Resilience and, in more specific terms, robustness is more relevant now than ever, with the increase of extreme weather events in frequency and intensity due to the effects of climate change. This is especially relevant here in New Zealand, where we are very susceptible to such events as can be seen last year with, for example, Cyclone Gabrielle.
It is important for the grid to be robust, a part of resilience which means the grid's ability to resist these extreme weather events. For example, the least amount of customers on the grid possibly losing power during a severe weather event.
Thiss project will focus on whether areas with more renewable energy experience fewer power outages during extreme weather events compared to areas that mostly rely on fossil fuels to generate their electricity. We will also investigate if Microgrids with small-scale renewable energy generation, like Solar and small-scale battery storage experience fewer outages during extreme weather events.
The project team will analyse Hazard Data like wind and precipitation, potentially from government entries like metservice, niwa and geonet. And compare it to outage data which we can get from Transpower for transmission outage data and the local distribution companies like Vector for more localised outage information. We will perform a Geospatial analysis on this data, and with the help of machine learning, we can determine which areas were the most prone to these outages and determine if the increased presence of renewable energy allowed certain regions to fare better during extreme weather events. From this, we could potentially recommend if further investment in renewable energy in the form of either large hydro generation or small-scale microgrids involving solar and battery storage would improve grid robustness.
Undergraduate
ELECTENG 731 and 703
Power Systems (405.628, Lab)