To Represent Electric Vehicles in Electricity Systems Modelling—Aggregated Vehicle Representation vs. Individual Driving Profiles

Maria Taljegard, Lisa Göransson, Mikael Odenberger and Filip Johnsson
Additional contact information
Maria Taljegard: Department of Space, Earth and Environment, Chalmers University of Technology, 412 96 Gothenburg, Sweden
Lisa Göransson: Department of Space, Earth and Environment, Chalmers University of Technology, 412 96 Gothenburg, Sweden
Mikael Odenberger: Department of Space, Earth and Environment, Chalmers University of Technology, 412 96 Gothenburg, Sweden
Filip Johnsson: Department of Space, Earth and Environment, Chalmers University of Technology, 412 96 Gothenburg, Sweden

Energies, 2021, vol. 14, issue 3, 1-25

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.

Keywords: energy system modelling; method; vehicle-to-grid; variability management; smart charging; energy storage (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
https://www.mdpi.com/1996-1073/14/3/539/pdf (application/pdf)
https://www.mdpi.com/1996-1073/14/3/539/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:14:y:2021:i:3:p:539-:d:484551

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().