The residential sector accounts for a significant portion of global energy consumption and greenhouse gas emissions. Traditional static life-cycle assessment (LCA) methods often fail to capture the dynamic nature of emissions resulting from fluctuating energy production, particularly as energy mixes evolve with the integration of renewable sources. This paper addresses these limitations by proposing a dynamic life-cycle assessment (DLCA) framework integrated with demand response (DR) strategies for residential buildings in Denmark. The framework optimizes the daily operation of household appliances—including washing machines, refrigerators, lighting, electric vehicles, laptops, and ovens—considering the real-time emission factors derived from the Danish energy mix. Integrating OpenLCA with Pyomo software, the optimization model minimizes global warming potential (GWP) based on dynamic energy consumption patterns and emission factors. The proposed methodology leverages DR to shift appliance usage to off-peak times, aligning with periods of lower carbon intensity in the energy grid. The paper demonstrates how DLCA and DR reduce residential GHG emissions by optimizing energy consumption and load-shifting strategies. This approach provides a more accurate representation of residential carbon footprints than static assessments, proving its effectiveness in decarbonizing the residential sector. The key contributions include developing a software-in-the-loop framework to dynamically assess energy use and emissions, proposing an emission-responsive load control strategy, and quantifying the environmental benefits of integrating DR with a dynamic energy mix. Results effectively convey that a 20% shift in load demand leads to a 13.9% reduction in GWP, highlighting the significance of dynamic modeling in optimizing residential energy.
Hemmati et al. (2026) studied this question.