Optimal scheduling for micro-grid considering EV charging-swapping-storage integrated station

被引:28
作者
Yuan, Hongtao [1 ]
Wei, Gang [1 ]
Zhu, Lan [1 ]
Zhang, Xin [2 ]
Zhang, He [3 ]
Luo, Zhigang [1 ]
Hu, Jue [1 ]
机构
[1] Shanghai Univ Elect Power, Coll Elect Engn, Shanghai 200090, Peoples R China
[2] Shanghai Puhaiqiushi Elect Power New Technol Co, Shanghai 200090, Peoples R China
[3] Shanghai Waigaoqiao 2 Power Generat Co Ltd, Shanghai 200137, Peoples R China
关键词
predictive control; secondary cells; distributed power generation; battery powered vehicles; battery storage plants; optimisation; electric vehicle charging; MG operation; electric vehicle charging-swapping-storage integrated station; traffic flow model; network topology; speed-flow relationship; battery swapping station; BSS; electric buses; CSSIS model; aggregates BCS; energy storage station; microturbines; model predictive control; day-ahead scheduling; intra-day rolling scheduling; optimal scheduling; microgrid optimal operation; queuing theory; photovoltaic cells; NETWORK;
D O I
10.1049/iet-gtd.2018.6912
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
In this study, a micro-grid (MG) optimal operation model considering the electric vehicle (EV) charging-swapping-storage integrated station (CSSIS) is presented. According to the behaviour characteristics of fast charging users, the battery fast charging station (BCS) model based on queuing theory is established. In addition, the traffic flow model could be determined by the network topology and speed-flow relationship in order to propose the battery swapping station (BSS) model considering the departure schedule and route arrangement of electric buses (Ebs). Meanwhile, the CSSIS model is also introduced, which aggregates BCS, BSS, and the energy storage station into one unit. In the MG, which takes the outputs of wind turbines, photovoltaic cells, micro-turbines, CSSIS and the demand of original load into account, this study proposes an optimal operation model in order to minimise the daily total operation cost and the problem is formulated in the framework of model predictive control (MPC). MPC based on a multi-time scale coordinated approach, which is formed by day-ahead scheduling, intra-day rolling scheduling, and real-time feedback correction, is introduced to handle uncertainties in renewable energy and load. The results demonstrate the effectiveness and benefit of the optimal scheduling strategy.
引用
收藏
页码:1127 / 1137
页数:11
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