Environmental Stress-Based Reliability Assessment of Power Distribution Systems: An Integrated Multi-Physics Methodology

Traditional reliability models for distribution grids often rely on static historical averages, overestimating the operational lifespan of power system assets by neglecting the dynamic interplay between electrical loading and microclimatic stressors. This paper addresses these limitations by introducing an innovative multi-physics methodology that shifts the analytical paradigm toward a Physics-of-Failure (PoF) approach. This methodology is operationalized through a novel simulation framework and a modular Python-based tool, integrating OpenDSS and Pandapower to perform high-fidelity reliability assessments. By calculating instantaneous failure rates and Mean Time Between Failures (MTBF) as functions of real-time environmental forcing—specifically temperature and humidity-induced stresses—the proposed system captures degradation dynamics that remain invisible to conventional models. The framework’s capabilities are demonstrated through a simulation on a rural distribution grid, which explicitly includes auxiliary digitalization components, such as Remote Terminal Units (RTUs), that are frequently overlooked in standard benchmarks. The results reveal that environmental forcing triggers a severe contraction in the MTBF of critical active assets, proving that asset seniority alone is an insufficient proxy for grid vulnerability. Furthermore, the integration of an advanced Reliability Dashboard enables Distribution System Operators (DSOs) to conduct sophisticated “What-If” analyses and quantify Expected Risk Costs (ERC) prior to physical deployment. Ultimately, this research provides a robust, innovation-driven decision-support system for the cost-effective hardening of smart grids, bridging the gap between theoretical power flow analysis and proactive, climate-resilient asset management.