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Article: Climate-Adaptive Transmission Network Expansion Planning Considering Evolutions of Resources

TitleClimate-Adaptive Transmission Network Expansion Planning Considering Evolutions of Resources
Authors
KeywordsClimate change
Climate-adaptive planning
climate-driven evolution
Data models
Investment
Planning
Power systems
Probability distribution
Solid modeling
transmission network expansion planning
Issue Date13-Jun-2023
PublisherInstitute of Electrical and Electronics Engineers
Citation
IEEE Transactions on Industrial Informatics, 2023, v. 20, n. 2, p. 2063-2078 How to Cite?
Abstract

Weather-sensitive resources are the main source of uncertainties in power systems. However, the unpredictable climate change further introduces ambiguity (i.e., unknown probability distribution) into the system, since the weather-sensitive resources would evolve with the climate and gradually exhibit a different probability distribution from the past in an uncertain manner. Lack of considering this climate-induced ambiguity in transmission network expansion planning (TNEP) may cause misunderstanding of future operational scenarios. Aiming at a higher security operation level under climate change yet less line investment, this paper proposes a climate-adaptive TNEP, which is essentially a robust TNEP equipped with a climate-adaptive uncertainty set (CUS) to embody injections from the weather-sensitive resources that have high probabilities in the target year despite the climate-induced ambiguity. Determination of the CUS involves three steps. First, model future unknown distribution under climate change. Specifically, the climate-driven evolution in distributions is quantified by an evolutionary distance between historical and future true distributions, whose upper bound is derived from practical data, while the future unknown distribution is then modeled by a distance-based ambiguity set; Second, determine the CUS which has a minimal volume yet a desired confidence level in the face of the ambiguous future distribution. To that end, a parametric Wasserstein distance-based distributionally robust optimization (p-WDRO) is developed over the ambiguity set; Third, solve the p-WDRO by a data-clustering-incorporated reformulation. After the CUS is determined, the overall climate-adaptive TNEP is solved by a column-and-constraint-generation method with an inner multi-loop algorithm tailored for the CUS. Simulations are conducted on three test systems with data from Australia and Coupled Model Intercomparison Project Phase 6 (CMIP6) under scenarios issued by Intergovernmental Panel on Climate Change (IPCC), which demonstrate that the climate-adaptive TNEP can improve operational security under climate change while reducing investment costs.


Persistent Identifierhttp://hdl.handle.net/10722/338401
ISSN
2023 Impact Factor: 11.7
2023 SCImago Journal Rankings: 4.420
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorChen, Y-
dc.contributor.authorSong, Z-
dc.contributor.authorHou, Y-
dc.date.accessioned2024-03-11T10:28:34Z-
dc.date.available2024-03-11T10:28:34Z-
dc.date.issued2023-06-13-
dc.identifier.citationIEEE Transactions on Industrial Informatics, 2023, v. 20, n. 2, p. 2063-2078-
dc.identifier.issn1551-3203-
dc.identifier.urihttp://hdl.handle.net/10722/338401-
dc.description.abstract<p>Weather-sensitive resources are the main source of uncertainties in power systems. However, the unpredictable climate change further introduces ambiguity (i.e., unknown probability distribution) into the system, since the weather-sensitive resources would evolve with the climate and gradually exhibit a different probability distribution from the past in an uncertain manner. Lack of considering this climate-induced ambiguity in transmission network expansion planning (TNEP) may cause misunderstanding of future operational scenarios. Aiming at a higher security operation level under climate change yet less line investment, this paper proposes a climate-adaptive TNEP, which is essentially a robust TNEP equipped with a climate-adaptive uncertainty set (CUS) to embody injections from the weather-sensitive resources that have high probabilities in the target year despite the climate-induced ambiguity. Determination of the CUS involves three steps. First, model future unknown distribution under climate change. Specifically, the climate-driven evolution in distributions is quantified by an evolutionary distance between historical and future true distributions, whose upper bound is derived from practical data, while the future unknown distribution is then modeled by a distance-based ambiguity set; Second, determine the CUS which has a minimal volume yet a desired confidence level in the face of the ambiguous future distribution. To that end, a parametric Wasserstein distance-based distributionally robust optimization (p-WDRO) is developed over the ambiguity set; Third, solve the p-WDRO by a data-clustering-incorporated reformulation. After the CUS is determined, the overall climate-adaptive TNEP is solved by a column-and-constraint-generation method with an inner multi-loop algorithm tailored for the CUS. Simulations are conducted on three test systems with data from Australia and Coupled Model Intercomparison Project Phase 6 (CMIP6) under scenarios issued by Intergovernmental Panel on Climate Change (IPCC), which demonstrate that the climate-adaptive TNEP can improve operational security under climate change while reducing investment costs.</p>-
dc.languageeng-
dc.publisherInstitute of Electrical and Electronics Engineers-
dc.relation.ispartofIEEE Transactions on Industrial Informatics-
dc.rightsThis work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.-
dc.subjectClimate change-
dc.subjectClimate-adaptive planning-
dc.subjectclimate-driven evolution-
dc.subjectData models-
dc.subjectInvestment-
dc.subjectPlanning-
dc.subjectPower systems-
dc.subjectProbability distribution-
dc.subjectSolid modeling-
dc.subjecttransmission network expansion planning-
dc.titleClimate-Adaptive Transmission Network Expansion Planning Considering Evolutions of Resources-
dc.typeArticle-
dc.identifier.doi10.1109/TII.2023.3284012-
dc.identifier.scopuseid_2-s2.0-85162712428-
dc.identifier.volume20-
dc.identifier.issue2-
dc.identifier.spage2063-
dc.identifier.epage2078-
dc.identifier.eissn1941-0050-
dc.identifier.isiWOS:001129375800001-
dc.identifier.issnl1551-3203-

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