File Download

There are no files associated with this item.

  Links for fulltext
     (May Require Subscription)
Supplementary

Article: Coordinated Planning of Wind Power Generation and Energy Storage With Decision-Dependent Uncertainty Induced by Spatial Correlation

TitleCoordinated Planning of Wind Power Generation and Energy Storage With Decision-Dependent Uncertainty Induced by Spatial Correlation
Authors
KeywordsCorrelation
Decision-dependent uncertainty (DDU)
energy storage
Gaussian mixture model (GMM)
Generators
Planning
Renewable energy sources
spatial correlation
stochastic programming
Uncertainty
Wind farms
Wind power generation
wind power generation planning
Issue Date1-Jun-2023
PublisherInstitute of Electrical and Electronics Engineers
Citation
IEEE Systems Journal, 2023, v. 17, n. 2, p. 2247-2258 How to Cite?
AbstractThe global experience on wind farm development reveals that due to the spatial correlation, the prediction error of wind power is related to the scale of wind farms. This evidence indicates that the uncertainty features of wind power output from large-scale wind farms are not fixed but dependent on expansion decisions. The decision-dependent uncertainty (DDU) will alter the traditional optimization process and pose solution challenges. This article proposes a coordinated planning model for large-scale wind farms and energy storage considering DDU. First, a DDU model, which quantifies the relationship between wind power prediction errors and the wind farm size, is established based on historical data. The proposed DDU model for a single wind farm is extended to multiple wind farms with their spatial correlation captured by a Gaussian Mixture Model. Then, tackling the coupling relationship between decisions and uncertainty, an affine function-based solution method for the stochastic model with decision-dependent probability distributions is proposed. The constructed affine function maps planning decisions to decision-dependent wind power scenario sets via linear transformation. The difference between the planning model with and without the DDU in wind power is compared and discussed. Case studies verify the proposed model and solution method.
Persistent Identifierhttp://hdl.handle.net/10722/338396
ISSN
2023 Impact Factor: 4.0
2023 SCImago Journal Rankings: 1.402
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorYin, W-
dc.contributor.authorLi, Y-
dc.contributor.authorHou, J-
dc.contributor.authorMiao, M-
dc.contributor.authorHou, Y-
dc.date.accessioned2024-03-11T10:28:32Z-
dc.date.available2024-03-11T10:28:32Z-
dc.date.issued2023-06-01-
dc.identifier.citationIEEE Systems Journal, 2023, v. 17, n. 2, p. 2247-2258-
dc.identifier.issn1932-8184-
dc.identifier.urihttp://hdl.handle.net/10722/338396-
dc.description.abstractThe global experience on wind farm development reveals that due to the spatial correlation, the prediction error of wind power is related to the scale of wind farms. This evidence indicates that the uncertainty features of wind power output from large-scale wind farms are not fixed but dependent on expansion decisions. The decision-dependent uncertainty (DDU) will alter the traditional optimization process and pose solution challenges. This article proposes a coordinated planning model for large-scale wind farms and energy storage considering DDU. First, a DDU model, which quantifies the relationship between wind power prediction errors and the wind farm size, is established based on historical data. The proposed DDU model for a single wind farm is extended to multiple wind farms with their spatial correlation captured by a Gaussian Mixture Model. Then, tackling the coupling relationship between decisions and uncertainty, an affine function-based solution method for the stochastic model with decision-dependent probability distributions is proposed. The constructed affine function maps planning decisions to decision-dependent wind power scenario sets via linear transformation. The difference between the planning model with and without the DDU in wind power is compared and discussed. Case studies verify the proposed model and solution method.-
dc.languageeng-
dc.publisherInstitute of Electrical and Electronics Engineers-
dc.relation.ispartofIEEE Systems Journal-
dc.rightsThis work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.-
dc.subjectCorrelation-
dc.subjectDecision-dependent uncertainty (DDU)-
dc.subjectenergy storage-
dc.subjectGaussian mixture model (GMM)-
dc.subjectGenerators-
dc.subjectPlanning-
dc.subjectRenewable energy sources-
dc.subjectspatial correlation-
dc.subjectstochastic programming-
dc.subjectUncertainty-
dc.subjectWind farms-
dc.subjectWind power generation-
dc.subjectwind power generation planning-
dc.titleCoordinated Planning of Wind Power Generation and Energy Storage With Decision-Dependent Uncertainty Induced by Spatial Correlation-
dc.typeArticle-
dc.identifier.doi10.1109/JSYST.2022.3196706-
dc.identifier.scopuseid_2-s2.0-85139390683-
dc.identifier.volume17-
dc.identifier.issue2-
dc.identifier.spage2247-
dc.identifier.epage2258-
dc.identifier.eissn1937-9234-
dc.identifier.isiWOS:000854596000001-
dc.identifier.issnl1932-8184-

Export via OAI-PMH Interface in XML Formats


OR


Export to Other Non-XML Formats