TY - JOUR
T1 - An Optimal Day-Ahead Scheduling Framework for E-Mobility Ecosystem Operation with Drivers Preferences
AU - Bagheri Tookanlou, Mahsa
AU - Pourmousavi, S. Ali
AU - Marzband, Mousa
N1 - Funding information: This work is funded by PGR scholarship at Northumbria University and supported by a project funded by the British Council under grant contract No. IND/CONT/GA/18-19/22.
PY - 2021/11
Y1 - 2021/11
N2 - The future e-mobility ecosystem will be a complex structure with different stakeholders seeking to optimize their operation and benefits. In this paper, a day-ahead grid-to-vehicle (G2V) and vehicle-to-grid (V2G) scheduling framework is proposed including electric vehicles (EVs), charging stations (CSs), and retailers. To facilitate V2G services and to avoid congestion at CSs, two types of trips, i.e., mandatory and optional trips, are defined and formulated. Also, EV drivers preferences are added to the model to enhance the practical aspects of the scheduling framework. An iterative process is proposed to solve the non-cooperative Stackelberg game by determining the optimal routes and CS for each EV, optimal operation of each CS and retailers, and optimal V2G and G2V prices. Extensive simulation studies are carried out for two different e-mobility ecosystems of multiple retailers and CSs as well as numerous EVs based on real data from San Francisco, the USA. The simulation results show that the optional trips not only reduces the cost of EVs and PV curtailment by 8.8-24.2% and 26.4-28.5% on average, respectively, in different scenarios but also mitigates congestion during specific hours.
AB - The future e-mobility ecosystem will be a complex structure with different stakeholders seeking to optimize their operation and benefits. In this paper, a day-ahead grid-to-vehicle (G2V) and vehicle-to-grid (V2G) scheduling framework is proposed including electric vehicles (EVs), charging stations (CSs), and retailers. To facilitate V2G services and to avoid congestion at CSs, two types of trips, i.e., mandatory and optional trips, are defined and formulated. Also, EV drivers preferences are added to the model to enhance the practical aspects of the scheduling framework. An iterative process is proposed to solve the non-cooperative Stackelberg game by determining the optimal routes and CS for each EV, optimal operation of each CS and retailers, and optimal V2G and G2V prices. Extensive simulation studies are carried out for two different e-mobility ecosystems of multiple retailers and CSs as well as numerous EVs based on real data from San Francisco, the USA. The simulation results show that the optional trips not only reduces the cost of EVs and PV curtailment by 8.8-24.2% and 26.4-28.5% on average, respectively, in different scenarios but also mitigates congestion during specific hours.
KW - Batteries
KW - Cascading style sheets
KW - E-mobility ecosystem
KW - EV drivers' preferences
KW - Ecosystems
KW - G2V and V2G operation
KW - Optimization
KW - Pricing
KW - State of charge
KW - Vehicle-to-grid
KW - optional trips
KW - three-layer optimization problem
KW - EV drivers' preferences
UR - https://www.scopus.com/pages/publications/85103794563
U2 - 10.1109/TPWRS.2021.3068689
DO - 10.1109/TPWRS.2021.3068689
M3 - Article
SN - 0885-8950
VL - 36
SP - 5245
EP - 5257
JO - IEEE Transactions on Power Systems
JF - IEEE Transactions on Power Systems
IS - 6
ER -
