Decarbonization is driving power systems toward more decentralized, self-governing models. While these technologies improve efficiency, planning, operations, and reduce the carbon footprint, they also introduce new challenges. In modern grids, particularly with the integration of power electronic devices and high penetration of Renewable Energy Sources (RES) and Inverter-Based Resources (IBRs), traditional reliability concepts may no longer ensure adequate performance due to systemic restructuring. This shift necessitates new or significantly modified reliability indices to capture the characteristics of the evolving power system. Ensuring converter reliability is essential for effective planning, which requires precise, component-to-system-level modeling, as different converters impact system performance indicators. However, the existing literature in this field faces a significant limitation, as most studies focus on a singular perspective. Some examine reliability at the device-level, others at the component-level, while broader reviews in power systems often emphasize system-level analysis. In this paper, we aim to bridge these gaps by comprehensively reviewing the interconnections between these levels and analyzing the mutual influence of power converter and system reliability. A key point to highlight is that, with the rapid evolution of modern power grids, decision-makers must adopt a multi-level approach that incorporates insights from all levels to enable more accurate and realistic planning and operational strategies. Our ultimate goal is to provide an in-depth investigation of studies addressing the unique challenges posed by modern power grids. Finally, we will highlight the gaps in the literature and suggest directions for future research.

Natural disasters like earthquakes, storms, and floods have an adverse impact on the critical infrastructures operation. The severity of the occurred damages on the infrastructure can be assessed by failure probabilities using fragility curves. In this paper, a S tochastic I nfrastructure D amage E valuation (SIDE) method is proposed. SIDE is a partition-based event tree approach. It estimates cumulative damage from disasters on distribution systems. The system is broken down based on equipment damage levels. SIDE evaluates the probability of damage in each partition. It uses reliability block diagram and the fragility curves of its components. The method improves event tree analysis by using partition-based modeling instead of traditional series equipment sets. Additionally, it enhances analytical efficiency by ignoring unlikely or non-expecting scenarios from the partition-based event trees (PBET). The method was applied to a power distribution system (RBTS4) as a critical infrastructure under an earthquake scenario, with results compared to Monte Carlo simulations (MCS). Finally, the merit of the proposed method is illustrated using sensitivity analysis, considering changing disaster severity and budget. The paper highlights a key observation, where in most budget allocation scenarios, the location of switches often overlaps. This suggests that beyond certain budget, the SIDE method stops adding more switches.