Quantitative Climate Vulnerability Assessment of Solar Energy Infrastructure under Extreme Weather Events

This investigation proposes a quantitative frame-work to assess the vulnerability of photovoltaic infrastructure under extreme meteorological phenomena. Using statistical thresholds and multi-percentile analysis (P5, P10, P90, P95), 365 days of solar irradiance data from the 20 MW Tacna Solar Plant in Peru were evaluated to characterize probabilistic risk and classify extreme events. The 10th percentile (P10) was applied as the main detection threshold, while the 5th percentile (P5) defined severity. Percentiles P90-P95 established baseline generation levels. This methodology identified 36 anomalous events, 16 severe and 20 moderate, representing 9.9% of annual operation. Severe episodes showed generation deficits surpassing 96 %. Temporal analysis revealed seasonal clustering, with higher frequencies of extreme events during austral winter and transitional months, reflecting persistent synoptic-scale meteorological systems. Economic vulnerability was quantified at USD 141,545 in losses, equivalent to 2,830.7 MWh of missed generation, assuming a 50/MWh tariff (subject to validation with official regulatory data). Post-event recovery analysis indicated het-erogeneous resilience: moderate events recovered faster, while severe disturbances required extended restoration. The study contributes robust statistical tools for risk assessment, seasonal vulnerability forecasting, and resilience planning. Its main advances include multi-percentile detection algorithms, quantitative economic impact assessment, and empirical recovery characterization. These findings support climate adaptation strategies and renewable energy risk management, while emphasizing the need for validated regional pricing in future evaluations.