Browsing by Subject "Grid resilience"

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Modeling electric grid vulnerability induced by natural events using machine learning and geospatial analysis
(2023-04-21) Agarwal, Khushboo; Pierce, Suzanne Alise, 1969-; Mobley, William; Kutanoglu, Erhan
The growing frequency of weather-induced power outages in recent decades has put the electric grid infrastructure of the United States at risk. Natural hazards, like hurricanes, floods, heat waves, and winter storms, can cause millions of dollars of loss to the grid infrastructure. Any damage to the electric grid can further impact other critical infrastructure, like water distribution and transportation. Past instances show that these events have more impact on low-income communities. Therefore, modeling the grid vulnerability to weather extremes is vital to protect these communities. This research involves the creation of a multi-step modeling method to predict the spatial extent and intensity of electric power outages for a case study in the Rio Grande Valley region of Texas. The study focuses on the impact of hydrologic flood events on the power grid using a step-wise workflow that scales geospatial analyses and applies a machine-learning approach to inform prevention, mitigation, and restoration strategies. The initial analysis generates a flood inundation model using the Height Above Nearest Drainage (HAND) method. This Python workflow uses the Stampede2 supercomputer to produce the HAND flood extent for one input DEM tile in 16 seconds and is made scalable for 135,000 DEM tiles in the Rio Grande Valley. The implementation presents ways to scale up and time-bound such hydrologic models on high-performance computing systems. Combining the HAND data with the Precipitation Frequency Estimate generated a Flood Vulnerability Raster (FVR) to provide the base dataset for subsequent steps in the analysis. The areas most prone to outages are identified using a spatial power outage model that combines information from the grid infrastructure maps, FVR, Social-Vulnerability Index (SVI) data, and the Weather Events dataset. The same set of input datasets with a total of 47 features for each of the 17 counties in the Rio Grande, along with their historical power outage data, is used to train a Random Forest model to predict the power outage intensity for a county. The Random Forest model performs well with a low normalized RMSE of 11.8%. Also, an analytical model for future outage prediction is developed based on linear regression. The simplified power-outage analytical model is created by using feature selection and utilizes only four important variables for an R-squared of 0.88. Furthermore, this research discusses possible practices that can improve power system resilience, such as deploying microgrids, expansion of transmission capacity and grid hardening. Research results show promise for use by urban planners, operators and decision-makers that make decisions related to resource allocation, critical infrastructure protection, investments, and managing emergency preparedness.
Resilience and operational benefits of electric vehicle and grid integration
(2023-12) Robbennolt, Jake; Boyles, Stephen David, 1982-
Shared autonomous electric vehicles (SAEVs) present new opportunities to control and optimize vehicle movements. Future deployment of these vehicles may reduce the need for individuals to own a personal vehicle and can have traffic flow and environmental benefits. However, to fully realize the benefits of this technology, vehicle dispatch needs to be optimized. Fleet operators will want to own and operate as few vehicles as possible while still maintaining a reasonable level of service for passengers. SAEVs can also be used for many purposes beyond moving individual passengers across the system. They could also be used to deliver food, provide last-mile delivery for packages, and interact with the electric grid. These services must be balanced to ensure that as many people are served as possible. SAEV dispatch can be of particular interest in the aftermath of a natural disaster when there may be failures in the electric grid. In this case, vehicles can be used to transport power across broken lines to power critical facilities or reduce the number of blackouts. However, this important service must be weighed against the continued need to provide transportation to critical workers and vulnerable populations that may be reliant on SAEV service. We develop a dispatch policy that is proven to serve all demands (for both electricity and transportation service) if any policy can serve those demand. This maximum throughput policy also enables an analytical characterization of the minimum fleet size (or minimum cost fleet if it can be heterogeneous) such that queues of passengers and energy will remain bounded in the long run. Based on the stable dispatch policy we relax some assumptions and develop a policy that is more realistic for implementation. We pay particular attention to constraints on power flow in the electric grid to ensure realistic charging and discharging behavior (which is important for distribution system service restoration). The analysis and simulation also distinguishes between several potential objective functions which have important equity and stability impacts. We demonstrate how serving passengers from the longest queues first (a technique based on the 'pressure' from maximum stability dispatch) can lead to more equitable outcomes for passengers. Finally, we examine the impact of the time horizon needed for the model predictive control algorithm. A long time horizon is needed to incorporate charging and discharging as well as longer term trends in electric demand. We suggest that future research should examine heuristics to solve this problem more quickly than commercial solvers to enable real-time implementation.