Dynamic reactive power optimization of distribution network with distributed generation based on fuzzy time clustering
In order to solve the coordinated optimization problem of distributed generation (DG), on‐load tap changer (OLTC) and capacitor banks, a dynamic reactive power optimization (DRPO) method based on fuzzy time clustering is proposed. In the method, fuzzy time clustering algorithm is used to cluster the...
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| Vydáno v: | IET generation, transmission & distribution Ročník 16; číslo 7; s. 1349 - 1363 |
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| Hlavní autoři: | , , , , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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Wiley
01.04.2022
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| ISSN: | 1751-8687, 1751-8695 |
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| Abstract | In order to solve the coordinated optimization problem of distributed generation (DG), on‐load tap changer (OLTC) and capacitor banks, a dynamic reactive power optimization (DRPO) method based on fuzzy time clustering is proposed. In the method, fuzzy time clustering algorithm is used to cluster the static optimal switching sequence of OLTC and capacitor banks. The time decoupling of control variables is realized and it meets the constraints of the maximum switching times. Next, the influencing factors of DG reactive power limit are analysed. Considering the control ability of OLTC, capacitor banks and DG in voltage and reactive power optimization, a coordinated optimization method is proposed. Since the adjustment of OLTC tap position directly affects the voltage and reactive power distribution of the whole line, the switching time and tap position of OLTC are determined based on the static optimization results of each time and fuzzy time clustering algorithm. Then, the switching time of capacitor banks is determined by fuzzy time clustering algorithm. At last, the switching capacity of capacitor banks and DG reactive power are jointly optimized to get the final control scheme. The proposed method demonstrated good performance in the modified IEEE33 node system and two actual power grid examples. |
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| AbstractList | Abstract In order to solve the coordinated optimization problem of distributed generation (DG), on‐load tap changer (OLTC) and capacitor banks, a dynamic reactive power optimization (DRPO) method based on fuzzy time clustering is proposed. In the method, fuzzy time clustering algorithm is used to cluster the static optimal switching sequence of OLTC and capacitor banks. The time decoupling of control variables is realized and it meets the constraints of the maximum switching times. Next, the influencing factors of DG reactive power limit are analysed. Considering the control ability of OLTC, capacitor banks and DG in voltage and reactive power optimization, a coordinated optimization method is proposed. Since the adjustment of OLTC tap position directly affects the voltage and reactive power distribution of the whole line, the switching time and tap position of OLTC are determined based on the static optimization results of each time and fuzzy time clustering algorithm. Then, the switching time of capacitor banks is determined by fuzzy time clustering algorithm. At last, the switching capacity of capacitor banks and DG reactive power are jointly optimized to get the final control scheme. The proposed method demonstrated good performance in the modified IEEE33 node system and two actual power grid examples. In order to solve the coordinated optimization problem of distributed generation (DG), on‐load tap changer (OLTC) and capacitor banks, a dynamic reactive power optimization (DRPO) method based on fuzzy time clustering is proposed. In the method, fuzzy time clustering algorithm is used to cluster the static optimal switching sequence of OLTC and capacitor banks. The time decoupling of control variables is realized and it meets the constraints of the maximum switching times. Next, the influencing factors of DG reactive power limit are analysed. Considering the control ability of OLTC, capacitor banks and DG in voltage and reactive power optimization, a coordinated optimization method is proposed. Since the adjustment of OLTC tap position directly affects the voltage and reactive power distribution of the whole line, the switching time and tap position of OLTC are determined based on the static optimization results of each time and fuzzy time clustering algorithm. Then, the switching time of capacitor banks is determined by fuzzy time clustering algorithm. At last, the switching capacity of capacitor banks and DG reactive power are jointly optimized to get the final control scheme. The proposed method demonstrated good performance in the modified IEEE33 node system and two actual power grid examples. |
| Author | Ji, Yuqi Chen, Xuehan Tao, Yukun Li, Congshan Wang, Tao He, Ping Jin, Nan |
| Author_xml | – sequence: 1 givenname: Yuqi surname: Ji fullname: Ji, Yuqi organization: Zhengzhou University of Light Industry – sequence: 2 givenname: Xuehan orcidid: 0000-0003-2934-3341 surname: Chen fullname: Chen, Xuehan organization: Zhengzhou University of Light Industry – sequence: 3 givenname: Tao surname: Wang fullname: Wang, Tao organization: Zhengzhou University of Light Industry – sequence: 4 givenname: Ping orcidid: 0000-0002-1692-5804 surname: He fullname: He, Ping email: hplkz@126.com organization: Zhengzhou University of Light Industry – sequence: 5 givenname: Nan surname: Jin fullname: Jin, Nan organization: Zhengzhou University of Light Industry – sequence: 6 givenname: Congshan surname: Li fullname: Li, Congshan organization: Zhengzhou University of Light Industry – sequence: 7 givenname: Yukun surname: Tao fullname: Tao, Yukun organization: Zhengzhou University of Light Industry |
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| Cites_doi | 10.1109/TPWRS.2017.2724058 10.1049/iet-gtd.2016.1912 10.1049/iet-gtd.2016.0036 10.1109/TPWRS.2018.2875032 10.1109/TSTE.2015.2494587 10.1109/ISGT-Asia.2019.8880890 10.1109/TPWRS.2019.2914923 10.1049/iet-gtd.2016.0303 10.1049/iet-gtd.2016.1195 10.1109/ISGT-Asia.2019.8881289 10.1109/ICIEA.2018.8397906 10.1049/iet-gtd.2015.1317 10.1109/TSG.2016.2539824 10.1049/iet-gtd.2017.0674 10.1049/iet-gtd.2015.1051 10.1049/iet-gtd.2017.0666 10.1109/TPWRS.2015.2466435 10.1109/ISGT-Asia.2019.8880807 10.1109/TSTE.2016.2607146 10.1109/TPWRD.2018.2868158 10.1109/TSTE.2015.2433957 |
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| Notes | Funding information National Natural Science Foundation of China (51707176), the Scientific and Technological Research Project of Henan Province (212102210257), the Science and Technology Project of State Grid Henan Electric Power Company (5217I020000G), and the High School Key Research Program of Henan Province (20A470014) |
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| References | 2017; 41 2016; 7 2017; 8 2017; 1 2015; 39 2015; 6 2019; 4 2017; 11 2019; 34 2016; 10 2014; 38 2019 2016; 31 2018 2018; 12 2018; 33 e_1_2_10_23_1 e_1_2_10_24_1 e_1_2_10_21_1 e_1_2_10_20_1 e_1_2_10_2_1 e_1_2_10_4_1 e_1_2_10_18_1 e_1_2_10_3_1 e_1_2_10_19_1 e_1_2_10_6_1 e_1_2_10_16_1 e_1_2_10_5_1 e_1_2_10_17_1 Ji Y.Q. (e_1_2_10_8_1) 2017; 41 e_1_2_10_14_1 e_1_2_10_7_1 e_1_2_10_15_1 e_1_2_10_9_1 e_1_2_10_10_1 e_1_2_10_11_1 Badal F.R. (e_1_2_10_22_1) 2019; 4 Liu G.B. (e_1_2_10_13_1) 2015; 39 Tan H. (e_1_2_10_12_1) 2014; 38 e_1_2_10_25_1 e_1_2_10_26_1 |
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