Priority-based vehicle-to-grid scheduling for minimization of power grid load variance

• Main Author: Mohd Syahmi Hashim, Mr.
• Format: text::Thesis
• Language: English
• Published: 2023
• Subjects: Priority-based vehicle-to-grid scheduling for minimization of power grid load variance

The growth of electric vehicle has increased exponentially as its stock had reached more than 7 million units globally in 2019. The rapid penetration of electric vehicle was encouraged by global warming and the excessive carbon emission from the internal combustion engine vehicle. Despite the economic and environmental benefits, electric vehicle still needs to receive regular charging from the power grid. Uncoordinated electric vehicle charging may introduce negative impacts to the power grid, such as increasing the peak load demand, disturbing the grid voltage stability and injecting harmonics into the power grid. Moreover, Vehicle-to-Grid algorithm is usually deployed to manage electric vehicle charging loads without focusing the benefits of vehicle users, such as the urgency to charge an electric vehicle with low battery stateof-charge. Hence, this study aims to find a central point that can benefit both power utility and electric vehicle users. This thesis presents an optimal priority-based Vehicleto-Grid scheduling with the objective to minimize the grid load variance. The developed algorithm also optimizes the amount of charging/discharging power based on the electric vehicle battery's state-of-charge. The algorithm can operate in three modes, which are valley filling, peak load shaving and priority charging. The charging of the electric vehicle can be performed in all three modes, as long as the charging of the electric vehicle is required. Meanwhile, discharging of the electric vehicle only occurs during peak load shaving mode. To ensure the practicability of this study, numerous constraints were considered and the study was conducted in a commercial-residential area with electric vehicle mobility of 1300. The results indicated that the algorithm was able to minimize the grid load variance while prioritizing the electric vehicle with a low percentage of state-of-charge. Thus, the optimal algorithm was able to utilize the electric vehicle battery energy to support the power grid while alleviating the vehicle user's range anxiety problem.