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Article: Data-Driven Probabilistic Net Load Forecasting With High Penetration of Behind-the-Meter PV

TitleData-Driven Probabilistic Net Load Forecasting With High Penetration of Behind-the-Meter PV
Authors
Keywordsbehind-the-meter PV
copula
discrete dependent convolution
maximal information coefficient (MIC)
net load
photovoltaic generation
Probabilistic load forecasting
Issue Date2018
Citation
IEEE Transactions on Power Systems, 2018, v. 33, n. 3, p. 3255-3264 How to Cite?
AbstractDistributed renewable energy, particularly photovoltaics (PV), has expanded rapidly over the past decade. Distributed PV is located behind the meter and is, thus, invisible to the retailers and the distribution system operator. This invisible generation, thus, injects additional uncertainty in the net load and makes it harder to forecast. This paper proposes a data-driven probabilistic net load forecasting method specifically designed to handle a high penetration of behind-the-meter (BtM) PV. The capacity of BtM PV is first estimated using a maximal information coefficient based correlation analysis and a grid search. The net load profile is then decomposed into three parts (PV output, actual load, and residual) which are forecast individually. Correlation analysis based on copula theory is conducted on the distributions and dependencies of the forecasting errors to generate a probabilistic net load forecast. Case studies based on ISO New England data demonstrate that the proposed method outperforms other approaches, particularly when the penetration of BtM PV is high.
Persistent Identifierhttp://hdl.handle.net/10722/308734
ISSN
2023 Impact Factor: 6.5
2023 SCImago Journal Rankings: 3.827
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorWang, Yi-
dc.contributor.authorZhang, Ning-
dc.contributor.authorChen, Qixin-
dc.contributor.authorKirschen, Daniel S.-
dc.contributor.authorLi, Pan-
dc.contributor.authorXia, Qing-
dc.date.accessioned2021-12-08T07:50:01Z-
dc.date.available2021-12-08T07:50:01Z-
dc.date.issued2018-
dc.identifier.citationIEEE Transactions on Power Systems, 2018, v. 33, n. 3, p. 3255-3264-
dc.identifier.issn0885-8950-
dc.identifier.urihttp://hdl.handle.net/10722/308734-
dc.description.abstractDistributed renewable energy, particularly photovoltaics (PV), has expanded rapidly over the past decade. Distributed PV is located behind the meter and is, thus, invisible to the retailers and the distribution system operator. This invisible generation, thus, injects additional uncertainty in the net load and makes it harder to forecast. This paper proposes a data-driven probabilistic net load forecasting method specifically designed to handle a high penetration of behind-the-meter (BtM) PV. The capacity of BtM PV is first estimated using a maximal information coefficient based correlation analysis and a grid search. The net load profile is then decomposed into three parts (PV output, actual load, and residual) which are forecast individually. Correlation analysis based on copula theory is conducted on the distributions and dependencies of the forecasting errors to generate a probabilistic net load forecast. Case studies based on ISO New England data demonstrate that the proposed method outperforms other approaches, particularly when the penetration of BtM PV is high.-
dc.languageeng-
dc.relation.ispartofIEEE Transactions on Power Systems-
dc.subjectbehind-the-meter PV-
dc.subjectcopula-
dc.subjectdiscrete dependent convolution-
dc.subjectmaximal information coefficient (MIC)-
dc.subjectnet load-
dc.subjectphotovoltaic generation-
dc.subjectProbabilistic load forecasting-
dc.titleData-Driven Probabilistic Net Load Forecasting With High Penetration of Behind-the-Meter PV-
dc.typeArticle-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1109/TPWRS.2017.2762599-
dc.identifier.scopuseid_2-s2.0-85031824974-
dc.identifier.volume33-
dc.identifier.issue3-
dc.identifier.spage3255-
dc.identifier.epage3264-
dc.identifier.isiWOS:000430733300093-

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