STOCHASTIC OPTIMIZATION OF HYBRID RENEWABLE ENERGY SYSTEMS FOR GRID RESILIENCE

Abstract

Grid resilience has emerged as a critical concern for modern power systems facing increasing frequency of extreme weather events, cyber-physical threats, and demand uncertainties. Hybrid renewable energy systems combining solar, wind, and storage technologies offer promising solutions for enhancing grid stability while advancing decarbonization goals. However, the inherent variability and uncertainty in renewable generation create complex optimization challenges that traditional deterministic approaches fail to adequately address. This research develops a stochastic optimization framework for designing and operating hybrid renewable energy systems that maximize grid resilience under uncertainty. The study employs multi-stage stochastic programming to model renewable generation variability, demand fluctuations, and disruption scenarios across different time horizons. Analysis of three case study regions demonstrates that stochastically optimized hybrid systems achieve 34-48% better resilience performance compared to deterministically designed systems while maintaining economic viability. The optimal energy mix varies substantially by location and resilience requirements, with battery storage capacity emerging as the critical enabler of resilience benefits. Findings indicate that incorporating uncertainty explicitly in system design reduces expected unserved energy by 42% and decreases recovery times following disruptions by 56%. This research contributes a comprehensive stochastic optimization methodology for renewable energy planning and provides practical insights for utilities and policymakers pursuing resilient, sustainable power systems.