An Energy Management System for Industrial Manufacturing with demand response based on a Hybrid Robust MPC

This paper presents a Robust Scenario-based Model Predictive Control (RMPC) strategy for a real-world system, where both energy management and a portion of the demand are controlled. The proposed solution employs a hybrid approach, combining Genetic Algorithms (GA) for job scheduling and Mixed Integer Linear Programming (MILP) to minimize the total cost of energy and, indirectly, increase the penetration of renewable energy sources while enforcing production constraints. The strategy is applied to a factory microgrid scenario, considering energy production from wind turbines, photovoltaic panels, and combined heat and power (CHP) plants, alongside battery energy storage systems (BESS). The controller employs GA and MILP to ensure both rapid response times and near-optimal performance. The performance of this controller has been evaluated against real data with an ideal Model Predictive Controller (MPC), and with a classical MPC. The results demonstrate that incorporating the robust approach into a traditional MPC framework leads to improvements in systems characterized by high levels of uncertainty.