TY  - GEN
AB  - The energy transition brings an increasing penetration of electric vehicles (EVs) aligning Swiss Energy Strategy 2050. It is important for the grid planner to anticipate the possible impact of EV charging on the hosting capacity (HC) of electricity distribution network in the next years. To identify and quantify such impact, the evolution model of EV fleets, the electrical network topology and the access to proprietary data are equivalently necessary. This work studies a low voltage distribution network (LVDN) for a touristic area located in Swiss Alpine region in collaboration with the local distribution system operators (DSOs). We approach this issue with an innovative method that combines inspired agent-based model implementing insights from a Social Sciences and Humanities (SSH) and an engineering model of the local distribution grid. The co-simulation results indicate that the power demand from EVs charging will have a significant impact on the LVDN's hosting capacity, especially on holidays' eves. The DSOs should advance proactive measures to prepare for future scenarios. The developed co-simulation model can be used to explore and evaluate potential solutions for network planning and design.
AD  - School of Engineering, HES-SO Valais-Wallis, HEI, HES-SO University of Applied Sciences and Arts Western Switzerland
AD  - School of Engineering, HES-SO Valais-Wallis, HEI, HES-SO University of Applied Sciences and Arts Western Switzerland
AD  - School of Engineering, HES-SO Valais-Wallis, HEI, HES-SO University of Applied Sciences and Arts Western Switzerland
AD  - School of Engineering, HES-SO Valais-Wallis, HEI, HES-SO University of Applied Sciences and Arts Western Switzerland
AU  - Guo, Baoling
AU  - Piana, Valentino
AU  - Pouget, Julien
AU  - Zurbrügg, Yann
DA  - 2024-06
EP  - 162
ID  - 15277
JF  - Proceedings of CIRED 2024 Conference, 19-20 June 2024, Vienna, Austria
KW  - low voltage distribution network
KW  - electrical vehicle
KW  - social sciences and humanities
KW  - engineering model
KW  - hosting capacity
L1  - https://arodes.hes-so.ch/record/15277/files/Guo_2024_co-simulation_ehavioral_technical_decisions.pdf
L2  - https://arodes.hes-so.ch/record/15277/files/Guo_2024_co-simulation_ehavioral_technical_decisions.pdf
L4  - https://arodes.hes-so.ch/record/15277/files/Guo_2024_co-simulation_ehavioral_technical_decisions.pdf
LA  - eng
LK  - https://arodes.hes-so.ch/record/15277/files/Guo_2024_co-simulation_ehavioral_technical_decisions.pdf
N2  - The energy transition brings an increasing penetration of electric vehicles (EVs) aligning Swiss Energy Strategy 2050. It is important for the grid planner to anticipate the possible impact of EV charging on the hosting capacity (HC) of electricity distribution network in the next years. To identify and quantify such impact, the evolution model of EV fleets, the electrical network topology and the access to proprietary data are equivalently necessary. This work studies a low voltage distribution network (LVDN) for a touristic area located in Swiss Alpine region in collaboration with the local distribution system operators (DSOs). We approach this issue with an innovative method that combines inspired agent-based model implementing insights from a Social Sciences and Humanities (SSH) and an engineering model of the local distribution grid. The co-simulation results indicate that the power demand from EVs charging will have a significant impact on the LVDN's hosting capacity, especially on holidays' eves. The DSOs should advance proactive measures to prepare for future scenarios. The developed co-simulation model can be used to explore and evaluate potential solutions for network planning and design.
PY  - 2024-06
SP  - 162
T1  - Co-simulation of behavioral and technical decisions :an application to impact analysis of EV charging on hosting capacity of LV distribution network
TI  - Co-simulation of behavioral and technical decisions :an application to impact analysis of EV charging on hosting capacity of LV distribution network
UR  - https://arodes.hes-so.ch/record/15277/files/Guo_2024_co-simulation_ehavioral_technical_decisions.pdf
VL  - 2024
Y1  - 2024-06
ER  -