Increasing charging demand with the widespread use of electric vehicles leads to negative effects such as load imbalance, sudden load changes, harmonics and voltage fluctuations in the electricity distribution network. Furthermore, irregular charging demand negatively impacts electric vehicle user comfort and traffic management. This study presents a dynamic pricing-based energy management model developed for use in urban electric vehicle charging infrastructures to address these challenges. The proposed model considers price not only as an economic output but also as a control variable that manages grid load balance. There are four input parameters (traffic, station occupancy rate, location and state of charge) in the pricing model and these parameters are dynamically updated at each iteration. The model was developed in MATLAB environment and was employed real-time traffic data obtained through the Google Maps API. The model tested for ten iterations. The results show that the pricing model prioritizes low charge levels vehicles. But the model maintaining balanced grid load simultaneously. Furthermore, price output increases high occupancy rates charging stations in order to encourage users to choose stations with lower occupancy rates. Results of this study demonstrates that pricing mechanism can be used as a decision variable both economic reasons and system efficiency. In future works, the model might be extended with artificial intelligence and optimization-based methods. Pricing model serves as a potential solution to challenges in energy and transportation networks with the help of test systems.
Çetin et al. (Fri,) studied this question.