Artificial Intelligence for Microgrid Resilience: A Data-Driven and Model-Free Approach

IEEE Power and Energy Magazine - Tập 22 Số 6 - Trang 18-27 - 2024
Dawei Qiu1, Goran Strbac1, Yi Wang1, Yujian Ye2, Jiawei Wang3,1, Pierre Pinson1, Vera Silva4, Fei Teng1
1Imperial College London, London, U.K.
2Southeast University, Nanjing, China
3Northumbria University, Newcastle-upon-Tyne, U.K.
4General Electric Grid Solutions, Paris, CS, France

Tóm tắt

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.

Từ khóa

#Adaptation models #Uncertainty #Microgrids #Power system stability #Stability analysis #Power system reliability #Optimization #Resilience #Meteorology #Load modeling #Power system restoration

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