Predictive CO2-Efficient Scheduling of Hybrid Electric and Thermal Loads

The current energy transition leading towards decentralized generation and low emission sources challenges our energy systems. With multiple energy sources available, the complexity of households as energy systems increases, while growing energy consumptions imply a higher environmental impact that, in the context of decarbonization, needs to be addressed. We investigate the potential for smart homes to lower their environmental impact in terms of CO
₂
emissions through a home energy management system (HEMS). This is achieved by adopting a model predictive control (MPC) to minimize CO
₂
emissions due to the consumptions of gas and electricity by scheduling the operation of time-flexible appliances and by changing the energy carriers to supply electric and thermal loads. The scheduling problem is formulated as a mixed-integer linear programming (MILP) program subject to constraints that reflect the users' preferences in operation and allowed delays of individual appliances, as well as main characteristics of different household technologies. Using statistical data available for German households and emission factor of the German power grid, we show that the proposed approach can reduce the daily emissions of a group of 300 households by up to 27% (i.e., 599 kgCO
₂
eq), while satisfying users' comfort preferences.