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.

Faults and unintended conditions in grid-connected photovoltaic systems often cause a change of the residual current. This article describes a novel machine learning based approach to detecting anomalies in the residual current of a photovoltaic system. It can be used to detect faults or critical states at an early stage and extends conventional threshold-based detection methods. For this study, a power-hardware-in-the-loop approach was carried out, in which typical faults have been injected under ideal and realistic operating conditions. The investigation shows that faults in a photovoltaic converter system cause a unique behaviour of the residual current and fault patterns can be detected and identified by using pattern recognition and variational autoencoder machine learning algorithms. In this context, it was found that the residual current is not only affected by malfunctions of the system, but also by volatile external influences. One of the main challenges here is to separate the regular residual currents caused by the interferences from those caused by faults. Compared to conventional methods, which respond to absolute changes in residual current, the two machine learning models detect faults that do not affect the absolute value of the residual current.

The decommissioning of conventional power plants and the installation of inverter-based renewable energy technologies decrease the overall power system inertia, increasing the rate of change of frequency of a system (RoCoF). These expected high values of RoCoF shorten the time response needed before load shedding or generation curtailment takes place. In a future scenario where renewables are predominant in power systems, the ability of synchronous machines to meet such conditions is uncertain in terms of capacity and time response. The implementation of fast power reserve and synthetic inertia from inverter-based sources was assessed through the simulation of two scenarios with different grid sizes and primary reserve responses. As main results it was obtained that the full activation time for a fast power reserve with penetration above 80% of inverter-based generation would need to be 100 ms or less for imbalances up to 40%, regardless of the synchronous response and grid size, meaning that the current frequency measurement techniques and the time for fast power reserve deployment would not ensure system stability under high unbalanced conditions. At less-unbalanced conditions, the grid in the European scale was found to become critical with imbalances starting at 3% and a non-synchronous share of 60%.

The expansion of renewable energies, the growing number of consumers, e. g., in e-mobility, and their connection to low voltage grids pose new challenges for grid operation, particularly regarding voltage control and energy losses. In this context, comprehensive knowledge about the surrounding grid facilitates the intelligent control of photovoltaic (PV)-battery systems, creating a need to observe grid participants. In that regard, a previously developed load recognition method is successfully applied in a use case to recognize two electric vehicles and a heat pump in a simulative grid environment, yielding an accuracy of around 96%. Considering to couple that method with a subsequent control algorithm, an approach is developed to estimate a virtual voltage signal, which represents the grid voltage without the impact of control actions. It is validated that this enables the effective application of load recognition in control conditions. Finally, it is demonstrated how to integrate the load recognition method into a control setup using the virtual voltage concept. The corresponding control algorithm manages the power flow between a PV-battery system and the grid in conjunction with a static Q(U) algorithm. It leverages the information about active loads with the objective of maintaining a stable and balanced grid voltage and optimizing energy consumption in the example use case. Thereby, the voltage deviation is reduced by around 26% and the grid unbalance by approximately 38%, compared to only using the Q(U) algorithm without a battery. It can be concluded that the load recognition method can successfully gather information from the surroundings of a grid node even in control applications.

Modern electrical grids are evolving into smart, decentralized systems with high integration of distributed energy resources, bidirectional power flows, and real-time control complexity, motivating the need for virtual modeling frameworks. This paper presents a workflow for building and experimentally validating a high-fidelity digital twin of a laboratory-scale reference grid implemented in real hardware. The grid consists of one 100 kVA supply terminal and six 30 kVA load terminals interfaced through a Speedgoat real-time system and monitored via DEWESoft acquisition tools. The digital twin is implemented in MATLAB/Simulink using fixed-step integration and is iteratively refined. Starting from nominal parameters, the model is calibrated by incorporating measured cable impedances and parasitic capacitance derived from the hardware’s behavior. For validation, five representative load profiles are defined and applied in both the simulated and physical grid environments, including static, dynamic, harmonic, and asymmetric conditions. In addition, a coordinated multi-terminal scenario applies varied profiles simultaneously across all six terminals, further confirming the twin’s robustness. The voltage behavior from the hardware is closely reproduced in simulation, with RMS deviations of less than 0.5 V across all scenarios. The twin’s accuracy is demonstrated across multiple loading scenarios, including asymmetrical and harmonics-rich conditions, where it reliably reproduced per-phase voltage dynamics and neutral current behavior observed in hardware. The results confirm that calibrated digital twins can improve development efficiency and reliability in grid research by reducing physical iteration cycles and enabling safe, repeatable evaluation of grid behavior under complex and dynamic conditions.