方案(数学)
网格
阶段(地层学)
电气工程
单级
计算机科学
拓扑(电路)
控制理论(社会学)
工程类
数学
控制(管理)
航空航天工程
几何学
数学分析
古生物学
人工智能
生物
作者
Hossein Saber,Hossein Ranjbar,Ehsan Hajipour,Mohammad Shahidehpour
出处
期刊:IEEE Transactions on Transportation Electrification
日期:2024-01-01
卷期号:: 1-1
被引量:2
标识
DOI:10.1109/tte.2024.3357217
摘要
The rapid proliferation of electric vehicles (EVs), if not properly integrated, has the potential to impact the secure and economic operation of distribution systems. To tackle this challenge, the system operators and planners have two primary options: i ) expanding system capacity, ii ) implementing charging management programs. In this paper, we consider an exclusive DC feeder for supplying multiple EV charging stations, connected to the main grid via the grid-tie converter. Additionally, a two-stage coordination algorithm is proposed in which the EV charging stations’ daily quotas from the grid-tie converter capacity and their day-ahead scheduling plans are determined in the first stage. In this regard, the EV charging stations determine their profit curves, and then the local market operator (LMO) collects these curves and calculates the quotas by maximizing the total profit respecting the capacity constraint. Considering the calculated quotas, the EV charging stations determine and submit their day-ahead scheduling plans to the LMO. In the second stage, each EV charging station manages its real-time operation under the uncertainties of EV parking activities. To this end, the model predictive control (MPC) method is employed to effectively handle model uncertainties. Further, to encourage EV owners’ participation in the charging control program, the EV charging station’s profit is fairly distributed among EVs based on their provided flexibility. Finally, the simulation study is provided to illustrate the effectiveness and applicability of the proposed model from the perspective of system operation and EV owners.
科研通智能强力驱动
Strongly Powered by AbleSci AI