Contribution of Model Predictive Control in the Integration of Renewable Energy Sources within the Built Environment

Integrating intermittent renewable energy sources has renders the power network operator task of balancing electricity generation and consumption increasingly challenging. Aside from heavily investing in additional storage capacities, an interesting solution might be the use predictive control methods to shift controllable loads towards production periods. Therefore, this paper introduces a systematic approach to provide a preliminary evaluation of the thermo-economic impact of model predictive control (MPC) when being applied to modern and complex building energy systems (BES). The proposed method applies an e-constraint multi-objective optimization to generate a large panel of different BES configurations and their respective operating strategies. The problem formulation relies on a holistic BES framework to satisfy the different building service requirements using a mixed integer linear programming technique. In order to illustrate the contribution of MPC, different applications on the single and multi-dwelling level are presented and analysed. The results suggest that MPC can facilitate the integration of renewable energy sources within the built environment by adjusting the heating and cooling demand to the fluctuating renewable generation, increasing the share of self-consumption by up to 27% while decreasing the operating expenses by up to 3% on the single building level. Finally, a preliminary assessment of the national-wide potential is performed by means of an extended implementation on the Swiss building stock.