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.
In Canada, there are over 280 communities that are off the grid, the majority of which are First Nation. They use stand-alone diesel-powered electricity generators to supply their basic needs, and whilst there are plans to provide renewable energy alternatives, this will take time. In addition, there are a growing number of generators that burn biogas from landfill sites or wastewater treatment plants.
Small Modular Reactors (SMRs) represent the next generation in nuclear power reactor technology with the benefits of being non-greenhouse-gas emitting forms of power production and providing inherent safety, lower construction and operating costs and proliferation resistance. As part of the recently announced New Brunswick SMR Research and Development Cluster, Moltex Energy proposes to build their first-of-a-kind Stable Salt Reactor (SSR) at the Point Lepreau site in southwestern NB.
When working with massive and complex structures such as dam-type structures it is required to generate a scale numerical model to simulate the real behaviour of the dam. The problem of this approach is that to be able to generate all possible hazard scenarios and system configuration, several computing hours are required. This represents a huge problem when the decision-making process need to be done in presence of natural disaster such as earthquakes.
SeeO2 energy and the Birss group (UCalgary) have developed world-leading catalysts for RSOFC systems with promising performance for the production of syngas and power from H2O/CO2 feeds. Today, the company is aiming to scale-up this technology and move towards commercialization by building larger cells, up to 5 x 5 cm2 (16 cm2 electrode area). However, the process of scaling-up RSOFCs presents many challenges in understanding the effects of fabrication and operation parameters on the cell performance at larger scale.