Quantum power system state estimation (QPSSE) offers an inspiring direction for tackling the challenge of state estimation through quantum computing. Nevertheless, the current bottlenecks originate from the scarcity of practical and scalable QPSSE methodologies in the noisy intermediate-scale quantum (NISQ) era. This paper devises a NISQ−QPSSE algorithm that facilitates state estimation on real NISQ devices. Our new contributions include: (1) A variational quantum circuit (VQC)-based QPSSE formulation that empowers QPSSE analysis utilizing shallow-depth quantum circuits; (2) A variational quantum linear solver (VQLS)-based QPSSE solver integrating QPSSE iterations with VQC optimization; (3) An advanced NISQ-compatible QPSSE methodology for tackling the measurement and coefficient matrix issues on real quantum computers; (4) A noise-resilient method to alleviate the detrimental effects of noise disturbances. The encouraging test results on the simulator and real-scale systems affirm the precision, universality, and scalability of our QPSSE algorithm and demonstrate the vast potential of QPSSE in the thriving NISQ era.

Green hydrogen has shown great potential to power microgrids as a primary source, whereas the resilient operation methodology under extreme events remains an open area. To fill this gap, this letter establishes an operational optimization strategy towards resilient hydrogen-powered microgrids. The frequency and voltage regulation characteristics of primary hydrogen sources under droop control and their electrical-chemical conversion process with nonlinear stack efficiency are accurately modeled by piecewise linear constraints. A resilience-oriented multi-time-slot stochastic optimization model is then formulated for an economic and robust operation under changing uncertainties. Test results show that the new formulation can leverage the primary hydrogen sources to achieve a resilience and safety-assured operation plan, supplying maximum critical loads while significantly reducing the frequency and voltage variations.

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.