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dc.contributor.authorHerrmann, J. K.en
dc.contributor.authorSavin, I.en
dc.date.accessioned2021-08-31T15:01:57Z-
dc.date.available2021-08-31T15:01:57Z-
dc.date.issued2017-
dc.identifier.citationHerrmann J. K. Optimal policy identification: Insights from the German electricity market / J. K. Herrmann, I. Savin. — DOI 10.1016/j.techfore.2017.04.014 // Technological Forecasting and Social Change. — 2017. — Vol. 122. — P. 71-90.en
dc.identifier.issn401625-
dc.identifier.otherFinal2
dc.identifier.otherAll Open Access, Green3
dc.identifier.otherhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85019064081&doi=10.1016%2fj.techfore.2017.04.014&partnerID=40&md5=128549b0ddce7a5438ac208eef497d11
dc.identifier.otherhttps://publikationen.bibliothek.kit.edu/1000055424/3854092m
dc.identifier.urihttp://elar.urfu.ru/handle/10995/102125-
dc.description.abstractThe diffusion of renewable electricity technologies is widely considered as crucial for establishing a sustainable energy system in the future. However, the required transition is unlikely to be achieved by market forces alone. For this reason, many countries implement various policy instruments to support this process, also by re-distributing related costs among all electricity consumers. This paper presents a novel history-friendly agent-based study aiming to explore the efficiency of different mixes of policy instruments by means of a Differential Evolution algorithm. Special emphasis of the model is devoted to the possibility of small scale renewable electricity generation, but also to the storage of this electricity using small scale facilities being actively developed over the last decade. Both combined pose an important instrument for electricity consumers to achieve partial or full autarky from the electricity grid, particularly after accounting for decreasing costs and increasing efficiency of both due to continuous innovation. Among other things, we find that the historical policy mix of Germany introduced too strong and inflexible demand-side instruments (like feed-in tariff) too early, thereby creating strong path-dependency for future policy makers and reducing their ability to react to technological but also economic shocks without further increases of the budget. © 2017 Elsevier Inc.en
dc.format.mimetypeapplication/pdfen
dc.language.isoenen
dc.publisherElsevier Inc.en
dc.rightsinfo:eu-repo/semantics/openAccessen
dc.sourceTechnol. Forecast. Soc. Change2
dc.sourceTechnological Forecasting and Social Changeen
dc.subjectDIFFERENTIAL EVOLUTIONen
dc.subjectELECTRICITY STORAGEen
dc.subjectENERGY GRIDen
dc.subjectFEED-IN TARIFFen
dc.subjectRENEWABLE ENERGYen
dc.subjectBUDGET CONTROLen
dc.subjectCOMMERCEen
dc.subjectELECTRIC ENERGY STORAGEen
dc.subjectELECTRIC POWER UTILIZATIONen
dc.subjectEVOLUTIONARY ALGORITHMSen
dc.subjectOPTIMIZATIONen
dc.subjectPOWER MARKETSen
dc.subjectDIFFERENTIAL EVOLUTIONen
dc.subjectELECTRICITY STORAGESen
dc.subjectENERGY GRIDSen
dc.subjectFEED-IN TARIFFen
dc.subjectRENEWABLE ENERGIESen
dc.subjectELECTRIC POWER TRANSMISSION NETWORKSen
dc.subjectALGORITHMen
dc.subjectELECTRICITY GENERATIONen
dc.subjectELECTRICITY SUPPLYen
dc.subjectENERGY MARKETen
dc.subjectFUTURE PROSPECTen
dc.subjectPOLICY ANALYSISen
dc.subjectPOLICY IMPLEMENTATIONen
dc.subjectPOLICY MAKINGen
dc.subjectRENEWABLE RESOURCEen
dc.subjectTARIFF STRUCTUREen
dc.subjectGERMANYen
dc.titleOptimal policy identification: Insights from the German electricity marketen
dc.typeArticleen
dc.typeinfo:eu-repo/semantics/articleen
dc.typeinfo:eu-repo/semantics/publishedVersionen
dc.identifier.doi10.1016/j.techfore.2017.04.014-
dc.identifier.scopus85019064081-
local.contributor.employeeHerrmann, J.K., Friedrich Schiller University of Jena, Faculty of Economics and Business Administration, Germany
local.contributor.employeeSavin, I., Chair for Economic Policy, Karlsruhe Institute of Technology, Germany, Bureau d'Economie Théorique et Appliquée, UMR 7522 Université de Strasbourg - CNRS, Strasbourg, France, Chair for Econometrics and Statistics, Graduate School of Economics and Management, Ural Federal University, Yekaterinburg, Russian Federation
local.description.firstpage71-
local.description.lastpage90-
local.volume122-
dc.identifier.wos000407184300007-
local.contributor.departmentFriedrich Schiller University of Jena, Faculty of Economics and Business Administration, Germany
local.contributor.departmentChair for Economic Policy, Karlsruhe Institute of Technology, Germany
local.contributor.departmentBureau d'Economie Théorique et Appliquée, UMR 7522 Université de Strasbourg - CNRS, Strasbourg, France
local.contributor.departmentChair for Econometrics and Statistics, Graduate School of Economics and Management, Ural Federal University, Yekaterinburg, Russian Federation
local.identifier.pure9b3a3abc-59ed-48cc-8883-1a592eb58e63uuid
local.identifier.pure1971245-
local.identifier.eid2-s2.0-85019064081-
local.identifier.wosWOS:000407184300007-
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