Price and carbon-based energy flexibility of residential heating and cooling loads using model predictive control

Model predictive controllers (MPC) have shown great potential for activating the energy flexibility of thermal loads, especially in buildings equipped with heat pump systems. In this work, an MPC controller is developed and tested within a co-simulation framework which couples an optimization software with a dynamic building simulation tool. The development phase is described in detail, in particular the methods to obtain simplified models to be used by the controller. The building envelope and the heat pump performance (based on experimental data) were thus modelled, both in heating and cooling seasons. Three different objective functions of the MPC are tested on a study case consisting of a Spanish residential building: promising results are obtained when the controller aims at minimizing operational costs (savings of 13-29%) or CO2 marginal emissions (savings of 19-29%). The development efforts, the required tuning and sensitivity of the MPC algorithm parameters, the adaptations needed between the cooling and heating operations are also discussed and put into perspective with the obtained benefits in terms of savings, comfort and load-shifting.