Utilizing distributed energy resources (DERs) within active distribution networks (ADNs) for primary frequency regulation (PFR) is of great value to power system frequency safety and stability. Nevertheless, the frequency support capability (FSC) of ADN is constrained by both the capacities of DERs and the carrying capacity of network power flow (PF). To ensure PF security, assessing the FSC of ADNs before PFR services is essential. However, the lack of line parameters in low-voltage ADNs results in inaccuracy in physics-based assessment models. Furthermore, the multitude of nodes and DERs contributes to the high-dimensional nonlinearity of PF model in ADNs, rendering optimization problems challenging to solve analytically. Therefore, this article proposes a data-driven online assessment method for FSC of ADNs, which uses the Koopman operator to augment dimension of partial state space, converts the original complex nonlinear PF into a high-dimensional linear model, and then constructs a readily solvable linear assessment optimization model. On this basis, considering the interactive characteristics between the FSC of bulk grid and ADNs, an alternating iterative method is proposed to precisely evaluate the maximum FSC of ADNs. A modified IEEE 82-node system is used to verify the effectiveness and feasibility of the proposed method. Numerical results indicate that the proposed method enhances computational efficiency by an average of 99.34% compared to traditional physics-based methods, achieving nearly 100% PF capacity utilization during the PFR process.

The DC-DC converter severs as one of the crucial components in DC microgrid applications. In this paper, a novel low frequency current-mode control strategy is proposed to improve overshoot suppression performance for the boost converter against load current and source voltage disturbances. The control strategy is presented as a cascade dual-loop structure composed of a dynamic current-loop controller with asymmetric saturation and a PI form voltage-loop controller. With the low frequency information of disturbances, the designed dynamic current-loop controller delivers not only promising disturbance suppression but also superior reference current tracking. Simulation and experiment results are provided to illustrate the superiority of the proposed strategy on overshoot suppression.

To reveal the dynamic stability mechanism of DC systems connected to weak grids in DC voltage control timescale from the perspective of equivalent circuit, a reduced-order model is proposed in this paper. The DC voltage control unit can be modeled as an equivalent RLC parallel circuit, by which the influence of DC voltage control dynamic and its interaction with phase locked loop (PLL) control on system dynamic stability in weak grid condition can be characterized with corresponding equivalent resistors and inductors quantitatively. In addition, the equivalent circuit reduced-order model of the whole system can be obtained, and the essential cause of the system dynamic stability in DC voltage control timescale can be uncovered intuitively. Eventually, the proposed reduced-order model is verified based on a back-to-back DC system connected to a weak grid established in PSCAD/EMTDC, the impact of system parameters on system dynamic stability can be characterized by dynamic indexes (damping coefficient and damped oscillation frequency) in a straightforward approach.

Virtual synchronous generator (VSG) control can improve the system inertia and frequency stability of microgrids including diesel generator and high penetration of renewable energy sources efficiently. In most of existing works, VSG parameters (such as effective damping and inertia) are always designed with known frequency dynamic of diesel generator. However, in most real applications, we cannot get the actual inertia and primary control parameters of diesel generator due to some reasons. Thus, how to apply appropriate VSG control in microgrids with unknown frequency dynamic of diesel generator has become the main motivation of this paper. A frequency dynamic identification method of diesel generator based on inertia and primary control parameters estimation is proposed firstly. Then, with the identified frequency dynamic of diesel generator, the frequency analysis model of the entire microgrid can be obtained, and the control parameter design optimization of VSG can be realized in practical microgrids to enhance the frequency stability. Finally, the proposed method and VSG control performance has been verified in a real experimental platform consisting of a 50kVA diesel generator and an energy storage system with a 50kVA DC-AC converter.

Low-frequency dynamic (LFD) (around 1~10Hz) of PLL-synchronized weak grid linked voltage source converter (WG-VSC) has been observed and dominated by the "outer loop-PLL-weak grid" interaction. To provide a clear insight to LFD of WG-VSC, a PLL-equivalent model is proposed in this paper. Firstly, a generic LFD analysis model is derived, which contains PLL and a ‘outer loop-WG' part which integrating outer loop dynamics, grid strength and VSC operation point. Then, this complicated ‘outer loop-WG' part is divided into two parallel loops, representing the influence of active side outer loop and reactive side control on LFD of WG-VSC respectively. Furthermore, a reduced PLL-equivalent model which can maintain sufficient accuracy for LFD analysis of WG-VSC is derived by simplifying the ‘outer loop-WG' part into a firstorder loop and combining original PI controller of PLL. With this model, comprehensive theoretical analysis for LFD instability mechanism has been carried out. The effectiveness of PLL-equivalent model and accuracy of analysis results are verified by time-domain simulations in PSCAD/EMTDC.

Distributed cooperative control strategies of DC microgrids (DCMG) reduce the detriment of communication delays, packet loss and link failure compared with centralized control. However, they are vulnerary to cyber-attacks. The operating objectives can be deviated by false data. Firstly, the adverse effects of false data are explained and modeled. A data-driven strategy based on linear regression is then proposed to remove the false data by offline learning and online judging of the transient process in DCMG without affecting the dynamic response. It successfully solves the problem of parameter selection of resilient control. Finally, the detection strategy is verified by detailed time-domain simulation.