Development and real-world implementation of a Digital Twin-enhanced supervisory system for RES-green hydrogen microgrid

• Digital twin of a smart microgrid based on RES and green hydrogen is implemented. • DT-enhanced SCADA system enables real-time visualization of results and estimations. • Design and implementation of mathematical models are described in detail. • Experimental results are obtained on a real-world physical microgrid. • DT suitability is confirmed with R 2 values ranging between 0.88 and 0.98. The Digital Twin (DT) paradigm is being investigated in several scopes, but its real-world application is still scarce and, hence, under development. DT of Renewable Energy Sources (RES) and green hydrogen facilities are specially limited, receiving increasing research efforts from industry and academy. To contribute to the effective advancement of DT, this paper presents the design, development and implementation of a DT-enhanced Supervisory Control and Data Acquisition (SCADA) system for a real-world microgrid which integrates RES and green hydrogen. The DT is developed using well-established mathematical models of the microgrid main components (photovoltaic (PV) generator, Proton Exchange Membrane (PEM) electrolyzer (EL) and fuel cell (FC)) and exchanges data with the physical counterparts through an industrial automation and data acquisition system. Namely, a set of Programmable Logic Controllers (PLC) collect data from sensors and transmit them via a communication network. Taking advantage of DT capabilities for monitoring purposes, the versatile software LabVIEW is applied to implement the DT-enhanced SCADA system. Experimental results under real operation are reported to prove the successful implementation of the microgrid supervisory system, employing statistical metrics such as RMSE, MAE, MAPE and R 2 to verify the suitability of the implemented DT. As evidence of this analysis, the DT PV generator, DT PEM EL, and DT FC present an R 2 of 0.982, 0.899, and 0.888, respectively. This paper addresses the gap between theoretical or simulated DT frameworks and real-world implementations by introducing a novel approach that incorporates real-world data, details on software for DT implementation and visualization, and technical insights into automation, communication, and data acquisition technologies—limitations often overlooked in previous studies.