适用于大规模可再生能源整合的高效单级鲁棒输电扩展规划方法

IF 4.8 2区 工程技术 Q2 ENERGY & FUELS Sustainable Energy Grids & Networks Pub Date : 2024-07-27 DOI:10.1016/j.segan.2024.101486
X. Zeng , J. Chen , X. Yin , H. Chen , Z. Liang , S. Zhang , B. Tan
{"title":"适用于大规模可再生能源整合的高效单级鲁棒输电扩展规划方法","authors":"X. Zeng ,&nbsp;J. Chen ,&nbsp;X. Yin ,&nbsp;H. Chen ,&nbsp;Z. Liang ,&nbsp;S. Zhang ,&nbsp;B. Tan","doi":"10.1016/j.segan.2024.101486","DOIUrl":null,"url":null,"abstract":"<div><p>Robust transmission expansion planning (RTEP) approaches are crucial for addressing the uncertainty associated with renewable energy sources (RESs). However, existing methods often yield overly conservative solutions and exhibit low computational efficiency, especially when dealing with a large number of RES units. To overcome these limitations, we propose a simplified single-level RTEP framework based on scenarios captured in advance from historical data by searching the vertices of a convex hull. These scenarios, referred to as robust scenarios, are guaranteed to produce robust solutions that are consistent with the traditional two-stage adaptive robust TEP (ARTEP) approach in terms of robustness and optimality. Finally, the speed for solving the single-level model is increased by applying a probability-based method to determine the odds of the robust scenarios being the worst-case scenario. Numerical results obtained for the Garver 6-bus system, the IEEE 118-bus system, and the Polish 2383-bus system demonstrate that the proposed approach saves 91.71 %, 93.39 %, and 98.84 % of the required computational time, respectively, compared to the ARTEP approach.</p></div>","PeriodicalId":56142,"journal":{"name":"Sustainable Energy Grids & Networks","volume":"39 ","pages":"Article 101486"},"PeriodicalIF":4.8000,"publicationDate":"2024-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Highly-efficient single-level robust transmission expansion planning approach applicable to large-scale renewable energy integration\",\"authors\":\"X. Zeng ,&nbsp;J. Chen ,&nbsp;X. Yin ,&nbsp;H. Chen ,&nbsp;Z. Liang ,&nbsp;S. Zhang ,&nbsp;B. Tan\",\"doi\":\"10.1016/j.segan.2024.101486\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>Robust transmission expansion planning (RTEP) approaches are crucial for addressing the uncertainty associated with renewable energy sources (RESs). However, existing methods often yield overly conservative solutions and exhibit low computational efficiency, especially when dealing with a large number of RES units. To overcome these limitations, we propose a simplified single-level RTEP framework based on scenarios captured in advance from historical data by searching the vertices of a convex hull. These scenarios, referred to as robust scenarios, are guaranteed to produce robust solutions that are consistent with the traditional two-stage adaptive robust TEP (ARTEP) approach in terms of robustness and optimality. Finally, the speed for solving the single-level model is increased by applying a probability-based method to determine the odds of the robust scenarios being the worst-case scenario. Numerical results obtained for the Garver 6-bus system, the IEEE 118-bus system, and the Polish 2383-bus system demonstrate that the proposed approach saves 91.71 %, 93.39 %, and 98.84 % of the required computational time, respectively, compared to the ARTEP approach.</p></div>\",\"PeriodicalId\":56142,\"journal\":{\"name\":\"Sustainable Energy Grids & Networks\",\"volume\":\"39 \",\"pages\":\"Article 101486\"},\"PeriodicalIF\":4.8000,\"publicationDate\":\"2024-07-27\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Sustainable Energy Grids & Networks\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2352467724002157\",\"RegionNum\":2,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"ENERGY & FUELS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Sustainable Energy Grids & Networks","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2352467724002157","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENERGY & FUELS","Score":null,"Total":0}
引用次数: 0

摘要

稳健的输电扩展规划(RTEP)方法对于解决与可再生能源(RES)相关的不确定性至关重要。然而,现有的方法往往会产生过于保守的解决方案,而且计算效率较低,尤其是在处理大量可再生能源设备时。为了克服这些局限性,我们提出了一种简化的单级 RTEP 框架,该框架基于通过搜索凸壳顶点提前从历史数据中捕捉到的情景。这些情景被称为鲁棒情景,可保证产生与传统两阶段自适应鲁棒 RTEP(ARTEP)方法在鲁棒性和最优性方面一致的鲁棒解决方案。最后,通过采用基于概率的方法来确定稳健方案成为最坏情况的概率,从而提高了单层模型的求解速度。针对 Garver 6 总线系统、IEEE 118 总线系统和波兰 2383 总线系统得出的数值结果表明,与 ARTEP 方法相比,建议的方法分别节省了 91.71%、93.39% 和 98.84% 的所需计算时间。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Highly-efficient single-level robust transmission expansion planning approach applicable to large-scale renewable energy integration

Robust transmission expansion planning (RTEP) approaches are crucial for addressing the uncertainty associated with renewable energy sources (RESs). However, existing methods often yield overly conservative solutions and exhibit low computational efficiency, especially when dealing with a large number of RES units. To overcome these limitations, we propose a simplified single-level RTEP framework based on scenarios captured in advance from historical data by searching the vertices of a convex hull. These scenarios, referred to as robust scenarios, are guaranteed to produce robust solutions that are consistent with the traditional two-stage adaptive robust TEP (ARTEP) approach in terms of robustness and optimality. Finally, the speed for solving the single-level model is increased by applying a probability-based method to determine the odds of the robust scenarios being the worst-case scenario. Numerical results obtained for the Garver 6-bus system, the IEEE 118-bus system, and the Polish 2383-bus system demonstrate that the proposed approach saves 91.71 %, 93.39 %, and 98.84 % of the required computational time, respectively, compared to the ARTEP approach.

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
Sustainable Energy Grids & Networks
Sustainable Energy Grids & Networks Energy-Energy Engineering and Power Technology
CiteScore
7.90
自引率
13.00%
发文量
206
审稿时长
49 days
期刊介绍: Sustainable Energy, Grids and Networks (SEGAN)is an international peer-reviewed publication for theoretical and applied research dealing with energy, information grids and power networks, including smart grids from super to micro grid scales. SEGAN welcomes papers describing fundamental advances in mathematical, statistical or computational methods with application to power and energy systems, as well as papers on applications, computation and modeling in the areas of electrical and energy systems with coupled information and communication technologies.
期刊最新文献
A hybrid machine learning-based cyber-threat mitigation in energy and flexibility scheduling of interconnected local energy networks considering a negawatt demand response portfolio An equilibrium-based distribution market model hosting energy communities and grid-scale battery energy storage The clearing strategy of primary frequency control ancillary services market from the point of view ISO in the presence of synchronous generations and virtual power plants based on responsive loads Optimal scheduling of smart home appliances with a stochastic power outage: A two-stage stochastic programming approach Cooperative price-based demand response program for multiple aggregators based on multi-agent reinforcement learning and Shapley-value
×
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
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 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 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1