Cooperative Optimization of Electric Vehicles in Microgrids Considering Across-Time-and-Space Energy Transmission

Plug-in electric vehicles (PEVs) tend to be treated as a new form of the mobile energy storage system with the potentiality to promote energy management in microgrids (MGs) and smart grid. However, a conflict of interest exists regarding the optimal capacity configuration and the optimal economic di...

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Bibliographic Details
Published in:IEEE transactions on industrial electronics (1982) 2019-02, Vol.66 (2), p.1532-1542
Main Authors: Chen, Changsong, Xiao, Liangle, Duan, Shanxu Duan, Chen, Jin
Format: Article
Language:English
Subjects:
Across-time-and-space energy transmission
Batteries
Configurations
cooperative optimization
Economic analysis
Economic models
Economics
Electric power grids
Electric vehicles
Electricity pricing
Energy management
Energy storage
Energy transmission
Impact analysis
Microgrids
microgrids (MGs)
Optimization
Particle swarm optimization
plug-in electric vehicles (PEVs)
Power dispatch
Smart grid
Wind turbines
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Summary:Plug-in electric vehicles (PEVs) tend to be treated as a new form of the mobile energy storage system with the potentiality to promote energy management in microgrids (MGs) and smart grid. However, a conflict of interest exists regarding the optimal capacity configuration and the optimal economic dispatch of PEVs. On one hand, MGs can maximize their benefits by mobile energy transmission as much as possible. On the other hand, MGs can also minimize total cost by reducing the investment cost of PEV charging/discharging infrastructures. Both objective functions need to be considered simultaneously. This paper presents a cooperative optimization method for capacity configuration and economic dispatch of electric vehicles in MGs considering across-time-and-space energy transmission (ATSET). Considering the differences of electricity prices in different time and space, the impact of ATSET on MG economic operation is analyzed, and a two-loop optimization model is utilized to optimize capacity configuration and economic dispatch of PEVs. Improved particle swarm optimization is used to implement cooperative optimization procedure, and the effectiveness of the proposed method is validated by detailed case studies. Results show that the total costs of the MG system and PEV owners can be reduced by the proposed cooperative optimization method.
ISSN:0278-0046
1557-9948
DOI:10.1109/TIE.2017.2784410