A hybrid-MPC based energy management system with time series constraints for a bioclimatic building

Microgrids play a crucial role in transitioning from the current energy grid model to one that seamlessly integrates renewable energy sources. However, the variability and unpredictability inherent in renewable energy pose new challenges in controlling energy flows within the microgrid. To ensure smooth operation in isolated mode or to minimize reliance on polluting energy sources, microgrids need to incorporate energy buffers and accurately predict renewable energy production, considering its intermittent nature. These factors significantly influence the development of effective microgrid controllers. This work presents a hybrid model-based predictive control to manage the energy flows in a microgrid. The proposed approach addresses both the discrete and continuous dynamics of the system through an optimized solution, incorporating, as main contribution, temporary constraints, which allows for integration of electric vehicle operating modes. The controller is tested in simulation with real data from a microgrid located at the CIESOL centre in Almería (Spain). Moreover, a comparison with a rule-based controller and a switching model predictive control is provided. Results demonstrate that the proposed approach effectively decreases the economic costs of microgrid management, outperforming the rule-based controller and the switching model predictive controller by 8.23% and 1.94%, respectively, over a thirty-day scenario.