Predictive Multi-Objective Scheduling with Dynamic Prices and Marginal CO2-Emission Intensities

Buildings' energy consumption accounts for approximately 35% of emissions in most industrialized countries. In spite of several studies on the economy of energy management in buildings, the environmental aspect has often been overlooked. Therefore, in the context of decarbonization, we investigate the potential for smart homes to lower their CO2 footprint while saving on their energy bills. We model a smart home as a multi-energy system equipped with several technologies to satisfy both electric and thermal demands. A home energy management system (HEMS) coordinates the supply and demand of energy carriers by shifting consumption in time and by changing energy vectors based on dynamic energy prices and marginal CO2-emission intensities. The HEMS aims at reducing daily CO2 emissions and/or energy costs preserving user's satisfaction. Due to the binary nature of on-off decisions and information uncertainty, we formulate a multi-objective mixed-integer linear programming (MILP) problem within a model predictive control (MPC) framework. Using prices and CO2-emission intensities of the German power grid, our approach is effective in reducing both energy costs and CO2 emissions, balancing between the two objectives. The results show that integrating energy carriers has a higher impact than time-flexible loads. If solar panels are available, emissions and costs strongly depend on the importance given by the users to the environmental and economic goals.