TY - GEN
T1 - Towards Sustainable Mobility
T2 - 9th IEEE International Smart Cities Conference, ISC2 2023
AU - Gonzalez-Garrido, Amaia
AU - Cortes Borray, Andres Felipe
AU - Santos-Mugica, Maider
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - The widespread use of the electric vehicle (EV) will put a strain on the electricity charging cost, distribution network stress, and environmental impact due to polluting emissions from the generation mix. This vision leads to developing smart charging strategies, especially in low-voltage distribution networks, to take advantage of local photovoltaic (PV) generation and long car parking periods at home or at the workplace. This paper presents a novel EV smart charging model for private charging stations as a deterministic mixed-integer linear programming problem. It pursues to minimize the EV charging cost while efficiently managing PV self-consumption, PV surplus, and vehicle-to-grid capability via retail price signals. The dependency on the distribution network is reduced through a limited contracted power. The users' environmental concern is included, being influenced by the share of generation technologies free of CO2 emission. The results demonstrate that the smart EV charging optimization in individual or collective charging stations has huge potential to reduce the energy supply cost and enhance the use of existing EV infrastructure, reducing the need for network upgrades and enabling a higher hosting capacity. Indeed, the results show that the EV charging cost has been reduced by more than 40.4%, and the contracted power up to 68.7% in collective charging stations compared to uncontrolled EV charging.
AB - The widespread use of the electric vehicle (EV) will put a strain on the electricity charging cost, distribution network stress, and environmental impact due to polluting emissions from the generation mix. This vision leads to developing smart charging strategies, especially in low-voltage distribution networks, to take advantage of local photovoltaic (PV) generation and long car parking periods at home or at the workplace. This paper presents a novel EV smart charging model for private charging stations as a deterministic mixed-integer linear programming problem. It pursues to minimize the EV charging cost while efficiently managing PV self-consumption, PV surplus, and vehicle-to-grid capability via retail price signals. The dependency on the distribution network is reduced through a limited contracted power. The users' environmental concern is included, being influenced by the share of generation technologies free of CO2 emission. The results demonstrate that the smart EV charging optimization in individual or collective charging stations has huge potential to reduce the energy supply cost and enhance the use of existing EV infrastructure, reducing the need for network upgrades and enabling a higher hosting capacity. Indeed, the results show that the EV charging cost has been reduced by more than 40.4%, and the contracted power up to 68.7% in collective charging stations compared to uncontrolled EV charging.
KW - electric vehicle
KW - environmental concern
KW - optimization
KW - photovoltaic systems
KW - smart charging
UR - http://www.scopus.com/inward/record.url?scp=85178367780&partnerID=8YFLogxK
U2 - 10.1109/ISC257844.2023.10293634
DO - 10.1109/ISC257844.2023.10293634
M3 - Conference contribution
AN - SCOPUS:85178367780
T3 - Proceedings of 2023 IEEE International Smart Cities Conference, ISC2 2023
BT - Proceedings of 2023 IEEE International Smart Cities Conference, ISC2 2023
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 24 September 2023 through 27 September 2023
ER -