Designing off-grid hybrid renewable energy systems under uncertainty: A two-stage stochastic programming approach

The decarbonization of remote energy systems presents both technical and economic challenges, due to their dependance on fossil fuels and the variability of renewable energy sources. This study introduces a Two-Stage Stochastic Programming approach to optimize Hybrid Renewable Energy Systems under uncertainty in renewable energy production. The methodology is applied to the island of Pantelleria, aiming to minimize Total Annualized Costs and CO 2 emissions using an ε-constraint approach. Results show that, within the set of optimized configurations, stricter CO 2 emissions constraints increase costs, due to the need for oversized components to ensure supply reliability. Nevertheless, even the zero-emissions scenario offers significant economic benefits compared to the current diesel-based system. Total Annualized Costs are reduced from 15.5 M€ to 8.10 M€ in the deterministic case and to 9.37 M€ in the stochastic one. The additional cost in the stochastic configuration is offset by improved reliability, ensuring demand is met under all scenarios. A sensitivity analysis on electricity demand reveals the necessity of further larger components, leading to a 27.0 % cost increase in a fully renewable scenario with stochastic optimization for a 10 % demand increase. These findings highlight the importance of stochastic optimization in designing cost-effective off-grid renewable energy systems. • Renewable energy integration reduces system emissions and costs. • Hybrid storage with hydrogen enhances system performance. • Storage systems are essential for energy balancing. • Two-stage stochastic programming ensures reliability under uncertainty. • Uncertainty integration increases system size and costs.