With the integration of a voltage source converter (VSC), having variable internal voltages and source impedance, in a microgrid with high resistance to reactance ratio of short lines, angle-based transient stability techniques may find limitations. Under such a situation, the Lyapunov function can be a viable option for transient stability assessment (TSA) of such a VSC-interfaced microgrid. However, the determination of the Lyapunov function with the classical method is very challenging for a microgrid with converter controller dynamics. To overcome such challenges, this paper develops a physics-informed, Lyapunov function-based TSA framework for VSC-interfaced microgrids. The method uses the physics involved and the initial and boundary conditions of the system in learning the Lyapunov functions. This method is tested and validated under faults, droop-coefficient changes, generator outages, and load shedding on a small grid-connected microgrid and the CIGRE microgrid.

Quantum power flow (QPF) offers an inspiring direction for overcoming the computation challenge of power flow through quantum computing. However, the practical implementation of existing QPF algorithms in today’s noisy-intermediate-scale quantum (NISQ) era remains limited because of their sensitivity to noise. This paper establishes an NISQ-QPF algorithm that enables power flow computation on noisy quantum devices. The main contributions include: (1) a variational quantum circuit (VQC)-based alternating current (AC) power flow formulation, which enables QPF using short-depth quantum circuits; (2) NISQ-compatible QPF solvers based on the variational quantum linear solver (VQLS) and modified fast decoupled power flow; and (3) an error-resilient QPF scheme to relieve the QPF iteration deviations caused by noise; (3) a practical NISQ-QPF framework for implementable and reliable power flow analysis on noisy quantum machines. Extensive simulation tests validate the accuracy and generality of NISQ-QPF for solving practical power flow on IBM’s real, noisy quantum computers.

Federated-learning-based active fault management (AFM) is devised to achieve real-time safety assurance for microgrids and the main grid during faults. AFM was originally formulated as a distributed optimization problem. Here, federated learning is used to train each microgrid’s network with training data achieved from distributed optimization. The main contribution of this work is to replace the optimization-based AFM control algorithm with a learning-based AFM control algorithm. The replacement transfers computation from online to offline. With this replacement, the control algorithm can meet real-time requirements for a system with dozens of microgrids. By contrast, distributed-optimization-based fault management can output reference values fast enough for a system with several microgrids. More microgrids, however, lead to more computation time with optimization-based method. Distributed-optimization-based fault management would fail real-time requirements for a system with dozens of microgrids. Controller hardware-in-the-loop real-time simulations demonstrate that learning-based AFM can output reference values within 10 ms irrespective of the number of microgrids.