Wastewater is an abundant and severely underutilized energy source in North America. Sewers experience predictable flow profiles and nearly constant temperatures between 18 ?C and 20 ?C year-round. When wastewater is used in conjunction with heat pumps, it can serve as an energy source and sink to provide both heating and cooling to buildings. Therefore, there exists the potential to extract significant amounts of thermal energy from the wastewater using heat exchangers, resulting in substantial economic and environmental benefits.

The integration of significant capacities of distributed energy resources (DERs) such as renewable wind and solar generation for a more sustainable energy future creates several challenges to the reliable and efficient operation of power distribution systems. These include: (i) Uncertain and intermittent nature of renewable generation compromises power quality for end-customers.  (ii) Up-to-date distribution system network topologies are not well known and their real-time monitoring is limited. As a result, effective management of DERs is challenging.

The Province of Alberta (AB) has decided to phase out coal power generation by 2030 and increase renewable electricity production to 30% of total power generation, also by 2030 with the remaining 70% of the power generation being dependent on natural gas. It has been conjectured that part of generation portfolio could be diversified to include nuclear power generation.

The main duty of Hydro-Québec is to repond efficiently to the energy demand of customers, in a safe and secure way while remaining competitive in the markets as well. The main goal of this start-up project is to support Hydro-Québec in developing a future-oriented energy system by proposing innovative technical solutions. Among these solutions, deep learning has been the final choice. Using a deep learning approach, satellite images, weather model outputs and data from solar radiation measurement stations, will be use for the development of a solar radiation nowcasting model.

Hydro-Québec is a public utility that generates and distributes electricity. Despite selling most of its electricity in Québec, its most lucrative sales are in the neighboring markets. To ensure the best possible quality of service, the transmission system must remain stable, but to maximize profits, the company also wants to increase its transmission capacity to maximize energy exports. The transfer limit is now conservatively estimated based on a certain combination of simulated network configurations.

Load forecasting is an essential activity for a company like Hydro-Québec. It is necessary for objectives as varied as the management of production or the management and maintenance of the electricity network. Any significant forecasting error can result in reliability issues, loss of opportunity, or additional costs to the business. On the other hand, a good prediction would allow Hydro-Québec to generate additional sales in neighboring markets. With the deployment of its Advanced Measurement Infrastructure (AMI), Hydro-Québec now has a significant amount of new consumption data.

The overall objective for this project is to support the research of two master’s students who would help advance the methods for modelling energy-climate policies, a field in which EMRG in the School of Resource and Environmental Management at SFU is one of the leading research units in the country and in which Navius Research Incorporated is the leading Canadian consulting firm, providing support to governments and other stakeholders in the development and assessment of energy and climate policy.