Distributed generators (DGs) can be connected to loads in practical islanded microgrids (MGs) using a wide variety of feeder impedances. Mismatched line impedance and various nonlinear load limitations present challenges for the technology that is currently used for controlling distributed generators in microgrids with complex architectures. This study presents a method for generating harmonic virtual impedance as a response to the issues that have been described above. This method lowers the harmonic voltage at the point of common coupling (PCC), which enables harmonic current sharing at the same time. The approach can be broken down into two distinct components. First, the constraints of harmonic current sharing are formulated according to the topology of the microgrid. Next, the optimal harmonic virtual impedance value is determined through the optimization algorithm, which initially reduces the harmonic voltage of the PCC. After that, the disruption of the PCC harmonic voltage is corrected by voltage compensation block, which leads to an even more significant improvement in the voltage quality. In conclusion, the applicability and efficiency of the method are demonstrated through the use of experimental works.

In naval direct current (DC) microgrids, pulsed power loads (PPLs) are becoming more prominent. A solar system, an energy storage system, and a pulse load coupled directly to the DC bus compose a DC microgrid in this study. For DC microgrids equipped with sonar, radar, and other sensors, pulse load research is crucial. Due to high pulse loads, there is a possibility of severe power pulsation and voltage loss. The original contribution of this paper is that we are able to address the nonlinear problem by applying the Takagi-Sugeno (TS) model formulation for naval DC microgrids. Additionally, we provide a nonlinear power observer for estimating major disturbances affecting DC microgrids. To demonstrate the TS-potential, we examine three approaches for mitigating their negative effects: instantaneous power control (IPC) control, model predictive control (MPC) formulation, and TS-MPC approach with compensated PPLs. The results reveal that the TS-MPC approach with adjusted PPLs effectively shares power and regulates bus voltage under a variety of load conditions, while greatly decreasing detrimental impacts of the pulse load. Additionally, the comparison confirmed the efficiency of this technique.

Accurate photovoltaic (PV) power prediction has been a subject of ongoing study in order to address grid stability concerns caused by PV output unpredictability and intermittency. This paper proposes an ultra-short-term hybrid photovoltaic power forecasting method based on a dendritic neural model (DNM) in this paper. This model is trained using improved biogeography-based optimization (IBBO), a technique that incorporates a domestication operation to increase the performance of classical biogeography-based optimization (BBO). To be more precise, a similar day selection (SDS) technique is presented for selecting the training set, and wavelet packet transform (WPT) is used to divide the input data into many components. IBBO is then used to train DNM weights and thresholds for each component prediction. Finally, each component's prediction results are stacked and reassembled. The suggested hybrid model is used to forecast PV power under various weather conditions using data from the Desert Knowledge Australia Solar Centre (DKASC) in Alice Springs. Simulation results indicate the proposed hybrid SDS and WPT-IBBO-DNM model has the lowest error of any of the benchmark models and hence has the potential to considerably enhance the accuracy of solar power forecasting (PVPF).