Potential of PV and Wind Energy-Based EV Charging Stations to Minimize Peak Load Demand
File version
Author(s)
Sanjari, Mohammad J
Du, Bo
Lu, Junwei
Griffith University Author(s)
Primary Supervisor
Other Supervisors
Editor(s)
Date
Size
File type(s)
Location
Sydney, Australia
License
Abstract
The increasing demand for electric vehicles (EVs) presents a challenge for electric utilities during peak load periods. To overcome these challenges demand response (DR) and time-of-use (ToU) pricing strategies are used, however, they disrupt user routines and make them less attractive. This study investigates the potential of integrating solar and wind energy-based EV charging stations as a solution to minimize peak load demand. The study considers a case study at the beach location of Brisbane, Australia. The case study considers the installation of 2 MW of solar and 1.2 MW of wind capacity. Results demonstrate a 45% reduction in grid energy consumption, with contributions of 24% from solar and 20.9% from wind energy. Additionally, the system achieves a 41% reduction in carbon emissions, significant cost savings, a 19.0% return on investment, and a 4.18-year payback period. Moreover, the energy cost is also reduced from 0.373 $ / kWh to 0.256 $ / k W h. These findings highlight the effectiveness of renewable energy in reducing peak load demand and associated costs while offering a sustainable solution for EV charging infrastructure.
Journal Title
Conference Title
2025 4th International Conference on Smart Grid and Green Energy (ICSGGE)
Book Title
Edition
Volume
Issue
Thesis Type
Degree Program
School
Publisher link
Patent number
Funder(s)
Grant identifier(s)
Rights Statement
Rights Statement
Item Access Status
Note
Access the data
Related item(s)
Subject
Hybrid and electric vehicles and powertrains
Transport planning
Electrical energy transmission, networks and systems
Electrical energy storage
Persistent link to this record
Citation
Rehman, AU; Sanjari, MJ; Du, B; Lu, J, Potential of PV and Wind Energy-Based EV Charging Stations to Minimize Peak Load Demand, 2025 4th International Conference on Smart Grid and Green Energy (ICSGGE), 2025, pp. 443-448