Modelling of electricity mix in temporal differentiated life-cycle-assessment to minimize carbon footprint of a cloud computing service

The information and communications technologies (ICT) sector is seeking to reduce the electricity consumption of data processing centres. Among the initiatives to improve energy efficiency is the shift to cloud computing technology. Thanks to very favourable geographical conditions, the Canadian energy mix is highly suited to the implementation of data centres, especially in light of the significant potential of renewable energy, which can help to curb greenhouse gas emissions. In the green sustainable Telco cloud (GSTC) project, an efficient cloud computing network would be set up to optimize renewable energy use based on several data centres. This study aimed to develop a temporally differentiated life cycle assessment (LCA) model, adapted to short-term predictions, to provide a regionalized inventory to model electricity generation. Purpose of this model is (i) to calculate more accurately the carbon emissions of ICT systems and (ii) to minimize the daily carbon emissions of the GSTC servers. This paper focuses mainly on the electricity generation modelling during the use phase in the context of the life cycle assessment methodology. Considering the time scale of the model, the difference between the annual fixed average and a shorter period may be highly relevant, in particular when assessing the green house gases (GHG) emissions of a process such as an ICT system, which mainly operates during peak load hours. The time dependent grid mix modelling makes it possible to manage the server load migrations between data centres on an hourly basis.