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 , J. Chen , X. Yin , H. Chen , Z. Liang , S. Zhang , 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 , J. Chen , X. Yin , H. Chen , Z. Liang , S. Zhang , 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}
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.
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.