In this paper, to address the problem of multiple actuator faults in autonomous AC microgrids, a cooperative fault-tolerant control (CFTC) algorithm is designed. The proposed algorithm is responsible for the potential heterogeneous faults in units’ actuators, like loss of effectiveness (LOE) and unknown bias faults. Furthermore, the communication network is considered as a directed switching graph, which can be viewed as an unreliable network and makes the CFTC problem more challenging. Also, in addition to the distributed generation (DG) units, we consider the presence of distributed energy storage (DES) systems that operate in the islanded microgrid. In addition to the voltage and frequency regulation, it is required to balance the state of charge (SoC) of DESs to achieve accurate active power-sharing. Finally, to evaluate the performance of the proposed CFTC algorithm, simulation studies are carried out on a test microgrid system in MATLAB/Simulink environment, and the results are compared with several previously-reported methods. Simulation results and comparison with other previously-reported techniques reveal the effectiveness and accuracy of the proposed method in regulating microgrid voltage and frequency, balancing of SoC, and providing accurate proportional active power-sharing.

Investigations of the dynamic behaviour of power electronic components integrated into electric networks require suitable and established simulation methodologies. Real-time simulation represents a frequently applied methodology for analyzing the steady-state and transient behavior of electric power systems. This work introduces a guideline on how to model power electronics converters in digital real time simulators, taking into account the trade-off between model accuracy and the required computation time. Based on this concept, possible execution approaches with respect to the usage of central processing unit and field-programmable gate array components are highlighted. Simulation test scenario, such as primary frequency regulation and low voltage ride through, have been performed and accuracy indices are discussed for each implemented real-time model and each test scenario, respectively. Finally, a run-time analysis of presented real-time setups is given and real-time simulation results are compared. This manuscript demonstrates important differences in real-time simulation modelling, providing useful guidelines for the decision making of power engineers.

Low‐inertia power systems suffer from a high rate of change of frequency (ROCOF) during a sudden imbalance in supply and demand. Inertia emulation techniques using storage systems, such as flywheel energy storage systems (FESSs), can help to reduce the ROCOF by rapidly providing the needed power to balance the grid. In this work, a new adaptive controller for inertia emulation using high‐speed FESS is proposed. The controller inertia and damping coefficients vary using a combination of bang–bang control approaches and self‐adaptive ones, to simultaneously improve both the ROCOF and the frequency nadir. The performance of the proposed adaptive controller has been initially validated and compared with several existing adaptive controllers by means of offline simulations, and then validated with experimental results. The proposed controller has been implemented on a real 60 kW high‐speed FESS, and its performance has been evaluated by means of power hardware‐in‐the‐loop (PHIL) testing of the FESS in realistic grid conditions. Both simulations and PHIL testing results confirm that the proposed inertia emulation control for the FESS outperforms several previously reported controllers, in terms of reducing the maximum ROCOF and improving the frequency nadir during large disturbances.

A hybrid Energy Storage Systems (ESS) consists of two or more energy storage technologies, with different power and energy characteristics. Using a hybrid ESS, both high-frequency and low-frequency power variations can be addressed at the same time. For an accurate sizing of a hybrid ESS, the use of high-resolution data is required. However, high-resolution data over long periods leads to large data sets, which are difficult to handle. In this paper, an improved motif discovery algorithm is introduced to find the most recurring daily consumption patterns within the time series of interest. The most recurring pattern is selected as the representative of the time series for sizing the hybrid ESS. Next, a simple optimization framework is proposed for selecting the cut-off frequency of a low-pass filter, used for allocating the power to different storage technologies. Finally, the proposed sizing approach is applied for sizing a hybrid battery-flywheel ESS at four different low voltage distribution grids in southern Germany using real measurement data. It is demonstrated that a hybrid ESS, with the characteristics derived from the most recurring patterns only, can effectively provide their intended grid services for most of the days during the whole period of the time series.

A flywheel and lithium-ion battery's complementary power and energy characteristics offer grid services with an enhanced power response, energy capacity, and cycling capability with a prolonged system lifetime. Real-time power management and considering storage components' state of charge (SoC) and ramp rate are crucial for optimizing performance. However, there is a need for further improvements in SoC correction techniques and rigorous, realistic testing to ensure accurate and efficient management strategies. This article proposes a Moving Average (MA) and fuzzy logic-based power management for a Hybrid Flywheel and battery energy storage system that optimally share the power among the two technologies, considering the flywheel's SoC and the battery's ramp rate as the most concerning variable of each technology. The system minimizes SoC and ramp rate imbalances by integrating MAs for dynamic energy demand adjustments and fuzzy logic controllers for efficient power redistribution. A power Hardware-in-the-Loop experimental validation utilizing a 120 kW, 7.2 kWh flywheel-based energy storage system coupled with a simulated battery demonstrates improved SoC correction and ramp rate management performance.