Global Sensitivity Analysis for the Hybrid Energy System Optimization Model

Abstract

The optimization of energy systems represents a potent strategy for climate change mitigation through the reduction of emissions. Canadian Nuclear Laboratories’ Hybrid Energy System Optimization model optimizes nuclear-renewable hybrid energy systems by minimizing the cost and emission. Given the inherent uncertainty in energy systems, understanding the impact of input parameters on the model's output is critical in controlling the uncertainty and robust development of energy systems. Sensitivity analysis is standard practice for quantifying the influence of inputs on the model’s outputs. This study presented a framework for applying a Global Sensitivity Analysis on the Hybrid Energy System Optimization model using a case study on an off-grid nuclear hybrid energy system for a research campus. Various time-dependent data on renewable energy sources and economic and technical parameters of the nuclear source were selected for the sensitivity analysis. The results showed that the nuclear capital cost emerged as the most critical parameter in the examined case, highlighting the decision-maker's strategy for managing this pivotal system factor. Moreover, the Global Sensitivity Analysis framework proved robust in conducting the sensitivity analysis.