Real-Time Optimization Scheduling Method for Electric Melting Magnesium Load Based on MPC

The participation of electric melting magnesium industry load in power system optimization scheduling can effectively reduce its overall energy consumption, decrease electricity costs, and improve the flexibility of power system regulation. This paper proposes a real-time optimization scheduling method for electric melting magnesium load based on Model Predictive Control (MPC), aiming to achieve energy efficiency optimization and system operational cost minimization by dynamically adjusting the electric melting magnesium load power. First, by analyzing the technological process and model characteristics of the electric melting magnesium load, the impact of the melting current setpoint on the energy consumption per ton is revealed. Secondly, considering the power constraints of the electric melting magnesium load, uncertainty in wind and solar power generation, and the power purchase and sale strategy of the grid, an MPC-based optimization control model for electric melting magnesium load is established. Simulation examples verify the advantages of this method in improving system flexibility, reducing electric melting magnesium load electricity purchase costs, and increasing renewable energy sales revenue. Compared with traditional scheduling methods, the MPC method can more effectively reduce grid power fluctuations, increase the renewable energy consumption rate, and optimize system economics while meeting load demands.