experimental practice. By mapping each platform to specific curricular modules, such as power systems,

renewable energy integration, control engineering, and optimization, we demonstrate their capacity to foster

experiential learning and strengthen competencies in modeling, system analysis, and real-time implementation.

Case examples illustrate how students can engage in project-based learning by designing controllers, analyzing

grid fault responses, or optimizing microgrid performance using digital twin frameworks. Beyond enhancing

local curricula, these platforms enable global knowledge exchange through open-source models, remote

laboratory access, and international collaborative initiatives. This work contributes to educational research by

showing how cutting-edge laboratory infrastructures can enrich pedagogy, cultivate practical engineering

interdisciplinary and system-level challenges of modern power systems. As a result, experiential and project-

based learning has gained prominence, in line with broader STEM education trends emphasizing active

learning, competency-based training, and industry-aligned curricula [3][4]. Within engineering education

research, frameworks such as CDIO (Conceive–Design–Implement–Operate) and experiential learning theory

have highlighted the importance of authentic, hands-on activities that foster not only technical proficiency but

also problem-solving, teamwork, and innovation [5].

To support this paradigm shift, many research institutions have developed advanced experimental platforms.

Examples include grid emulators, which replicate real grid conditions for testing distributed generation;

microgrid platforms, enabling studies on hybrid renewable energy systems; impedance emulators, allowing

hardware-in-the-loop testing of dynamic power interactions; and digital twin models, which bridge physical

However, the integration of such infrastructures into teaching and training activities remains limited. In many

cases, the facilities are primarily designed for research objectives, making them complex, costly, and

inaccessible for routine student use. Moreover, while simulation tools are widely available in curricula, they

often lack the realism and constraints that experimental platforms provide. Consequently, there exists a gap

between research-grade infrastructures and their adaptation for pedagogy, which limits opportunities for

students to engage with authentic, hands-on experiences that mirror real-world challenges.

This paper addresses this gap by examining how advanced laboratory infrastructures can be repurposed and

adapted for educational contexts [10][11]. Specifically, it focuses on the pedagogical potential of grid and

impedance emulators, microgrid platforms, and digital twin environments. The aim is twofold: first, to

demonstrate how these platforms can enhance student learning by bridging theory and practice through

global knowledge exchange.

The paper is organized as follows. Section 2 presents the developed laboratory platforms, including the grid

emulator, microgrid platform, and digital twin environment. Section 3 discusses their pedagogical integration,

teaching methodologies, and example assignments. Section 4 highlights educational outcomes and assessment

strategies, while Section 5 addresses global knowledge exchange through remote access and international

collaboration

In our laboratory, several experimental test benches have been designed and implemented to support both

advanced research and student training in power and energy engineering. These platforms combine hardware,

real-time control, and digital monitoring, providing an environment where theoretical concepts can be directly

The grid emulator reproduces different voltage, frequency, and fault conditions of real distribution networks. It

enables students to study grid-connected renewable energy systems, analyze voltage stability, and evaluate the

impact of disturbances. By exposing learners to realistic scenarios, the emulator bridges the gap between

classroom simulations and real-world grid dynamics.

The experimental setup presented in Figure 1 is composed of a power stage and a control stage. The power

stage consists of a 4 kVA isolation transformer, a 4 kVA autotransformer for reduced-voltage testing, an L

filter (20 mH, 3 Ω), two Semikron Semi-Teach converters (AC/DC and DC/AC), an LCL low-pass filter, and a

4 kW variable resistive load adjustable in 5% increments. The LCL filter includes converter-side inductors (4

mH, 20 A), grid-side inductors (2 mH, 20 A), and capacitors (30 µF, 400 V). The control stage incorporates

Figure 1. Architecture of the Grid Emulator for Educational Use

deficits during autonomous operation and for supporting transient load profiles, owing to its high energy

density and adequate power capability.

The experimental setup installed in the laboratory consists of PV panels with a total capacity of 3.12 kWp and

battery storage rated at 200 Ah. The platform also includes emulated loads, comprising both a variable resistive

load and a variable inductive load, each rated at a maximum power of 4 kVA [23].

Figure 2. Microgrid Test Platform with Renewable and Storage Units

The digital twin environment integrates real-time data acquisition from physical test benches with a virtual

simulation layer, creating a cyber-physical framework for advanced experimentation. It allows students to

deploying new algorithms directly on the physical system. Unlike traditional test systems that rely solely on

physical components, the digital twin serves as a virtual counterpart, enabling flexible, risk-free testing,

scenario analysis, and performance optimization [24][25][26][27].

As illustrated in Figure 3, the digital twin structure operates by continuously collecting and storing asset data,

which is then analyzed to optimize the energy management system (EMS) algorithms until predefined accuracy

metrics are achieved. The dataset includes historical records obtained under various operating conditions and

meteorological scenarios, covering a wide spectrum of load profiles. Current measurements are acquired via

dedicated sensors, transferred through a data acquisition system, and processed in LabVIEW for real-time

monitoring and interface with the virtual environment.

Each laboratory platform offers opportunities to address key topics in engineering education:

To illustrate practical applications, the following course assignments can be implemented using the laboratory

platforms:

acquire.

transversal skills:

problem-solving skills, preparing students to operate in multidisciplinary engineering environments

potential for international collaboration and knowledge dissemination. By leveraging open-source tools, digital

twins, and remote-access capabilities, these infrastructures can support a broader community of learners and

educators worldwide.

The digital twin environment and software interfaces of our test benches enable remote operation and

experimentation, allowing students and researchers from different institutions to interact with physical systems

virtually. Open-access models, simulation scripts, and experimental protocols promote reproducibility,

transparency, and adaptability. This approach extends the reach of advanced laboratory setups to institutions