Distributed Coordination of EV Charging With Renewable Energy in a Microgrid of Buildings

被引:103
作者
Yang, Yu [1 ]
Jia, Qing-Shan [1 ]
Deconinck, Geert [2 ]
Guan, Xiaohong [1 ,3 ,4 ]
Qiu, Zhifeng [5 ]
Hu, Zechun [2 ,6 ]
机构
[1] Tsinghua Univ, Dept Automat, Ctr Intelligent & Networked Syst, Beijing 100084, Peoples R China
[2] Univ Leuven, Dept Elect Engn, Elect Energy, EnergyVille, B-3001 Leuven, Belgium
[3] Xi An Jiao Tong Univ, Minist Educ, Key Lab Intelligent Networks, Xian 710049, Shaanxi, Peoples R China
[4] Xi An Jiao Tong Univ, Network Secur Lab, Xian 710049, Shaanxi, Peoples R China
[5] Cent S Univ, Dept Elect Engn, Changsha 410000, Hunan, Peoples R China
[6] Tsinghua Univ, Dept Elect Engn, Beijing 100084, Peoples R China
基金
中国国家自然科学基金;
关键词
Electric vehicles (EVs); building mounted wind power; Markov decision process; distributed optimization; EVENT-BASED OPTIMIZATION; WIND TURBINES; MANAGEMENT;
D O I
10.1109/TSG.2017.2707103
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
With the rapid development of electric vehicles (EVs), the consequent charging demand represents a significant new load on the power grids. The huge number of high-rise buildings in big cities and modern technological advances have created conditions to mount on-site wind power generators on the buildings. Since modern buildings are usually equipped with large parking lots for EVs, it shows vital practical significance to utilize the on-site wind power generation to charge EVs parked in the buildings. In this paper, we first use a case study in Beijing to show that the on-site wind power generation of high-rise buildings can potentially support all the EVs in the city. Considering that the charging demand of EVs usually does not align with the uncertain wind power, the coordination of EV charging with the locally generated wind power in a microgrid of buildings is investigated and three main contributions are made. First, we investigate the problem and formulate it as a Markov decision process, which incorporates the random driving requirements of EVs among the buildings. Second, we develop a distributed simulation-based policy improvement (DSBPI) method, which can improve from heuristic and experience-based policies. Third, the performance of the distributed policy improvement method is proved. We compare DSBPI with a central version method on two case studies. The DSBPI method demonstrates good performance and scalability.
引用
收藏
页码:6253 / 6264
页数:12
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