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Article: Chance-constrained co-expansion planning for power systems under decision-dependent wind power uncertainty

TitleChance-constrained co-expansion planning for power systems under decision-dependent wind power uncertainty
Authors
Keywordschance-constrained stochastic programming
decision-dependent uncertainty
expansion planning
mixed-integer second-order cone programming
power transmission planning
power-generation planning
programming
stochastic
wind power
wind power generation
Issue Date27-Apr-2023
PublisherWiley Open Access
Citation
IET Renewable Power Generation, 2023, v. 17, n. 6, p. 1342-1357 How to Cite?
AbstractVariability and uncertainty in wind resources pose significant challenges to the expansion planning of wind farms and associated flexible resources. In addition, the spatial smoothing effect, indicating the impact of wind farm scale on aggregated wind power prediction errors, further aggravates the challenge. This paper proposes a chance-constrained co-expansion planning method considering the spatial smoothing effect, where the expansion of wind farm capacity, batter energy storage capacity, and power transmission lines are co-optimized. Specifically, a decision-dependent uncertainty (DDU) model is established capturing the dependency of wind power uncertainties on wind farm expansion decisions under the spatial smoothing effect. Unlike traditional optimization diagram where decisions are made under only decision-independent uncertainty (DIU) with fixe properties, properties of decision-dependent uncertain parameters would be inversely altered by decisions. To effectively tackle the coupling relation between decisions and DDU, DDU-based chance constraints are formulated in an analytical manner, where the decisions and decision-dependent uncertain parameters are expressed in a closed form. Eventually, with piecewise linearization of the DDU model and the polynomial approximation of cumulative distribution function of uncertain parameters, the proposed chance-constrained optimization model with DDU is converted into a mixed-integer second-order cone program (MISOCP). Case studies verify the effectiveness of the proposed method.
Persistent Identifierhttp://hdl.handle.net/10722/338395
ISSN
2023 Impact Factor: 2.6
2023 SCImago Journal Rankings: 0.859
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorYin, W-
dc.contributor.authorFeng, S-
dc.contributor.authorLiu, RP-
dc.contributor.authorHou, Y-
dc.date.accessioned2024-03-11T10:28:31Z-
dc.date.available2024-03-11T10:28:31Z-
dc.date.issued2023-04-27-
dc.identifier.citationIET Renewable Power Generation, 2023, v. 17, n. 6, p. 1342-1357-
dc.identifier.issn1752-1416-
dc.identifier.urihttp://hdl.handle.net/10722/338395-
dc.description.abstractVariability and uncertainty in wind resources pose significant challenges to the expansion planning of wind farms and associated flexible resources. In addition, the spatial smoothing effect, indicating the impact of wind farm scale on aggregated wind power prediction errors, further aggravates the challenge. This paper proposes a chance-constrained co-expansion planning method considering the spatial smoothing effect, where the expansion of wind farm capacity, batter energy storage capacity, and power transmission lines are co-optimized. Specifically, a decision-dependent uncertainty (DDU) model is established capturing the dependency of wind power uncertainties on wind farm expansion decisions under the spatial smoothing effect. Unlike traditional optimization diagram where decisions are made under only decision-independent uncertainty (DIU) with fixe properties, properties of decision-dependent uncertain parameters would be inversely altered by decisions. To effectively tackle the coupling relation between decisions and DDU, DDU-based chance constraints are formulated in an analytical manner, where the decisions and decision-dependent uncertain parameters are expressed in a closed form. Eventually, with piecewise linearization of the DDU model and the polynomial approximation of cumulative distribution function of uncertain parameters, the proposed chance-constrained optimization model with DDU is converted into a mixed-integer second-order cone program (MISOCP). Case studies verify the effectiveness of the proposed method.-
dc.languageeng-
dc.publisherWiley Open Access-
dc.relation.ispartofIET Renewable Power Generation-
dc.rightsThis work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.-
dc.subjectchance-constrained stochastic programming-
dc.subjectdecision-dependent uncertainty-
dc.subjectexpansion planning-
dc.subjectmixed-integer second-order cone programming-
dc.subjectpower transmission planning-
dc.subjectpower-generation planning-
dc.subjectprogramming-
dc.subjectstochastic-
dc.subjectwind power-
dc.subjectwind power generation-
dc.titleChance-constrained co-expansion planning for power systems under decision-dependent wind power uncertainty-
dc.typeArticle-
dc.identifier.doi10.1049/rpg2.12664-
dc.identifier.scopuseid_2-s2.0-85150797909-
dc.identifier.volume17-
dc.identifier.issue6-
dc.identifier.spage1342-
dc.identifier.epage1357-
dc.identifier.eissn1752-1424-
dc.identifier.isiWOS:000949083200001-
dc.identifier.issnl1752-1416-

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