Co-simulation of optimal EVSE and techno-economic system design models for electrified fleets.

Erdogan, Nuh; Kucuksari, Sadik; Cali, Umit

Abstract

As the transition to electric mobility is expanding at a rapid pace, operationally feasible and economically viable charging infrastructure is needed to support electrified fleets. This paper presents a co-simulation of optimal electric vehicle supply equipment (EVSE) and techno-economic system design models to investigate the behaviors of various EVSE configurations from cost and technical aspects. While the system design optimization is performed for a grid-tied PV system, the optimal EVSE model considers all EVSE options which are currently installed at workplaces. To investigate the impact of EV utilization rate, three fleet sizes are considered that are generated based on real EV fleet data. Furthermore, the impact of electricity rates is also explored through an innovative EV-specific (BEV) rate and a conventional time-of-use (ToU) tariff. It is shown that investing in grid-tied renewable energy technologies for workplace charging infrastructure supply can lower charging costs. Cost savings differ from EVSE types and fleet size under the BEV rate while EVSEs display similar cost-saving behavior under the ToU tariff irrespective of fleet size. DC Fast Charging (DCFC) EVSE is found to be highly sensitive to fleet size as compared to AC EVSEs. Moreover, DCFCs make better use of the BEV rate which makes their economics competitive as much as AC EVSEs. Finally, it is found that the fleet size and AC EVSE types have a minor effect on the use of renewable energy in contrast to the DCFC case.

Citation

ERDOGAN, N., KUCUKSARI, S. and CALI, U. 2022. Co-simulation of optimal EVSE and techno-economic system design models for electrified fleets. IEEE access [online], 10, pages 18988-18997. Available from: https://doi.org/10.1109/ACCESS.2022.3150359

• Journal Article Type: Article
• Acceptance Date: Feb 2, 2022
• Online Publication Date: Feb 9, 2022
• Publication Date: Dec 31, 2022
• Deposit Date: Feb 10, 2022
• Publicly Available Date: Feb 10, 2022
• Journal: IEEE Access
• Electronic ISSN: 2169-3536
• Publisher: Institute of Electrical and Electronics Engineers (IEEE)
• Peer Reviewed: Peer Reviewed
• Volume: 10
• Pages: 18988-18997
• DOI: https://doi.org/10.1109/ACCESS.2022.3150359
• Keywords: Costs; Biological system modeling; Optimization; Employment; Data models; System analysis and design; Tariffs; Electric vehicles; Electric fleet; EVSE; Optimization; PV; Smart charging
• Public URL: https://rgu-repository.worktribe.com/output/1592187