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Conference Paper: Data-driven Stochastic Distribution Network Reconfiguration

TitleData-driven Stochastic Distribution Network Reconfiguration
Authors
Huang, WZheng, WHill, DJ
KeywordsStochastic optimization
data-driven
renewable energy
unbalanced distribution network
distribution network reconfiguration
Issue Date2020
Citation
The 21st International Federation of Automatic Control (IFAC) World Congress, Virtual Conference (IFAC-V 2020), Berlin, Germany, 12-17 July 2020 How to Cite?
AbstractDistribution network reconfiguration (DNR) is indispensable for the operation of active distribution networks. To address the uncertainties of renewables and variables loads, a data-driven stochastic DNR model is proposed for day-ahead DNR of three-phase unbalanced distribution networks. The switching cost and expected costs resulted from power losses and load balance are minimized. Based on the analysis of historical data, the probability distribution of DG output and load demand is derived using a data-driven method. To improve computation efficiency, a mixed-integer linear programming problem is formulated using linearization techniques. Numerical tests are carried out in an IEEE unbalanced benchmark. The comparison with the conventional deterministic method verifies the effectiveness of the proposed method.
DescriptionVI125-01: Uncertainty Quantification in Control and Optimization – Tools, Methods and Applications - Paper VI125-01.2
Persistent Identifierhttp://hdl.handle.net/10722/288230

 

DC FieldValueLanguage
dc.contributor.authorHuang, W-
dc.contributor.authorZheng, W-
dc.contributor.authorHill, DJ-
dc.date.accessioned2020-10-05T12:09:48Z-
dc.date.available2020-10-05T12:09:48Z-
dc.date.issued2020-
dc.identifier.citationThe 21st International Federation of Automatic Control (IFAC) World Congress, Virtual Conference (IFAC-V 2020), Berlin, Germany, 12-17 July 2020-
dc.identifier.urihttp://hdl.handle.net/10722/288230-
dc.descriptionVI125-01: Uncertainty Quantification in Control and Optimization – Tools, Methods and Applications - Paper VI125-01.2-
dc.description.abstractDistribution network reconfiguration (DNR) is indispensable for the operation of active distribution networks. To address the uncertainties of renewables and variables loads, a data-driven stochastic DNR model is proposed for day-ahead DNR of three-phase unbalanced distribution networks. The switching cost and expected costs resulted from power losses and load balance are minimized. Based on the analysis of historical data, the probability distribution of DG output and load demand is derived using a data-driven method. To improve computation efficiency, a mixed-integer linear programming problem is formulated using linearization techniques. Numerical tests are carried out in an IEEE unbalanced benchmark. The comparison with the conventional deterministic method verifies the effectiveness of the proposed method.-
dc.languageeng-
dc.relation.ispartofThe 21st International Federation of Automatic Control (IFAC) World Congress, 2020-
dc.subjectStochastic optimization-
dc.subjectdata-driven-
dc.subjectrenewable energy-
dc.subjectunbalanced distribution network-
dc.subjectdistribution network reconfiguration-
dc.titleData-driven Stochastic Distribution Network Reconfiguration-
dc.typeConference_Paper-
dc.identifier.emailHuang, W: wjhuang@eee.hku.hk-
dc.identifier.emailHill, DJ: dhill@eee.hku.hk-
dc.identifier.authorityHill, DJ=rp01669-
dc.identifier.hkuros315159-

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