Bi-Objective Optimization of EV Charging in a Workplace Parking Lot
- Resource Type
- Conference
- Authors
- Shariatzadeh, Mahla; Antunes, Carlos Henggeler; Lopes, Marta A. R.
- Source
- 2023 International Conference on Smart Energy Systems and Technologies (SEST) Smart Energy Systems and Technologies (SEST), 2023 International Conference on. :1-6 Sep, 2023
- Subject
- Communication, Networking and Broadcast Technologies
Components, Circuits, Devices and Systems
Power, Energy and Industry Applications
Signal Processing and Analysis
Transportation
Vehicle-to-grid
Costs
Sensitivity analysis
Employment
Transportation
Quality of service
Charging stations
electric vehicle
smart charging
bi-objective optimization
charging preferences
- Language
- ISSN
- 2836-4678
The dissemination of Electric Vehicles (EVs) is essential to decarbonizing the transportation sector. However, the uncoordinated charging of EVs can negatively interfere with the power grid. An adequate scheduling scheme is crucial for Charging Managers (CMs) to avert undesirable effects. This study aims to develop a bi-objective optimization model for the scheduling of EVs in Charging Stations (CS) in workplace Parking Lots (PL) addressing the EV users' preferences. Two bi-objective optimization models are proposed to address the economic and Quality-of-Service (QoS) dimensions by means of minimizing both cost and the deviation from desired State-of-Charge (SoC). Two perspectives to minimize this deviation are considered: minimizing the sum of deviations and minimizing the worst deviation (a fairness criterion based on a min-max approach). The Epsilon-constraint method is used to obtain a representation of the non-dominated solution set corresponding to the scheduling plan for each EV. This study further conducts a sensitivity analysis to investigate the influence of selling energy prices in Vehicle-to-Grid (V2G) mode. The findings indicate that the model aiming to minimize the charging cost and the sum of deviations is more sensitive to variations in selling energy prices, highlighting the impact of price variations in scheduling plans.