Climate Aware Reliability Analysis of Renewable Energy Systems Using Nonstationary Stochastic Hazard Models

Renewable energy assets are certified against hazard functions that classical reliability theory treats as fixed in time, yet the climate driving heat stress, humidity, wind extremes, and flooding is shifting in ways that make stationarity untenable for long‑lived infrastructure. This article develops a hazard model in which asset failure intensity combines a Weibull baseline for intrinsic wear with a climate‑driven component following an affine jump diffusion, joining mean‑reverting drift from gradual thermal and humidity loading with discrete jumps from cyclones and floods. A closed‑form reliability function is derived through a Riccati equation, transferring machinery from interest rate, credit risk, and stochastic mortality modeling to infrastructure reliability, and is extended to trending climate means and extreme event frequencies through a time‑inhomogeneous quadrature formula. The model yields a Climate Resilience Index and agrees with forty thousand path Monte Carlo simulations within one third of one percent over sixty years. Calibrated applications to solar photovoltaic arrays, wind turbines, battery storage units, and coastal transmission towers, grounded in published Indian degradation and cyclone fragility data, show that climate‑driven hazard erodes mean time to failure most severely for cyclone‑exposed transmission towers, whose lifetime resilience index in coastal Odisha falls from 0.44 under a stationary climate to 0.36 under severe warming, and least severely for enclosed battery systems.