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Presenter: Bahareh Torabidavan
Supervisor:

Date: Tue, November 30, 2021
Time: 09:00:00 - 10:00:00
Place: ZOOM - Please see below.

ABSTRACT

Zoom meeting link: https://uvic.zoom.us/j/89958974290?pwd=dXVPNlVPTlNYcXpUTGozQ1ZsNGg1UT09

Meeting ID:  899 5897 4290
Password:    168839

Note:  Please log in to Zoom via SSO and your UVic Netlink ID.

Summary:

Over the recent decades, the energy system decarbonization has played an essential role in the greenhouse gas emissions reduction required to limit the climate change impacts. Accordingly, the use of renewable energy resources such as solar power plants has become more critical. Hence, by integrating more solar plants into the power system generation mix, it becomes undeniable to properly model their temporal and spatial variability. To capture renewables variability in an energy system model, a high temporal resolution is ideal. However, computational restrictions pose design and implementation-related constraints and make it infeasible or computationally expensive in practice. Many of the current models only include a limited number of representative time slices that aggregate periods with similar input data profile patterns to reduce the time resolution of energy models, which in turn increases the computational tractability. The proper selection of the time slices to consider in a model is vital to downscale the time dimension in a way that results in a minimum error on the model outputs. However, available methods are limited in terms of applying to the input data with many time segments, which is a disadvantage of models with high shares of renewable energy. This project presents a computational efficient time slice clustering approach applicable to hourly solar generation input data for multiple locations. This method determines representative days to be utilized in the energy system modeling procedure.