In response to the challenges faced by high-energy-consuming enterprises in utilizing renewable energy and implementing low-carbon operations, this paper proposes a multi-objective optimization strategy based on source–storage–load collaborative scheduling. The strategy establishes a refined model of aluminum electrolysis load, thoroughly considering the coupling relationship between temperature, production output, and power consumption. Additionally, it develops a dynamic coupling model between multi-functional crane loads and aluminum electrolysis production to reveal the influence mechanism of auxiliary equipment on the main production process. Based on this foundation, this paper constructs a multi-objective optimization model that targets the minimization of operating costs, the minimization of carbon emissions, and the maximization of the renewable energy consumption rate. An improved heuristic intelligent optimization algorithm is employed to solve the model. The simulation results demonstrate that, under a renewable energy penetration of 67.8%, the proposed multi-objective optimization strategy achieves a maximum reduction in carbon emissions of 1677.35 t and an increase in renewable energy consumption rate of 12.11%, compared to the conventional single-objective economic optimization approach, while ensuring the stability of aluminum electrolysis production. Furthermore, when the renewable energy penetration is increased to 76.2%, the maximum reduction in carbon emissions reaches 8260.97 t, and the renewable energy consumption rate is improved by 18.86%.