Online Distribution Network Scheduling via Provably Robust Learning Approach

Distribution network scheduling (DNS) is the basis for distribution network management, which is computed in a periodical way via solving the formulated mixed-integer programming (MIP). To achieve the online scheduling, a provably robust learn-to-optimize approach for online DNS is proposed in this...
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Autor*in:	Naixiao Wang [verfasserIn] Xinlei Cai [verfasserIn] Linwei Sang [verfasserIn] Tingxiang Zhang [verfasserIn] Zhongkai Yi [verfasserIn] Ying Xu [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2024

Schlagwörter:	distribution network scheduling learn-to-optimize online scheduling machine learning

Übergeordnetes Werk:	In: Energies - MDPI AG, 2008, 17(2024), 6, p 1361
Übergeordnetes Werk:	volume:17 ; year:2024 ; number:6, p 1361

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/en17061361

Katalog-ID:	DOAJ10051894X

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allfields	10.3390/en17061361 doi (DE-627)DOAJ10051894X (DE-599)DOAJfa3417c9c596413a807f89b4da51c197 DE-627 ger DE-627 rakwb eng Naixiao Wang verfasserin aut Online Distribution Network Scheduling via Provably Robust Learning Approach 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Distribution network scheduling (DNS) is the basis for distribution network management, which is computed in a periodical way via solving the formulated mixed-integer programming (MIP). To achieve the online scheduling, a provably robust learn-to-optimize approach for online DNS is proposed in this paper, whose key lies in the transformation of the MIP-based DNS into the simple linear program problem with a much faster solving time. It formulates the parametric DNS model to construct the offline training dataset and then proposes the provably robust learning approach to learn the integer variables of MIP. The proposed learning approach is adversarial to minor perturbation of input scenario. After training, the learning model can predict the integer variables to achieve online scheduling. Case study verifies the acceleration effectiveness for online DNS. distribution network scheduling learn-to-optimize online scheduling machine learning Technology T Xinlei Cai verfasserin aut Linwei Sang verfasserin aut Tingxiang Zhang verfasserin aut Zhongkai Yi verfasserin aut Ying Xu verfasserin aut In Energies MDPI AG, 2008 17(2024), 6, p 1361 (DE-627)572083742 (DE-600)2437446-5 19961073 nnns volume:17 year:2024 number:6, p 1361 https://doi.org/10.3390/en17061361 kostenfrei https://doaj.org/article/fa3417c9c596413a807f89b4da51c197 kostenfrei https://www.mdpi.com/1996-1073/17/6/1361 kostenfrei https://doaj.org/toc/1996-1073 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 17 2024 6, p 1361
spelling	10.3390/en17061361 doi (DE-627)DOAJ10051894X (DE-599)DOAJfa3417c9c596413a807f89b4da51c197 DE-627 ger DE-627 rakwb eng Naixiao Wang verfasserin aut Online Distribution Network Scheduling via Provably Robust Learning Approach 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Distribution network scheduling (DNS) is the basis for distribution network management, which is computed in a periodical way via solving the formulated mixed-integer programming (MIP). To achieve the online scheduling, a provably robust learn-to-optimize approach for online DNS is proposed in this paper, whose key lies in the transformation of the MIP-based DNS into the simple linear program problem with a much faster solving time. It formulates the parametric DNS model to construct the offline training dataset and then proposes the provably robust learning approach to learn the integer variables of MIP. The proposed learning approach is adversarial to minor perturbation of input scenario. After training, the learning model can predict the integer variables to achieve online scheduling. Case study verifies the acceleration effectiveness for online DNS. distribution network scheduling learn-to-optimize online scheduling machine learning Technology T Xinlei Cai verfasserin aut Linwei Sang verfasserin aut Tingxiang Zhang verfasserin aut Zhongkai Yi verfasserin aut Ying Xu verfasserin aut In Energies MDPI AG, 2008 17(2024), 6, p 1361 (DE-627)572083742 (DE-600)2437446-5 19961073 nnns volume:17 year:2024 number:6, p 1361 https://doi.org/10.3390/en17061361 kostenfrei https://doaj.org/article/fa3417c9c596413a807f89b4da51c197 kostenfrei https://www.mdpi.com/1996-1073/17/6/1361 kostenfrei https://doaj.org/toc/1996-1073 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 17 2024 6, p 1361
allfields_unstemmed	10.3390/en17061361 doi (DE-627)DOAJ10051894X (DE-599)DOAJfa3417c9c596413a807f89b4da51c197 DE-627 ger DE-627 rakwb eng Naixiao Wang verfasserin aut Online Distribution Network Scheduling via Provably Robust Learning Approach 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Distribution network scheduling (DNS) is the basis for distribution network management, which is computed in a periodical way via solving the formulated mixed-integer programming (MIP). To achieve the online scheduling, a provably robust learn-to-optimize approach for online DNS is proposed in this paper, whose key lies in the transformation of the MIP-based DNS into the simple linear program problem with a much faster solving time. It formulates the parametric DNS model to construct the offline training dataset and then proposes the provably robust learning approach to learn the integer variables of MIP. The proposed learning approach is adversarial to minor perturbation of input scenario. After training, the learning model can predict the integer variables to achieve online scheduling. Case study verifies the acceleration effectiveness for online DNS. distribution network scheduling learn-to-optimize online scheduling machine learning Technology T Xinlei Cai verfasserin aut Linwei Sang verfasserin aut Tingxiang Zhang verfasserin aut Zhongkai Yi verfasserin aut Ying Xu verfasserin aut In Energies MDPI AG, 2008 17(2024), 6, p 1361 (DE-627)572083742 (DE-600)2437446-5 19961073 nnns volume:17 year:2024 number:6, p 1361 https://doi.org/10.3390/en17061361 kostenfrei https://doaj.org/article/fa3417c9c596413a807f89b4da51c197 kostenfrei https://www.mdpi.com/1996-1073/17/6/1361 kostenfrei https://doaj.org/toc/1996-1073 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 17 2024 6, p 1361
allfieldsGer	10.3390/en17061361 doi (DE-627)DOAJ10051894X (DE-599)DOAJfa3417c9c596413a807f89b4da51c197 DE-627 ger DE-627 rakwb eng Naixiao Wang verfasserin aut Online Distribution Network Scheduling via Provably Robust Learning Approach 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Distribution network scheduling (DNS) is the basis for distribution network management, which is computed in a periodical way via solving the formulated mixed-integer programming (MIP). To achieve the online scheduling, a provably robust learn-to-optimize approach for online DNS is proposed in this paper, whose key lies in the transformation of the MIP-based DNS into the simple linear program problem with a much faster solving time. It formulates the parametric DNS model to construct the offline training dataset and then proposes the provably robust learning approach to learn the integer variables of MIP. The proposed learning approach is adversarial to minor perturbation of input scenario. After training, the learning model can predict the integer variables to achieve online scheduling. Case study verifies the acceleration effectiveness for online DNS. distribution network scheduling learn-to-optimize online scheduling machine learning Technology T Xinlei Cai verfasserin aut Linwei Sang verfasserin aut Tingxiang Zhang verfasserin aut Zhongkai Yi verfasserin aut Ying Xu verfasserin aut In Energies MDPI AG, 2008 17(2024), 6, p 1361 (DE-627)572083742 (DE-600)2437446-5 19961073 nnns volume:17 year:2024 number:6, p 1361 https://doi.org/10.3390/en17061361 kostenfrei https://doaj.org/article/fa3417c9c596413a807f89b4da51c197 kostenfrei https://www.mdpi.com/1996-1073/17/6/1361 kostenfrei https://doaj.org/toc/1996-1073 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 17 2024 6, p 1361
allfieldsSound	10.3390/en17061361 doi (DE-627)DOAJ10051894X (DE-599)DOAJfa3417c9c596413a807f89b4da51c197 DE-627 ger DE-627 rakwb eng Naixiao Wang verfasserin aut Online Distribution Network Scheduling via Provably Robust Learning Approach 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Distribution network scheduling (DNS) is the basis for distribution network management, which is computed in a periodical way via solving the formulated mixed-integer programming (MIP). To achieve the online scheduling, a provably robust learn-to-optimize approach for online DNS is proposed in this paper, whose key lies in the transformation of the MIP-based DNS into the simple linear program problem with a much faster solving time. It formulates the parametric DNS model to construct the offline training dataset and then proposes the provably robust learning approach to learn the integer variables of MIP. The proposed learning approach is adversarial to minor perturbation of input scenario. After training, the learning model can predict the integer variables to achieve online scheduling. Case study verifies the acceleration effectiveness for online DNS. distribution network scheduling learn-to-optimize online scheduling machine learning Technology T Xinlei Cai verfasserin aut Linwei Sang verfasserin aut Tingxiang Zhang verfasserin aut Zhongkai Yi verfasserin aut Ying Xu verfasserin aut In Energies MDPI AG, 2008 17(2024), 6, p 1361 (DE-627)572083742 (DE-600)2437446-5 19961073 nnns volume:17 year:2024 number:6, p 1361 https://doi.org/10.3390/en17061361 kostenfrei https://doaj.org/article/fa3417c9c596413a807f89b4da51c197 kostenfrei https://www.mdpi.com/1996-1073/17/6/1361 kostenfrei https://doaj.org/toc/1996-1073 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 17 2024 6, p 1361
language	English
source	In Energies 17(2024), 6, p 1361 volume:17 year:2024 number:6, p 1361
sourceStr	In Energies 17(2024), 6, p 1361 volume:17 year:2024 number:6, p 1361
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	distribution network scheduling learn-to-optimize online scheduling machine learning Technology T
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authorswithroles_txt_mv	Naixiao Wang @@aut@@ Xinlei Cai @@aut@@ Linwei Sang @@aut@@ Tingxiang Zhang @@aut@@ Zhongkai Yi @@aut@@ Ying Xu @@aut@@
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author	Naixiao Wang
spellingShingle	Naixiao Wang misc distribution network scheduling misc learn-to-optimize misc online scheduling misc machine learning misc Technology misc T Online Distribution Network Scheduling via Provably Robust Learning Approach
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title_auth	Online Distribution Network Scheduling via Provably Robust Learning Approach
abstract	Distribution network scheduling (DNS) is the basis for distribution network management, which is computed in a periodical way via solving the formulated mixed-integer programming (MIP). To achieve the online scheduling, a provably robust learn-to-optimize approach for online DNS is proposed in this paper, whose key lies in the transformation of the MIP-based DNS into the simple linear program problem with a much faster solving time. It formulates the parametric DNS model to construct the offline training dataset and then proposes the provably robust learning approach to learn the integer variables of MIP. The proposed learning approach is adversarial to minor perturbation of input scenario. After training, the learning model can predict the integer variables to achieve online scheduling. Case study verifies the acceleration effectiveness for online DNS.
abstractGer	Distribution network scheduling (DNS) is the basis for distribution network management, which is computed in a periodical way via solving the formulated mixed-integer programming (MIP). To achieve the online scheduling, a provably robust learn-to-optimize approach for online DNS is proposed in this paper, whose key lies in the transformation of the MIP-based DNS into the simple linear program problem with a much faster solving time. It formulates the parametric DNS model to construct the offline training dataset and then proposes the provably robust learning approach to learn the integer variables of MIP. The proposed learning approach is adversarial to minor perturbation of input scenario. After training, the learning model can predict the integer variables to achieve online scheduling. Case study verifies the acceleration effectiveness for online DNS.
abstract_unstemmed	Distribution network scheduling (DNS) is the basis for distribution network management, which is computed in a periodical way via solving the formulated mixed-integer programming (MIP). To achieve the online scheduling, a provably robust learn-to-optimize approach for online DNS is proposed in this paper, whose key lies in the transformation of the MIP-based DNS into the simple linear program problem with a much faster solving time. It formulates the parametric DNS model to construct the offline training dataset and then proposes the provably robust learning approach to learn the integer variables of MIP. The proposed learning approach is adversarial to minor perturbation of input scenario. After training, the learning model can predict the integer variables to achieve online scheduling. Case study verifies the acceleration effectiveness for online DNS.
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title_short	Online Distribution Network Scheduling via Provably Robust Learning Approach
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