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dc.contributor.authorBordin, Chiara
dc.contributor.authorTomasgard, Asgeir
dc.description.abstractAbstract The increasing demand of electric vehicles creates challenges for the electric grid both on the transmission level and distribution level. Charging sites in particular will have to face strong challenges especially in those countries where a massive penetration of electric vehicles happened in the last years and even more is expected in the forthcoming future. Such an increased forecast demand will lead to a capacity lack within the existing charging sites, therefore new investments in design and expansion have to be planned. We propose the so called SMACS MODEL that stands for Stochastic Multihorizon Approach for Charging Sites Management, Operations, Design and Expansion under Limited capacity conditions. The model is built to analyse critical decisions in terms of transformer expansion, grid reinforcements, renewable installation and storage integration, over a time horizon of 10 years, with a particular focus on the long term uncertainty in the price variations of the available resources. Long term investment decisions and short term operational decisions are addressed simultaneously in a holistic approach that includes also battery degradation issues and is able to tackle the optimal trade off between battery replacements, grid reinforcements and renewable installations throughout the chosen time horizon. Compared to traditional decision approaches the model is able to take more precise decisions due to its higher insight on the long term costs projections, the inclusion of battery degradation issues and the inclusion of grid rules and regulations limits that affect the final decisions.nb_NO
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internasjonal*
dc.titleSMACS MODEL, a stochastic multihorizon approach for charging sites management, operations, design, and expansion under limited capacity conditionsnb_NO
dc.typeJournal articlenb_NO
dc.typePeer reviewednb_NO
dc.source.journalJournal of Energy Storagenb_NO
dc.description.localcode© 2019 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (
cristin.unitnameInstitutt for industriell økonomi og teknologiledelse

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Attribution-NonCommercial-NoDerivatives 4.0 Internasjonal
Except where otherwise noted, this item's license is described as Attribution-NonCommercial-NoDerivatives 4.0 Internasjonal