A centralized vehicle-to-grid scheme with distributed computing capacity engaging internet of smart charging points: Case study

Shang, Yitong1,2,3; Liu, Man2,3; Shao, Ziyun4; Jian, Linni2,3

2020-09-15

10.1002/er.5967

INTERNATIONAL JOURNAL OF ENERGY RESEARCH   影响因子和分区

0363-907X

1099-114X

In order to accommodate additional plug-in electric vehicle (PEV) charging loads for existing distribution power grids, the vehicle-to-grid (V2G) technology has been regarded as a cost-effective solution. Nevertheless, it can hardly scale up to large PEVs fleet coordination due to the computational complexity issue. In this paper, a centralized V2G scheme with distributed computing capability engaging internet of smart charging points (ISCP) is proposed. Within ISCP, each smart charging point equips a computing unit and does not upload PEV sensitive information to the energy coordinator, to protect PEV users' privacy. Particularly, the computational complexity can be decreased dramatically by employing distributed computing, viz., by decomposing the overall scheduling problem into many manageable sub-problems. Moreover, six typical V2G scenarios are analyzed deliberately, and based on that, a load peak-shaving and valley-filling scheduling algorithm is built up. The proposed algorithm can be conducted in real-time to mitigate the uncertainties in arrival time, departure time, and energy demand. Finally, the proposed scheme and its algorithm are verified under the distribution grid of the SUSTech campus (China). Compared with uncoordinated charging, the proposed scheme realizes load peak-shaving and valley-filling by 11.98% and 12.68%, respectively. The voltage values are ensured within the limitation range by engaging power flow calculation, in which the minimum voltage values are increasing and the maximum voltage values are decreasing with the expansion of PEV penetration. What is more, the computational complexity of peak-shaving and valley-filling strategy is near-linear, which verifies the proposed scheme can be carried out very efficiently.

distributed computing internet of smart charging points (ISCP) load peak-shaving and valley-filling plug-in electric vehicle vehicle-to-grid

SCI ; EI ; SSCI

英语

通讯

National Natural Science Foundation of China[61773195] ; Natural Science Foundation of Guangdong Province[2019B101001022] ; Science and Technology Innovation Committee of Shenzhen[ZDSYS201604291912175]

Energy & Fuels ; Nuclear Science & Technology

Energy & Fuels ; Nuclear Science & Technology

WOS:000569803800001

WILEY

20203809203573

Cost effectiveness ; Distribution functions ; Filling ; Charging (batteries) ; Electric power transmission networks ; Landforms ; Computational complexity ; Electric load flow ; Vehicle-to-grid ; Scheduling algorithms

Geology:481.1 ; Materials Handling Methods:691.2 ; Secondary Batteries:702.1.2 ; Electric Power Systems:706.1 ; Electric Power Transmission:706.1.1 ; Computer Theory, Includes Formal Logic, Automata Theory, Switching Theory, Programming Theory:721.1 ; Industrial Economics:911.2 ; Probability Theory:922.1

ENGINEERING

Web of Science

1.Harbin Inst Technol, Harbin, Peoples R China
2.Southern Univ Sci & Technol, Shenzhen Key Lab Elect Direct Drive Technol, Dept Elect & Elect Engn, Shenzhen 518055, Peoples R China
3.Southern Univ Sci & Technol, Univ Key Lab Adv Wireless Commun Guangdong Prov, Shenzhen, Peoples R China
4.Guangzhou Univ, Sch Elect & Commun Engn, Guangzhou, Peoples R China

Shang, Yitong,Liu, Man,Shao, Ziyun,et al. A centralized vehicle-to-grid scheme with distributed computing capacity engaging internet of smart charging points: Case study[J]. INTERNATIONAL JOURNAL OF ENERGY RESEARCH,2020,45(1):841-863.

Shang, Yitong,Liu, Man,Shao, Ziyun,&Jian, Linni.(2020).A centralized vehicle-to-grid scheme with distributed computing capacity engaging internet of smart charging points: Case study.INTERNATIONAL JOURNAL OF ENERGY RESEARCH,45(1),841-863.

Shang, Yitong,et al."A centralized vehicle-to-grid scheme with distributed computing capacity engaging internet of smart charging points: Case study".INTERNATIONAL JOURNAL OF ENERGY RESEARCH 45.1(2020):841-863.