chargers should endorse standard chargers, in order to be utilized for various types of
EVs. Home charging usually uses AC charging and it includes two types of charging; level 1 charging
(120 V) and level 2 charging (240). On the other hand, DC faster chargers are largely used in the
commercial chargers stations. The capital cost of the charging station will be cut-price as the prices of
wind and solar PV generator apparatus come down. Any overabundant energy from WT and PV can
be accumulated in the energy storage batteries. The optimal sizing of WT, PV and battery capacities
could depend on the variance of wind velocities and solar irradiance. Table 1shows the renewable
sources-based specifications of the charging station.
Table 1. The output of the charging station.
Output Qty Working Hours
Faster DC charger 50 kW 2 24
DC standard charger 10 kW 4 24
Lights and other loads 10 kW 12
The total output 150 kW
The charging station is designed, such as the maximal output power is 150 kW. The daily operation
time is 24 h and seven days a week. The typical battery bank capacity is 24 kWh (Nissan Leaf, 2014).
The calculated maximal demand for the charging station is 4728 kWh every day. The most popular
charging technology, based on Japan’s EV association standard [
36
,
37
], can deliver 50 kW output.
However, the power rating of the charger is 90 kW at Tesla’s supercharger station. Then the car can
travel about 240 km after charging for 30 min. In this paper, it is assumed that the DC fast charger rates
50 kW output.

Energies 2019,12, 4240 6 of 23
3. Control Strategy for PV/Wind/Storage Hybrid System
3.1. Control Scheme of the Boost DC-DC Converter Interfacing PV Array
The boost conversion stage is used to regulate the voltage from the PV panel and extract the
maximum power. The PV panel voltage V
pv
and the input current I
pv
are sensed frequently. Then the
MPPT control algorithm utilizes these two values and calculates the reference power that the PV panel
requires to be operated at MPP conditions. The MPPT is achieved using an inner current loop and an
outer voltage loop, as shown in Figure 3. By increasing the current drawn from the boost converter,
results in reducing the panel output voltage. Therefore, the outer voltage compared with a reference
value and feedback is regulated using PI controller gains. Hence, the output voltage is prevented from
exceeding the adjusted value. On the other hand, the resulting signal from the MPPT controller is
regulated using a PI controller. Then the output of the internal loop is compared with the reference
current produced by the outer loop to generate the PWM signal [38–40].
Energies 2016, 9, x 6 of 5
3.1. Control Scheme of the Boost DC-DC Converter Interfacing PV Array
The boost conversion stage is used to regulate the voltage from the PV panel and extract the
maximum power. The PV panel voltage Vpv and the input current Ipv are sensed frequently. Then the
MPPT control algorithm utilizes these two values and calculates the reference power that the PV
panel requires to be operated at MPP conditions. The MPPT is achieved using an inner current loop
and an outer voltage loop, as shown in Figure 3. By increasing the current drawn from the boost
converter, results in reducing the panel output voltage. Therefore, the outer voltage compared with
a reference value and feedback is regulated using PI controller gains. Hence, the output voltage is
prevented from exceeding the adjusted value. On the other hand, the resulting signal from the MPPT
controller is regulated using a PI controller. Then the output of the internal loop is compared with
the reference current produced by the outer loop to generate the PWM signal [38–40].
Figure 3. Maximum power point tracker (MPPT)control of boost DC-DC converter.
3.2. Control of the Boost DC-DC Converter Interfacing Wind Turbine
The control scheme of the WT generator includes maximum power point extractor for
standalone variable speed WT with a permanent magnet synchronous generator (PMSG) and DC bus
voltage control. The boost power converter is correctly adjusted to supply the maximum available
generated power from the WT using the rectified DC voltage and current drawn from the rectifier
output. The charging station works in standalone operation mode. Hence, the generated energy
should be transferred through the DC microgrid to the electric vehicle loads. The power reference is
generated from the comparison of the DC-link actual and reference values. The generated control
signals are adjusted by using PI controllers to give power reference. For maintaining the DC
microgrid voltage at its desired value, the PWM modulation signals of each converter are controlled
regardless of variations in wind speeds and vehicles charging loads. The aim of using the boost
converter is to regulate the rectified DC voltage to a higher voltage level for supplying generated
power to the station DC microgrid. The DC microgrid voltage will be in the range of 280–320 V. DC-
DC converters are controlled to obtain maximum power point operation MPP to maximize gathered
wind power and to optimize the electrical energy produced by the PV panels. Figure 4 explains the
control scheme of the boost converter. The parameters of PMSG are listed in Table 2. Thus, the
measured input current and voltage values are used in the power optimizing algorithm or power
tracker MPPT. The rectified DC voltage value (VDC) is provided to a look-up table that defines a
predefined maximum power point (MPP) characteristic curve.
Figure 3. Maximum power point tracker (MPPT)control of boost DC-DC converter.
3.2. Control of the Boost DC-DC Converter Interfacing Wind Turbine
The control scheme of the WT generator includes maximum power point extractor for standalone
variable speed WT with a permanent magnet synchronous generator (PMSG) and DC bus voltage
control. The boost power converter is correctly adjusted to supply the maximum available generated
power from the WT using the rectified DC voltage and current drawn from the rectifier output.
The charging station works in standalone operation mode. Hence, the generated energy should be
transferred through the DC microgrid to the electric vehicle loads. The power reference is generated
from the comparison of the DC-link actual and reference values. The generated control signals are
adjusted by using PI controllers to give power reference. For maintaining the DC microgrid voltage at
its desired value, the PWM modulation signals of each converter are controlled regardless of variations
in wind speeds and vehicles charging loads. The aim of using the boost converter is to regulate
the rectified DC voltage to a higher voltage level for supplying generated power to the station DC
microgrid. The DC microgrid voltage will be in the range of 280–320 V. DC-DC converters are controlled
to obtain maximum power point operation MPP to maximize gathered wind power and to optimize
the electrical energy produced by the PV panels. Figure 4explains the control scheme of the boost
converter. The parameters of PMSG are listed in Table 2. Thus, the measured input current and voltage
values are used in the power optimizing algorithm or power tracker MPPT. The rectified DC voltage
value (V
DC
) is provided to a look-up table that defines a predefined maximum power point (MPP)
characteristic curve.

Energies 2019,12, 4240 7 of 23
Energies 2016, 9, x 7 of 5
Figure 4. Control of the wind turbine (WT) boost converter interfacing DC microgrid.
Table 2. PMSG parameters [29].
Parameter Value Unit
Stator resistance 0.02 Ω
d-axis inductance Ld 7 mH
q-axis inductance 7 mH
Vpk/krpm 98.7
No. of poles (P) 8
Moment of inertia 8 × 10−3 N-msec2
Mechanical time constant 0.04
3.3. Control of Bidirectional DC-DC Converter Interfacing Battery Bank
As shown in Figure 5, the bidirectional converter consists of a high-frequency inductor L,
filtering capacitor CDC and two half-bridge switches (S1 and S2), which enable a bidirectional flow of
current. There are two voltage controllers with appropriate control blocks to realize the desired
energy flow in various conditions. The controller produces a reference current of energy charging
and discharging. The first controller is for DC-bus voltage regulation, and the second controller is for
battery voltage control. To improve energy management in the charging station and the DC
microgrid, backup energy storage batteries are used. The battery bank is connected to the DC-
microgrid employing a bidirectional DC-DC power converter. This converter carries out double
tasks: A battery charging regulator and a boost converter to supply power from the battery bank to
the DC microgrid when the PV panels and wind sources have insufficient power to charge the electric
vehicle loads. As a standalone charging station, the most convenient operating condition takes place
when the electric vehicle power and the PV and wind extracted power agree. However, too deep
discharge of the battery bank is not recommended, as, at a low battery bank voltage, there is a
confined range of charging energy, which may cause over-voltage in DC microgrid during, e.g.,
energy recovery from the EVs side. On the other hand, there is a limited range of discharging energy,
and the batteries have to be protected.
Figure 4. Control of the wind turbine (WT) boost converter interfacing DC microgrid.
Table 2. PMSG parameters [29].
Parameter Value Unit
Stator resistance 0.02 Ω
d-axis inductance Ld 7 mH
q-axis inductance 7 mH
Vpk/krpm 98.7
No. of poles (P) 8
Moment of inertia 8×10−3N-msec2
Mechanical time constant 0.04
3.3. Control of Bidirectional DC-DC Converter Interfacing Battery Bank
As shown in Figure 5, the bidirectional converter consists of a high-frequency inductor L, filtering
capacitor C
DC
and two half-bridge switches (S
1
and S
2
), which enable a bidirectional flow of current.
There are two voltage controllers with appropriate control blocks to realize the desired energy flow in
various conditions. The controller produces a reference current of energy charging and discharging.
The first controller is for DC-bus voltage regulation, and the second controller is for battery voltage
control. To improve energy management in the charging station and the DC microgrid, backup
energy storage batteries are used. The battery bank is connected to the DC-microgrid employing a
bidirectional DC-DC power converter. This converter carries out double tasks: A battery charging
regulator and a boost converter to supply power from the battery bank to the DC microgrid when
the PV panels and wind sources have insufficient power to charge the electric vehicle loads. As a
standalone charging station, the most convenient operating condition takes place when the electric
vehicle power and the PV and wind extracted power agree. However, too deep discharge of the battery
bank is not recommended, as, at a low battery bank voltage, there is a confined range of charging
energy, which may cause over-voltage in DC microgrid during, e.g., energy recovery from the EVs side.
On the other hand, there is a limited range of discharging energy, and the batteries have to be protected.

Energies 2019,12, 4240 8 of 23
Energies 2016, 9, x 8 of 5
PWM
Vdc
Ibat
L
IGBT
Vbat
PI-Ibat
Cdc
Vdc-bus
Ibat
Iref
Current controller
Battery
Voltage
controller
Battery voltage
reference
DC-bus
Voltage
controller
DC-bus
voltage
reference
Iref-grid
Iref-isl. Charge/
discharge
mode
+
-
+
S1
S2
Figure 5. Control scheme of battery energy storage.
3.4. DC Microgrid Control Method
In the studied DC microgrid, a control scheme has been implemented to balance the DC voltage
bus and to control the power supply to meet the load demand in islanded mode. In this control, one
unit source acts as a master controlling the full system, while the rest of the units work as current
sources (i.e., as “Slaves”). In this way, there will not be the voltage difference between the outputs of
the DC sources, because the Master unit regulates the voltage values of all the output units; therefore,
current will not circulate between the sources.
The DC microgrid is measured and compared with a predefined reference voltage, and the
voltage error is processed through a compensator (PI block) to obtain the desired impedance current
reference for the current loop. This compensator can be expressed in the following way [33]:
−+−= dtVVkVVkI MGrefiMGrefpLref )()( (1)
where ILref is the reference current for the DC-DC converter. Vref is the reference voltage for DC
microgrid, and VMG is the actual voltage. Kp and ki are the proportional and integral controller
coefficients for voltage loop. The power flow is controlled by a current controller who compares the
impedance current in the master unit with the reference current desired to stabilize the system, the
error is processed through another PI block to obtain the desired duty cycle for the converter which
acts as a Master. The PI block can be expressed as:
−+−= dtIIkIIkd LLrefiiLLrefip )()( (2)
where ILref is the reference current for the DC-DC converter. IL is the actual measured current. Kip and
kii are the proportional and integral controller coefficients for the current loop. The problem of this
control topology is the dependence on the master unit, and if there is a fault in this unit, the control
will stop working properly [27]. To increase the reliability of the system, three different sources can
act as a master unit, decreasing the chance to fault in the microgrid control. The energy storage system
(ESS) can control the voltage level and the power flow through a bidirectional converter. When the
microgrid is working in an islanded mode, this source will act as a “master” remaining the voltage
at 300 V and meeting the load demand. If there is a fault in the ESS or the state of charge (SOC) level
is not properly to control the microgrid in an islanded mode. There is a voltage controller
implemented with a voltage and a current loop as it is shown in Figure 6.
Figure 5. Control scheme of battery energy storage.
3.4. DC Microgrid Control Method
In the studied DC microgrid, a control scheme has been implemented to balance the DC voltage
bus and to control the power supply to meet the load demand in islanded mode. In this control,
one unit source acts as a master controlling the full system, while the rest of the units work as current
sources (i.e., as “Slaves”). In this way, there will not be the voltage difference between the outputs of
the DC sources, because the Master unit regulates the voltage values of all the output units; therefore,
current will not circulate between the sources.
The DC microgrid is measured and compared with a predefined reference voltage, and the voltage
error is processed through a compensator (PI block) to obtain the desired impedance current reference
for the current loop. This compensator can be expressed in the following way [33]:
ILre f =kp(Vre f −VMG) + kiZ(Vre f −VMG)dt (1)
where I
Lref
is the reference current for the DC-DC converter. V
ref
is the reference voltage for DC
microgrid, and V
MG
is the actual voltage. K
p
and k
i
are the proportional and integral controller
coefficients for voltage loop. The power flow is controlled by a current controller who compares
the impedance current in the master unit with the reference current desired to stabilize the system,
the error is processed through another PI block to obtain the desired duty cycle for the converter which
acts as a Master. The PI block can be expressed as:
d=kip(ILre f −IL) + kiiZ(ILre f −IL)dt (2)
where I
Lref
is the reference current for the DC-DC converter. I
L
is the actual measured current. K
ip
and
k
ii
are the proportional and integral controller coefficients for the current loop. The problem of this
control topology is the dependence on the master unit, and if there is a fault in this unit, the control
will stop working properly [
27
]. To increase the reliability of the system, three different sources can act
as a master unit, decreasing the chance to fault in the microgrid control. The energy storage system
(ESS) can control the voltage level and the power flow through a bidirectional converter. When the
microgrid is working in an islanded mode, this source will act as a “master” remaining the voltage at
300 V and meeting the load demand. If there is a fault in the ESS or the state of charge (SOC) level is

Energies 2019,12, 4240 9 of 23
not properly to control the microgrid in an islanded mode. There is a voltage controller implemented
with a voltage and a current loop as it is shown in Figure 6.
Energies 2016, 9, x 9 of 5
i
Vo
*
Pdc
Vo
*
mdc1
mdc2
Pdc2 max
Pdc1 max Pdc2
Pdc1
Vo
n
Vo
a
Vo
min
Vo
b
Vo
n
Vo
a
Vo
b
ibia
(a) (b)
Figure 6. Representation of DC droop (a) The particular droop of two DG units; (b) v-I droop for the
DC microgrid.
The DG units are interfaced through DC-DC converters to the DC microgrid. The individual
droop-based power-sharing in the DC microgrid is represented in Figure 6a. The DC-link voltage of
each DC-DC power converter is drooped with the DC generated power (PDCJ) utilizing the droop
coefficient mDCj as represented in the equation:
*n
oj oj DCj DCj
VVmP=− (3)
where
*
oj
V,
n
oj
V are the reference and no-load DC-link voltages of the DC-DC power converter,
whereas the subscript j refers to a DG unit in the DC microgrid. The delivered DC power from each
DG unit (Pacj) is wirelessly specified to supply the connected DC-load (PDCL), following the equality
mDC1PDC1 = mDC2PDC2 (4)
where PDCL = PDC1 + PDC2.
The primary requirement for a DC microgrid operation is to maintain the common DC-link
voltage within a predefined range. Different measures shall be taken by each terminal of DC
microgrid according to microgrid operation conditions. Therefore, a reliable and fast control scheme
is essential for acknowledging system operation status. The DC-link voltage is a proper indicator of
the DC microgrid’s operational condition. An equivalent circuit of the DC-mircogrid, including the
BESS and PEV is simplified, as shown in Figure 7, where PDC and PAC represent to the total power
flow on the DC side of microgrid (PV panel, Battery bank and DC/DC power converters) and the AC-
side (inverter and the AC load). From Figure 7, the instantaneous power relationship in the DC-
microgrid is described by
)()()()()( tPtPtptPtP acPEVcBESSdc +++= (5)
where PDC is the DC power delivered by the DC-DC converter to the DC-microgrid, PBESS is the power
supplied to (or by) the BESS, Pc is the power to the DC-link capacitor, PPEV is the power required for
charging the plug-in electric vehicles PEV, and PAC is the power required by the inverter for supplying
the AC load.
Figure 6.
Representation of DC droop (
a
) The particular droop of two DG units; (
b
) v-I droop for the
DC microgrid.
The DG units are interfaced through DC-DC converters to the DC microgrid. The individual
droop-based power-sharing in the DC microgrid is represented in Figure 6a. The DC-link voltage of
each DC-DC power converter is drooped with the DC generated power (P
DCJ
) utilizing the droop
coefficient mDCj as represented in the equation:
V∗
oj =Vn
oj −mDCj PDCj (3)
where
V∗
oj
,
Vn
oj
are the reference and no-load DC-link voltages of the DC-DC power converter, whereas
the subscript j refers to a DG unit in the DC microgrid. The delivered DC power from each DG unit
(Pacj) is wirelessly specified to supply the connected DC-load (PDCL), following the equality
mDC1PDC1=mDC2PDC2(4)
where PDCL =PDC1+PDC2.
The primary requirement for a DC microgrid operation is to maintain the common DC-link
voltage within a predefined range. Different measures shall be taken by each terminal of DC microgrid
according to microgrid operation conditions. Therefore, a reliable and fast control scheme is essential
for acknowledging system operation status. The DC-link voltage is a proper indicator of the DC
microgrid’s operational condition. An equivalent circuit of the DC-mircogrid, including the BESS
and PEV is simplified, as shown in Figure 7, where P
DC
and P
AC
represent to the total power flow
on the DC side of microgrid (PV panel, Battery bank and DC/DC power converters) and the AC-side
(inverter and the AC load). From Figure 7, the instantaneous power relationship in the DC-microgrid
is described by
Pdc(t) = PBESS (t) + pc(t) + PPEV (t) + Pac (t)(5)
where P
DC
is the DC power delivered by the DC-DC converter to the DC-microgrid, P
BESS
is the power
supplied to (or by) the BESS, Pc is the power to the DC-link capacitor, P
PEV
is the power required for
charging the plug-in electric vehicles PEV, and P
AC
is the power required by the inverter for supplying
the AC load.

Energies 2019,12, 4240 10 of 23
Energies 2016, 9, x 10 of 5
Figure 7. DC power flow diagram.
4. Energy Management Control Strategy
The amount of energy that can be produced by the PV or wind generator is calculated using the
input data, such as hourly solar irradiance, wind speed, and ambient temperature. The total value of
generated output power (Psources) is compared with the load demand energy (PPEVs) to estimate
the energy flow distribution between the energy storage unit and the loads. Surplus energy is stored
in the battery banks. The control strategy of the hybrid PV-wind charging station is described by the
flowchart in Figure 8. According to Figure 8, the control strategy is applied according to four different
cases as follows:
If Psourcs (Ppv + Pw) > PPEVs, then PESU = Psources − PPEVs. If the irradiance level and wind speed are high
enough, the output power empowers the connected electric vehicles, and the exceeding power is
stored in the battery bank.
If Psourcs·(Ppv + Pw) = PPEVs, then PESU = 0. That is, if the irradiance level and wind speed are just
enough, to empowers the connected electric vehicles and no excess power to charge the battery bank.
If Psources < PPEVs and PPEVs − Psources ≤ PESU, then PPEVs − Psources = PESU. That is, if the PV and wind
generators cannot supply the load, then the load is supplied directly from the DC-microgrid, and the
battery converter is switched on.
If Psources < PEVS and PEVS − Psources > PESU, in this case, the energy stored in the battery bank is not
enough to charge connected PEVs. Then the PEVS and battery bank are disconnected.
The supervisory controller is divided into two main functions. The first function identifies the
mode of operation according to the conditions and situation of individual microgrid components.
The second function is integrated into intelligent systems, such as converters and inverters that
determine the performance of individual components in that mode of operation. The switch over in
the battery charging mode takes place either when the state of charge of the battery is lower than the
minimum SOClow or when there is a sudden decrease in the required power for load and state of
charge of the battery is lower than maximum SOChigh. Therefore, when loading power decreases, the
surplus power is utilized to charge the batteries if it is not fully charged. The individual components
can be controlled easily using a built-in controller, such as the DC-DC converter controller. Thus, the
energy management system (EMS) is responsible for achieving the optimal operation of the DC
microgrid.
Figure 7. DC power flow diagram.
4. Energy Management Control Strategy
The amount of energy that can be produced by the PV or wind generator is calculated using the
input data, such as hourly solar irradiance, wind speed, and ambient temperature. The total value of
generated output power (Psources) is compared with the load demand energy (PPEVs) to estimate the
energy flow distribution between the energy storage unit and the loads. Surplus energy is stored in
the battery banks. The control strategy of the hybrid PV-wind charging station is described by the
flowchart in Figure 8. According to Figure 8, the control strategy is applied according to four different
cases as follows:
If P
sourcs
(P
pv
+P
w
)>P
PEVs
, then P
ESU
=P
sources −
P
PEVs
. If the irradiance level and wind speed
are high enough, the output power empowers the connected electric vehicles, and the exceeding power
is stored in the battery bank.
If P
sourcs·
(P
pv
+P
w
)=P
PEVs
, then P
ESU
=0. That is, if the irradiance level and wind speed are just
enough, to empowers the connected electric vehicles and no excess power to charge the battery bank.
If P
sources
<P
PEVs
and P
PEVs −
P
sources ≤
P
ESU
, then P
PEVs −
P
sources
=P
ESU
. That is, if the PV and
wind generators cannot supply the load, then the load is supplied directly from the DC-microgrid,
and the battery converter is switched on.
If P
sources
<P
EVS
and P
EVS −
P
sources
>P
ESU
, in this case, the energy stored in the battery bank is
not enough to charge connected PEVs. Then the PEVS and battery bank are disconnected.
The supervisory controller is divided into two main functions. The first function identifies the
mode of operation according to the conditions and situation of individual microgrid components.
The second function is integrated into intelligent systems, such as converters and inverters that
determine the performance of individual components in that mode of operation. The switch over in
the battery charging mode takes place either when the state of charge of the battery is lower than the
minimum SOC
low
or when there is a sudden decrease in the required power for load and state of charge
of the battery is lower than maximum SOC
high
. Therefore, when loading power decreases, the surplus
power is utilized to charge the batteries if it is not fully charged. The individual components can be
controlled easily using a built-in controller, such as the DC-DC converter controller. Thus, the energy
management system (EMS) is responsible for achieving the optimal operation of the DC microgrid.
The energy management control algorithms overcome the unbalance between power produced
from distributed generation (DG) units and load. This can be done when the SOCs of ESS are sufficient.
In the case of ESS failure or an inappropriate SOC value, the master unit becomes the wind turbine.
In case of that, the load is greater than the available energy production, and the controller of the
DC-microgrid is not able to balance the power flow of the system, the solution will be the load shedding.
In case that the power generated by the sources is bigger than the load consumption, one of the
distributed generators will be disconnected from the microgrid.

Energies 2019,12, 4240 11 of 23
Energies 2016, 9, x 11 of 5
Figure 8. Flow chart of energy management control for the PV/wind/storage system.
The energy management control algorithms overcome the unbalance between power produced
from distributed generation (DG) units and load. This can be done when the SOCs of ESS are
sufficient. In the case of ESS failure or an inappropriate SOC value, the master unit becomes the wind
turbine. In case of that, the load is greater than the available energy production, and the controller of
the DC-microgrid is not able to balance the power flow of the system, the solution will be the load
shedding. In case that the power generated by the sources is bigger than the load consumption, one
of the distributed generators will be disconnected from the microgrid.
The operation of the charging station DC-microgrid can be divided into four operation modes.
The power flow direction is changing during various operation modes of the DC-microgrid based
charging station, as illustrated in Figure 9. These modes of operation can be explained as follows:
Mode 1: VDC ≥ VDC3: PEV charging and battery bank charging mode
The PV panels produce enough power, and this appears in an increase of the DC-link voltage to
be higher than VDC3. This additional energy produced by the PV panels and the wind turbine is
supplied to the batteries through a bidirectional DC-DC converter. As soon as the PEVs are fully
charged, all the power produced by the PV and wind sources is delivered to the battery banks.
Mode 2: VDC1 > VDC > VDC3: Charging by PV power
At this operating mode, the PEV is charged using the power generated by the PV system. In this
case, the controller ensures that the PEV battery is not exceeding the over-charging limit. Thus, the
controller terminates PEV charging when PEV voltage exceeds VBH (the voltage relating to 95 % state
Figure 8. Flow chart of energy management control for the PV/wind/storage system.
The operation of the charging station DC-microgrid can be divided into four operation modes.
The power flow direction is changing during various operation modes of the DC-microgrid based
charging station, as illustrated in Figure 9. These modes of operation can be explained as follows:
Mode 1:VDC ≥VDC3: PEV charging and battery bank charging mode
The PV panels produce enough power, and this appears in an increase of the DC-link voltage to be
higher than V
DC3
. This additional energy produced by the PV panels and the wind turbine is supplied
to the batteries through a bidirectional DC-DC converter. As soon as the PEVs are fully charged, all the
power produced by the PV and wind sources is delivered to the battery banks.
Mode 2:VDC1>VDC >VDC 3: Charging by PV power
At this operating mode, the PEV is charged using the power generated by the PV system. In this
case, the controller ensures that the PEV battery is not exceeding the over-charging limit. Thus,
the controller terminates PEV charging when PEV voltage exceeds VBH (the voltage relating to 95 %
state of charge of the PEV battery). This interval continues as long as the value of DC-link voltage is in
a range between VDC1and VDC 3.
Mode 3:VDC3≤VDC <VDC 2: Wind turbine supplying power and battery bank discharging
During this mode, the power produced by the wind turbine is less than the required power for
charging the PEV. Therefore, the whole power produced by the wind turbine is transferred to the PEV,

Energies 2019,12, 4240 12 of 23
and the additional amount is supplied by the battery bank. However, the DC-link voltage changes
with the variation in solar irradiation and wind speed. Thus, any variation in the DC-link voltage
at the DC-microgrid is monitored by the controller to produce a proper voltage at the output of the
bidirectional DC-DC power converter. The renewable energy sources continue charging the PEV;
whereas, the battery banks cover the peak load demand.
Mode 4: Case-1: VDC <VDC-1 and IDMD <IDMD-max
In this mode, the PV panels and wind turbines do not produce any power, due to inconvenient
weather conditions. The boost DC-DC power converter is isolated, and the battery bank supplies the
power required for charging PEVs. At any instant, during this mode, if the DC-link voltage VDC
exceeds V
DC-1
, the controller moves the system to work in Mode 2. The bidirectional DC-DC power
converter regulates the output current and voltage for charging the PEV battery. As the battery bank is
at off-peak, it continues to supply energy until the vehicles are completely charged. The controller
terminates the charging process of PEV by disabling the DC-DC converter when the battery voltage
VBat exceeds its maximum value VBH.
Case-2: VDC <VDC-1 and IDMD ≥IDMD -max
This case is similar to case 1, but local demand exceeds the maximum demand of the microgrid.
During this period, the PEV can be charged using the stored energy in the BESS if it is enough to
cater to the charging process of PEVs. This continues until the state of charge of BESS (battery bank)
decreases below its minimum value (SOC <SOCmin). At this moment, the charging process of PEVs
is stopped tentatively by de-activating the bidirectional DC-DC power converter. Once the renewable
energy power is back to off-peak conditions (i.e., I
DMD
<I
DMD-max
) the charging process of the PEVs is
restored, and the controller supervises charging parameters.
Energies 2016, 9, x 12 of 5
of charge of the PEV battery). This interval continues as long as the value of DC-link voltage is in a
range between VDC1 and VDC3.
Mode 3: VDC3 ≤ VDC < VDC2: Wind turbine supplying power and battery bank discharging
During this mode, the power produced by the wind turbine is less than the required power for
charging the PEV. Therefore, the whole power produced by the wind turbine is transferred to the
PEV, and the additional amount is supplied by the battery bank. However, the DC-link voltage
changes with the variation in solar irradiation and wind speed. Thus, any variation in the DC-link
voltage at the DC-microgrid is monitored by the controller to produce a proper voltage at the output
of the bidirectional DC-DC power converter. The renewable energy sources continue charging the
PEV; whereas, the battery banks cover the peak load demand.
Mode 4: Case-1: VDC < VDC-1 and IDMD < IDMD-max
In this mode, the PV panels and wind turbines do not produce any power, due to inconvenient
weather conditions. The boost DC-DC power converter is isolated, and the battery bank supplies the
power required for charging PEVs. At any instant, during this mode, if the DC-link voltage VDC
exceeds VDC-1, the controller moves the system to work in Mode 2. The bidirectional DC-DC power
converter regulates the output current and voltage for charging the PEV battery. As the battery bank
is at off-peak, it continues to supply energy until the vehicles are completely charged. The controller
terminates the charging process of PEV by disabling the DC-DC converter when the battery voltage
VBat exceeds its maximum value VBH.
Case-2: VDC < VDC-1 and IDMD ≥ IDMD-max
This case is similar to case 1, but local demand exceeds the maximum demand of the microgrid.
During this period, the PEV can be charged using the stored energy in the BESS if it is enough to cater
to the charging process of PEVs. This continues until the state of charge of BESS (battery bank)
decreases below its minimum value (SOC < SOCmin). At this moment, the charging process of PEVs
is stopped tentatively by de-activating the bidirectional DC-DC power converter. Once the renewable
energy power is back to off-peak conditions (i.e., IDMD < IDMD-max) the charging process of the PEVs is
restored, and the controller supervises charging parameters.
Figure 9. Different modes of operation.
An example of the electric vehicle load supplied by the DC-microgrid is shown in Figure 10 for
one day period. Figure 11 shows the solar irradiance, temperature and wind velocity profiles during
a typical day in Sohag city, Egypt. The wind speed profile starts with 5 m/s at time 1 h, rises to 9 m/s
at time 6 h, then falls down to 5 m/s at time 10 h, etc., as shown in Figure 11. The generators meet the
load during the night. In the morning when the sun comes up, and the PV starts generating, the PV
charges the BESS until the PV generation and the BESS state of charge are high enough that they can
Figure 9. Different modes of operation.
An example of the electric vehicle load supplied by the DC-microgrid is shown in Figure 10 for
one day period. Figure 11 shows the solar irradiance, temperature and wind velocity profiles during a
typical day in Sohag city, Egypt. The wind speed profile starts with 5 m/s at time 1 h, rises to 9 m/s at
time 6 h, then falls down to 5 m/s at time 10 h, etc., as shown in Figure 11. The generators meet the load
during the night. In the morning when the sun comes up, and the PV starts generating, the PV charges
the BESS until the PV generation and the BESS state of charge are high enough that they can meet the
load on their own without the generators. The load is then transferred to the PV/BESS system, and the
generators turn off. The BESS and PV power the load together from 7–8 a.m. when PV generation is
not yet high enough to meet the load by itself. At 8 a.m., when PV can fully meet the load, the BESS
stops discharging. Excess PV generation is used first to charge the BESS and then remaining excess is
curtailed. In the evening, PV generation decreases, until the PV and BESS can no longer meet the full

Energies 2019,12, 4240 13 of 23
load. Some of the PV generation is curtailed because the BESS is already fully charged. At this point,
the generators turn on again and supply the load overnight.
Energies 2016, 9, x 13 of 5
meet the load on their own without the generators. The load is then transferred to the PV/BESS
system, and the generators turn off. The BESS and PV power the load together from 7–8 a.m. when
PV generation is not yet high enough to meet the load by itself. At 8 a.m., when PV can fully meet the
load, the BESS stops discharging. Excess PV generation is used first to charge the BESS and then
remaining excess is curtailed. In the evening, PV generation decreases, until the PV and BESS can no
longer meet the full load. Some of the PV generation is curtailed because the BESS is already fully
charged. At this point, the generators turn on again and supply the load overnight.
Figure 10. Electric vehicle load profile supplied by the DC-microgrid.
Figure 11. The solar radiation, temperature and wind velocity profiles during a typical day.
5. Simulation Results and Discussion
Figure 10. Electric vehicle load profile supplied by the DC-microgrid.
Energies 2016, 9, x 13 of 5
meet the load on their own without the generators. The load is then transferred to the PV/BESS
system, and the generators turn off. The BESS and PV power the load together from 7–8 a.m. when
PV generation is not yet high enough to meet the load by itself. At 8 a.m., when PV can fully meet the
load, the BESS stops discharging. Excess PV generation is used first to charge the BESS and then
remaining excess is curtailed. In the evening, PV generation decreases, until the PV and BESS can no
longer meet the full load. Some of the PV generation is curtailed because the BESS is already fully
charged. At this point, the generators turn on again and supply the load overnight.
Figure 10. Electric vehicle load profile supplied by the DC-microgrid.
Figure 11. The solar radiation, temperature and wind velocity profiles during a typical day.
5. Simulation Results and Discussion
Figure 11. The solar radiation, temperature and wind velocity profiles during a typical day.
5. Simulation Results and Discussion
Simulation results are obtained based on the typical daily load profile of the studied EV presented
in Figure 11. The calculated produced renewable power and load during a typical day in the studied
system is shown in Figure 12. The hybrid system model is verified by implementing the detailed
models in a MATLAB/Simulink environment. This model presents an alternative emergency power
system based on lithium-ion batteries. This model also features an energy management system for

Energies 2019,12, 4240 14 of 23
hybrid electric sources. The energy management system regulates the power between the energy
sources and loads according to a predetermined control strategy. The Simulink model of the studied
DC microgrid is shown in Figure 13. The specifications of the studied DC-microgrid are shown in
Table 3:
Energies 2016, 9, x 14 of 5
Simulation results are obtained based on the typical daily load profile of the studied EV
presented in Figure 11. The calculated produced renewable power and load during a typical day in
the studied system is shown in Figure 12. The hybrid system model is verified by implementing the
detailed models in a MATLAB/ Simulink environment. This model presents an alternative emergency
power system based on lithium-ion batteries. This model also features an energy management system
for hybrid electric sources. The energy management system regulates the power between the energy
sources and loads according to a predetermined control strategy. The Simulink model of the studied
DC microgrid is shown in Figure 13. The specifications of the studied DC-microgrid are shown in
Table 3:
Figure 12. Daily profiles of renewable power generation and load in the studied system.
Table 3. System parameters.
Item Description
PV Array composed of 330 modules SunPower SPR-305E-WHT-D with series and
parallel combination (Nser = 5 Npar = 66) rating 100 kW
Wind turbine
Rated output power = 10 kW
Wind speed base = 12 m/s
Base rotational speed = 500 rpm
Initial rotational speed = 200 rpm
Moment of inertia = 0.08 p.u
Li-ion battery A 48 V, 500 Ah, system
Battery state of charge SOCmin–SOCmax: 60–90 [%]
Bidirectional DC-DC
converter A 50 kW, A controlled voltage/current outputs
Inverter system A 150 kVA, 270 V DC in, 200 V AC, 60 Hz
The DC microgrid voltage is shown in Figure 14. The DC-link voltage is an indication for the DC
generated power. Figure 15 represents the output voltage and output current of PV panels. The
photovoltaic power generation is set to the Maximal Power Point Tracking, which is proportional to
the irradiance solar radiation and (W/m2). These typical weather data at intervals of one hour are
collected. There is much meteorological software can estimate the solar radiation and the ambient
Figure 12. Daily profiles of renewable power generation and load in the studied system.
Table 3. System parameters.
Item Description
PV Array
composed of 330 modules SunPower
SPR-305E-WHT-D with series and parallel
combination (Nser =5 Npar =66) rating 100 kW
Wind turbine
Rated output power =10 kW
Wind speed base =12 m/s
Base rotational speed =500 rpm
Initial rotational speed =200 rpm
Moment of inertia =0.08 p.u
Li-ion battery A 48 V, 500 Ah, system
Battery state of charge SOCmin–SOCmax: 60–90 [%]
Bidirectional DC-DC converter A 50 kW, A controlled voltage/current outputs
Inverter system A 150 kVA, 270 V DC in, 200 V AC, 60 Hz
The DC microgrid voltage is shown in Figure 14. The DC-link voltage is an indication for the
DC generated power. Figure 15 represents the output voltage and output current of PV panels.
The photovoltaic power generation is set to the Maximal Power Point Tracking, which is proportional
to the irradiance solar radiation and (W/m
2
). These typical weather data at intervals of one hour are
collected. There is much meteorological software can estimate the solar radiation and the ambient
temperature. In this figure, the solar irradiation is reduced from 1000
W/m2
to 850
W/m2
at time 2 s.
Consequently, the total generated current from PV panels is reduced from 29.6 A to 25 A. The battery
bank compensates the fluctuations of the difference between the microgrid reference power and all
the passive power variations of the DC microgrid (PV/wind power and total loads). The battery bank

Energies 2019,12, 4240 15 of 23
voltage, charging and discharging current is shown in Figure 16. The corresponding state of charge
SOC of the battery bank is shown in Figure 17.
The wind turbine model comprises mathematical models of wind turbines and wind speed
simulation. Figure 18 shows how the voltage at generator terminals (instantaneous value) changes
with time. Figure 18 shows the corresponding generated current. The output power of the wind
generator is proportional to the cube of the wind speed. A sudden variation of wind speed from 12 m/s
to 9 m/s happens at a time of 3 s. However, the temporal variations of the PMSG rotational speed,
torque, voltage, and output power follow that of the wind speed. The rectified output voltage of the
wind generator is shown in Figure 19.
Energies 2016, 9, x 15 of 5
temperature. In this figure, the solar irradiation is reduced from 1000 W∕m
 to 850 W∕m
 at time
2 s. Consequently, the total generated current from PV panels is reduced from 29.6 A to 25 A. The
battery bank compensates the fluctuations of the difference between the microgrid reference power
and all the passive power variations of the DC microgrid (PV/wind power and total loads). The
battery bank voltage, charging and discharging current is shown in Figure 16. The corresponding
state of charge SOC of the battery bank is shown in Figure 17.
The wind turbine model comprises mathematical models of wind turbines and wind speed
simulation. Figure 18 shows how the voltage at generator terminals (instantaneous value) changes
with time. Figure 18 shows the corresponding generated current. The output power of the wind
generator is proportional to the cube of the wind speed. A sudden variation of wind speed from 12
m/s to 9 m/s happens at a time of 3 s. However, the temporal variations of the PMSG rotational speed,
torque, voltage, and output power follow that of the wind speed. The rectified output voltage of the
wind generator is shown in Figure 19.
Figure 13. The Simulink model of the studied DC microgrid.
Figure 14. DC-microgrid DC-link voltage.
Figure 13. The Simulink model of the studied DC microgrid.
Energies 2016, 9, x 15 of 5
temperature. In this figure, the solar irradiation is reduced from 1000 W∕m
 to 850 W∕m
 at time
2 s. Consequently, the total generated current from PV panels is reduced from 29.6 A to 25 A. The
battery bank compensates the fluctuations of the difference between the microgrid reference power
and all the passive power variations of the DC microgrid (PV/wind power and total loads). The
battery bank voltage, charging and discharging current is shown in Figure 16. The corresponding
state of charge SOC of the battery bank is shown in Figure 17.
The wind turbine model comprises mathematical models of wind turbines and wind speed
simulation. Figure 18 shows how the voltage at generator terminals (instantaneous value) changes
with time. Figure 18 shows the corresponding generated current. The output power of the wind
generator is proportional to the cube of the wind speed. A sudden variation of wind speed from 12
m/s to 9 m/s happens at a time of 3 s. However, the temporal variations of the PMSG rotational speed,
torque, voltage, and output power follow that of the wind speed. The rectified output voltage of the
wind generator is shown in Figure 19.
Figure 13. The Simulink model of the studied DC microgrid.
Figure 14. DC-microgrid DC-link voltage.
Figure 14. DC-microgrid DC-link voltage.

Energies 2019,12, 4240 16 of 23
Energies 2016, 9, x 16 of 5
Figure 15. PV panels output voltage and current.
Figure 16. Battery bank voltage and current.
Figure 17. Battery bank SOC.
Figure 15. PV panels output voltage and current.
Energies 2016, 9, x 16 of 5
Figure 15. PV panels output voltage and current.
Figure 16. Battery bank voltage and current.
Figure 17. Battery bank SOC.
Figure 16. Battery bank voltage and current.
Energies 2016, 9, x 16 of 5
Figure 15. PV panels output voltage and current.
Figure 16. Battery bank voltage and current.
Figure 17. Battery bank SOC.
Figure 17. Battery bank SOC.

Energies 2019,12, 4240 17 of 23
Energies 2016, 9, x 17 of 5
Figure 18. Wind generator output voltage and current.
Figure 19. The rectified voltage of PMSG output.
The voltage in the line and the current fed to the inverter for AC charging are shown in Figures
20 and 21, respectively. The current drawn from the AC system is sinusoidal, due to the AC filters
employed. The lack of such AC filters will directly feed the harmonics into the grid source. A 3-phase
AC load is used to emulate the EV charging load profile. The load profiles were generated using
SIMULINK, and then the hourly energy results were configured into a suitable format. For each
month, three day types were used to represent the annual load: Peak day, weekday, and weekend.
The total powers from different sources and loads are shown in Figure 22. A step change in load
power occurs at time 4–5 s from 20 kW to 60 kW. During this period, the peak power is compensated
from the battery bank. The electrical power performance, current-voltage characteristics and system
response confirm that the system has satisfactory performance under conditions of a step changing
power reference and loads disturbances.
Figure 18. Wind generator output voltage and current.
Energies 2016, 9, x 17 of 5
Figure 18. Wind generator output voltage and current.
Figure 19. The rectified voltage of PMSG output.
The voltage in the line and the current fed to the inverter for AC charging are shown in Figures
20 and 21, respectively. The current drawn from the AC system is sinusoidal, due to the AC filters
employed. The lack of such AC filters will directly feed the harmonics into the grid source. A 3-phase
AC load is used to emulate the EV charging load profile. The load profiles were generated using
SIMULINK, and then the hourly energy results were configured into a suitable format. For each
month, three day types were used to represent the annual load: Peak day, weekday, and weekend.
The total powers from different sources and loads are shown in Figure 22. A step change in load
power occurs at time 4–5 s from 20 kW to 60 kW. During this period, the peak power is compensated
from the battery bank. The electrical power performance, current-voltage characteristics and system
response confirm that the system has satisfactory performance under conditions of a step changing
power reference and loads disturbances.
Figure 19. The rectified voltage of PMSG output.
The voltage in the line and the current fed to the inverter for AC charging are shown in
Figures 20 and 21
, respectively. The current drawn from the AC system is sinusoidal, due to the AC
filters employed. The lack of such AC filters will directly feed the harmonics into the grid source.
A 3-phase AC load is used to emulate the EV charging load profile. The load profiles were generated
using SIMULINK, and then the hourly energy results were configured into a suitable format. For each
month, three day types were used to represent the annual load: Peak day, weekday, and weekend.
The total powers from different sources and loads are shown in Figure 22. A step change in load
power occurs at time 4–5 s from 20 kW to 60 kW. During this period, the peak power is compensated
from the battery bank. The electrical power performance, current-voltage characteristics and system
response confirm that the system has satisfactory performance under conditions of a step changing
power reference and loads disturbances.

Energies 2019,12, 4240 18 of 23
Energies 2016, 9, x 18 of 5
Figure 20. AC side voltage for AC charging.
Figure 21. AC side charging current.
Figure 22. Different power values for sources and loads.
6. Resilient DC-Microgrid
Figure 20. AC side voltage for AC charging.
Energies 2016, 9, x 18 of 5
Figure 20. AC side voltage for AC charging.
Figure 21. AC side charging current.
Figure 22. Different power values for sources and loads.
6. Resilient DC-Microgrid
Figure 21. AC side charging current.
Energies 2016, 9, x 18 of 5
Figure 20. AC side voltage for AC charging.
Figure 21. AC side charging current.
Figure 22. Different power values for sources and loads.
6. Resilient DC-Microgrid
Figure 22. Different power values for sources and loads.

Energies 2019,12, 4240 19 of 23
6. Resilient DC-Microgrid
DC microgrid configurations are evaluated that can be used to integrate PV and wind generators
alongside existing batteries’ energy storage systems (BESS) to increase resiliency at the site. The BESS
units are sized to support the charging station load for one hour in this operation condition. There
will be cloudy intervals or early morning/late afternoon hours when PV and wind generators will
not be capable of delivering the required charging load. During those times, the BESS is required to
supply the EV loads until PV or wind generation supplies the charging station load. An integrated
solution was proposed so that all energy sources operate in an integrated manner and are centrally
controlled. Therefore, the BESS does not require to be sized for the full load in this operation scenario.
The DC-bus Voltage of microgrid effects time of charging. Then the time of charging can be calculated
according to the voltage and capacity of the battery. This measure takes into account the robustness of
the system against disturbances and the quickness of the recovery [27].
R=RTa
t0Vdc−bus(t)dt
Ta(6)
where VDC-bus is the DC bus voltage. Ris the resiliency, Tais the recovery time.
The resilience of an electrical microgrid can be defined as “the ability of the microgrid to sustain
against disturbances and return to its normal state quickly”. This definition includes the two remarkable
attributes, recovery and response, and is compatible with the definitions given in references [
27
,
28
].
The schematic diagram of the system’s resilience concept is illustrated in Figure 10. However, disruption
occurs at time t
0
, as shown in this figure, and the system performance (DC bus voltage) falls from Q
0
to Q1. By taking adequate action, the system returns finally to original DC-link voltage at time t1.
As shown in Equation (6), the resilience measure is able to comprehensively represent the ability
of the system to withstand the disruption and recover rapidly. Here, 0 <R<1. Therefore, when
R=1
,
it means that the system has perfect resilience: Either its performance degradation is 0, or it can recover
from disruption instantaneously. In case R=0, it designates that the system is completely troubled
immediately upon disruption and cannot recover within the maximum permissible recovery time. It is
obvious that systems with higher values of Rare more resilient.
The performance curve Q(t) is used to describe the system resilience of microgrid. The performance
loss function from disruption is defined by the integral of the curve, followed by a gradual recovery
(i.e., the shadowed area in Figure 23). This measure achieves the robustness of the system versus
disturbances (load disturbance and intermitted generated energy from renewable sources) and the
quickness of the recovery action. By calculating the area under the curve of Figure 24, the DC-bus
voltage is recovered from V
DC
=96% to 100% and the time from 4 to 4.06 s and dividing this by time
0.06; then the resiliency will be 0.98. It means the system is near perfect resilient.
A new resilience measure is proposed in this paper for DC microgrid. It comprises using the
maximum admissible recovery time as the considered time interval and enabling an estimation method.
Resilience measurement scheme is used to estimate the resilience of different microgrid designs. It is
also used to verify whether the resilience goal of microgrid can be satisfied, and choose a resilient
method that can sustain the disruption and return the microgrid to the normal state as quickly
as possible.

Energies 2019,12, 4240 20 of 23
Energies 2016, 9, x 20 of 5
Time
tot1
Q0
Q1
Performance
Recover
Disruption
Response
Figure 23. The schematic representation of resilience [27].
to t1
Q0
Q1
Figure 24. Dc-link voltage for calculating the resilience.
7. Conclusions
This paper has presented an energy management technique for an isolated DC-microgrid
supplying EV charging station. The standalone DC microgrid is verified by implementing the
charging station model in a MATLAB/Simulink environment. The EMS control system is designed to
regulate renewable energy sources status, battery SOC, and load demand. For an accurate evaluation
of EMS strategy, hourly variations of renewable generation and a typical EV load are utilized as input
data. The solar irradiation is reduced from 1000 W/m2 to 850 W/m2 at time 2 s. A sudden variation of
wind speed from 12 m/s to 9 m/s happened at a time of 3 s. However, the temporal variations of the
PMSG rotational speed, torque, voltage, and output power follow that of the wind speed. A step
Figure 23. The schematic representation of resilience [27].
Energies 2016, 9, x 20 of 5
Time
tot1
Q0
Q1
Performance
Recover
Disruption
Response
Figure 23. The schematic representation of resilience [27].
to t1
Q0
Q1
Figure 24. Dc-link voltage for calculating the resilience.
7. Conclusions
This paper has presented an energy management technique for an isolated DC-microgrid
supplying EV charging station. The standalone DC microgrid is verified by implementing the
charging station model in a MATLAB/Simulink environment. The EMS control system is designed to
regulate renewable energy sources status, battery SOC, and load demand. For an accurate evaluation
of EMS strategy, hourly variations of renewable generation and a typical EV load are utilized as input
data. The solar irradiation is reduced from 1000 W/m2 to 850 W/m2 at time 2 s. A sudden variation of
wind speed from 12 m/s to 9 m/s happened at a time of 3 s. However, the temporal variations of the
PMSG rotational speed, torque, voltage, and output power follow that of the wind speed. A step
Figure 24. Dc-link voltage for calculating the resilience.
7. Conclusions
This paper has presented an energy management technique for an isolated DC-microgrid supplying
EV charging station. The standalone DC microgrid is verified by implementing the charging station
model in a MATLAB/Simulink environment. The EMS control system is designed to regulate renewable
energy sources status, battery SOC, and load demand. For an accurate evaluation of EMS strategy,
hourly variations of renewable generation and a typical EV load are utilized as input data. The solar
irradiation is reduced from 1000 W/m
2
to 850 W/m
2
at time 2 s. A sudden variation of wind speed from
12 m/sto9m/s happened at a time of 3 s. However, the temporal variations of the PMSG rotational
speed, torque, voltage, and output power follow that of the wind speed. A step change is applied

Energies 2019,12, 4240 21 of 23
in the load power then the battery bank compensates the fluctuations of the difference between the
microgrid reference power and all power variations of the DC microgrid (PV/wind power and total
loads). The electrical power performance, current-voltage characteristics and system response confirm
that the system has satisfactory performance under conditions of a step-changing power reference and
loads disturbances. The results indicated that the integration of intermittent wind and solar energy
sources in microgrid should be designed carefully in standalone operation. The proposed control
strategies can provide excellent performance under different operating conditions. BESSs can increase
the reliability of the system because they can store excess renewable energy during low-demand
periods and can supply during high-demand periods.
Author Contributions:
Conceptualization, K.S.; Methodology, A.G.A.-K. and A.S.A.; Software, A.G.A.-K.;
Validation, K.S.; Formal Analysis, A.S.A.; Investigation, A.G.A.-K.; Resources, A.S.A.; Data Curation, K.S.;
Writing-Original Draft Preparation, A.G.A.-K.; Writing—Review and Editing, A.S.A.; Visualization, K.S.;
Supervision; Project Administration, A.G.A.-K.; Funding Acquisition, A.S.A.
Funding: This research received no external funding.
Acknowledgments:
The authors extend their appreciation to the Deanship of Scientific Research at Majmaah
University for funding this work under project number No (RGP-2019-19).
Conflicts of Interest: The authors declare no conflict of interest.
Abbreviations
The following abbreviations are used in this manuscript:
DC Direct Current
AC Alternating Current
EMI Electromagnetic Interference
DG Distributed Generation
BMS Battery Management Systems
SOC State of charge
EV Electric vehicle
PMSG Permanent Magnet Synchronous Generator
IDMD Maximum demand current
PEV Plug in Electric Vehicle
BESS batteries energy storage system
EMS Energy management Strategy
EMU Energy management unit
ESS Energy Storage system
DG distributed generation
MPP Maximum power point
MPPT Maximum power point tracking
PV Photovoltaic
PI Proportional-integral
WT Wind Turbine
WTCS Wind Turbine Conversion System
DERs Distributed Energy Resources
HVAC heating, ventilation, and air conditioning
PWM pulse-width modulation
RE Renewable Energy
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2019 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 (http://creativecommons.org/licenses/by/4.0/).