Low carbon integrated energy system management framework considering multiple energy production and supply uncertainties

This study proposes an effective stochastic optimization framework for determining the facility capacities and operational conditions of a renewable energy-based low carbon integrated energy system for hydrogen and ammonia production. The framework accounts for uncertainties in renewable energy availability as well as in hydrogen and ammonia demand. A key contribution is the introduction of a novel carbon intensity-based cut that addresses a fundamental limitation of conventional Benders decomposition methods. While traditional decomposition approaches struggle to produce effective solutions under multiple sources of uncertainty, the proposed framework directly incorporates the expected carbon intensity from scenario-based operational outcomes into capacity planning. This allows the master model to respond more effectively to violations of the carbon intensity threshold. In addition, the carbon intensity-based cut operates within a separate iterative loop that incrementally tightens the constraints based on prior iteration results, thereby overcoming the stagnation often encountered in conventional iterative methods. The proposed framework serves as a practical decision-support tool for planning the capacity and operations of the integrated energy system considering low carbon intensity under multi-process uncertainties.