鲁棒电网的仿生设计

Varuneswara Panyam, Hao Huang, Bogdan Pinte, K. Davis, A. Layton
{"title":"鲁棒电网的仿生设计","authors":"Varuneswara Panyam, Hao Huang, Bogdan Pinte, K. Davis, A. Layton","doi":"10.1109/TPEC.2019.8662130","DOIUrl":null,"url":null,"abstract":"Extreme events continue to show that existing power grid configurations can be vulnerable to disturbances. Drawing inspiration from naturally robust biological ecosystems presents a potential source of robust design guidelines for modern power grids. The robust network structure of ecosystems is partially derived from a unique balance between pathway efficiency and redundancy. Structural and basic-functional similarities support the application of ecological properties and analysis techniques to power grid design. The work presented here quantitatively investigates the level of similarity between ecosystems and power grids by applying ecological network metrics to a basic, realistic hypothetical 5-bus power system. A comparison between the power grid’s performance and average ecosystem performance substantiates the use of the ecological robustness metric for the development of a bio-inspired power grid optimization model. The bio-inspired optimization model re-configures the five bus grid to mimic ecosystem robustness. The results demonstrate the potential of ecosystems to provide new robust design principles for power grids.","PeriodicalId":424038,"journal":{"name":"2019 IEEE Texas Power and Energy Conference (TPEC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"19","resultStr":"{\"title\":\"Bio-Inspired Design for Robust Power Networks\",\"authors\":\"Varuneswara Panyam, Hao Huang, Bogdan Pinte, K. Davis, A. Layton\",\"doi\":\"10.1109/TPEC.2019.8662130\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Extreme events continue to show that existing power grid configurations can be vulnerable to disturbances. Drawing inspiration from naturally robust biological ecosystems presents a potential source of robust design guidelines for modern power grids. The robust network structure of ecosystems is partially derived from a unique balance between pathway efficiency and redundancy. Structural and basic-functional similarities support the application of ecological properties and analysis techniques to power grid design. The work presented here quantitatively investigates the level of similarity between ecosystems and power grids by applying ecological network metrics to a basic, realistic hypothetical 5-bus power system. A comparison between the power grid’s performance and average ecosystem performance substantiates the use of the ecological robustness metric for the development of a bio-inspired power grid optimization model. The bio-inspired optimization model re-configures the five bus grid to mimic ecosystem robustness. The results demonstrate the potential of ecosystems to provide new robust design principles for power grids.\",\"PeriodicalId\":424038,\"journal\":{\"name\":\"2019 IEEE Texas Power and Energy Conference (TPEC)\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-02-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"19\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2019 IEEE Texas Power and Energy Conference (TPEC)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/TPEC.2019.8662130\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 IEEE Texas Power and Energy Conference (TPEC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/TPEC.2019.8662130","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 19

摘要

极端事件不断表明,现有的电网结构很容易受到干扰。从自然健壮的生物生态系统中汲取灵感,为现代电网提供了健壮设计指南的潜在来源。生态系统健壮的网络结构部分来源于路径效率和冗余之间的独特平衡。结构和基本功能的相似性支持了生态特性和分析技术在电网设计中的应用。本文提出的工作通过将生态网络指标应用于一个基本的、现实的假设的5总线电力系统,定量地调查了生态系统和电网之间的相似性水平。电网性能和平均生态系统性能之间的比较证实了生态稳健性指标用于开发仿生电网优化模型的使用。仿生优化模型重新配置五总线网格模拟生态系统的鲁棒性。研究结果表明,生态系统有潜力为电网提供新的稳健设计原则。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Bio-Inspired Design for Robust Power Networks
Extreme events continue to show that existing power grid configurations can be vulnerable to disturbances. Drawing inspiration from naturally robust biological ecosystems presents a potential source of robust design guidelines for modern power grids. The robust network structure of ecosystems is partially derived from a unique balance between pathway efficiency and redundancy. Structural and basic-functional similarities support the application of ecological properties and analysis techniques to power grid design. The work presented here quantitatively investigates the level of similarity between ecosystems and power grids by applying ecological network metrics to a basic, realistic hypothetical 5-bus power system. A comparison between the power grid’s performance and average ecosystem performance substantiates the use of the ecological robustness metric for the development of a bio-inspired power grid optimization model. The bio-inspired optimization model re-configures the five bus grid to mimic ecosystem robustness. The results demonstrate the potential of ecosystems to provide new robust design principles for power grids.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信