Performance analysis of hybrid renewable energy systems under variable operating conditions

Abstract

Hybrid renewable electricity structures (HRES) offer a promising technique to address the intermittency of standalone renewable resources, but their overall performance under variable environmental situations remains tough. This study evaluates a solar-wind-battery hybrid gadget via Python-primarily based dynamic modeling, integrating real-global datasets for sun irradiance, wind velocity, temperature, and residential load profiles. The system's efficiency, reliability, and price had been analyzed underneath each ideal conditions (excessive irradiance, stable wind) and risky conditions (low irradiance, erratic wind). Results show sun dominates electricity production (62 % contribution) during best situations, even as wind supplements nighttime demand. Volatile situations substantially increase the Loss of Load Probability (LOLP) from 0.8 % to 12.4 % and elevate the Levelized Cost of Energy (LCOE) by way of 64 %, highlighting essential sensitivity to environmental fluctuations. The 100-kWh battery reduces grid dependency but proves inadequate at some point of multi-day low-generation intervals, requiring capacity expansion. Sensitivity analysis exhibits a 20 % wind speed discount will increase LCOE by means of 36 %, demonstrating wind's disproportionate fee impact as compared to solar. While HRES drastically beautify reliability over single-source systems, they require optimized garage and different era to cope with intermittency. Future paintings should explore AI-driven predictive manipulate and inexperienced hydrogen integration to stabilize long-time period overall performance, presenting actionable insights for resilient hybrid device layout in climate-susceptible regions.