Modern Power System Risk Assessment Using Quantum Computing

The incorporation of renewable energy and the expanding size of power systems have elevated operational uncertainty, thereby raising the risk of load loss. Additionally, the analysis duration has been extended as a consequence. Swift and imperative risk assessments are vital for guaranteeing the safety and reliability of modern power systems operations. In this paper, the utilization of Quantum computing methodologies to enhance the efficiency of the risk assessment in modern power systems is explored. Two prevalent methods, namely Monte Carlo simulation and Bayesian networks, are the focus of this study. The first part introduces a Quantum Monte Carlo circuit designed to analyze risk with greater efficiency compared to the conventional Monte Carlo simulations commonly employed on classical computers. Moreover, the exploration of Quantum computing in Bayesian networks offers a novel approach for modeling and analyzing dependencies within power systems. The potential of Quantum computing to redefine risk assessment methodologies in the domain of power system reliability is illuminated by this research.