Context-Aware Probabilistic Numerical Framework for Solar PV Grid Stability Under Extreme Weather Conditions

Abstract

Increased extreme weather occurrences are destabilizing solar photovoltaic (PV) power systems by causing voltage and frequency variations in current grids. Deterministic and numerical models fail to reflect nonlinear and stochastic transients, excluding context from weather dynamics. To address this gap, our context-aware probabilistic numerical framework for solar grid stability prediction combines five distinct analytical methods in a stochastic pipeline. Contextual radiance shock encoder (CRSE) learns weather-induced irradiance shock precursors for stochastic forcing. The context-steered adaptive meshing CoSTA–Mesh SPDE Solver anticipates and improves computation near expected irradiance shocks. The Bayes–Galerkin grid propagator (BGGP) propagates voltage and frequency dynamics risk-awarely using Bayesian Petrov–Galerkin from its output. TAILOR uses differentiable CVaR-based optimization to calibrate and control severe tail behaviors. Finally, context-hardware in-the-loop validation (C-HIL VERTEX) validates C-HIL tests’ whole probabilistic loop during real-time playback, retaining posterior distributions. These phases cut irradiance prediction errors by 30%–40%, voltage/frequency changes by 25%–30%, and tail-risk calibration by 1%. This system uses data-driven context learning and physically grounded stochastic solvers for real-time grid dependability to establish a probabilistic foundation for adaptive, robust PV-grid operation under climate variability in process.