This study investigates the integration of high-penetration renewable energy into energy-intensive aluminumAluminum smeltingSmelting, addressing the dual challenges of grid stability and industrial decarbonization. By developing a Model Predictive ControlModel predictive control (MPC)-based rolling optimization framework, we harmonize real-time electricity market dynamics (spot prices and contract-for-difference settlements) with the operational constraints of smelters equipped with EnPot technology, enabling ± 20% power modulation capability. SimulationSimulation results demonstrate that our MPC strategy outperforms rule-based approaches, increasing profit by 2.1 million CNY despite prediction uncertainties. Furthermore, we identify an optimal renewable capacity threshold (800 MW wind power), beyond which forecasting errors diminish returns, and quantify how enhanced load flexibility boosts profitability by 16.6%. This work provides a critical pathway for grid-interactive sustainable manufacturing in high-VRE penetration grids, emphasizing the economic viability of flexible industrial demand in supporting China’s energy transition.