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A Modular IoT-Enabled Remote Laboratory Platform for Hybrid Energy System Research and Engineering Education
Lamine Chalal, Louis Olivier, Pierre Liennard, Allal Saadane, Ahmed Rachid
TL;DR
Conventional laboratories limit access because of cost, safety, scheduling, and geographic constraints. This paper develops and validates a modular IoT-enabled remote laboratory for hybrid energy systems, finding close local–remote behavior with modest energy-balance differences, while its cloud loop is not strictly real-time.
Problem
Conventional laboratories have limited accessibility because of equipment cost, safety constraints, scheduling, maintenance, and geographic barriers.
Method
The paper develops a modular hybrid-energy remote laboratory combining industrial PLC and IoT infrastructure with renewable, storage, and load emulators and three remote interfaces.
Results
Local and remote executions under identical operating conditions showed modest energy-balance differences, preserving the overall behavior of the hierarchical energy-management algorithm.
Takeaways & Limitations
The platform supports supervisory experimentation, controller validation, project-based learning, and collaborative research in hybrid energy systems.
Takeaways & Limitations
The cloud-based remote control loop is not intended for strictly real-time applications; typical one-way delays are on the order of 100 ms.
Abstract
from arXiv · showhide
Remote laboratory systems improve accessibility in engineering education and research by enabling Internet-based interaction with physical equipment. This paper presents a modular IoT-enabled remote laboratory platform for hybrid energy system studies, combining renewable energy emulators, battery storage, and programmable loads within a three-interface architecture based on a web HMI, TIA Portal, and MATLAB/Simulink, all connected through a Talk2M VPN cloud. An industrial PLC and IoT gateway provide deterministic local control as well as secure remote access and monitoring. A hierarchical energy-management algorithm is validated by comparing local and remote executions under identical wind and irradiance profiles. The results show small differences in the energy balances of the renewable sources, battery, and load, while typical communication delays are on the order of 100 ms. Consequently, the platform supports research-grade remote experimentation and project-based learning in control and energy systems engineering.
1. INTRODUCTION
Remote laboratories address the limited accessibility of conventional, safety-sensitive facilities, while remaining underdeveloped for collaborative research. This paper introduces a modular IoT-enabled platform for hybrid energy systems that combines industrial control, secure remote access, and multi-interface experimentation.
- Conventional laboratories are constrained by equipment cost, safety, scheduling, maintenance, and supervision requirements, especially for high-power or high-voltage systems.
- Remote laboratories and digital twins enable safe, repeatable, geographically independent interaction with physical equipment for education and research.
- RL adoption progressed through initial growth, stagnation, rapid COVID-19-era expansion, and recent normalization, but collaborative research applications remain less developed.
- The proposed platform combines renewable emulators, storage, adjustable loads, power-electronics interfaces, PLCs, and IoT sensors for remote control and near-real-time supervision.
- The platform targets research-grade hybrid microgrid operation, unlike earlier educational systems focused on low-power photovoltaic/thermal experiments.
- Its contributions include a modular PLC–IoT architecture, local-versus-remote validation of hierarchical energy management, and support for learning and collaborative research.
2. RELATED WORK AND LITERATURE REVIEW
Remote laboratories have evolved from educational setups into more complex IoT- and cloud-connected infrastructures, but research-oriented platforms still face interoperability, scalability, and accessibility limitations.
- Early remote laboratories established shared engineering-education infrastructure and reduced costs through multi-institutional access.
- Most RL architectures integrate user interfaces, hardware control layers, and communication software to support Internet-based interaction with physical equipment.
- IoT- and cloud-connected platforms improve accessibility through low-cost microcontrollers, lightweight protocols, remote monitoring, and web dashboards.
- These low-cost platforms remain primarily oriented toward low-power teaching experiments rather than research-grade hybrid microgrid applications.
- Research-oriented platforms use MATLAB/Simulink, SCADA, or PLC controllers but often face interoperability, remote scalability, and multi-interface accessibility issues.
- Table 1 compares representative remote laboratory platforms with the system proposed in this work.
3. PLATFORM OVERVIEW AND HARDWARE CONFIGURATION
The platform is a modular laboratory-scale hybrid energy system combining real industrial devices with programmable renewable and battery emulators. Its hierarchical interfaces support progressive access from monitoring and supervision to PLC programming, research experimentation, and digital-twin validation.
- Platform architecture: The laboratory uses independent plug-and-play functional units and a relay matrix for dynamic reconfiguration, topology switching, and hardware integration or upgrades.
- Hardware configuration: Real PLC, HMI, IoT gateway, smart meters, relay matrix, and communication infrastructure are combined with programmable PV, wind, and battery emulators.
- Hardware configuration: The emulator-based design provides safe, repeatable conditions independent of weather variability while preserving realistic control and hardware-in-the-loop interactions.
- Experimental scope: The system supports on-grid and off-grid experiments involving converter control, renewable-source prioritization, and energy-management strategies.
- Multi-interface control: Three access levels connect web supervision, IEC 61131-3 PLC programming, and MATLAB/Simulink research interfaces through unified PLC and IoT-gateway control.
- Educational and research access: The hierarchical design enables progressive skill development while preserving a single unchanged physical setup for supervision, advanced control, and digital-twin validation.
- Educational and research access: Semester-long remote student projects reported high satisfaction and perceived realism, with learning outcomes covering energy management, PLC programming, industrial communications, and digital twins.
- Operational support: A numerical model supports offline algorithm validation when the experimental setup is occupied, while session controls, watchdogs, and PLC protections safeguard remote operation.
4. REMOTE EXPERIMENTATION ARCHITECTURE AND VALIDATION STRATEGY
The platform validates hierarchical hybrid-microgrid control by comparing local and remote executions under identical renewable profiles. Remote operation closely preserves local behavior, while communication latency remains a boundary for time-critical control.
- Control strategy: The hierarchical controller selects PV-dominant, wind-dominant, and battery-support modes to prioritize renewable generation and maintain load supply.The battery buffers surplus generation by charging and supports the load when renewable generation is insufficient, subject to state-of-charge constraints.
- Validation strategy: Identical local and remote experiments used the MATLAB/Simulink interface under the same wind-speed and irradiance profiles.Power profiles and integrated energy balances were recorded for renewable sources, the battery, and the load.
- Validation results: Remote trajectories closely followed local trajectories across wind-, PV-, and battery-dominated phases, while maintaining load supply and smooth mode transitions.The comparison included renewable sources, battery, load, and operating modes from the sequential control algorithm.
- Energy balance: 18.07 Wh locally versus 18.79 Wh remotely was delivered by renewable sources, with deviations of 4.0% for renewable sources, 1.4% for the battery, and 2.6% for the load.Battery balances were -16.66 Wh locally and -16.43 Wh remotely; load consumption was 25.89 Wh locally and 26.55 Wh remotely. Negative battery values indicate net discharging.
- Communication boundary: Typical one-way remote communication delays were on the order of 100 ms, with a maximum observed latency peak of about 4 s.The cloud-based loop performed adequately for the reported supervisory and validation experiments but is not strictly real-time and may limit more time-critical closed-loop applications.
5. CONCLUSION AND FUTURE WORK
The platform supports engineering education and collaborative research through modular IoT-enabled remote experimentation. Future work targets lower-latency connectivity, broader experiments, richer digital-twin collaboration, advanced control, and electric-vehicle applications.
- Conclusion: The platform supports engineering education and collaborative research for hybrid energy systems.Its conclusion identifies supervisory experimentation, controller validation, and project-based learning as supported uses.
- Conclusion: Comparative experiments under identical operating conditions showed modest differences between local and remote execution.The conclusion presents these results as confirming suitability for supervisory experimentation and controller validation.
- Future work: Future work includes lower-latency communication, more diverse experimental scenarios, and extensions of the digital-twin framework.The paper also proposes richer collaborative studies, advanced control strategies, and electric-vehicle-oriented applications.