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Data-Driven Predictive Voltage Control for Distributed Energy Storage in Active Distribution Networks
Integration of distributed energy storage (DES) is beneficial for mitigating voltage fluctuations in highly distributed generator (DG)-penetrated active distribution networks (ADNs). Based on an accurate physical model of ADN, conventional model-based methods can realize optimal control of DES. Howe...
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Published in: | CSEE Journal of Power and Energy Systems 2024-09, Vol.10 (5), p.1876-1886 |
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container_end_page | 1886 |
container_issue | 5 |
container_start_page | 1876 |
container_title | CSEE Journal of Power and Energy Systems |
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creator | Yanda Huo Peng Li Haoran Ji Hao Yu Jinli Zhao Wei Xi Jianzhong Wu Chengshan Wang |
description | Integration of distributed energy storage (DES) is beneficial for mitigating voltage fluctuations in highly distributed generator (DG)-penetrated active distribution networks (ADNs). Based on an accurate physical model of ADN, conventional model-based methods can realize optimal control of DES. However, absence of network parameters and complex operational states of ADN poses challenges to model-based methods. This paper proposes a data-driven predictive voltage control method for DES. First, considering time-series constraints, a data-driven predictive control model is formulated for DES by using measurement data. Then, a data-driven coordination method is proposed for DES and DGs in each area. Through boundary information interaction, voltage mitigation effects can be improved by inter-area coordination control. Finally, control performance is tested on a modified IEEE 33-node test case. Case studies demonstrate that by fully utilizing multi-source data, the proposed predictive control method can effectively regulate DES and DGs to mitigate voltage violations. |
doi_str_mv | 10.17775/CSEEJPES.2022.02880 |
format | article |
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Based on an accurate physical model of ADN, conventional model-based methods can realize optimal control of DES. However, absence of network parameters and complex operational states of ADN poses challenges to model-based methods. This paper proposes a data-driven predictive voltage control method for DES. First, considering time-series constraints, a data-driven predictive control model is formulated for DES by using measurement data. Then, a data-driven coordination method is proposed for DES and DGs in each area. Through boundary information interaction, voltage mitigation effects can be improved by inter-area coordination control. Finally, control performance is tested on a modified IEEE 33-node test case. 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Case studies demonstrate that by fully utilizing multi-source data, the proposed predictive control method can effectively regulate DES and DGs to mitigate voltage violations.</description><subject>data-driven</subject><subject>distributed energy storage (DES)</subject><subject>distributed generators (DGs)</subject><subject>Distribution network</subject><subject>predictive voltage control</subject><issn>2096-0042</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><sourceid>DOA</sourceid><recordid>eNo9jEtOwzAYhL0Aiar0Bix8gZTfdhzbyyoNUFRBpQLbyK9ULiFGjgH19hSKWM1o5ptB6IrAnAgh-HW9bZr7TbOdU6B0DlRKOEMTCqoqAEp6gWbjuAc4FlxQKCfIL3XWxTKFTz_gTfIu2Hz0-CX2We88ruOQU-xxFxNehjGnYD6yd7gZfNod8DbH9IOFAS9Ow38oxAE_-PwV0-t4ic473Y9-9qdT9HzTPNV3xfrxdlUv1oUjnOSCcCm9NNoRabgDr501rKKWcOU9VbLqnAUjaAmScVVSyUhnK1kqqwQ4J9gUrU6_Lup9-57Cm06HNurQ_gYx7VqdcrC9b5WTnjsjO8JUCZobZywTEpxSkpmuYt9Ug2Za</recordid><startdate>20240901</startdate><enddate>20240901</enddate><creator>Yanda Huo</creator><creator>Peng Li</creator><creator>Haoran Ji</creator><creator>Hao Yu</creator><creator>Jinli Zhao</creator><creator>Wei Xi</creator><creator>Jianzhong Wu</creator><creator>Chengshan Wang</creator><general>China electric power research institute</general><scope>DOA</scope></search><sort><creationdate>20240901</creationdate><title>Data-Driven Predictive Voltage Control for Distributed Energy Storage in Active Distribution Networks</title><author>Yanda Huo ; Peng Li ; Haoran Ji ; Hao Yu ; Jinli Zhao ; Wei Xi ; Jianzhong Wu ; Chengshan Wang</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-d151t-1588e8bad18b5d0eadcb362c159ee2986fdc0b7240835942831fc6849c970dd73</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2024</creationdate><topic>data-driven</topic><topic>distributed energy storage (DES)</topic><topic>distributed generators (DGs)</topic><topic>Distribution network</topic><topic>predictive voltage control</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Yanda Huo</creatorcontrib><creatorcontrib>Peng Li</creatorcontrib><creatorcontrib>Haoran Ji</creatorcontrib><creatorcontrib>Hao Yu</creatorcontrib><creatorcontrib>Jinli Zhao</creatorcontrib><creatorcontrib>Wei Xi</creatorcontrib><creatorcontrib>Jianzhong Wu</creatorcontrib><creatorcontrib>Chengshan Wang</creatorcontrib><collection>Directory of Open Access Journals</collection><jtitle>CSEE Journal of Power and Energy Systems</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Yanda Huo</au><au>Peng Li</au><au>Haoran Ji</au><au>Hao Yu</au><au>Jinli Zhao</au><au>Wei Xi</au><au>Jianzhong Wu</au><au>Chengshan Wang</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Data-Driven Predictive Voltage Control for Distributed Energy Storage in Active Distribution Networks</atitle><jtitle>CSEE Journal of Power and Energy Systems</jtitle><date>2024-09-01</date><risdate>2024</risdate><volume>10</volume><issue>5</issue><spage>1876</spage><epage>1886</epage><pages>1876-1886</pages><issn>2096-0042</issn><abstract>Integration of distributed energy storage (DES) is beneficial for mitigating voltage fluctuations in highly distributed generator (DG)-penetrated active distribution networks (ADNs). Based on an accurate physical model of ADN, conventional model-based methods can realize optimal control of DES. However, absence of network parameters and complex operational states of ADN poses challenges to model-based methods. This paper proposes a data-driven predictive voltage control method for DES. First, considering time-series constraints, a data-driven predictive control model is formulated for DES by using measurement data. Then, a data-driven coordination method is proposed for DES and DGs in each area. Through boundary information interaction, voltage mitigation effects can be improved by inter-area coordination control. Finally, control performance is tested on a modified IEEE 33-node test case. 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subjects | data-driven distributed energy storage (DES) distributed generators (DGs) Distribution network predictive voltage control |
title | Data-Driven Predictive Voltage Control for Distributed Energy Storage in Active Distribution Networks |
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