Rui Qiu

This paper proposes a wideband composite sequence impedance model (WCSIM)-based analysis method to evaluate the interactions in power-electronic-based power systems subjected to unbalanced grid faults or with unbalanced loads. The WCSIM-based method intuitively assesses the impact of the small-signal interconnection among the positive-, negative-, and zero-sequence circuits on the interaction stability of unbalanced power systems. The effectiveness of this method is demonstrated using a permanent magnet synchronous generator-based weak grid system under a single-line-to-ground fault (SLGF). Frequency scanning results and controller hardware-in-loop tests validate both the correctness of the WCSIM and the effectiveness of the WCSIM-based analysis method.

Fueled by pressing global climate concerns, the integration of large-scale renewable distributed generation sources, including distributed wind power and photovoltaics, along with electricity substitution loads into the distribution network has been accelerated to diminish carbon emissions. This shift introduces significant challenges and necessitates the advanced operation and control of distribution systems to accommodate these changes effectively. Against this backdrop, there is a growing expectation for an open and scalable central control mode, equipped with compatible interfaces, to offer a visionary development platform for the grid. This platform is anticipated to meet the evolving needs of future distribution system development, ensuring adaptability and forward compatibility. The aforementioned platform requires open, scalable, and interface-compatible models of key distribution network equipment as its foundation. To address the challenges presented, this paper proposes a data–physics-driven modeling approach for automating simulations in distribution systems. This method employs a simplified and standardized system of linear differential equations with undetermined coefficients to capture the common physical characteristics of specific device types. The models designed through this approach are notably open, allowing for real-time data to refine undetermined coefficients and accurately depict the dynamic behavior of equipment over various periods. Their scalability also stands out, rendering them apt for large-scale distribution network simulations. The paper elaborates on models for distributed photovoltaic, wind turbine, energy storage, and electric vehicle, and demonstrates their application within an IEEE-33 node distribution network topology built on Python.