Abstract
Both the frequency and intensity of extreme weather events have been trending higher in recent years, leading to significant infrastructure damage in the electric grid. The impact of these extreme weather events is desired to be analyzed and quantified to help transmission and distribution system operators prepare for and prevent significant damage and subsequent loss of power. In this paper, we develop an approach that models the impact of extreme weather on the grid and identifies the worst impact zone using Q-learning (a reinforcement learning approach). The identification results reveal grid vulnerability to weather events and provide insights for system operators to help achieve optimal resource allocation and crew dispatch to minimize the adverse impacts of extreme weather. Simulation studies are conducted on the IEEE 123-node system to demonstrate the performance of the proposed approach.
Original language | American English |
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Number of pages | 5 |
DOIs | |
State | Published - Feb 2020 |
Event | 2020 IEEE Power and Energy Society Innovative Smart Grid Technologies Conference, ISGT 2020 - Washington, United States Duration: 17 Feb 2020 → 20 Feb 2020 |
Conference
Conference | 2020 IEEE Power and Energy Society Innovative Smart Grid Technologies Conference, ISGT 2020 |
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Country/Territory | United States |
City | Washington |
Period | 17/02/20 → 20/02/20 |
Bibliographical note
See NREL/CP-5D00-74737 for preprintNREL Publication Number
- NREL/CP-5D00-77282
Keywords
- Distribution system
- Extreme weather
- Impact analysis
- Intensity
- Reinforcement learning
- Vulnerability