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

Laura Fiorini, Marco Aiello

Research output: Contribution to conferencePaperAcademic

4 Citations (Scopus)


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.
Original languageEnglish
Number of pages12
Publication statusPublished - 12-Jun-2020
EventThe Eleventh ACM International Conference on Future Energy Systems (ACM e-Energy) - Melbourne, Australia, Melbourne, Australia
Duration: 22-Jun-202026-Jun-2020


ConferenceThe Eleventh ACM International Conference on Future Energy Systems (ACM e-Energy)
Abbreviated titleACM e-Energy 2020


  • Marginal emission
  • Multi-objective optimization
  • Hybrid loads

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