Abstract
Vehicle to Grid (V2G) refers to the optimal management of the charging and discharging behavior of electric vehicles through reasonable strategies and advanced communication. In the process of interaction, there are three stakeholders: the power grid, operators (charging stations), and EV users. In real life, the impact of peak‐valley difference caused a lot of power loss when charging. At the same time, the loss of current is also a loss for power grid companies and EV users. In this paper, we propose a multiobjective optimization method to reduce the current loss and determine the relationship between the parameters and the objective function and constraints. This optimization method uses a genetic algorithm for multiobjective optimization. Through the analysis of the number of vehicles and load curve of AC class I and AC class II electric vehicles before and after optimization in each period, we found that the charging load of electric vehicles played a role of valley filling in the low valley price stage and played a peak‐cutting role in a peak price period.
Highlights
Automobile energy consumption accounts for nearly a quarter of the world’s total energy consumption
Our main contribution is to propose a multiobjective optimization method to reduce the current loss of electric vehicles during the charging process, which is of benefit to both electric power companies and electric vehicle owners
Assuming that node 8 is connected to 100 electric vehicles, 10% of them are charged by AC level 1 and 90% are charged by AC level 2
Summary
Automobile energy consumption accounts for nearly a quarter of the world’s total energy consumption. Electric vehicles (EVS) are considered a solution to reduce air pollution and greenhouse gas emissions. As a result, their market share has increased exponentially in recent years [3]. Researchers [6, 7] studied the energy management strategies and charging technology for pure electric vehicles, discussed the main challenges and corresponding solutions faced by pure electric vehicles, and introduced the latest developments in pure electric vehicles. Our main contribution is to propose a multiobjective optimization method to reduce the current loss of electric vehicles during the charging process, which is of benefit to both electric power companies and electric vehicle owners. In addition to the power limit of the line, the vehicle’s own charging and discharging power limit, the battery SOC limit, etc. are constraints, and the final power of each vehicle is 50 kWh
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