Open AccessCoordinated charging strategy of plug‐in electric vehicles for maximising the distributed energy based on time and location
Author(s) -
Wang ZhiHui,
Fan ShiXiong,
Liu BaoZhu,
Liu XingWei,
Wei ZeChen
Publication year - 2017
Publication title -
the journal of engineering
Language(s) - English
Resource type - Journals
ISSN - 2051-3305
DOI - 10.1049/joe.2017.0630
Subject(s) - distributed generation , computer science , plug in , function (biology) , node (physics) , interval (graph theory) , load balancing (electrical power) , energy (signal processing) , load profile , energy consumption , distributed computing , automotive engineering , electrical engineering , engineering , grid , electricity , renewable energy , statistics , mathematics , geometry , structural engineering , combinatorics , evolutionary biology , biology , programming language
The rapid expansion of distributed generations (DGs) in the distributed network creates a new challenge that it is hard to make use of DG output efficiently. This study investigates the coordinated charging strategy of plug‐in electric vehicles to optimise the distributed energy accommodation capacity. Based on the demand‐side management, the time interval of peak‐valley charging demand is found. In order to determine the suitable transferred charging load to the valley period, the optimal function of transferred charging load is established. Furthermore, the load allocation function is established which can guide users to charging at appropriate location. The method is applied to the IEEE 33‐node distributed network to verify that transferring charging load to valley period and the load allocation scheme are both important to enhance the consumption of distributed energy.