Microgrid Optimal Scheduling and Risk Analysis

Risk analysis is currently not quantified in microgrid resource scheduling optimization. This thesis proposes a conditional value at risk (cVaR) analysis on a disconnected residential microgrid with distributed energy resources (DER). We assume the infrastructure to set up an ad-hoc microgrid is already in place for a residential neighborhood with power sources such as PV, diesel, and battery generation. With this scenario in mind, we employ optimization using day-ahead scheduling to allocate various resources to match demand in scenarios where neighborhoods, especially residential, are disconnected from the overall grid such as in flooding, hurricanes, winter storms, or operational failures. These allocations are then analyzed through a cVaR algorithm to calculate the worst-case scenarios the microgrid would face with abnormally high demand. The goal is to provide an alternative framework to optimize power availability for priority customers and strengthen the overall grid against dips in power outside of normal operating considerations. Natural disasters have been increasing in severity and length due to climate change. Additionally, the existing electric grid has been strained due to an increase in residential and commercial solar power, as well as other renewable systems and electric vehicles. This has created more reliability concerns for the overall health of the grid. It has also made it more difficult to provide consistent and reliable electricity especially when faced with large-scale disaster scenarios such as flooding, wildfires, hurricanes, or winter freezes. The focus of this research will be taking in renewable energy sources from photovoltaic (PV) combined with diesel and Battery Energy Storage System (BESS) while minimizing cost. This will allow for compensating on a distribution level for short-term usage in a residential microgrid configuration. Lastly, by utilizing existing infrastructure with a new energy management system, microgrids can be implemented to be for more resilient for new reliability challenges.