4.4 Article

Enhancing the resilience of distribution systems through optimal restoration of sensitive loads based on hybrid network zoning

期刊

ELECTRICAL ENGINEERING
卷 105, 期 2, 页码 745-760

出版社

SPRINGER
DOI: 10.1007/s00202-022-01695-1

关键词

Resilience; Distribution network; Microgrid; Sensitive loads; Distributed Generation

向作者/读者索取更多资源

Recent statistical analysis shows that the number and intensity of natural catastrophes worldwide are increasing due to climate change. Therefore, there is an urgent need to use cutting-edge technology to mitigate the damaging impacts. This paper presents a novel method for increasing resilience by installing intelligently managed remote switches and optimizing the capacity of Distributed Generations (DGs).
Recent statistical analysis of natural catastrophes reveals a rise in the number and intensity of incidents worldwide due to climate change. As a result, the urgency of utilizing cutting-edge technology to mitigate its damaging impacts is palpable. This paper presents a novel method aiming at increasing resilience which is based on (1) the installation of intelligently managed remote switches and (2) the optimization of the capacity of the Distributed Generations (DGs) considering the number of sensitive loads. The purpose of this study is to determine appropriate zoning by considering established limits and technical constraints imposed by a certain number of DGs. Each DG is considered to cover a portion of the loads, allowing for the restoration of additional high priority loads with the lowest available capacity in each region. The optimized system's primary function is input using the LP-metric technique with equal weight coefficients. The MILP optimization subject will identify the ideal Microgrid (MG) combinations for reclaiming the highest amount of load. The findings imply that the proposed scenarios are efficient in terms of the problem's stated objective function.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.4
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据