Two‐stage multi‐objective stochastic optimization of a wind‐solar powered steam and green methanol co‐production system

Abstract The transition to low‐carbon refineries requires integrated systems that coordinate renewable energy with industrial production. This work bridges a critical gap by proposing a novel Renewable Energy‐Integrated Steam and Methanol Production (RE‐ISMP) system, which couples a steam network with a carbon capture‐enabled methanol process. A two‐stage stochastic multi‐objective optimization framework is developed to jointly address investment and operational decisions under wind‐solar uncertainty, solved efficiently via Benders decomposition. Application to an industrial refinery demonstrates that the RE‐ISMP system significantly reduces annual cost and CO 2 emissions versus conventional baselines. Crucially, the stochastic solution guarantees operational robustness across scenarios and identifies the optimal cost‐CO 2 emissions Pareto frontier. This work establishes a generalizable paradigm for transforming energy‐intensive industries into resilient, low‐carbon integrated hubs.