A multi-agent based integrated volt-var optimization engine for fast vehicle-to-grid reactive power dispatch and electric vehicle coordination

作者：Zhang, Wenjie^*; Gandhi, Oktoviano; Quan, Hao; Rodriguez-Gallegos, Carlos D.; Srinivasan, Dipti

来源：Applied Energy, 2018, 229: 96-110.

DOI：10.1016/j.apenergy.2018.07.092

• Electric vehicle
• Volt-var optimization
• Multi-agent system
• Charging coordination
• Vehicle-to-grid
• Var dispatch

Electric Vehicles have been receiving increasing attention. As the number of electric vehicles increases, uncoordinated charging of electric vehicles can lead to voltage and frequency instability in microgrids. Various methods have been proposed for electrical vehicle coordination, where most of them focused on controlling active power. Vehicle-to-grid var has been recently included in volt-var optimization approaches, which aim at improving voltage stability using var sources. However, most of these approaches are based on computationally inefficient heuristic methods, which are not applicable to handle fast-changing vehicle-to-grid var. Furthermore, the uncertainties and charging demands of electric vehicles have not been considered thoroughly. In this paper, an integrated volt-var optimization engine is proposed for distributed electric vehicle charging coordination and fast vehicle-to-grid var dispatch, considering the uncertainties and charging demands of electric vehicles. The proposed method is based on a multi-agent system, which distributes complex optimization processes to enhance computational efficiency. Case studies show that the proposed distributed method reduces up to 92% computational time without economic losses, compared with the central coordination. It is also observed that the costly usage of diesel generators can be reduced by employing more vehicle-to-grid var due to their similar functionality in voltage regulation. Surprisingly, it is found that when utilizing the power support from electric vehicles and diesel generators, the computational time decreases even when more decision variables are added.

• 出版日期2018-11-1
• 单位南京理工大学