Using Power-Hardware-in-the-Loop Experiments
together with Co-simulation for the Holistic
Validation of Cyber-Physical Energy Systems
Van Hoa NGUYEN1, Yvon BESANGER1, Quoc Tuan TRAN2, Cédric BOUDINNET1, Tung Lam NGUYEN1,2,
Ron BRANDL3, Thomas I. STRASSER4
1
University Grenoble Alpes, G2Elab, F-38000 Grenoble, France
CNRS, G2Elab, F-38000 Grenoble, France
2
CEA-INES, 50 Avenue du Lac Léman, 73370 Le Bourget-du-lac, France
3
Fraunhofer Institute of Wind Energy and Energy System Technology, Kassel, Germany
4
AIT Austrian Institute of Technology, A-1210 Vienna, Austria
Email: van-hoa.nguyen@grenoble-inp.fr
Abstract—Composed of a large variety of technologies and applications with unprecedented complexity, the smart grid, as a
cyber-physical energy system, needs careful investigation of the interactions between the various domains involved, especially the
coupling between the power and information systems. In this paper, two modern ways of modeling and simulating complex cyberphysical energy systems are considered: Co-simulation and PowerHardware-in-the-Loop experiments. An analysis of these two approaches shows that a complementary and joint setup with realistic
behaviors of hardware equipment under a variety of complex environments, co-simulated by several simulators from different domains, create a complete and high performance environment to
achieve a holistic approach for smart grid validation and roll out.
In the scope of coupling these two techniques, major technical challenges are identified and advanced solutions are outlined.
Index Terms—Co-simulation, Cyber-Physical Energy System, Holistic Validation Approach, Power-Hardware-in-theLoop, Smart Grid Systems Testing.

I. INTRODUCTION
A variety of changes and developments have been carried
out during the past, presenting a portfolio of initiatives and
perspectives of different smart grid solutions on European but
also on international level [1]–[3]. In general, an increased
consumption of electricity, with important peak loading due to
electrification of transport is expected [1]. The decarbonized
scenario requires a high penetration of distributed and renewable energy resources, over levels of 15% to 20%, leading to
an increasingly difficulty to ensure the reliable and stable
management of electricity systems [3]. The integration of Information and Communication Technology (ICT) into the
electrical energy infrastructure, along with smart metering is
shifting from demonstration phase to large scale deployment.
This will have a strong impact on system architectures as well
as it raises concerns about cyber-security issue. The electric
power grid integrated with communication systems and distributed energy resources (solar, heat, etc.) has become a
cyber-physical energy system (smart grid) nowadays.
A general framework for smart grid validation and roll out,
which takes into account their mutual interactions and interde-

pendencies, is required. One of the main barriers to this has been
the lack of design and validation tools that are capable of analyzing power and communication systems in a holistic manner.
Extending power system simulation tools for the ICT domain, or vice versa, demands a lot of effort and collaboration
among experts of both areas, because the life cycle and technical specifications of the electrical and communication
equipment (in terms of reliability requirements, round trip time,
determinism, temporal consistency and hierarchy) are significantly different. By creating a so-called co-simulation environment for the integrated analysis of both domains, via means
of ad-hoc connections or in a master/slave fashion, one can understand the impacts of different communication solutions used
for the operation of power systems much better. Although simulation architectures may vary, a co-simulation framework allows in general the joint and simultaneous investigation of
models developed with different tools, in which the intermediate results are exchanged during the execution of the simulations. However, the sub-systems are usually solved independently by their corresponding domain-specific simulators
[4]. Co-simulation allows to have a complete view of both
network behavior and the physical energy system states, while
power system and communication networks are simulated with
the most suitable solver and the calculation loads are shared.
Power-Hardware-in-the-Loop (PHIL) technology is increasingly used by the industry and the research community
for testing hardware components, devices or systems in real
conditions and scale, where a part of the whole test components are simulated in a Digital Real-Time Simulator (DRTS)
[5]. The PHIL approach allows a safe and repeatedly testing of
a device also in faulty and extreme conditions without damaging lab equipment, while providing also flexibility in setting
up the test setup in transient and steady state operation [5], [6].
In the European ERIGrid project, a survey was addressed to
the experts in 12 top European power and energy systems research institutions about mandatory future improvements of
PHIL technology1. 63.6% of the experts stated that the power
and software interfaces in PHIL technology should be im1
81.8% have PHIL testing capacity, in which 54.5% are in welldeveloped/experienced state.

proved to enable the capacity of remote/distributed testing and
integration with co-simulation.
In this paper, we explore the possibilities of integrating
PHIL in co-simulation in order to enable a holistic evaluation
of smart grid solutions (addressing mainly power and ICT
domains). By offering experiments very closed to real situations, this approach provides an important tool during the design, implementation and roll out of smart grid technologies,
solutions and corresponding products. Beyond the added value
to testing methods, this approach would also offer international and multi-laboratory cooperation, which in turn, have a positive impact to interoperability and confidence in the applicability of the researches under different grid conditions.
The main parts of the paper are structured as follows: The
fundamental points of PHIL and co-simulation relevant for
this investigation are outlined in Section II. A general architecture is also proposed in this section. Major technical obstacles towards a seamless integration of PHIL and co-simulation
are discussed in Section III together with possible solutions.
The paper is concluded with a discussion and an outlook about
future research in Section IV.

face (API) is often missing. On top of that, the fundamentally
different concepts behind power and communication systems
are also a challenge; detecting, linking, and handling related
events in both domains can be a complex task (cf. Fig. 1)2:


Power system simulation is usually continuous with the
possibility of detecting events associated to values crossing a certain threshold.
Communication network simulation is based on discrete
events whose occurrence usually unevenly distributed
with respect to time. Corresponding domain-specific simulators provide an event scheduler to record current system time and process the events in an event list.



II. INTEGRATION OF PHIL AND CO-SIMULATION
In this section, we outline the principal elements of the cosimulation and the PHIL approaches followed by a generic architecture for an integration of them.
A. Co-simulation of Power and ICT Systems
Most of the work related to co-simulation in the field of
smart grid solutions is mainly related to the necessity of interconnecting power system and communication network simulations. As the traditional passive electric power grid (with unidirectional power flows) evolves towards and active power system (with bi-directional power flows), the existing energy infrastructure suffer from several drawbacks (fragmented architecture, lack of adequate bandwidth for two-way communication, in-ability to handle the increasing amount of data from
smart devices, etc.) [7]. It is therefore crucial to take the communication network in the development of smart grids – in
terms of efficient topology, latency and security – into account.
Usually, communication networks used in the context of
lab experiments have very low latency due to short geographical distances. This does not reflect real scenarios whereas the
long geographic distance between different, networked devices may cause unexpected delays and signal losses resulting in
an unexpected and faulty control behavior. Therefore, the
communication network is usually separately analyzed using
dedicated software tools in order to study the effect of realistic
latencies, packet losses or failures in the ICT/automation system [8]. Communication simulators facilitate also cybersecurity related investigations, such as denial-of-service protection, confidentiality and integrity testing.
Co-simulation of the power and the communication system
for an integrated analysis of both domains is however, not an
easy task since the synchronizing of both simulation packages
during runtime is required. Moreover, existing simulation tools
are usually provided limited coupling possibilities with external
tools; an adequate and suitable Application Programming Inter-

Fig. 1. Time synchronization between power and communication simulation.

Once an event occurs, the associated information is passed
to the other domain where the other simulator will create the
reaction. A co-simulation framework then has to execute some
algorithms to ensure the synchronous and deterministic execution of both domains simultaneously. Scientists have come up
with various methods and techniques to deal with the synchronization issue. We can classify them into four main synchronization techniques:


“Offline” Co-simulation or “Model Exchange”: In this
approach, the model of power system is exported to Ccodes and then be compiled and imported to the network
simulator for co-simulation. This is usually used as an alternative when direct co-simulation is hard to achieve.
Master-Slave: In this approach, one simulator (usually the
communication simulator, due to discrete timeline) is given
higher priority and will coordinate the co-simulation steps.
Point-based or time-stepped method: The individual simulators run their simulations independently but pause at
fixed synchronization points where information is exchanged between simulators. In this approach, a middleware is normally needed.
Global Event-Driven: In this approach, a global event list
is created by mixing up the power system iteration steps
with the communication network events according to their
timestamps. Then only one simulator is allowed to pro-






2

The software mentioned in the figures is representative and serves only
for illustration purpose, to aide with reader comprehension. They are, by no
mean, suggestion or recommendation from the authors for simulator selection.

ceed at a time and the other will halt. This structure leads
to a limited speed of co-simulation.
A quite general review of existed works on the cosimulation of power and communication systems can be found
in [7]–[9]. Generally, one can acknowledge two different
structures of co-simulation:


Ad-hoc Co-simulation: Most of the work in the literature
falls into this category (usually coupling directly one
power system simulator and one communication network
simulator).
 Co-simulation with Master Algorithm: In this approach, a
master algorithm (e.g., HLA [10]) or a co-simulation framework (e.g., mosaik3, Ptolemy4) will orchestrate the process.
This master algorithm is responsible for synchronizing different timelines of involved simulators and for directing the
information exchange among simulator’s inputs/outputs.
In order to improve interoperability and reusability of the
models developed in co-simulation frameworks mainly two
major standards have been issued: Functional Mockup Interface5 (FMI) and High Level Architecture framework (HLA)
[10]. While FMI is oriented towards model exchange and the
coupling of simulators for co-simulation, HLA provides a kind
of master algorithm to orchestrate the co-simulated processes
(which is addressed as “federate”). It appears that while both
standards serve for co-simulation, FMI and HLA are not exactly at the same level of abstraction. Individually, HLA allows highly parallelized simulations of large-scale systems,
but introduces additional time-synchronization issues [8].
B. Power-Hardware-in-the-Loop Experiments
The high ratio of Distributed Energy Resources (DER) integration in a decarbonized scenario leads also to technical difficulty to preserve the security and reliability of the network
operation and to ensure the fulfilment of the established voltage quality standards [11]. The Hardware-in-the-Loop (HIL)
approach used in the power and energy systems domain is an
efficient testing method for DER devices, for manufacturers to
adapt their products to the increasingly demanding requirements, as well as for network operators and regulation authorities to establish new testing and certification procedures [6],
[11]. In this approach, a real hardware setup for a domain (or
part of a domain) is coupled with a simulation tool to allow
testing of hardware or software components under realistic
conditions. The execution of the simulator in that case requires
strictly small simulation time steps in accordance to the real
time constraint of the physical target. Since the HIL approach
usually involve coupling of different domains, it deals with
quite similar challenges as co-simulation. On the other hand,
HIL provides the advantage of replacing error-prone or incomplete models with real-world counterparts and the possibility of scalable testing in faulty and extreme conditions.
HIL in smart grids is generally classified into Controller
Hardware-in-the-loop (CHIL) and Power-Hardware-in-the3 http://mosaik.offis.de/
4 http://ptolemy.eecs.berkeley.edu/
5 https://www.fmi-standard.org/

loop (PHIL) experiments [5], [6], [11]. CHIL involves in testing of a device (usually a controller) where signals are exchanged between a DRTS and the device under test via its information ports. The interface consists most of the time only
Analogue to Digital and Digital to Analogue converters. In
contrary, PHIL involves in testing a device which absorbs or
generates power (e.g., inverter-based DER). A power interface
is therefore necessary (see Fig. 2). In this paper, the focus is
mainly on the PHIL approach.
D

Power
Amplification

A
A

Measurement

D
DIGITAL
REAL-TIME
SIMULATOR
(DRTS)

HARDWARE-

POWER INTERFACE (PI)

UNDER-TEST

(HUT)

Fig. 2. General architecture of a PHIL experiment.

A general PHIL setup consists of three main elements: (i)
the DRTS, (ii) the Hardware-under-Test (HuT), and (iii) the
Power Interface (PI):






The DRTS computes the simulation model and offer I/O
capacities. As aforementioned, the simulation time-step of
the DRTS must be small enough to reproduce the behavior of the simulated system under dynamic condition (Fig.
3). The simulator allows designing and performing various test scenarios with a great flexibility.
The HuT is usually a wide variety of different DER devices and networks (e.g., inverter-based DER, electric vehicles, smart transformers) or a whole microgrid can also
be tested in a realistic environment.
A PI generally consists of a power amplifier and sensors
that transmit measurements in feedback. It allows the interaction of the virtual simulated system with the HuT.

Computation step
Simulation Clock

n1

n2

n3

n4

t0

t1

t2

t3

t4

Real-time Clock t
0

t1

t2

t3

t4

Fig. 3. Time step restriction of a real-time simulation.

While offering a great flexibility of testing, PHIL requires
serious consideration on stability and accuracy [11]. The introduction of power interface to the test setup creates an additional close loop, which possibly injects errors, time delay,
and distortion that may cause severe instability issues or inaccurate results [12]. Generally, the power amplifier also affects
the magnitude and phase of the signal under amplification.
However, the inserted time delay is the main obstacle that limits the current capacity of PHIL over a large geographical area
or remote PHIL.
The two principal characteristics of a PI in PHIL experiments are the power amplification unit and the interface algorithm. There is a variety of options for power amplifier with

diverse performance characteristics. A review on power amplification units and their topologies can be found in [6].
Comparison of different types of amplifier, as well as recommendations for selection can be found in [13]. In general, the
following three types of power amplifiers are common in
PHIL experiments:


Switched-mode Power Amplification: Commonly used for
small scale PHIL simulation in megawatt range. It is less
expensive but represents a higher level of time-delay and
lower accuracy than the others.
 Generator Type Power Amplification: Is used extensively
for interfacing of balanced three phase grid simulations at
low and medium power range.
 Linear Power Amplification: Is the most suitable aggregate for PHIL applications in small to medium power
range. The linear amplifier has very high dynamic performance, short time delay and less stability issues.
Configuration and impact of the power amplifier (I/O
boundaries, galvanic isolation, short circuit behavior, slewrate, etc.) must be addressed and evaluated to match the specific requirement for each PHIL setup as it strongly influence
the determination of system stability, bandwidth, and the expected accuracy.
The interface algorithm between the DRTS and the hardware part in a PHIL experiment may be either voltage type
(for voltage amplifier) or current type (for current amplifier).
Three commonly employed interface algorithms are:




Ideal Transformer Method (ITM)
Partial circuit duplication method (PCD)
Damping impedance method (DIM)
A complete review of various interface algorithms and
recommendations for selection in PHIL experiments can be
found in [11] and [12].
Offering a wide range of possibilities for validation and testing of smart grid solutions, PHIL simulations still be restricted
by some limitations, mostly due to the technical challenges related to the introduction of a power interface (e.g., simulation of
nonlinearities, studies of high harmonics, stability and power
level of amplifier, accuracy of measurements in transient phase,
bandwidth limitation). The main difficulty towards integration
of PHIL into a holistic validation framework is, inter alia, the issue of signal latency, compensation of loop delay and time synchronization. Besides, the issue of time synchronization also
limits the capacity of PHIL simulation of complex systems. Due
to the aforementioned obstacles, especially the stability issue,
there does not exist any interface or standard which enables interoperable PHIL applications. The harmonization and standardization of PHIL testing is therefore also a topic of common interest to the power and energy domain.
C. Integration of PHIL and Co-simulation
We investigate in this section the possibility of integrating
PHIL technology in a co-simulation framework in a holistic
approach for cyber-physical energy systems. Combining the
strong points of both approaches, we can study multi-domain
experiments with realistic behaviors from hardware equipment

under a variety of complex environments, co-simulated by
several simulators from different domains. It will enable a
complete consideration of electrical grid interconnected with
other domains and is an important contribution to a holistic
approach for smart grid system validation and roll out. A general architecture for this integration is proposed in
Fig. 4.
D
A
A

MASTER ALGORITHM

D

Power
Amplification

Measurement

POWER INTERFACE
CO-SIMULATION

HUT

DRTS
SOFTWARE
INTERFACE

Fig. 4. General architecture for the integration of PHIL and co-simulation.

This architecture also enables the possibility to cooperate
multiple research infrastructures’ resources to actualize collaborated experiments and provides a way to include valuable
knowledge and intelligence of researcher from different domains to study, in a holistic manner, the cyber-physical energy
system (i.e., smart grid). This desired scenario requires however a strong interoperability among partner’s platforms in
various levels.
Most of the current works involving the integration of the
HIL approach into a co-simulation framework use only a direct coupling with the DRTS [14] or a kind of CHIL setup
[15]. Only until recently, scientists have investigated the possibility of extending PHIL beyond laboratory geographical
boundaries, and mostly, for latency tolerant applications, i.e.
monitoring [16]. These developments, along with deeper studies on impact of latency in distributed DRTS [17], would create a technical base to enable the integration of PHIL to cosimulation framework.
III. TECHNICAL CHALLENGES AND PROPOSED SOLUTIONS
In order to run the holistic experiment correctly and seamlessly, the following major technical challenges arise.
A. Data Flow and Concurrency
Within the process of integrating PHIL into co-simulation,
it is crucial to ensure a synchronous data flow among the individual components, as well as the concurrency of the different
simulators. In the general architecture from
Fig. 4, three spots should be considered:
1) Power Interface
Basically, the challenge here is to synchronize and compensate the loop delay in order to stabilize the system and increase the accuracy of the test. The first step should be the selection of an appropriated interface algorithms and corresponding power amplification where recommendations from
[11] and [12] should be considered.
Secondly, a time delay compensation method could be applied, such as introducing phase shifting, low-pass filter to the
feedback signal [18], extrapolation prediction to compensate
for time delays [19], phase advance calibration [20] or multirate real-time simulation [21].

2) Co-simulation Interface
The issue at the co-simulation interface was already addressed in Section II.A. Besides synchronizing time steps of
different simulators using the aforementioned techniques, the
master algorithm or the co-simulation framework has to deal
with the harmonization of the continuous/discrete event timelines of the power/communication interface.
3) Software Interface
On top of that, when integrating with real-time simulation
and PHIL, it is necessary to ensure that the harmonized time
steps should be small enough to be coupled in real-time.
Therefore, the interfacing of real-time and offline simulations
needs to be taken into consideration.
The principle of real-time simulation is presented in Fig. 3.
Offline simulation, on the other hand, may have a simulation
clock speed different to real time clock. Two kinds of nonreal-time simulations are classified (i) slow, and (ii) fast as depicted in Fig. 5.

C. Remote coupling PHIL/Cosimulation
In the context of coupling PHIL and co-simulation for
multi-laboratory experiments, there are scenarios where the
DRTS and offline simulation are geographically separated. In
that case, the latency may accumulate and surpass the limitation of the time synchronization algorithm (see Fig. 6). Moreover, random packet loss due to network congestion outside of
a communication network (e.g., LAN) may alter the information and cause malfunction to the DRTS, as well as any
connected hardware.
Where the command
should be applied

State1

State1'

State2

State2

U0

U0

U0

U1

t0

Real-time Clock t
0

n1 n2

n3

n4

t1

t3

t4

t2
t1

S1

t2

t3

t4

t0

Real-time Clock t
0

t1

n2

n3

n4

t1

t2

t3

t4

t3

Step

TCal

RT2

n1

t2

S2

RT1

State1

Computation step

T2 ≠ T1 + ∆T
Tsim

Non Real-Time Simulation - Fast

Simulation Clock

T1 + ∆T

T1

Computation step
Simulation Clock

does not cover the ICT domain, an interface with the communication simulators must be provided as well.

t4

U1 &
∆T = ∆T1 + ∆T2

WAN Connection
to Partners

State1 &
U1
∆T1

Non Real-Time Simulation - Slow

Fig. 5. Non-real-time simulation types.

The non real-time simulations step, in case of coupling,
has to be adapted to the real-time simulation step, either by delaying the step in case of fast simulation, or increasing the
computation speed in case of slow simulation.
B. Interoperability, Data Model, and System Topology
Besides the above technical challenges, when the experiments involve multiple domains or multi-laboratory, it is required to have a certain degree of interoperability among the
different actors as well as among different elements of the experiments. A common information model or at least a conversion interface is necessary.
In a power system simulation, the exact and proper representation of a system’s topology is critical, proportionally with
scale and complexity. The information model should be capable to represent, encapsulate and exchange static and dynamic
data, as well as, to inform any modification in topology and
current state of the network in real-time and in a standardized
way. It is suggested in [22] that IEC 61970/61968
(CIM/XML/RDF) and OPC UA could be combined to provide
a seamless and meaningful communication among applications and a strong support for multiplatform experiment,
which is capable of transmitting static and dynamic data of
system’s topology in real-time. This combination, however,

Controller

Fig. 6. Delayed application of command due to unexpected latency.

Therefore, in case of coupling PHIL together with cosimulation in geographically distributed experiments, the time
synchronization algorithm must be adapted. The PI compensation has to take into account communication latency.
IV. DISCUSSION AND OUTLOOK
Two modern ways of modeling and simulating complex
cyber-physical energy systems were presented. While cosimulation includes and combines knowledges in various domains in order to consider the system in a holistic manner,
PHIL provides users with the advantage of replacing errorprone or incomplete models with real-world counterparts and
the possibility of scalable testing in faulty and extreme conditions. An analysis of these two tools shows that it will make
sense to combine the strong points of both approaches to study
multi-domain experiments. The advantage is a complementary
and joint setup, benefited from realistic behaviors of hardware
equipment under a variety of complex environments, cosimulated by several simulators from different domains. The
goal is to create a complete and high performance environment to achieve a holistic approach for smart grid validation
and roll out.

Major technical challenges have been identified and some
solutions were suggested. This contribution gives way for further proposals in future developments of coupling PHIL and
co-simulation.

[10]

[11]
[12]

ACKNOWLEDGEMENT
This work is supported by the European Community's
Horizon 2020 Programme (H2020/2014-2020) under project
“ERIGrid” (Grant Agreement No. 654113, www.erigrid.eu).
The work of G2Elab and CEA-INES is also partially supported by the Carnot Institute “Energies du Futur” under the
PPInterop II project (www.energiesdufutur.eu).

[13]

[14]

REFERENCES
[1]
[2]
[3]

[4]

[5]

[6]

[7]

[8]

[9]

International Energy Agency, “Smart Grids in Distribution Network Roadmap development and Implementation,” 2015.
European Network of Transmission System Operators for Electricity,
“Research and development roadmap 2013-2022,” 2013.
European Commission, “Energy Roadmap 2050,” Smart Grid Task
Force, Brussels, Communication from the commission to the european
parliament, the council, the european economic and social committee
and the committee of the regions, 2011.
M. Faschang, F. Kupzog, E. Widl, S. Rohjans, and S. Lehnhoff, “Requirements for Real-Time Hardware Integration into Cyber-Physical
Energy System Simulation,” presented at the Workshop on Modeling
and Simulation of Cyber-Physical Energy Systems (MSCPES), Seattle, WA, USA, 2015.
M. D. O. Faruque, T. Strasser, G. Lauss, V. Jalili-Marandi, P. Forsyth,
and C. Dufour, “Real-Time Simulation Technologies for Power Systems Design, Testing, and Analysis,” IEEE Power Energy Technol.
Syst. J., vol. 2, no. 2, pp. 63–73, Jun. 2015.
G. Lauss, M. D. O. Faruque, K. Schoder, C. Dufour, A. Viehweider,
and J. Langston, “Characteristics and Design of Power Hardware-inthe-Loop Simulations for Electrical Power Systems,” IEEE Trans.
Ind. Electron., vol. 63, no. 1, pp. 406–417, Jan. 2016.
K. Mets, J. . Ojea, and C. Develder, “Combining Power and Communication Network Simulation for Cost-Effective Smart Grid Analysis,”
IEEE Commun. Surv. Tutor., vol. 16, no. 3, pp. 1771–1796, Mar.
2014.
S. C. Mueller et al., “Interfacing Power System and ICT Simulators:
Challenges, State-of-the-Art, and Case Studies,” IEEE Trans. Smart
Grid, vol. PP, no. 99, pp. 1–1, 2016.
L. Weilin and Z. Xiaobin, “Simulation of the smart grid communications: Challenges, techniques, and future trends,” Comput. Electr.
Eng., vol. 40, no. 1, pp. 270–288, Jan. 2014.

[15]

[16]

[17]

[18]

[19]
[20]

[21]

[22]

IEEE Computer Society, “IEEE SA - 1516-2010 - IEEE Standard for
Modeling and Simulation (M&S) High Level Architecture (HLA)-Framework and Rules.” 2010.
E. De Jong et al., “European White Book on Real-Time Power Hardware in the Loop Testing,” DERlab Report R-005.0, 2012.
W. Ren, M. Steurer, and T. L. Baldwin, “Improve the Stability and the
Accuracy of Power Hardware-in-the-Loop Simulation by Selecting
Appropriate Interface Algorithms,” in 2007 IEEE/IAS Industrial
Commercial Power Systems Technical Conference, 2007, pp. 1–7.
F. Lehfuss, G. Lauss, P. Kotsampopoulos, N. Hatziargyriou, P. Crolla,
and A. Roscoe, “Comparison of multiple power amplification types
for power Hardware-in-the-Loop applications,” in 2012 Complexity in
Engineering (COMPENG). Proceedings, 2012, pp. 1–6.
D. Bian, M. Kuzlu, M. Pipattanasomporn, S. Rahman, and Y. Wu,
“Real-time co-simulation platform using OPAL-RT and OPNET for
analyzing smart grid performance,” in 2015 IEEE Power Energy Society General Meeting, 2015, pp. 1–5.
S. Rotger-Griful, S. Chatzivasileiadis, R. H. Jacobsen, E. M. Stewart,
J. M. Domingo, and M. Wetter, “Hardware-in-the-Loop co-simulation
of distribution Grid for demand response,” in 2016 Power Systems
Computation Conference (PSCC), 2016, pp. 1–7.
B. Palmintier, B. Lundstrom, S. Chakraborty, T. Williams, K. Schneider, and D. Chassin, “A Power Hardware-in-the-Loop Platform With
Remote Distribution Circuit Cosimulation,” IEEE Trans. Ind. Electron., vol. 62, no. 4, pp. 2236–2245, Apr. 2015.
R. Liu, M. Mohanpurkar, M. Panwar, R. Hovsapian, A. Srivastava,
and S. Suryanarayanan, “Geographically distributed real-time digital
simulations using linear prediction,” Int. J. Electr. Power Energy
Syst., vol. 84, pp. 308–317, Jan. 2017.
P. Kotsampopoulos, V. Kleftakis, G. Messinis, and N. Hatziargyriou,
“Design, development and operation of a PHIL environment for Distributed Energy Resources,” in IECON 2012 - 38th Annual Conference on IEEE Industrial Electronics Society, 2012, pp. 4765–4770.
W. Ren et al., “Interfacing Issues in Real-Time Digital Simulators,”
IEEE Trans. Power Deliv., vol. 26, no. 2, pp. 1221–1230, Apr. 2011.
A. J. Roscoe, A. Mackay, G. M. Burt, and J. R. McDonald, “Architecture of a Network-in-the-Loop Environment for Characterizing AC
Power-System Behavior,” IEEE Trans. Ind. Electron., vol. 57, no. 4,
pp. 1245–1253, Apr. 2010.
A. Viehweider, G. Lauss, and L. Felix, “Stabilization of Power Hardware-in-the-Loop simulations of electric energy systems,” Simul.
Model. Pract. Theory, vol. 19, no. 7, pp. 1699–1708, Aug. 2011.
V. H. Nguyen, Q. T. Tran, and Y. Besanger, “SCADA as a service
approach for interoperability of micro-grid platforms,” Sustain. Energy Grids Netw., vol. 8, pp. 26–36, Dec. 2016.

