Artificial intelligence for microgrid resilience: a data-driven and model-free approach
File(s)
Accepted version
Author(s)
Type
Journal Article
Abstract
Extreme weather events, which are characterized by high impact and low probability, can disrupt power system components and lead to severe power outages. The increasing adoption of renewable energy resources in the power sector, as part of decarbonization efforts, introduces further system operation challenges because of their fluctuating nature, potentially worsening the impact of these extreme weather events. To address the challenges from these high-impact and low-probability events, the concept of resilience has been introduced into the power industry. Considering the potential serious disruptions, the primary goal of resilient power system operation during extreme events is to ensure the continuous supply of critical loads, such as hospitals, police stations, data centers, traffic lights, etc., across various power sectors, which constitutes a system-wide load restoration problem.
Date Issued
2024-11-01
Date Acceptance
2024-11-01
Citation
IEEE Power and Energy Magazine, 2024, 22 (6), pp.18-27
ISSN
1540-7977
Publisher
Institute of Electrical and Electronics Engineers
Start Page
18
End Page
27
Journal / Book Title
IEEE Power and Energy Magazine
Volume
22
Issue
6
Copyright Statement
Copyright © 2024 IEEE. This is the author’s accepted manuscript made available under a CC-BY licence in accordance with Imperial’s Research Publications Open Access policy (www.imperial.ac.uk/oa-policy)
License URL
Subjects
Adaptation models
Engineering
Engineering, Electrical & Electronic
Load modeling
Meteorology
Microgrids
Optimization
Power system reliability
Power system stability
Resilience
Science & Technology
Stability analysis
Technology
Uncertainty
Publication Status
Published
Date Publish Online
2024-11-20