Smart Control of an Electric Vehicle for Ancillary Service in DC Microgrid

被引:17
作者
Yu, Yue [1 ]
Nduka, Onyema Sunday [2 ]
Pal, Bikash C. [1 ]
机构
[1] Imperial Coll London, Control & Power Grp, London SW7 2AZ, England
[2] Royal Holloway Univ London, Dept Elect Engn, London TW20 0EX, England
关键词
Batteries; Optimization; State of charge; Vehicle-to-grid; Resistance; Load modeling; Microgrids; EV integration; dc microgrid; control; V2G; battery degradation; multi-objective optimisation; optimal power flow; modified Dijkstra’ s algorithm; power losses; ENERGY MANAGEMENT; POWER QUALITY; SYSTEM; OPTIMIZATION; ALGORITHMS; OPERATION; NETWORKS; DESIGN; PV;
D O I
10.1109/ACCESS.2020.3034496
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
This article presents a two-stage framework for optimal Electric Vehicle (EV) charging/discharging strategy for DC Microgrid (MG) with Distributed Generators (DGs). A multi-objective optimisation task aimed at minimising system losses and EV battery degradation with Vehicle-to-Grid (V2G) peak shaving service has been realised. This coordinated EV integration into the DCMG was formulated as a directed weighted single source shortest path problem that was solved using a modified Dijkstra's algorithm. The weights of the edges were obtained using primal-dual interior point method. The proposed framework has been experimentally verified using simulations with a test DCMG system with practical IEEE European low voltage test feeder load profiles. Results show realisation of peak demand shaving leveraging on EV discharge with minimal on-board battery degradation as well as reduced system losses. It is also shown that the proposed two-stage framework reduces the battery state of charge (SOC) sample space requirements in the analysis, thus, reducing the computational burden.
引用
收藏
页码:197222 / 197235
页数:14
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