TY - GEN
T1 - Techno-Economic Optimization of Electric Vehicle Charging Station with Virtual Power Plant - A University Campus Use Case
AU - Ahmed, S. M.Masum
AU - Alvi, Anas Abdullah
AU - Pantazis, Konstantinos
AU - Bagaini, Annamaria
AU - Croci, Edoardo
AU - Romero-Cadaval, Enrique
AU - Martins, João
N1 - Funding Information:
This project has received funding from the SmartGYsum project which is the European Union's Horizon 2020 research and innovation programme under the Marie Sk\u0142odowska-Curie grant agreement no. 955614. Also, this research was funded (in part) by the Portuguese FCT - Fundacao para a Ciencia e a Tecnologia no ambito da Unidade de Investigacao CTS - Centro de Tecnologia e Sistemas/UNINOVA/FCT/NOVA, with reference CTS/00066. Numerous icons developed by Freepik have been utilized, accessible at https://www.flaticon.com.
Publisher Copyright:
© 2025 IEEE.
PY - 2025/7/15
Y1 - 2025/7/15
N2 - Electric Vehicles (EVs) are crucial for decarbonizing the transport sector, which accounts for about one-quarter of the EU's total Greenhouse Gas (GHG) emissions. However, most EV Charging Stations (EVCS) still rely on fossil fuel-based energy generation, contributing to grid-related emissions. Integrating Renewable Energy Sources (RES) with EVCS is a key strategy to lower the emissions intensity of the EV supply chain, though its techno-economic benefits remain largely underexplored. This study addresses this gap by conducting a techno-economic feasibility analysis of RESpowered EVCS at NOVA University Lisbon, either through onsite RES integration or via a Virtual Power Plant (VPP). Two datasets were combined-including parking hours, building energy demand, and economic variables-to evaluate three scenarios using HOMER and a genetic algorithm: (i) Grid-toVehicle (base), (ii) EVCS with on-site RES, and (iii) EVCS with VPP. The VPP scenario is most optimal, achieving the lowest cost of energy (0.111 €/kWh), the highest RES fraction (34.6%), and a 16.15% reduction in energy bills relative to the base scenario.
AB - Electric Vehicles (EVs) are crucial for decarbonizing the transport sector, which accounts for about one-quarter of the EU's total Greenhouse Gas (GHG) emissions. However, most EV Charging Stations (EVCS) still rely on fossil fuel-based energy generation, contributing to grid-related emissions. Integrating Renewable Energy Sources (RES) with EVCS is a key strategy to lower the emissions intensity of the EV supply chain, though its techno-economic benefits remain largely underexplored. This study addresses this gap by conducting a techno-economic feasibility analysis of RESpowered EVCS at NOVA University Lisbon, either through onsite RES integration or via a Virtual Power Plant (VPP). Two datasets were combined-including parking hours, building energy demand, and economic variables-to evaluate three scenarios using HOMER and a genetic algorithm: (i) Grid-toVehicle (base), (ii) EVCS with on-site RES, and (iii) EVCS with VPP. The VPP scenario is most optimal, achieving the lowest cost of energy (0.111 €/kWh), the highest RES fraction (34.6%), and a 16.15% reduction in energy bills relative to the base scenario.
KW - Cost of energy
KW - Electric vehicle charging station
KW - G2V
KW - Genetic algorithm
KW - Virtual power plant
UR - http://www.scopus.com/inward/record.url?scp=105012245472&partnerID=8YFLogxK
U2 - 10.1109/EUROCON64445.2025.11073409
DO - 10.1109/EUROCON64445.2025.11073409
M3 - Conference contribution
AN - SCOPUS:105012245472
T3 - Proceedings - EUROCON 2025: 21st International Conference on Smart Technologies
BT - Proceedings - EUROCON 2025
A2 - Czarnowski, Ireneusz
A2 - Jasinski, Marek
PB - Institute of Electrical and Electronics Engineers (IEEE)
T2 - 21st IEEE International Conference on Smart Technologies, EUROCON 2025
Y2 - 4 June 2025 through 6 June 2025
ER -
