Grant: Canada Research Chairs

Dr. Schell’s research program aims to address critical and costly gaps in operating and planning the energy system as it moves to net zero greenhouse gas emissions. The overall objective is to provide evidence-based recommendations on strategies for optimal renewable energy investment, deployment, and integration that maximize the operational flexibility of the energy system. Understanding how best to utilize this operational flexibility will help achieve Canada’s net zero targets, while also delivering significant savings to the taxpayer. This research program will study how best to operationalize flexibility in the net zero energy system by developing models and representations of key uncertainties. Renewable energy uncertainty remains a key challenge in energy system operation, and has resulted in the curtailment, or wastage, of available power from existing wind and solar farms because it cannot yet be predicted with high enough accuracy to be relied upon. New machine and deep learning forecasting models of renewable energy production will be developed to achieve near-zero error for wind power prediction. Improved renewable energy forecasting has the potential to transform the deployment of renewable energy in the real world and translate to economic savings on the order of hundreds of millions of dollars. Similarly, uncertainties are growing on the demand-side as the energy transition unfolds, with new technologies—such as smart grid technologies (SGTs)—expected to play a large role in maintaining electric system reliability and affordability by 2050. Increasing electricity demand will necessitate the expansion of low-carbon energy generation to 2–3 times current capacity. New, large sources of demand—both industrial and SGTs aggregated across the system—also present opportunities for flexible operation. This research will develop and validate models to characterize the uncertainty of new demand that can be tractably incorporated into net zero energy system models. This will enable the study of electricity demand, opportunities for demand side management, and efficient expansion of the power grid to accommodate new demand. Both supply-side and demand-side uncertainty models will be incorporated into a large-scale energy system optimization framework to study optimal pathways to achieve net zero. The ultimate goal of this research program is to improve uncertainty representation in net zero modelling to maximize efficiency and minimize decision error.