Multi-Criteria Model Predictive Controller for Hybrid Heating Systems in Buildings

With more hybrid heating systems available, there is a need to optimize energy use intelligently from the end-consumer perspective. This paper focuses on a multi-criteria heating system optimization to optimize cost, carbon emission, and comfort level of building occupants. A discrete Multi-Objective Model Predictive Controller (MO-MPC) algorithm is proposed to optimally utilize two heating sources connected to a building, namely district heating (DH) and a building-integrated electrical heat pump (HP). The model is tested on a real-world building case simulated with a gray box building model. The results are compared to a conventional PID controller as well as the MPC scheme, each with a single heating input, and eight different cases are constructed to make this comparison more visible. The results indicate that, using MO-MPC, a cost saving of up to 10% and emission saving of up to 13% can be reached without additional thermal discomfort, while the potential savings on cost and emission with the hybrid system can be up to 25% and 77%, respectively. Further, a sensitivity analysis on price and emission parameters is conducted to investigate the changes in the provided solution.