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With increasing changes in the contemporary energy system, it becomes essential to test the autonomous control strategies for distributed energy resources in a controlled environment to investigate power grid stability. Power hardware-in-the-loop (PHIL) concept is an efficient approach for such evaluations in which a virtually simulated power grid is interfaced to a real hardware device. This strongly coupled software-hardware system introduces obstacles that need attention for smooth operation of the laboratory setup to validate robust control algorithms for decentralized grids. This paper presents a novel methodology and its implementation to develop a test-bench for a real-time PHIL simulation of a typical power distribution grid to study the dynamic behavior of the real power components in connection with the simulated grid. The application of hybrid simulation in a single software environment is realized to model the power grid which obviates the need to simulate the complete grid with a lower discretized sample-time. As an outcome, an environment is established interconnecting the virtual model to the real-world devices. The inaccuracies linked to the power components are examined at length and consequently a suitable compensation strategy is devised to improve the performance of the hardware under test (HUT). Finally, the compensation strategy is also validated through a simulation scenario.
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Energies 2021, 14, 593. https://doi.org/10.3390/en14030593 www.mdpi.com/journal/energies
Article
Power Hardware-in-the-Loop: Response of Power Components
in Real-Time Grid Simulation Environment
Moiz Muhammad *, Holger Behrends, Stefan Geißendörfer, Karsten von Maydell and Carsten Agert
German Aerospace Center (DLR)—Institute of Networked Energy Systems, Carl-von-Ossietzky Strasse,
26129 Oldenburg, Germany; Holger.Behrends@dlr.de (H.B.); Stefan.Geissendoerfer@dlr.de (S.G.);
Karsten.Maydell@dlr.de (K.v.M.); Carsten.Agert@dlr.de (C.A.)
* Correspondence: Moiz.MuhammadAyubBalol@dlr.de; Tel.: +49-441-99906-228
Abstract: With increasing changes in the contemporary energy system, it becomes essential to test
the autonomous control strategies for distributed energy resources in a controlled environment to
investigate power grid stability. Power hardware-in-the-loop (PHIL) concept is an efficient ap-
proach for such evaluations in which a virtually simulated power grid is interfaced to a real
hardware device. This strongly coupled software
-
hardware system introduces obstacles that need
attention for smooth operation of the laboratory setup to validate robust control algorithms for
decentralized grids. This paper presents a novel methodology and its implementation to develop a
test-bench for a real-time PHIL simulation of a typical power distribution grid to study the dy-
namic behavior of the real power components in connection with the simulated grid. The applica-
tion of hybrid simulation in a single software environment is realized to model the power grid
which obviates the need to simulate the complete grid with a lower discretized sample-time. As an
outcome, an environment is established interconnecting the virtual model to the real-world devic-
es. The inaccuracies linked to the power components are examined at length and consequently a
suitable compensation strategy is devised to improve the performance of the hardware under test
(HUT). Finally, the compensation strategy is also validated through a simulation scenario.
Keywords: power hardware-in-the-loop (PHIL); power interface (PI); hardware under test (HUT);
hybrid simulation; real-time simulator (RTS)
1. Introduction
The past few years have experienced an unprecedented growth of distributed en-
ergy resources (DER) globally. The fast moving energy transition towards decentralized
energy systems leads towards several technical challenges for the grid operations, espe-
cially at low voltage distribution levels highlighted in a detailed report by CIGRE [1].
Performing simulation studies in a virtual environment is one of the ways to analyze
these challenges and observe the “what-if” scenarios for power system operations. In
simulation studies, the DER units interfacing grids can be modelled in detail to study the
dynamics and also simply as a power (P–Q) source [2]. The latter is convenient while
performing simulation studies to analyze voltage stability and power flow analysis.
However, the simulation approach using a simplified DER unit model have its limitation
and may sometimes be inaccurate owing to the complications in modelling the power
electronic interfaces [3].
The simulation studies might not indicate the challenges involved during real op-
erations of a device in the field such as undesirable power flow and response time to state
change. Power hardware-in-the-loop (PHIL) simulation approach has provided an effi-
cient platform to perform such experiments integrating real hardware and testing of
control algorithms on the hardware to study its operational response in real-time with a
virtual grid. Motivated by the increasing importance of PHIL simulation for DER inte-
Citation: Muhammad, M.; Behrends,
H.; Geißendörfer, S.; von Maydell,
K.; Agert, C. Power
Hardware-in-the-Loop: Response of
Power Components in Real-Time
Grid Simulation Environment.
Energies 2021, 14, 593. https://doi.org/
10.3390/en14030593
Received: 15 December 2020
Accepted: 20 January 2021
Published: 25 January 2021
Publisher’s Note: MDPI stays
neutral with regard to jurisdictional
claims in published maps and
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Copyright: © 2021 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/).
Energies 2021, 14, 593 2 of 22
gration studies, this paper presents the development of a PHIL test-bench to simulate a
low voltage distribution grid (LVDG) in connection with a real power component with
the ability to operate dynamically as load and source. The idea behind is to test the dy-
namic behavior of the component and the inaccuracies which affects the smooth opera-
tion of a PHIL setup. Understanding these challenges gives an insight on the perfor-
mance of the components in PHIL environment and enables to devise relevant remedies
to eliminate the associated errors which may otherwise not be highlighted in simula-
tion-based studies.
In most of the literature concerning PHIL framework for DER integration the com-
mercial devices [2,4–6] and their controls are tested in a PHIL environment. This study
rather focuses on an amplifier in current-controlled (CC) mode as hardware under test
(HUT) to obtain desired power flow into or from the grid through reference signals gen-
erated from the virtual environment. In this paper, the operation of the amplifier as
power interface (PI) and HUT will be analyzed in a PHIL setup to characterize its re-
sponse and its impact on the virtual power grid simulated in real-time. This in turn
would lead to the effective use of amplifier in load/regenerative mode for future PHIL
tests to perform DER integration studies and analyze advance control strategies. Ap-
parently, the use of such hardware devices coupled in a simulation environment poses
operational challenges due to the dynamic behavior of its components. This paper aims
to investigate the same and to propose the required adjustments in a bid to improve the
components performance. A very recent study by Ustun et al. [7] analyzes the behavior of
smart inverters during faults. It has also shown that software/hardware adjustments are
required to study the inverters dynamics in simulation environments. The novelty of the
setup in this study is also the fact, that the power flow for the HUT is controlled from the
virtual environment through reference current signals evaluation algorithm synchro-
nized with the grid voltage and based on power set-points.
A PHIL setup requires a PI to transmit signals (voltage/current) from the simulated
environment to the real hardware and vice versa. Stable operation of PHIL is ensured by
means of an interface to minimize the influence of measurement probes and power am-
plifiers on system dynamics [8]. In referenced studies [8,9] the common interface algo-
rithms for PHIL simulation are discussed in detail. A lot of research has been carried out
to improve in general the accuracy of the PHIL simulation [10–12], compensation of the
PHIL simulation interface [13], and design considerations [14]. The issues related to sta-
bility of a PHIL experiment are also highlighted in detail in referenced studies [15–17].
Similarly Zha et al. [18] discusses the accuracy of the complete setup using virtual im-
pedance method. However, in this paper the characterization is performed at component
level by observing its dynamic behavior. The stability analysis of the system is not the
focus but rather the inaccuracies resulting from specific power components in a PHIL
setup. For the interface, the basic structure with an ideal transformer model–voltage type
interface as shown in Figure 1 is adopted. The modelled grid is represented by an equiv-
alent voltage source and impedance. The complete workflow is explained in detail in
Section 3.2.
The real-time simulators (RTSs) have discrete behavior [3] and the solution is com-
puted at discrete time instances known as simulation time-step or sample-time. For 50/60
Hz power systems, the simulator needs to be roughly executed with a 50 µs time-step or
lower to analyze the power system transients [19]. Executing power systems at such low
time-steps can overload the processors of the RTS. For simulating large power systems,
the parallel computational capabilities of the RTSs can be utilized by distributing and
executing the grid model into multiple cores of the RTSs as shown in Hooshyar et al. [20].
In this paper, the hybrid simulation approach is implemented to model the distribution
grid to ease up the burden on the processors of RTS. The general idea is mainly adopted
from the PhD dissertation [21] and similar approach with different interfacing techniques
is explained in referenced studies [22–25]. The complete hybrid model is developed in a
Energies 2021, 14, 593 3 of 22
single environment of Simulink®, MATLAB [26], which is also used as a software inter-
face for real-time simulation.
Figure 1. Power hardware-in-the-loop (PHIL) system architecture with ideal transformer mod-
el-voltage type interface.
The remaining paper is organized in the following structure. Section 2 presents the
use of Simulink to develop a complete hybrid simulation model. Multiple scenarios are
implemented, and the result is illustrated to explain the functionality and relevance of
hybrid simulation with respect to PHIL simulation. Section 3 explains the development
and approach related to the PHIL setup. The method to execute the setup is scripted in
detail along with the scenarios to be implemented. Section 4 illustrates the comprehen-
sive results concerning the performance of the PI and HUT. Section 5, summarizes the
discussion of the results, main outcomes, and optimization recommendations for future
work. Finally, Section 6, concludes the paper.
2. Development of Hybrid Simulation Model
2.1. Equivalent Circuit Representation
The phasor simulation method for power system analysis has limitations as it only
computes the phasor values of the parameters such as voltages and currents at each
time-step. The fundamental frequency is usually maintained throughout the simulation.
On the contrary, detailed studies using electromagnetic transient (EMT) simulation
method with much lower time-steps (microseconds to seconds) for large power networks
to be executed on RTS would require high computational power. Hybrid simulation
serves an interim purpose by incorporating both techniques, as part of the system is
simulated in phasor domain and the remaining in EMT or discrete domain in which the
high frequency transients needs to be studied and instantaneous values of the signals can
be analyzed. The approach to define interface practices between the two connected
sub-systems in a hybrid model can be found in the literature [27] along with equivalent
circuit representation on both sides [22,28]. To develop the hybrid simulation interface,
the important aspect is the equivalent circuit and the interface bus at which the exchange
of data takes place. The equivalent circuit representation followed in this paper to ex-
change the boundary conditions in hybrid simulation is illustrated in Figure 2 and also
summarized in PhD dissertation [21] from different studies. As shown, the discrete
sub-system simulated at a time-step of 100 µs is represented by a current source in phasor
domain whereas the phasor sub-system simulated at the fundamental frequency is rep-
resented by a Thevenin circuit with voltage source and grid impedance.
Energies 2021, 14, 593 4 of 22
Figure 2. Equivalent circuits and boundary conditions for interaction between two simulation domains.
The Thevenin equivalent impedance for phasor sub-system representation in dis-
crete domain needs to be calculated at the interface bus in phasor domain in order to
update the Thevenin voltage source [21]. Mathematically, the relations can be repre-
sented as the following Equations (1) and (2) modified from [23] using subscripts as
shown in Figure 2.
() =
() +
∗
∗
() (1)
() =−∗
()=
()∗
 (2)
where
th
(
p
) is the equivalent Thevenin impedance characterizing the grid modelled in
phasor domain,
th
(
p
) is the Thevenin voltage phasor in phasor domain,
V
and are the
phasor domain voltage and current injection at the interface bus. Using the current
source to represent the discrete sub-system is a direct approach compared to other
equivalent methods which includes representation via a power source or a Thevenin
(Norton) equivalent [29].
2.2. Data Exchange
As both the sub-systems are coupled, the output from one simulation domain is
transmitted as an input to the other. Desired equivalent circuit values including imped-
ance, voltage amplitude, voltage phase angle, current, and frequency are required to be
extracted dynamically at each time-step. Firstly, in discrete domain the values from the
phasor domain at the fundamental frequency are transformed into a waveform. Sec-
ondly, the phasors (magnitude and angle) are extracted from waveforms in discrete do-
main using fast-Fourier transform (FFT) at the fundamental frequency before feeding the
values back to the phasor domain [23]. Data must be transformed into appropriate forms
before exchanging between the two domains as shown in the Figure 3 below:
Figure 3. Data exchange during hybrid simulation between two different simulation environments.
Energies 2021, 14, 593 5 of 22
2.3. Co-Simulation Workflow
In PHIL simulation, the interface bus represents the point of common coupling
(PCC) at which the voltage is emulated in real-time and at which the hardware is con-
nected. The simple radial LVDG network being considered in this paper follows the
MONA–8002 structure from the MONA Project 2030 [30]. The topology of the distribu-
tion grid and details about the distribution line parameters are defined in Appendix A.
The hybrid simulation workflow of the LVDG developed in Simulink is shown in Figure
4 with the equivalent circuits interconnected at the selected interface bus.
Figure 4. Development and workflow of hybrid simulation model for a power distribution grid with one interface point.
The corresponding voltage (magnitude and phase) at the interface bus is extracted
from the phasor domain. The values are transformed into three time-varying waveforms
in discrete domain at the fundamental frequency using Equation (3).
()=
∗sin
(2∗∗
∗+
)
()=
∗sin
(2∗∗∗+
)
()=
∗sin
(2∗∗
∗+
)
(3)
where U
m
and Ɵ represents the magnitude and angle of the respective voltage phasors
obtained from the phasor domain. A balanced grid network is assessed for convenience;
therefore voltage magnitudes will be similar with an almost fixed phase difference of
±120 degree. The voltage waveforms for each phase are fed to the controlled-voltage (CV)
sources as a source signal after which the sinusoidal voltage is generated. To evaluate the
Thevenin equivalent impedance of the phasor model the impedance measurement block
in Simulink is used. The block measures the impedance between the two phases as seen
from the interface bus, as a function of frequency. The impedance is computed during
initialization. For a three-phase circuit, to acquire the positive-sequence impedance the
multiplication factor of (½) is to be used to rescale the measured impedance [31]. The real
Energies 2021, 14, 593 6 of 22
part of the impedance represents the value of the resistance whereas for the reactive part
the corresponding value of the inductor is evaluated by using Equation (4).
 =
+
;
 =1
2∗
and =1
2
2∗∗
 1
2
2∗∗50
(4)
The equivalent impedance is only measured once and assumed constant as no
modifications are made in the network configuration. The two sub-systems (phasor and
discrete) are interconnected with each other in a single model which is the advantage of
using Simulink environment. Two different powergui blocks are segmented separately in
each sub-system. It provides the option to choose the simulation method for the circuit
and is required to simulate any model with Simulink-Simscape specialized power system
blocks. For the phasor sub-system the phasor solver is selected indicating that the phasor
values of electrical signals will be computed at each time-step at the fundamental fre-
quency of 50 Hz. While in discrete sub-system the solver is set to discrete with a sam-
ple-time of 100 µs indicating that this portion of the network is simulated as elec-
tro-magnetic transient with solution computed at each time-step. Both of the sub-systems
are operating simultaneously in different simulation domains constituting the idea of
hybrid simulation. In a nutshell, the instantaneous phasor values are transmitted to the
discrete domain to be converted to waveforms. Likewise, the phasor values are extracted
from waveforms in discrete domain and transmitted to phasor domain to represent the
total power being consumed/injected in the network at each time instant. The whole data
exchange takes place at the interface bus.
The hybrid simulation approach deems accurate for real-time simulation as part of
the grid model simulated in discrete domain can provide the desired discrete instanta-
neous waveforms. The discretized waveforms can be processed by the RTS in PHIL
simulation. That is why a small part of the distribution grid is modelled in discrete do-
main in this paper which serves the mentioned purpose. Similarly it also provides the
interface to treat the measured feedback signals of the HUT in discrete domain and inte-
grate it into the virtual grid model.
2.4. Offline Hybrid Simulation
The practical functioning of the hybrid simulation is illustrated in this sub-section
through two scenarios. The key parameters for the scenarios are defined in Table 1. For
the first scenario, a constant three-phase resistive load is connected at the PCC in discrete
domain at bus # 2. The voltage and current should be in phase at bus # 2 representing a
pure active power being drawn by the resistive load. The idea behind this scenario is to
observe whether the waveforms are purely in phase or if there is any sort of delay due to
the partition of the network and equivalent circuit representations. As a driving signal for
the CC sources in phasor domain the phasor values of the current flowing through the
circuit is fed back.
Table 1. Offline hybrid simulation scenarios to assess the functioning of the hybrid model.
Scheme Load
Network Partition
Point Phasor/Discrete Sample-Time
Constant Active Power TP Resistive Load (0.5 kW) Bus 2 100 ms/100 µs
Dynamic Load–Step Response TP Resistive Load (2.5 to 5 kW)
The result from the first offline simulation scenario is shown in Figure 5. The
waveforms represent an ideal resistive load. The voltages and currents are completely
synchronized and in phase with each other as observed at the zero-crossings. This pre-
sents an insight into the functioning of the hybrid model in case of a conventional load.
The results show that there seems to be no lag or delay and the exchange of variables at
the interface is carried out smoothly.
Energies 2021, 14, 593 7 of 22
Figure 5. Instantaneous three-phase voltage/current waveforms for a resistive load connected at the
interface bus in discrete domain. Represents in-phase synchronization with no undesired phase
shifts.
To further observe the fidelity of the hybrid model, in the second scenario the volt-
age and current at the interface bus is compared from both the domains. In principle, the
phasor voltage at the interface bus transmitted to the discrete domain should generate
the waveform of the same amplitude. Similarly, the current fluctuations should also be
captured in discrete domain at the same time instant. The load is replaced by a
three-phase dynamic load, the power demand of which is stepped-up during the simu-
lation to observe the imminent current change in both domains. The voltage and current
comparison is shown in Figure 6.
Figure 6. Step increase in power demand at 10 s. (upper) Voltage synchronization between the two
interconnected domains. (lower) Prompt change in current drawn analyzed in both domains due to
step increase in power demand.
Energies 2021, 14, 593 8 of 22
The plot in Figure 6a shows the comparison of the voltage measured from both
domains at the interface bus. In Figure 6b, it can be observed that there is a variation in
current magnitude due to the change in power demand at that instant which is also fol-
lowed by current waveform. The change in phasor magnitude is captured dynamically at
the same instant as seen by the instantaneous waveform results. The change of current
phasor to a new value happens over one grid cycle, i.e., 20 ms. The design of the hybrid
simulation in the Simulink platform is presented at length in this section. The imple-
mented scenarios help in understanding the functioning of the hybrid simulation model.
The results from the scenarios evidently show that the hybrid model is running in syn-
chronization, the equivalent circuit representations does not introduce any significant
fluctuations or delay in voltage and current at the interface bus. For the development of
PHIL simulation environment, the load connected in the discrete domain will be re-
moved and an actual power device in real-world will serve as a load.
3. PHIL Simulation
3.1. Description of PHIL Components
The PHIL system comprises of three main parts [4] and the respective components
used for this experiment are also defined as follows:
Hardware under Test (HUT): It refers to the power component, the response and
behavior of which is to be examined. In this experiment, the HUT is a
switched-mode 30 kVA power amplifier operating in CC mode from Regatron AG®,
Rorschach, Switzerland f type TopCon TC.ACS with a bandwidth of 5.0 kHz. This
series from Regatron AG supports the operation of CC amplifiers in both “Feeding
Mode” for positive power and “Regenerative Mode” for negative power [32]. This
characteristic is quite helpful while performing PHIL studies for DER integration.
As the HUT can serve dynamically as a load and also as a feed-in source based on
the power set-points.
Power Interface (PI): It enables to get the operating points of an electrical power
system from the simulation environment and makes it available in the real world
(for instance: voltage at the PCC). The power interface in this experiment is a
switched-mode voltage source, a 50 kVA 4-Quadrant grid simulator from Regatron
AG® also of type TopCon TC.ACS with a bandwidth of 5.0 kHz operating in volt-
age-controlled mode [32]. It operates as a grid simulator emulating the voltage at the
PCC. The TC.ACS amplifiers have built-in protection features programmed to
prompt circuit breakers in case of phase overcurrent and over voltage leading to
electrical isolation of the device.
The use of Regatron devices as grid simulators/power interface is quite common for
PHIL simulation. The application of Regatron power amplifier as a PI is discussed in [4],
to test the smart grid controls using two different PHIL setups. Similarly, in [33] a PHIL
setup is implemented to validate the developed battery-model in real-time using Rega-
tron’s amplifier as a power interface. The vibrant behavior of the power amplifier is
discussed in detail in this paper to illustrate the considerations that needs to be made if it
is used as a PI for PHIL simulation.
Real-Time Simulator (RTS): The real-time simulator simulates the grid model in re-
al-time and performs digital-to-analog (D/A) conversion of the reference signals and
vice versa. It transmits the scaled down analog values from the simulation envi-
ronment to the power interface and from the HUT (e.g., current) back to the simula-
tion environment. For this experiment, a Speedgoat real-time target machine [34] is
used as a RTS with the mathematical grid model developed in Simulink executed on
its multi-core processors. The IO334 field-programmable gate array (FPGA) boards
are used for analog-to-digital (A/D) and D/A conversion of the signals. The boards
Energies 2021, 14, 593 9 of 22
are interconnected with the target CPU through peripheral component interconnect
(PCI) express connection.
In general, concerning the real-time digital simulators various options are currently
being utilized. Some of the most popular RTSs with their applications and software in-
terfaces are defined in Table 2. For data acquisition during the real-time simulation, the
high bandwidth DEWESOFT devices [35] are used in this experiment.
Table 2. Real-time simulators for PHIL testing of multiple applications. Modified from [36].
Simulator Software Interface Applications
Opal-RT RTLAB, Simulink, MATLAB, Lab-
view and HYPERSIM
Power electronics, control systems, HIL, Power sys-
tems like smart grid.
RTDS RSCAD, MATLAB and Simulink
Speedgoat Real-Time
Target Machine Simulink, MATLAB
National Instruments (NI)
Hardware Labview
Typhoon Typhoon HIL Control Center,
MATLAB, and Simulink Power electronics, control systems, HIL, Microgrids.
dSPACE MATLAB and Simulink Power electronics, real-time control, rapid prototyping,
power systems like smart grid.
3.2. PHIL Simulation Workflow
The PHIL simulation environment is represented in a layered diagram in Figure 7
with the measured signals of interest followed by the explanation of the complete work-
flow.
Figure 7. PHIL simulation environment illustrated with the components, data acquisition, and feedback signal.
Energies 2021, 14, 593 10 of 22
The Vref is the reference voltage signal at the interface bus from the virtual simulated
grid. This digital signal is scaled-down and converted to analog signal by the RTS. It is
transmitted to the VC power amplifier operating as a PI. The PI which is connected to the
HUT amplifies the voltage to the nominal values. The operation of the HUT as load is
defined by the reference current signal Iref, which is synchronized with the Vref and is
evaluated based on power set-points. The Iref is scaled-down and sent via RTS to the
HUT. The analog current that flows between the two power components is measured and
extracted via the PI internal ports and converted from analog to digital by the RTS to
have the actual current in the virtual simulated model. This signal is then fed back to the
CC sources in the grid model. The RTS generates the reference values within the range of
±10 Vpk. The scaling factors for D/A conversion and vice versa are stated in Equation (5).
A Vref of 10 Vpk refers to a 432 Vpk from the PI output.
 =432
10 ×
 (5)
3.3. Startup and Execution
The step by step execution of the PHIL simulation setup is listed below. The method
is not lab specific but rather hardware components and simulation environment specific
and can be followed for a general PHIL setup comprising of the same arrangement.
The simulated LVDG modelled in Simulink platform is compiled in real-time by
setting the simulation mode in Simulink to “external”. After the model is built on the
Speedgoat RT target machine, the simulation is executed which produces a
scaled-down Vref signal at the chosen PCC bus for the PI.
The interface produces the amplified voltage at its terminals based on the set-points
received by the RTS. The voltage limits and the reference root-mean square (rms)
values must be cross checked before switching on the PI.
After the voltage is successfully established, the scaled down Iref current command is
sent to the RTS. The AC voltage at the power interface is then applied to the HUT by
switching it on. In this manner, the external power connection between the PI and
the HUT is established.
The measurements (current and voltage) are logged externally by means of data
measurement devices from DEWESOFT. Additionally, the current of the HUT is fed
back to the simulation environment. The currents can be obtained directly from the
out ports of the PI by establishing a connection from respective port of the amplifier
to the RTS and ultimately extracting it from analog-in channels of the RTS in simu-
lation environment.
Finally, the injection of acquired external currents to the CC sources in the virtual
grid model closes the PHIL loop.
The reference current injection signals for the HUT are evaluated based on power
set-points and voltage at the PCC. Figure 8 illustrates the simplified process flow behind
the evaluation of the reference three-phase injection currents for the HUT. The
three-phase voltage splits into direct and quadrature axis values inside the discrete
three-phase phase-locked loop (PLL) block. It is further treated to calculate the frequency
and wt of the signal respectively by means of a variable frequency block and PI control-
ler. The discrete three-phase PLL block is part of the three-phase dynamic load block
mask in Simulink and is developed by P, Girroux and G, Sybille from the Power System
Laboratory, Hydro-Québec research institute (IREQ). Detailed model of the respective
Simulink block and its use in current evaluation block is shown in Appendix B.
Energies 2021, 14, 593 11 of 22
Figure 8. Reference current evaluation for current-controlled (CC)-amplifier based on voltage at the point of common
coupling (PCC) and desired power set-points.
3.4. PHIL Simulation Scenarios
To observe the functioning of developed PHIL setup, the two main scenarios im-
plemented are defined in Figure 9. The first scenario intends to observe the response of
the power interface at no load (i.e., I = 0). It is to establish the characterization of the
power interface to find out the associated errors, noise, or delay. In this scenario the aim
is to observe whether the commanded V
ref
signal at the PCC bus sent by the RTS is fol-
lowed by the power interface or not.
Figure 9. Scenarios to analyze the developed PHIL simulation environment, power components inaccuracies and dy-
namics.
The second scenario represents a closed loop PHIL setup in which the HUT is sub-
jected to draw power through I
ref
signals. In this scenario, the capability of the HUT is
analyzed to follow up the commanded I
ref
signals, to observe the desired power flow and
more importantly the dynamic behavior of the hardware coupled with the simulation
environment.
4. Results
4.1. Scenario 1: Voltage Comparison–Open Circuit Test
Energies 2021, 14, 593 12 of 22
The plot in Figure 10 shows the comparison between the single-phase voltages. The
V
ref
is a scaled-down analog signal from the simulator that is being amplified by the in-
terface. For the convenience of comparison analysis, the V
ref
is scaled-up using the
mathematical relation shown in Equation (5). From the sub-plot in Figure 10a, it seems
that the V
ref
is accurately superimposed by V
PI
. To get more details, the second sub-plot in
Figure 10b is presented which is a zoomed-in version at one of the peaks and the sub-plot
in Figure 10c further illustrates the zoomed-in version at the zero-crossing to visualize
the time delay.
Figure 10. Voltage Comparison. (a) Reference and power interface generated voltage waveforms in real-time. (b)
Zoomed-in version at one of the instants to analyze the inaccuracies between the simulated and real-world interface
output voltage. (c) Zoomed-in version at the zero-crossing to analyze the time delay.
The noise associated with the simulator output can be easily distinguished from the
sub-plots. As far as the power interface is concerned, the default built-in filters of the
amplifier eradicated the noise to some extent and the output appears out to be smoother.
The effective difference between the two signals results from the latency of the setup and
introduces minute phase-shift in all the phases. To investigate the magnitude difference
i.e., the offset due to the power interface, a test case is performed as explained in the fol-
lowing sub-section.
4.1.1. Test Case: Voltage Step Response
The inaccuracies in magnitude are analyzed through this interim test case. A simple
grid is modelled using a Thevenin equivalent circuit depicted by a voltage source and
impedance. The same parameters for impedance are utilized as defined in Figure 9
whereas for the voltage source a dynamic step-input is used to vary the voltage levels of
the reference signal from (50–300 V
rms
) at regular intervals in steps of 25 V
rms
. The result
depicting the rms offset from the interface at each V
ref
is shown in Figure 11. An almost
direct relation is thereby observed between the two. The magnitude of the reference and
amplified signal is almost the same at low voltage levels with minimal difference of 0.1
V
rms
. At standard voltage of 230 V
rms
, the offset is quite significant and increases further
with the increase in V
ref
.
Energies 2021, 14, 593 13 of 22
Figure 11. Relation between the rms power interface offset (VPI) based on reference voltage (Vref)
from the real-time simulator (RTS).
() = 0.0049 ×  − 0.012 (6)
To characterize the power interface behavior, a line of fit is generated shown by
red-dotted line in Figure 11. The relation between the reference input signal and the ac-
tual generated offset by the PI at that particular input is defined by Equation (6). It is
evident that for a range of low simulated reference values, the PI outputs the voltage
signal of approximately same magnitude.
4.2. Scenario 2: Closed Loop PHIL Simulation
In this scenario the complete closed loop PHIL simulation is executed and the re-
sponse of HUT as an active load coupled with the simulation environment is analyzed as
shown in Figure 12. The instantaneous waveforms in Figure 12a show that there is a
phase difference between the voltage and current as observed at the zero-crossings with
power factor (pf) measured to be leading 0.97. The statement is supported with the power
flow of HUT shown in Figure 12b. Apart from the commanded active power, the capaci-
tive reactive power is also present and observed by the virtual simulated grid. The os-
cillatory behavior in power is due to the current harmonics introduced by the HUT.
Although the Iref is being generated based on active power set-points, the HUT exhibits
the nature of a capacitive-resistive load due to its internal design components. This could
be due to the parasitic capacities being used in switched-mode power amplifiers behav-
ing as a low-pass filter for the output. The dynamics of the HUT’s component results in
an uncontrolled and undesired reactive power flow.
Energies 2021, 14, 593 14 of 22
Figure 12. Response of the hardware under test (HUT). (a) Waveforms depicting phase difference between voltage and
current for a pure active power demand. (b) Power flow of the hardware under test (HUT) as experienced by the virtual
grid. (c) Dynamic behavior of the hardware under test (HUT) with variation in reference voltage (V
ref
) to examine internal
parasitic capacities.
To acquire more insight on the configuration of parasitic capacities of the HUT, an
interim test is performed. No power is being demanded from the HUT and the power
exchange between the interface and HUT is established. The input voltage levels were
varied again with steps of 25 V
rms
to observe the dynamic behavior of the HUT under
different voltage levels and no external active power demand. The response is shown in
Figure 12c. By default, the HUT behaves as a capacitive load for the simulated grid ex-
porting reactive power. At the standard 230 V
rms
, a capacitive reactive power of around
1.18 kvar is present. The reactive power increases with the voltage as shown by the blue
line. The green line represents the active power consumption of the HUT and it can be
seen that it is almost negligible. Therefore, the dynamic behavior of the HUT can be
simply modelled as a capacitor and using the reactive power value at the standard 230
V
rms
the default parasitic capacitance is approximated as shown in the following Equation
(7).
=

= 232
1
2∗∗50∗
→
=25 (7)
where, C
p
represents capacitance per phase. The presence of the default capacitance cre-
ates reactive power flow depending on the voltage level at the HUT terminals irrespec-
tive of the active power demand.
4.3. Power Quadrants
The operation of the power components in general during the PHIL simulation can
be characterized through power quadrants shown in Figure 13. The PI emulating the grid
voltage is operating in the fourth quadrant and the HUT is operating in the second
quadrant. As per IEC 62053-23 explanation of the power quadrants, the interface is the
source of active power for the HUT but at the same time it is receiving reactive power
from it. Similarly, the second quadrant for the HUT indicates that it is operating as an
active power sink and reactive power source.
Energies 2021, 14, 593 15 of 22
Figure 13. Power quadrants illustrating specific operation of the power components in PHIL setup.
4.4. Static Compensation
The compensation method is based on introducing an intentional delay in
time-domain for the generated I
ref
signal; the process flow of which is illustrated in Figure
14. The static time delay to be introduced is measured by comparing the phase difference
between the voltage/current at the HUT terminals and at the PCC in simulation envi-
ronment. This would lead to the reduction of undesired power flow from HUT. In prin-
cipal, the phase angle of the I
ref
and I
HUT
should be the same but due to the latency of the
RTS and dynamic components of the HUT there is a difference which the stated com-
pensation method targets to account for.
Figure 14. Process flow of static compensation algorithm to control undesired power flow from hardware under test
(HUT).
After acquisition of the feedback current signals of the HUT in simulation environ-
ment the fast-Fourier transform (FFT) of the signal is performed to obtain the
phase-by-phase angles in frequency domain. Only the fundamental components are
considered, and the harmonics introduced by the HUT are not compensated. The dif-
ference between the phase angles of the I
ref
and I
HUT
is evaluated and eventually a static
time delay is introduced based on the following mathematical relation shown in Equa-
tion (8). Thus the inaccuracies between the power factor (pf) for a certain active load are
minimized.
360° = 20 → ° = 20
360° ∗° (8)
The results illustrating the performance of mentioned compensation are shown in
Figure 15. The power demand for the HUT was increased in steps of 1 kW in the range of
(1–8 kW). For simplification, only active power was demanded to visualize the promi-
nent difference as for each case the pf is supposed to be unity. The plot in Figure 15a
shows the improvement in pf of the HUT for each power set-point after implementing
Energies 2021, 14, 593 16 of 22
the compensation. At low power demands, the compensation seems to contribute sig-
nificantly whereas for high power demands also the compensation attempts to get close
to the actual desired pf.
Figure 15. Comparison between usual and compensated PHIL simulation. (a) Improvement of power factor for different
power set-points through compensation implementation. (b) Significant reduction of reactive power flow from the
hardware under test (HUT).
The average capacitive reactive power injected by the HUT into the simulated grid is
also analyzed to observe the credibility of the developed compensation method. For each
power set-point the reactive power was noted, and the statistical mean is generated to
visualize the difference in a usual scenario and compensated scenario as shown in Figure
15b. Operating the HUT in usual case results in an injection of 1.29 kvar on average for a
specific active power demand and the compensation reduces this default value to an av-
erage of 0.81 kvar.
5. Discussion
The discussions of the results are classified scenario-wise.
5.1. Scenario 1
The results of the first scenario in Section 4.1 presented a detailed overview of the
power amplifier operation as the PI. It is realized that there is a minute phase difference
between the reference and actual generated voltage signals due to the time delay. The
delay is contributed by the real-time simulator due to the computation and generation of
reference signals within a given time-step. The power amplifier also contributes to a time
delay. The amplifier used for this experiment is a switched-mode power amplifier which
has high time delays [37]. The time delay ultimately creates a phase difference; however,
a very minute phase difference was observed.
The associated noise of the reference signal can also be clearly seen from Figure 10b.
The RTS sends an output at each discrete time-step of 100 µs for the modelled grid, which
certainly seems to be inefficient for the quality of the signal. The simulator output would
probably get better with lower discrete time-steps in the range of 10–50 µs. The lower
sample-times were attempted, but as the model was executed on the RTS processors, due
to computational limitations, the processor overload error was prompted, and the simu-
lation was not executed. The use of FPGAs in the real-time target machine for the signal
generation blocks is recommended to achieve lower sample-times and is planned for the
next phase of PHIL tests.
Energies 2021, 14, 593 17 of 22
Additionally, the offset of the PI is also studied. On literature review it has been
found that the power amplifier has an impedance which prompts the rise in voltage
proportional to the load current and is the reason of difference between the Vref and VPI
magnitude [13]. In this paper, the voltage rise, i.e., the offset is found to be in direct rela-
tion with the reference voltage magnitude for open circuit configuration. The internal
impedance of the PI is not evaluated in this paper; the methods to do so are discussed by
Wang et al. [16]. The basics on the response of specific power amplifier in a PHIL setup
are laid down in detail through the scenario performed in this paper. The scenario pro-
vides a holistic picture of the issues encountered in employing the PI for PHIL setups.
Adding on to these, respective corrective measures can now be devised to enhance the
accuracy of the PHIL simulation. For a general approach on compensation methods, the
detailed analysis is conducted in [38] to characterize the power amplifier as power in-
terface. Further methods for the stability and compensation for the power interface are
also discussed in referenced studies [39,40].
The test case conducted as discussed in Section 4.1.1 refers to the output of the PI at
different input voltages (Vout/Vin). The same method can be extended to identify the gain
of the power interface just by operating it at multiple frequencies in addition to the fun-
damental component to estimate the transfer function. Details on the impedance and gain
(transfer function) would be helpful to accurately model the dynamics of the PI and also
carrying out the stability studies. The same is planned for the future work.
5.2. Scenario 2
The results of scenario 2 discussed in Section 4.2 provides insight on the dynamic
behavior of the HUT (CC amplifier). The default parasitic capacitance of the HUT is re-
sponsible for the presence of capacitive reactive power. The HUT exports reactive power
into the simulated grid at the PCC. The quality of the signals exchanged between the
hardware and simulator plays a significant role in accuracy of the real-time simulation,
and it is observed that the HUT also causes harmonics leading to a deteriorated current
waveform. At lower active power demands, the current waveform is found to be quite
distorted and the pf of the system is affected badly. On average, a reactive power in the
range of 1.27–1.3 kvar is exported into the simulated grid which also affects the voltage at
the PCC.
A simple yet effective compensation method is developed to mitigate the unwanted
reactive power. The performance of the HUT is improved after implementation of the
mentioned compensation. The method does not affect the topology of the system. The
method however has certain limitations as for each power set-point, first the need would
be to run the usual case without compensation, observe the phase difference between the
Iref and IHUT and then introduce the equivalent time delay. The information from the usual
case scenario is necessary to implement the static compensation. On the other hand the
advantage is that for active power demands, the compensation method can be imple-
mented directly as the desired pf would be unity and the delay is to be added accord-
ingly. For other power demand configurations (i.e., capacitive, or inductive) additional
measures would be required as the HUT already operates as a capacitive load by default.
Further, the compensation reduces the default reactive power significantly by oper-
ating the HUT close to the desired behavior. The closed-loop scenario exposes the power
flow challenges associated with HUT operated in amplifier mode in a PHIL environment.
It shows that deploying such dynamic power components to perform PHIL simulation
would require appropriate measures to deal with the challenges highlighted in this pa-
per. For future work, the need is to devise more precise compensation algorithm using
proportional integral derivative (PID) controller and implementing a dynamic method so
that the phase differences can be tracked more accurately. Successful development of
such an algorithm would not only compensate the effect of parasitic capacitance but also
reduce the time delay of the whole PHIL setup. It is also necessary to compensate for
harmonics as at low power demands the current harmonics are very dominant. An ap-
Energies 2021, 14, 593 18 of 22
proach is defined by Sansano et al. [41], in which the reference voltage signal for the PI is
subjected to phase-shifting harmonic-by-harmonic and phase-by-phase and it is shown
that the PHIL simulation accuracy of low impedance grids can be improved.
6. Conclusions
The power components dynamics interconnected with the simulated environment
are presented at length in this paper. The study contributes to the fundamental devel-
opment of a bench-mark PHIL setup to simulate a typical LVDG in a real-time environ-
ment. The main contribution of the paper is to comprehend the dynamic response of the
power components involved especially the HUT to have the desired power flow. Firstly,
the corresponding inaccuracies between the reference and generated voltage related to
the PI are discussed. A test case is implemented to define the correlation between the two
at different voltage levels. Secondly, the operational complexities of the HUT are high-
lighted, and subsequent recommendation is presented. The experimental result depicts
the viability of the developed compensation method. Irrespective of the Regatron device
as HUT, the compensation method defined in this paper can be implemented for any
power hardware component that exhibits the behavior of undesired reactive power flow
to have it operated as a pure active load.
Furthermore, in the established PHIL setup, the HUT is controlled virtually from the
simulation environment therefore it provides more fidelity over a commercial inverter
device which is usually the case in PHIL. It can be operated as a dynamic source/sink to
study integration of such dynamic prosumers at the distribution grid level. Further, ad-
vance control strategies such as machine-learning based reactive-power management can
be superimposed on it; its impact in RT gird operations can be assessed and ultimately
the same can be implemented on commercial inverters. To achieve that, initially the need
is to first develop the base setup and understand the basic dynamic response of the PI as
well as the HUT in controlled mode and this paper presents the same. The next steps
entail more research to standardize optimum compensation methods in response to the
challenges and decrease collective inaccuracy/instability of the complete PHIL simula-
tion.
Author Contributions: Conceptualization, M.M., H.B., and S.G.; Investigation, M.M.; Writ-
ing—original draft preparation, M.M.; Writing—review and editing, H.B., S.G., K.v.M., and C.A.;
Supervision, H.B. and S.G.; All authors have read and agreed to the published version of the
manuscript.
Funding: This research was partially funded by the Ministry of Science and Culture of the German
State of Lower Saxony as part of the research project ‘SiNED’ (Project Code: ZN3563). We are very
grateful for the sponsor’s contribution in making this research possible.
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Not applicable.
Data Availability Statement: Not applicable.
Conflicts of Interest: The authors declare no conflict of interest.
Abbreviations
Acronym Name
CC amplifier Current-Controlled amplifier
DER Distributed Energy Resources
EMT Electromagnetic Transient
FPGA Field-Programmable Gate Array
HUT Hardware under Test
ITM Ideal Transformer Model
LV Low Voltage
Energies 2021, 14, 593 19 of 22
LVDG Low Voltage Distribution Grid
PCC Point of Common Coupling
PCI Peripheral Component Interconnect
PHIL Power Hardware-in-the-Loop
PI Power Interface
PID Controller Proportional Integral Derivative Controller
PLL Phase-Locked Loop
RMS Root-Mean Square
RT Real-Time
RTS Real-Time Simulator
VC amplifier Voltage-Controlled amplifier
Appendix A. MONA Low Voltage Distribution Grid Topology
Figure A1. Single line diagram of MONA-8002 low voltage distribution grid (LVDG).
Table A1. Distribution line parameters with respect to line codes and specifications.
Line code Resistance (/km) Reactance (mH/km) Capacitance (nF/km)
Positive Sequence
(r1)
Zero Sequence
(r0)
Positive Sequence
(l1)
Zero Sequence
(l0)
Positive Sequence
(c1)
Zero Sequence
(c0)
NAYY 4 ×
50 0.642 2.568 0.083 0.312 670 275.7
NAYY 4 ×
120 0.255 1.02 0.08 0.292 797.3 365.9
NAYY 4 ×
150 0.208 0.832 0.08 0.292 830 385.9
NAYY 4 ×
185 0.167 0.668 0.08 0.292 868.2 409.3
NAYY 4 × 50
The first number represents the no. of conductors while the second number represents its cross-section
(mm
2
).
Energies 2021, 14, 593 20 of 22
Appendix B. Simulink Model Blocks
Appendix B.1. Discrete 3-Phase PLL Block
Figure A2. Discrete 3-phase Phase-Locked Loop (PLL) block.
Appendix B.2. Reference Current Evaluation Based on Power Setpoints and V
pcc
Figure A3. Current evaluation for the hardware under test (HUT) based on power set-points and voltage at the point of
common coupling (PCC) in Simulink.
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... In addition, a DUT such as a PV inverter may be interfaced on its AC side through a transformer to the power amplifier to match the voltage levels of the solar PV inverters and the grid voltages passed to the amplifier as in [6,7] In most of the literature on PHIL testing for DER integration, the DUTs and their controls are tested in a PHIL environment, not amplifiers [112]. In [112] the authors focus on a power amplifier in current-controlled mode as the actual DUT which could enable the effective use of amplifiers as hardware emulators for DERs in future PHIL integration studies. The methodology of the interface determines which signals are exchanged between the RTS and the external equipment, as well as the timing of those exchanges during the simulation timestep [57]. ...
... Some PHIL power hardware, such as PV and battery inverters, require a DC power input, e.g., provided by a controllable DC source [7]. In addition, a DUT such as a PV inverter may be interfaced on its AC side through a transformer to the power amplifier to match the voltage levels of the solar PV inverters and the grid voltages passed to the amplifier as in [6,7] In most of the literature on PHIL testing for DER integration, the DUTs and their controls are tested in a PHIL environment, not amplifiers [112]. In [112] the authors focus on a power amplifier in current-controlled mode as the actual DUT which could enable the effective use of amplifiers as hardware emulators for DERs in future PHIL integration studies. ...
... In addition, a DUT such as a PV inverter may be interfaced on its AC side through a transformer to the power amplifier to match the voltage levels of the solar PV inverters and the grid voltages passed to the amplifier as in [6,7] In most of the literature on PHIL testing for DER integration, the DUTs and their controls are tested in a PHIL environment, not amplifiers [112]. In [112] the authors focus on a power amplifier in current-controlled mode as the actual DUT which could enable the effective use of amplifiers as hardware emulators for DERs in future PHIL integration studies. ...
Article
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Over the past decade, the world’s electrical grid infrastructure has experienced rapid growth in the integration of grid-edge inverter-based distributed energy resources (DERs). This has led to operating concerns associated with reduced system inertia, stability and intermittent renewable power generation. However, advanced or “smart” inverters can provide grid services such as volt-VAR, frequency-Watt, and constant power factor capabilities to help sustain reliable grid and microgrid operations. To address the challenges and accelerate the benefits of smart inverter integration, new approaches are needed to test both the impacts of inverter-based resources (IBRs) on the grid as well as the impacts of changing grid conditions on the operation of IBRs. Power hardware-in-the-loop (PHIL) stands out as a strong testing solution, enabling a real-time simulated power system to be interfaced to hardware devices such as inverters which can be implemented to determine interactions between multiple inverters at multiple points of common coupling on the grid and microgrids. This paper presents a review of PHIL for grid and microgrid applications including recent advancements and requirements such as real-time simulators, hardware interfaces and communication and stability considerations. An illuminating case study is summarized followed by exemplary PHIL testbed developments around the world, concluding with a proposed research paradigm to advance the integration of smart grid-following and grid-forming inverters.
... Following a hybrid approach by Muhammad et al. based on the concept of Plumier et al. [28,29], the grid simulation was set to run in phasor mode, which allows it to simulate On the grid simulation side, the three-phase reference grid no. 8 for low voltage distribution grids from the MONA 2030 project [23] was modeled in MATLAB/Simulink (release 2020b) using the toolboxes Simscape and Simscape Electrical [24,25]. Therefore, the grid model contained a low voltage grid of 0.4 kV including ten GCPs and a transformer that connects to a virtual medium voltage level of 11 kV. ...
... Following a hybrid approach by Muhammad et al. based on the concept of Plumier et al. [28,29], the grid simulation was set to run in phasor mode, which allows it to simulate large power grids without over-loading the computational capacities of the real-time system. In addition, a conversion from phasor to EMT domain was included to transform phasor values into discrete sinusoidal signals of voltage and current. ...
Article
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Due to the increasing penetration of the power grid with renewable, distributed energy resources, new strategies for voltage stabilization in low voltage distribution grids must be developed. One approach to autonomous voltage control is to apply reinforcement learning (RL) for reactive power injection by converters. In this work, to implement a secure test environment including real hardware influences for such intelligent algorithms, a power hardware-in-the-loop (PHIL) approach is used to combine a virtually simulated grid with real hardware devices to emulate as realistic grid states as possible. The PHIL environment is validated through the identification of system limits and analysis of deviations to a software model of the test grid. Finally, an adaptive volt–var control algorithm using RL is implemented to control reactive power injection of a real converter within the test environment. Despite facing more difficult conditions in the hardware than in the software environment, the algorithm is successfully integrated to control the voltage at a grid connection point in a low voltage grid. Thus, the proposed study underlines the potential to use RL in the voltage stabilization of future power grids.
... Por lo tanto, la reducción de la latencia en las pruebas en bucle (HIL o PHIL), es algo crítico para aumentar el ancho de banda, aumentando a su vez la estabilidad y el determinismo de la prueba.3.1.1.2 Estado del arteExisten varios simuladores en tiempo real comerciales, los cuales han sido analizados en diferentes artículos[49,69,70,78,79,80]. El análisis más completo de las características de estos simuladores es el realizado por el grupo de trabajo del IEEE PES sobre simulación en tiempo real de sistemas de potencia y energía[49].En este trabajo destaca la tabla completa, con un resumen de las características más importantes de los simuladores digitales en tiempo real más utilizados, tanto en la industria como en el mundo académico. ...
Thesis
Los problemas asociados al uso de combustibles fósiles nos están llevando como sociedad a una transición energética hacia las energías renovables. Para su integración, la red eléctrica actual está evolucionando a una red eléctrica inteligente, o también denominada Smartgrid. Pero esta evolución supone un aumento en la complejidad de su funcionamiento, sobre todo en las redes de distribución. En las redes eléctricas se van a necesitar nuevos equipos, con nuevas funcionalidades, que interactúen y comuniquen con sistemas de otros fabricantes para garantizar en todo momento la calidad y seguridad de suministro, equilibrando la balanza entre generación y consumo. Este salto disruptivo pone en riesgo la gran fiabilidad que estos nuevos equipos deben mostrar durante su operación. Por ello, el desarrollo y mejora de los métodos de prueba para la validación de estos sistemas es clave para el éxito de su despliegue. El presente trabajo de investigación tiene dos objetivos principales. El primer objetivo es la identificación, análisis y mejora de los métodos de prueba actuales para la Smartgrid, centrándose en aquellos que permitan la verificación y validación de los sistemas de potencia. El segundo objetivo es el lanzamiento de un servicio tecnológico que permita mejorar las capacidades técnicas actuales en el territorio, necesarias para el despliegue del equipamiento requerido en el desarrollo de la Smartgrid. Para ello, en primer lugar se ha realizado un revisión de los métodos de prueba para equipos de la Smartgrid, clasificándolos según su precio, fidelidad y cobertura. Esta clasificación muestra que la técnica de pruebas que ofrece la mejor relación entre cobertura y fidelidad para sistemas de potencia es Power Hardware-In-the-Loop (PHIL). Además, la disposición de esta bancada por parte de los laboratorios también ofrece otras técnicas de prueba interesantes, tales como la simulación y las restantes técnicas en bucle cerrado (MIL, SIL, PIL y HIL). Dada las capacidades de esta técnica de pruebas, se ha desarrollado un nuevo procedimiento para el dimensionamiento de una bancada PHIL, recopilando información tanto del equipamiento comercial, como de prototipos y diferentes pruebas desarrolladas en laboratorios. Además, se ha definido y clasificado la información relevante a documentar de una prueba PHIL para poder reproducir el experimento y reutilizar las configuraciones utilizadas. Esta clasificación de la información ha sido utilizada para la creación de una base de datos en línea, permitiendo a los usuarios consultar la información de las pruebas realizadas por otros laboratorios a equipos de interés común, así como incluir la información de sus nuevos experimentos. También se ha realizado un análisis de los elementos que componen el lazo cerrado de control de una bancada PHIL, proponiendo mejoras que consiguen aumentar la máxima frecuencia de emulación hasta cinco veces respecto las bancadas actuales. Para la ejecución de estas mejoras, se ha realizado el diseño conceptual de una nueva plataforma PHIL, que posibilita la prueba de equipos en varios puntos de conexión de la red eléctrica. A su vez, también se ha desarrollado un simulador embebido que permite la ejecución de modelos multiplataforma y su modelado en herramienta gráfica. Estas mejoras se cierran con la propuesta de un amplificador de corriente de gran ancho de banda, basado en la topología de un convertidor masivamente paralelo. En el análisis de los requerimientos de los sistemas de ensayo, se ha observado que para aquellas pruebas de sistemas de potencia con un protocolo de maniobra hardware y de comunicaciones muy específico y repetitivo (como por ejemplo, los cargadores de vehículo eléctrico), la utilización de emuladores de potencia puede suponer un ahorro importante en el tiempo de desarrollo y ejecución de los ensayos. Estos emuladores además ofrecen también ventajas operativas en pruebas muy acotadas o repetitivas respecto el método PHIL. Por este motivo, se presenta el desarrollo de un emulador de vehículo eléctrico V2G para la comprobación de cargadores bidireccionales. El funcionamiento de este emulador ha sido verificado en laboratorio ante un equipo real. Para la realización de este prototipo, dentro de esta tesis se ha diseñado el control de tensión de salida de batería, el filtro de salida utilizando la técnica de espacio de diseño y la implementación del código de su tarjeta de control. También se ha diseñado el control de reactiva para compensar la potencia de los cargadores durante la prueba. Para finalizar, se presenta el Laboratorio de Estudios y Ensayos de Electrónica de Potencia (LE3P) en las instalaciones de CIRCE, desarrollado en el marco de esta tesis. Este laboratorio ofrece un servicio tecnológico a las universidades, centros de investigación tecnológicos y empresas para el despliegue de nuevo equipamiento de potencia para la Smartgrid. En este servicio se define un modelo de desarrollo en V, complementándolo con el equipamiento utilizable dentro del mismo y las diferentes técnicas de prueba disponibles. Como muestra de este lanzamiento, en los anexos se describen dos pruebas realizadas dentro del LE3P a prototipos desarrollados en proyectos europeos.
... The amplifier controls the voltage applied, as decided by the voltage regulation model. Focusing on distribution grid stability, in [19], an innovative bench focusing on dynamic behavior simulation was presented. An extensive study was provided on the inaccuracies between the real devices and the virtual model. ...
Article
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By empowering consumers and enabling them as active players in the power and energy sector, demand flexibility requires more precise and sophisticated load modeling. In this paper, a laboratory testbed was designed and implemented for surveying the behavior of laboratory loads in different network conditions by using real-time simulation. Power hardware-in-the-loop was used to validate the load models by testing various technical network conditions. Then, in the emulation phase, the real-time simulator controlled a power amplifier and different laboratory equipment to provide a realistic testbed for validating the load models under different voltage and frequency conditions. In the case study, the power amplifier was utilized to supply a resistive load to emulate several consumer load modeling. Through the obtained results, the errors for each load level and the set of all load levels were calculated and compared. Furthermore, a fixed consumption level was considered. The frequency was changed to survey the behavior of the load during the grid’s instabilities. In the end, a set of mathematical equations were proposed to calculate power consumption with respect to the actual voltage and frequency variations.
... Through this test, the dynamics (parasitic effects, unwanted power flow, harmonics, consumption deviation etc.) of the HUT as an individual component were observed in real-time. An insight into a similar PHIL simulation performed at DLR-NESTEC to analyze the components response is discussed in detail in this study [13]. Additionally, the accuracy of PHIL simulation, development complications of software-hardware coupled interfaces and latency of the real-time simulation are also being analyzed. ...
Article
This paper describes the Networked Energy Systems Emulation Center – DLR-NESTEC – a platform for research on power grids of the future. The DLR-NESTEC consists of a large number of networked power electronic components with which real hardware can be emulated using a real-time simulation system. The grid networking is realized via cable emulators. The laboratory works with real physical power flows and has a connected load of 800 kVA. In addition to the emulators, real network components can be integrated into the laboratory. The sector coupling is addressed by the coupling of charging infrastructure of electric cars as well as the integration of electricity-controlled heating systems. In addition, hydrogen technologies can be integrated. The laboratory is controlled by a SCADA system coupled to high-resolution measurement equipment. With the help of DLR-NESTEC, various future questions regarding robust and safe operation sector-coupled energy networks can be addressed – for instance the operation of a grid structure with a high share of controllable loads without a superordinate control.
... Device under Test (DuT) [8,21], Equipment under Test (EuT) [20] or Physical Power System (PPS) [18] are seldom used. In contrast, Hardware under Test (HuT), is most common [6,[13][14][15][16]20,[22][23][24][25], therefore this abbreviation will be used in this paper. The virtual part of the system which Electronics 2022, 11, 7 3 of 23 has to be modeled and simulated in real time will be referred to as Real Time Simulation (RTS) in the following. ...
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Power Hardware-in-the-Loop (PHiL) simulation is an emerging testing methodology of real hardware equipment within an emulated virtual environment. The closed loop interfacing between the Hardware under Test (HuT) and the Real Time Simulation (RTS) enables a realistic simulation but can also result in an unstable system. In addition to fundamentals in PHiL simulation and interfacing, this paper therefore provides a consistent and comprehensive study of PHiL stability. An analytic analysis is compared with a simulative approach and is supplemented by practical validations of the stability limits in PHiL simulation. Special focus is given on the differences between a switching and a linear amplifier as power interface (PI). Stability limits and the respective factors of influence (e.g., Feedback Current Filtering) are elaborated with a minimal example circuit with voltage-type Ideal Transformer Model (ITM) PHiL interface algorithm (IA). Finally, the findings are transferred to a real low-voltage grid PHiL application with residential load and photovoltaic system.
Conference Paper
The effective modeling of power distribution grid components has become extremely challenging especially considering the increase of power electronics interface based distributed energy resources (DER). The aim behind is, to optimize the accuracy of the models to precisely evaluate the component response and stability of the system. For this purpose, based on the fundamentals of power hardware-in-the-loop simulation, grid-in-the-loop (GIL) environment is proposed in this study to evaluate the grid stability due to integration of DER. A complete low-voltage (LV) distribution grid is emulated along with active grid participants. Additionally, to study the impact at high voltage levels the emulated grid is firmly synchronized with a medium-voltage (MV) simulated grid. An initial case-study is also performed to demonstrate the interesting dynamic behavior at coupling nodes, that otherwise may not have been depicted in simulation studies. Thus, the approach eases up the need for time-intensive detailed modeling of distribution grid participants and provides a setting to test actual components in grid-connected states. If required, the results can be used to replicate the behavior for simulation studies as an alternate to detailed component models.
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Grid stability becomes an issue when incorporating renewable distribution generation into an electrical grid due to voltage fluctuations. This work presents an innovative solution which assists grid planners in carrying out technical and economic analysis of future grids and in taking decisions based on it. A set of tools allows the determination of the renewable energy sources and energy storage systems impact to a given grid concerning technical and economic indicators. Using these tools, a study was conducted comparing model predictive control with photovoltaics-curtailment, volt-watt and volt-var methods for the control of photovoltaics and energy storage power in an existing grid. Some highlights of the analysis are: (i) the given grid supports maximal photovoltaics penetration level of 120% without exceeding the ±10% voltage level limits; (ii) the model predictive control method aiming at the minimization of power exchange in a grid with 60% storage penetration allowed significant increase of photovoltaics penetration to 190% and reduced the maximum voltage level to 1.089pu; (iii) a user-centered design and development of the interface for grid planners resulted in a system usability scale score of 63.9. The tools enable the grid planner to take decisions when planning the future grid.
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The real-time (RT) hardware-in-the-loop (HIL) simulation-based testing is getting popular for power systems and power electronics applications. The HIL testing provides the interactive environment between the actual power system components like control and protection devices and simulated power system networks including different communication protocols. Therefore, the results of the RT simulation and HIL testing before the actual implementation in the field are generally more acceptable than offline simulations. This paper reviews the HIL testing methods and applications in the recent literature and presents a step-by-step documentation of a new HIL testing setup for a specific case study. The case study evaluates improved version of previously proposed communication-dependent logically selective adaptive protection algorithm of AC microgrids using the real-time HIL testing of IEC 61850 generic object-oriented substation event (GOOSE) protocol. The RT model of AC microgrid including the converter-based distributed energy resources and battery storage along with IEC 61850 GOOSE protocol implementation is created in MATLAB/Simulink and RT-LAB software using OPAL-RT simulator platform. The Ethernet switch acts as IEC 61850 station bus for exchanging GOOSE Boolean signals between the RT target and the actual digital relay. The evaluation of the round-trip delay using the RT simulation has been performed. It is found that the whole process of fault detection, isolation and adaptive setting using Ethernet communication is possible within the standard low voltage ride through curve maintaining the seamless transition to the islanded mode. The signal monitoring inside the relay is suggested to avoid false tripping of the relay.
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Deep penetration of distributed generators have created several stability and operation issues for power systems. In order to address these, inverters with advanced capabilities such as frequency and reactive power support the grid. Known also as Smart Inverters (SIs), these devices are highly dynamic and contribute to the power flow in the system. Notwithstanding their benefits, such dynamic devices are new to distribution networks. Power system operators are very reluctant toward such changes as they may cause unknown issues. In order to alleviate these concerns and facilitate SIs integration to the grid, behavior studies are required. To that end, this paper presents a power hardware-in-the-loop test set up and tests that are performed to study fault behavior of SIs connected to distribution networks. The details of the software model, SI integration with the real-time simulator, test results, and their analyses are presented. This experience shows that it is not trivial to connect such novel devices with simulation environments. Adjustments are required on both software and hardware fronts on a case-by-case basis. The encountered integration issues and their solutions are presented herein. The fault behavior of the SI with respect to the fault location is documented. It is observed that for faults that are close to SIs, momentary cessation of generation is observed. This needs to be tackled by device manufacturers as this phenomenon is very detrimental to health of a power system under fault conditions. Extensive PHIL test results show that several factors affect the fault behavior of an SI: fault location and its duration, SI mode of operation as well as extra devices housed in the casing. These results and their in-depth analyses are presented for a thorough understanding of SI behavior under fault conditions.
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The Smart Grid is one of the most important solutions to boost electricity sharing from renewable energy sources. Its implementation adds new functionalities to power systems, which increases the electric grid complexity. To ensure grid stability and security, systems need flexible methods in order to be tested in a safe and economical way. A promising test technique is Power Hardware-In-the-Loop (PHIL), which combines the flexibility of Hardware-In-the-Loop (HIL) technique with power exchange. However, the acquisition of PHIL components usually represents a great expense for laboratories and, therefore, the setting up of the experiment involves making hard decisions. This paper provides a complete guideline and useful new tools for laboratories in order to set PHIL facilities up efficiently. First, a PHIL system selection guide is presented, which describes the selection process steps and the main system characteristics needed to perform a PHIL test. Furthermore, a classification proposal containing the desirable information to be obtained from a PHIL test paper for reproducibility purposes is given. Finally, this classification was used to develop a PHIL test online database, which was analysed, and the main gathered information with some use cases and conclusions are shown.
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The fundamental changes in the energy sector, due to the rise of renewable energy resources and the possibilities of the digitalisation process, result in the demand for new methodologies for testing Smart Grid concepts and control strategies. Using the Power Hardware-in-the-Loop (PHIL) methodology is one of the key elements for such evaluations. PHIL and other in-the-loop concepts cannot be considered as plug'n'play and, for a wider adoption, the obstacles have to be reduced. This paper presents the comparison of two different setups for the evaluation of components and systems focused on undisturbed operational conditions. The first setup is a conventional PHIL setup and the second is a simplified setup based on a quasi-dynamic PHIL (QDPHIL) approach which involves fast and continuously steady state load flow calculations. A case study which analyses a simple superimposed voltage control algorithm gives an example for the actual usage of the quasi-dynamic setup. Furthermore, this article also provides a comparison and discussion of the achieved results with the two setups and it concludes with an outlook about further research.
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To make the object of electromagnetic transient (EMT) simulation flexible to change, the authors propose using the method of electromagnetic transient-transient stability analysis (TSA) hybrid real-time simulation of the variable area of interest. The area where the fault is to be set, or where the operation takes place, is defined as the area of interest. The simulation object is divided into multiple sub-networks. The EMT simulation range is determined according to the voltage drop depth at the boundary of the adjacent sub-network caused by the three-phase short-circuit fault at the boundary of an area of interest. The Norton equivalent is obtained by using the sub-network as a basic unit. The electromagnetic sub-network forms its own Norton equivalent on the TSA side by means of the Norton equivalent admittance of its TSA model. Based on this, the overall framework of hybrid real-time simulation of the variable area of interest is constructed. The fundamental phasor prediction and Norton equivalent current source prediction are adopted to reduce the interface error. The performance of the proposed method in terms of feasibility, flexibility, and effectiveness have been verified by the simulation studies on the IEEE 118-bus system.
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In order to overcome challenges associated with the integration of distributed energy resources (DER) into state-of-the-art and future power grids require the development of a common basis for testing using appropriate benchmark systems. Real-time hardware-in-the-loop (HIL) simulation has proven to be an advanced and efficient tool for the analysis and validation of electric power systems and DER components. However, a common methodology for HIL testing of DER along with the required set of reference systems has not yet been developed. This Task-Force paper proposes a benchmark system for HIL testing incorporating DER into the real-time simulation environment. A low voltage (LV) benchmark system with detailed HIL setup is proposed for testing of DER performance. The modeling of DER for real-time applications is discussed and detailed laboratory procedures and setups for both controller hardware-in-the-loop (CHIL) and power hardware-in-the-loop (PHIL) are provided. Results from CHIL simulation related to centralized controls and experimental results of PHIL simulation related to local control on the benchmark system substantiate the suitability of the proposed real-time simulation approach.
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The importance of Power Hardware-in-the-Loop (PHIL) experiments is rising more and more over the last decade in the field of power system and components testing. Due to the bidirectional exchange between virtual and physical systems, a true-to-reality interface is essential; however, linking several dynamic systems, stability issues can challenge the experiments, the components under test, and the individuals performing the experiments. Over the time, several interface algorithms (IA) have been developed and analyzed, each having different advantages and disadvantages in view of combining virtual simulations with physical power systems. Finally, IA are very specific to the kind of PHIL experiment. This paper investigates the operational range of several IA for specific PHIL setups by calculations, simulations, and measurements. Therefore, a selection of the mainly used respectively optimized IA is mathematically described. The operational range is verified in a PHIL system testing environment. Furthermore, in order to study the influence of different PHIL setups, according to software and hardware impedance, different tests using linear and switching amplifiers are performed.
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This paper presents the wideband system identification (WSI) technique, i.e., an online method to identify power impedances over a wide frequency range from which the corresponding parametric impedance can be calculated online as well. The WSI technique exploits an existing custom 25-kW power electronic converter on the top of its power conversion function, which serves as power amplifier of an existing power hardware-in-the-loop (PHiL) simulation setup. The PHiL simulation technique allows connecting a real device under test (DUT) with the real-time simulated rest of system (ROS) at power level. An interface algorithm (IA) on simulation side and a power amplifier (the 25-kW power electronic converter) connect ROS and DUT. This paper shows the impact of the uncertainties in the WSI chain on the accuracy of the impedance identification and highlights how the WSI technique can be combined with the damping impedance method IA to improve both accuracy and stability of the PHiL test bench. The application of the method is illustrated for the scenario of a PHiL test of a dc microgrid with a passive load.
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This study establishes a new basis for understanding the stability of power hardware-in-the-loop (PHIL) systems considering their hybrid (analogue/digital) nature. Such systems are known to have closed-loop stability issues due to delays between the simulator and the power amplifier (PA). This work demonstrates that the conventional method for determining the stability criterion, which considers the system as a continuous model, is not appropriate. A new method of assessing the stability of a PHIL system based on discrete-time impedance frequency responses is thus presented. Hydro-Québec's Research Institute will use this innovative approach for the development of its own PHIL system, which will involve connecting the institute's real-life experimental distribution test line to its large-scale real-time digital simulator through a 25-kV, 10-MVA PA. The validity of the new method is demonstrated for simulation models of a well-known inductive system and of a distribution feeder connected to a large-scale power system.