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• W3124162018 abstract "This study describes, applies, and compares three different approaches to integrate electric vehicles (EVs) in a cost-minimising electricity system investment model and a dispatch model. The approaches include both an aggregated vehicle representation and individual driving profiles of passenger EVs. The driving patterns of 426 randomly selected vehicles in Sweden were recorded between 30 and 73 days each and used as input to the electricity system model for the individual driving profiles. The main conclusion is that an aggregated vehicle representation gives similar results as when including individual driving profiles for most scenarios modelled. However, this study also concludes that it is important to represent the heterogeneity of individual driving profiles in electricity system optimisation models when: (i) charging infrastructure is limited to only the home location in regions with a high share of solar and wind power in the electricity system, and (ii) when addressing special research issues such as impact of vehicle-to-grid (V2G) on battery health status. An aggregated vehicle representation will, if the charging infrastructure is limited to only home location, over-estimate the V2G potential resulting in a higher share (up to 10 percentage points) of variable renewable electricity generation and an under-estimation of investments in both short- and long-term storage technologies." @default.
• W3124162018 created "2021-02-01" @default.
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• W3124162018 date "2021-01-21" @default.
• W3124162018 modified "2023-10-06" @default.
• W3124162018 title "To Represent Electric Vehicles in Electricity Systems Modelling—Aggregated Vehicle Representation vs. Individual Driving Profiles" @default.
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• W3124162018 doi "https://doi.org/10.3390/en14030539" @default.
• W3124162018 hasPublicationYear "2021" @default.
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