A robust analysis of uncertainty in technology deployment to guide optimal investment priorities in industrial innovation for a net-zero Canada

The project focused on detailed modelling of Canada’s industrial sector and its transition pathways, addressing uncertainties and technological innovations. It evaluated techno-economic uncertainties through a multi-phase approach, including research and characterisation of both existing and new technologies, as well as scenario modelling using methods such as Endogenous Technological Learning (ETL), Monte Carlo simulations, and Reinforcement Learning (RL) to analyse the impacts of uncertainty in the industrial sector. An 8760-hour power dispatch optimisation ensured supply-demand energy adequacy, considering the increasing demand from the industrial sector, while also assessing renewable integration and storage. By leveraging proprietary models like NATEM and NATEM-Power, along with tools such as Datuum and PowerBI, the project developed and used additional models to assess uncertainties. The project documented over 50 emerging technologies in the industrial sector and modeled around 250 scenarios. Additionally, it included extensive stakeholder consultation and disseminated findings through data files, a visualisation dashboard, a technical report, and a webinar. The project’s outcomes guided investment decisions for industry and infrastructure, inform future decarbonisation policies, and defined priorities for technology developers in Canada.