Critical Flow-Based Fast Algorithm for the Total Supply Capability Curve of Distribution Networks
To alleviate the ‘curse of dimensionality’ and speed up the calculation of the total supply capability curve (TSC curve), this paper proposes a novel critical flow-based fast algorithm for distribution networks. Firstly, the critical flow and the TSC curve are found to have good correspondence throu...
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| Published in | Arabian journal for science and engineering (2011) Vol. 48; no. 11; pp. 14501 - 14515 |
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| Main Authors | , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Berlin/Heidelberg
Springer Berlin Heidelberg
01.11.2023
Springer Nature B.V |
| Subjects | |
| Online Access | Get full text |
| ISSN | 2193-567X 1319-8025 2191-4281 |
| DOI | 10.1007/s13369-023-07722-y |
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| Abstract | To alleviate the ‘curse of dimensionality’ and speed up the calculation of the total supply capability curve (TSC curve), this paper proposes a novel critical flow-based fast algorithm for distribution networks. Firstly, the critical flow and the TSC curve are found to have good correspondence through the comparison of their concepts and models. Secondly, a critical flow-based algorithm with three phases is proposed for calculating the TSC curve: (i). Determine the equivalent flow network of the distribution network; (ii). Solve critical flows of the equivalent flow network through a dimension reduction-sampling algorithm and obtain the corresponding TSC curve operating points; (iii). Calculate the flow value of the critical flow and plot the TSC curve. Finally, the proposed algorithm is verified by the expanded IEEE RBTS BUS4 case with soft open point (SOP). The application of the TSC curve in distribution network planning is demonstrated. In addition, the correctness and rapidity of the proposed algorithm are verified by comparing it with the existing sampling algorithm, and the reasons for the performance improvement are investigated. This paper applies the network flow theory to calculating the power supply capability of distribution networks for the first time, which simplifies the calculation of the TSC curve and increases the computational efficiency significantly. |
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| AbstractList | To alleviate the ‘curse of dimensionality’ and speed up the calculation of the total supply capability curve (TSC curve), this paper proposes a novel critical flow-based fast algorithm for distribution networks. Firstly, the critical flow and the TSC curve are found to have good correspondence through the comparison of their concepts and models. Secondly, a critical flow-based algorithm with three phases is proposed for calculating the TSC curve: (i). Determine the equivalent flow network of the distribution network; (ii). Solve critical flows of the equivalent flow network through a dimension reduction-sampling algorithm and obtain the corresponding TSC curve operating points; (iii). Calculate the flow value of the critical flow and plot the TSC curve. Finally, the proposed algorithm is verified by the expanded IEEE RBTS BUS4 case with soft open point (SOP). The application of the TSC curve in distribution network planning is demonstrated. In addition, the correctness and rapidity of the proposed algorithm are verified by comparing it with the existing sampling algorithm, and the reasons for the performance improvement are investigated. This paper applies the network flow theory to calculating the power supply capability of distribution networks for the first time, which simplifies the calculation of the TSC curve and increases the computational efficiency significantly. |
| Author | Zhang, Shihao Li, Chengjin Xiao, Jun Jiao, Heng Qu, Yuqing She, Buxin |
| Author_xml | – sequence: 1 givenname: Jun surname: Xiao fullname: Xiao, Jun organization: Key Laboratory of Smart Grid of Ministry of Education, Tianjin University – sequence: 2 givenname: Yuqing surname: Qu fullname: Qu, Yuqing organization: Key Laboratory of Smart Grid of Ministry of Education, Tianjin University – sequence: 3 givenname: Buxin orcidid: 0000-0002-2624-560X surname: She fullname: She, Buxin email: bshe@vols.utk.edu organization: Department of Electronic Engineering and Computer Science, The University of Tennessee – sequence: 4 givenname: Heng surname: Jiao fullname: Jiao, Heng organization: Key Laboratory of Smart Grid of Ministry of Education, Tianjin University – sequence: 5 givenname: Chengjin surname: Li fullname: Li, Chengjin organization: Key Laboratory of Smart Grid of Ministry of Education, Tianjin University – sequence: 6 givenname: Shihao surname: Zhang fullname: Zhang, Shihao organization: Key Laboratory of Smart Grid of Ministry of Education, Tianjin University |
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| Cites_doi | 10.1109/TSG.2022.3222323 10.1016/j.egyr.2022.10.326 10.1109/61.915497 10.1016/j.ijepes.2014.10.025 10.1049/iet-gtd.2018.5820 10.1109/TPWRS.2021.3091710 10.1016/j.ress.2019.03.007 10.1080/15325008.2017.1336582 10.1109/59.32623 10.1007/s13369-013-0623-0 10.1016/0020-0190(89)90084-7 10.1049/iet-gtd.2017.0725 10.1007/s13369-018-3200-8 10.1016/j.ejor.2022.04.012 10.1007/s13369-019-04309-4 10.1049/iet-gtd.2016.1071 10.1109/59.76730 10.1007/s13369-022-07213-6 10.1109/ACCESS.2020.2995620 10.1109/TPWRS.2015.2444792 10.1002/2050-7038.13030 10.1016/j.ijepes.2022.108193 10.1016/j.ress.2022.108974 10.1016/j.ijepes.2020.106238 10.1109/PESGM46819.2021.9638142 10.1016/j.ijepes.2021.107344 10.1016/j.ijepes.2021.107812 10.1016/j.apenergy.2015.12.022 10.1016/j.ijepes.2021.107676 |
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| Copyright | King Fahd University of Petroleum & Minerals 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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| Keywords | Total supply capability curve Flow network Total supply capability Critical flow Distribution network |
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| Title | Critical Flow-Based Fast Algorithm for the Total Supply Capability Curve of Distribution Networks |
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