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dc.contributor.authorCorbier, D.
dc.contributor.authorGonand, Frédéric
dc.contributor.authorBessec, Marie
dc.date.accessioned2019-07-12T09:00:15Z
dc.date.available2019-07-12T09:00:15Z
dc.date.issued2018
dc.identifier.issn1420-2026
dc.identifier.urihttps://basepub.dauphine.fr/handle/123456789/19227
dc.language.isoenen
dc.subjectRenewables
dc.subjectElectric utilities
dc.subjectDistribution networks
dc.subjectCluster analysis
dc.subjectC38
dc.subjectL94
dc.subjectQ42
dc.subject.ddc338en
dc.subject.classificationjelC.C3.C38en
dc.subject.classificationjelL.L9.L94en
dc.subject.classificationjelQ.Q4.Q42en
dc.titleImpacts of decentralised power generation on distribution networks: a statistical typology of European countries
dc.typeArticle accepté pour publication ou publié
dc.description.abstractenThe development of decentralized sources of power out of renewable sources of energies has been triggering far-reaching consequences for Distribution System Operators over the past decade in Europe. Our paper benchmarks across 23 European countries the impact of the development of renewables on the physical characteristics of power distribution networks and on their investments. It builds on a large spectrum of databases of quantitative indicators about the dynamics of installed capacity of renewable energy resources and the power generation out of them, electricity independence, quality of electricity distribution, smart grids investments, Network System Operators capital expenditures, length of the distribution networks, overall costs of power networks paid by private agents, and electricity losses, all in relation with the development of decentralized generation. The heterogeneity of these indicators across Europe appears to be wide notably because of physical constraints, historic legacies, or policy and regulatory choices. A cluster analysis allows for deriving six groups of countries that display statistically homogenous characteristics. Our results may provide decision makers and regulators with a tool helping them to concentrate on the main issues specific to their countries as compared to the European median, and to look for possible solutions in the experience of other clusters which are shown to perform better for some indicators.
dc.relation.isversionofjnlnameEnvironmental Modeling and Assessment
dc.relation.isversionofjnlvol23
dc.relation.isversionofjnlissue5
dc.relation.isversionofjnldate2018
dc.relation.isversionofjnlpages471–495
dc.relation.isversionofdoihttps://doi.org/10.1007/s10666-018-9621-7
dc.relation.isversionofjnlpublisherSpringer
dc.subject.ddclabelEconomie industrielleen
dc.relation.forthcomingnonen
dc.relation.forthcomingprintnonen
dc.description.ssrncandidatenon
dc.description.halcandidateoui
dc.description.readershiprecherche
dc.description.audienceInternational
dc.relation.Isversionofjnlpeerreviewedoui
dc.date.updated2021-05-05T07:22:10Z
hal.identifierhal-02181543*
hal.version1*
hal.update.actionupdateFiles*


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