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

被引:0
|
作者
Qiu, Dawei [1 ]
Strbac, Goran [1 ]
Wang, Yi [1 ]
Ye, Yujian [2 ]
Wang, Jiawei [1 ,3 ]
Pinson, Pierre [1 ]
Silva, Vera [4 ]
Teng, Fei [1 ]
机构
[1] Imperial Coll London, London SW7 2AZ, England
[2] Southeast Univ, Nanjing 210096, Peoples R China
[3] Northumbria Univ, Newcastle Upon Tyne, England
[4] Gen Elect Grid Solut, F-11140 Paris, France
来源
IEEE POWER & ENERGY MAGAZINE | 2024年 / 22卷 / 06期
基金
英国工程与自然科学研究理事会;
关键词
Adaptation models; Uncertainty; Microgrids; Power system stability; Stability analysis; Power system reliability; Optimization; Resilience; Meteorology; Load modeling;
D O I
10.1109/MPE.2024.3405893
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
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.
引用
收藏
页码:18 / 27
页数:10
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