Open AccessElectric Vehicles Charging Management Based on Flexible Load Aggregation
Author(s) -
Nannan Xu,
Nan Xu,
Yunpeng Ling,
Qiang Liu,
Hongyuan Ma,
Bo Zhou,
Yan Song,
Zihao Zhao
Publication year - 2020
Publication title -
iop conference series. earth and environmental science
Language(s) - English
Resource type - Journals
eISSN - 1755-1307
pISSN - 1755-1315
DOI - 10.1088/1755-1315/598/1/012091
Subject(s) - news aggregator , grid , scheduling (production processes) , computer science , power grid , electric vehicle , service provider , automotive engineering , mathematical optimization , power (physics) , service (business) , business , engineering , mathematics , physics , geometry , quantum mechanics , marketing , operating system
As the scale of electric vehicles grows year by year, disorderly charging of EVs brings great challenges to the safe operation of the power grid and charging management of EV charging service providers. The charging service providers play the role of EV aggregators and aggregate the flexible load of EVs through centralized scheduling strategy. Take profit of EV aggregators and variance of power grid as objectives. Based on the direct scheduling pattern, a multi-objective optimal scheduling model was established and solved by Genetic Algorithm (GA). The results show that under the centralized scheduling strategy of EV aggregators, the difference between peak and valley load of electric power system is reduced, the variance of the grid is lower, the benefits of the aggregator are increased and the EV charging costs are reduced.