Exploring dynamic greenhouse gas emissions of Canadian electric grids
Electrification has been considered as a viable pathway to decarbonize the Canadian economy. However, the Canadian electricity grid is not all clean, especially during the critical peak hours due to the operation of marginal combustion power plants. The greenhouse emissions of the grids are therefore variable from one hour to another. As grid operation data becomes more and more available, we could calculate the GHG emissions more dynamically. This would enhance the accuracy of the GHG emissions calculations.
The objective of this project is to compute the dynamic GHG emissions of the Canadian electric grids using the grid operation data and then analyze the average and marginal GHG emissions of the grids. The results will provide a more precise picture about the temporal aspect of the GHG emissions of the Canadian grids.
Required knowledge
- Knowledge of programming languages such as Python, especially the Pandas package;
- Familiarity with data analytics, data science.