DocumentCode :
596762
Title :
Intelligent PHEV charging and discharging strategy in smart grid
Author :
Jie Yu ; Wei Gu ; Zaijun Wu
Author_Institution :
Sch. of Electr. Eng., Southeast Univ., Nanjing, China
fYear :
2012
fDate :
18-20 Oct. 2012
Firstpage :
1107
Lastpage :
1112
Abstract :
The anticipation of a large penetration of Plug-in Hybrid Electric Vehicles (PHEVs) and Plug-in Electric Vehicles (PEVs) into the market brings up many new technical problems that need to be addressed. In the near future, a large number of PHEVs/PEVs connected to power grids will add a large-scale energy load, as well as add substantial energy resources that can be utilized. Vehicle-to-Grid (V2G) technology is a most promising opportunity in PHEV/PEV adoption. In this paper, the authors propose an intelligent method for optimally managing a large number of PHEVs/PEVs (e.g., 3,000) charging/discharging at a municipal parking deck. The authors used the Estimation of Distribution Algorithm (EDA) to determine the optimal charging/discharging times and patterns over a period of 24 hours. A mathematical framework for the objective function (i.e., maximizing the overall profit on a vehicle fleet base) is also given in detail. The authors characterized the performance of EDA-based energy management using a Matlab simulation, and compared it with other optimization techniques.
Keywords :
distributed algorithms; energy management systems; hybrid electric vehicles; smart power grids; EDA-based energy management; Matlab simulation; estimation of distribution algorithm; intelligent PHEV charging strategy; intelligent PHEV discharging strategy; large-scale energy load; mathematical framework; municipal parking deck; objective function; optimization techniques; plug-in hybrid electric vehicles; power grids; smart grid; substantial energy resources; vehicle-to-grid technology; Batteries; Electricity; Genetic algorithms; Lead; System-on-a-chip; US Department of Defense; Vehicles;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Advanced Computational Intelligence (ICACI), 2012 IEEE Fifth International Conference on
Conference_Location :
Nanjing
Print_ISBN :
978-1-4673-1743-6
Type :
conf
DOI :
10.1109/ICACI.2012.6463345
Filename :
6463345
Link To Document :
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