Performance and uncertainty analysis of rural waste-to-energy systems under stochastic feedstock supply

This study evaluates the operational performance of three configurations of an Integrated Resource and Recovery Centre (IRRC) as a decentralised rural waste-to-energy (WtE) system using Monte Carlo simulation. Twelve months of empirical feedstock data were incorporated into a probabilistic, scenario-based model to assess how stochastic variations in the availability of vegetable waste and manure affect daily electricity generation. The configurations examined include the existing business-as-usual system (S1), a modified system with buffer storage and thermal pre-treatment (S2), and a constrained system that excludes vegetable waste (S3). A total of 10,000 Monte Carlo iterations across 100 replications were conducted to generate probability distributions of electricity output. Results indicate that S2 Gross achieves the highest mean electricity output, while S2 Net under the electric-heating. Relative variability remains comparable across the reported outputs. The fifth-percentile output increases for S2 Gross but decreases when the thermal requirement is treated as an electrical load. Global sensitivity analysis identifies methane yield, electrical conversion efficiency, and feedstock level as the primary determinants of mean electricity-output uncertainty, while pre-treatment temperature becomes important for net S2 performance. Overall, buffering modestly reduces feedstock variability, while the energy benefit of thermal pre-treatment depends on how the required heat is supplied.