电力系统中大规模风电集成的模型预测控制进展:全面回顾

IF 13 Q1 ENERGY & FUELS
Peng Lu , Ning Zhang , Lin Ye , Ershun Du , Chongqing Kang
{"title":"电力系统中大规模风电集成的模型预测控制进展:全面回顾","authors":"Peng Lu ,&nbsp;Ning Zhang ,&nbsp;Lin Ye ,&nbsp;Ershun Du ,&nbsp;Chongqing Kang","doi":"10.1016/j.adapen.2024.100177","DOIUrl":null,"url":null,"abstract":"<div><p>Wind power exhibits low controllability and is situated in dispersed geographical locations, presenting complex coupling and aggregation characteristics in both temporal and spatial dimensions. When large-scale wind power is integrated into the power grid, it will bring a significant technical challenge: the highly variable nature of wind power poses a threat to the safe and stable control of the power, frequency, and voltage in the power system. Simultaneously, the model predictive control (MPC) technology provides more opportunities for investigating control issues related to large-scale wind power integration in power systems. This paper provides a timely and systematic overview of the applications of MPC in the field of wind power for the first time, innovatively embedding MPC technology into multi-level (e.g., wind turbines, wind farms, wind power cluster, and power grids) and multi-objective (e.g., power, frequency, and voltage) control. Firstly, the basic concept and classification criteria of MPC are developed, and the available modeling methods in wind power are carefully compared. Secondly, the application scenarios of MPC in multi-level and multi-objective wind power control are summarized. Finally, how to use a variety of optimization algorithms to solve these models is discussed. Based on the broad review above, we summarize several key scientific issues related to predictive control and discuss the challenges and future development directions in detail. This paper details the role of MPC technology in multi-level and multi-objective control within the wind power sector, aiming to help engineers and scientists understand its substantial potential in wind power integration in power systems.</p></div>","PeriodicalId":34615,"journal":{"name":"Advances in Applied Energy","volume":"14 ","pages":"Article 100177"},"PeriodicalIF":13.0000,"publicationDate":"2024-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666792424000155/pdfft?md5=8da5ee9be84dc66a46cbd485ccbef1b0&pid=1-s2.0-S2666792424000155-main.pdf","citationCount":"0","resultStr":"{\"title\":\"Advances in model predictive control for large-scale wind power integration in power systems\",\"authors\":\"Peng Lu ,&nbsp;Ning Zhang ,&nbsp;Lin Ye ,&nbsp;Ershun Du ,&nbsp;Chongqing Kang\",\"doi\":\"10.1016/j.adapen.2024.100177\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>Wind power exhibits low controllability and is situated in dispersed geographical locations, presenting complex coupling and aggregation characteristics in both temporal and spatial dimensions. When large-scale wind power is integrated into the power grid, it will bring a significant technical challenge: the highly variable nature of wind power poses a threat to the safe and stable control of the power, frequency, and voltage in the power system. Simultaneously, the model predictive control (MPC) technology provides more opportunities for investigating control issues related to large-scale wind power integration in power systems. This paper provides a timely and systematic overview of the applications of MPC in the field of wind power for the first time, innovatively embedding MPC technology into multi-level (e.g., wind turbines, wind farms, wind power cluster, and power grids) and multi-objective (e.g., power, frequency, and voltage) control. Firstly, the basic concept and classification criteria of MPC are developed, and the available modeling methods in wind power are carefully compared. Secondly, the application scenarios of MPC in multi-level and multi-objective wind power control are summarized. Finally, how to use a variety of optimization algorithms to solve these models is discussed. Based on the broad review above, we summarize several key scientific issues related to predictive control and discuss the challenges and future development directions in detail. This paper details the role of MPC technology in multi-level and multi-objective control within the wind power sector, aiming to help engineers and scientists understand its substantial potential in wind power integration in power systems.</p></div>\",\"PeriodicalId\":34615,\"journal\":{\"name\":\"Advances in Applied Energy\",\"volume\":\"14 \",\"pages\":\"Article 100177\"},\"PeriodicalIF\":13.0000,\"publicationDate\":\"2024-04-20\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.sciencedirect.com/science/article/pii/S2666792424000155/pdfft?md5=8da5ee9be84dc66a46cbd485ccbef1b0&pid=1-s2.0-S2666792424000155-main.pdf\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Advances in Applied Energy\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2666792424000155\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENERGY & FUELS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Advances in Applied Energy","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2666792424000155","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENERGY & FUELS","Score":null,"Total":0}
引用次数: 0

摘要

风力发电的可控性低,地理位置分散,在时间和空间维度上都具有复杂的耦合和聚集特性。当大规模风电并入电网时,将带来巨大的技术挑战:风电的高可变性对电力系统中功率、频率和电压的安全稳定控制构成威胁。与此同时,模型预测控制(MPC)技术为研究与大规模风电并入电力系统相关的控制问题提供了更多机会。本文首次对 MPC 在风电领域的应用进行了及时而系统的概述,创新性地将 MPC 技术嵌入到多层次(如风力涡轮机、风电场、风电集群和电网)和多目标(如功率、频率和电压)控制中。首先,提出了 MPC 的基本概念和分类标准,并仔细比较了现有的风电建模方法。其次,总结了 MPC 在多级多目标风电控制中的应用场景。最后,讨论了如何使用各种优化算法来求解这些模型。在上述综述的基础上,我们总结了与预测控制相关的几个关键科学问题,并详细讨论了所面临的挑战和未来的发展方向。本文详细介绍了 MPC 技术在风电领域的多级和多目标控制中的作用,旨在帮助工程师和科学家了解其在电力系统风电集成中的巨大潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Advances in model predictive control for large-scale wind power integration in power systems

Wind power exhibits low controllability and is situated in dispersed geographical locations, presenting complex coupling and aggregation characteristics in both temporal and spatial dimensions. When large-scale wind power is integrated into the power grid, it will bring a significant technical challenge: the highly variable nature of wind power poses a threat to the safe and stable control of the power, frequency, and voltage in the power system. Simultaneously, the model predictive control (MPC) technology provides more opportunities for investigating control issues related to large-scale wind power integration in power systems. This paper provides a timely and systematic overview of the applications of MPC in the field of wind power for the first time, innovatively embedding MPC technology into multi-level (e.g., wind turbines, wind farms, wind power cluster, and power grids) and multi-objective (e.g., power, frequency, and voltage) control. Firstly, the basic concept and classification criteria of MPC are developed, and the available modeling methods in wind power are carefully compared. Secondly, the application scenarios of MPC in multi-level and multi-objective wind power control are summarized. Finally, how to use a variety of optimization algorithms to solve these models is discussed. Based on the broad review above, we summarize several key scientific issues related to predictive control and discuss the challenges and future development directions in detail. This paper details the role of MPC technology in multi-level and multi-objective control within the wind power sector, aiming to help engineers and scientists understand its substantial potential in wind power integration in power systems.

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
Advances in Applied Energy
Advances in Applied Energy Energy-General Energy
CiteScore
23.90
自引率
0.00%
发文量
36
审稿时长
21 days
×
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:481959085
Book学术官方微信