Towards Next-Generation Power Systems Dominated by Inverter-Based Resources: Modeling, Control, and Stability Analysis

The growing integration of inverter-based resources (IBRs) into existing power systems brings significant advantages for improved sustainability. However, it also presents inevitable challenges, such as insufficient inertia, potential instability, and complex network dynamics, among others. The stability analyses for conventional power systems often assume bulk power grids and ignore the detailed dynamics of generators and networks. However, these assumptions no longer hold due to the increasing complexity of next-generation power systems dominated by extensive IBRs. To address the aforementioned challenges, this dissertation presents a set of approaches with a focus on enhancing the modeling, control, and stability analysis of IBR-dominated power systems.

Firstly, a modular and highly scalable small-signal modeling and stability analysis framework is proposed to capture full-spectrum dynamics from generators, loads, networks, and their coupled interactions. An extended power flow algorithm based on the Newton-Raphson method, along with IBR control systems, is further put forward to provide the varying steady-state operating points for small-signal modeling. The computational efficiency is improved by adopting matrix operations and algorithm optimization. Moreover, the proposed framework can be used to indicate the stability performance in terms of stability margin and minimum damping ratio, and identify the oscillation mechanisms through eigenvalue analysis, modal analysis, and participation factor analysis.

Secondly, a bi-level hierarchical control scheme and a mode transition strategy are proposed for IBR-dominated hybrid power plants (HPPs). At the plant level, the multiple operation modes are achieved by integrating into the inverter secondary controllers as separate compensatory terms. At the converter level, the droop control is implemented as the grid-forming (GFM) function, and a coordination strategy between photovoltaics (PVs), batteries, and inverters is developed to satisfy the grid-side demand under various conditions. Furthermore, the operation modes, as an additional control degree, are flexibly adjusted to augment the system stability margin and increase the damping ratio. By comparing the existing methods, the proposed approach exhibits a smaller overshoot, shorter settling time, and larger stability margin.

Finally, the accelerated region-based small-signal stability analysis and small-signal stability constrained power flow optimization approaches are proposed by using data-driven techniques. Kernel ridge regression (KRR) is employed to estimate the stability boundary and to enable real-time stability assessment. By comparing with the conventional point-by-point stability analysis, the proposed approach can be used to identify the impact of key parameters in a more intuitive way. Additionally, a multivariate adaptive regression splines (MARS) algorithm is adopted to generate analytical small-signal stability constraints for addressing the operational challenges in optimal power flow (OPF). The additional small-signal stability constraints are further incorporated into the OPF formulation. By comparing with conventional OPF, the proposed approach can achieve a more secure and reliable dispatch while maintaining high computational efficiency and minimal impact on operational costs.

Extensive case studies are presented through simulations and real-time hardware-in-the-loop (HIL) tests to demonstrate the effectiveness of the proposed approaches and strategies in IBR-dominated power systems. The results show that the proposed framework and approaches can be used to effectively identify the oscillation mechanism, promote higher penetration of IBRs, enhance stability margin and damping, and enable real-time and efficient stability assessment and stability-constrained power flow optimization. Future work includes investigating small-signal stability for power systems dominated by IBRs with unknown control schemes and parameters, as well as transient stability analysis for IBR-dominated power systems.