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http://dx.doi.org/10.3837/tiis.2019.10.010

An Emission-Aware Day-Ahead Power Scheduling System for Internet of Energy  

Huang, Chenn-Jung (Department of Computer Science & Information Engineering, National Dong Hwa University)
Hu, Kai-Wen (Department of Electrical Engineering, National Dong Hwa University)
Liu, An-Feng (Department of Computer Science & Information Engineering, National Dong Hwa University)
Chen, Liang-Chun (Department of Management, Fo Guang University)
Chen, Chih-Ting (Department of Computer Science & Information Engineering, National Dong Hwa University)
Publication Information
KSII Transactions on Internet and Information Systems (TIIS) / v.13, no.10, 2019 , pp. 4988-5012 More about this Journal
Abstract
As a subset of the Internet of Things, the Internet of Energy (IoE) is expected to tackle the problems faced by the current smart grid framework. Notably, the conventional day-ahead power scheduling of the smart grid should be redesigned in the IoE architecture to take into consideration the intermittence of scattered renewable generations, large amounts of power consumption data, and the uncertainty of the arrival time of electric vehicles (EVs). Accordingly, a day-ahead power scheduling system for the future IoE is proposed in this research to maximize the usage of distributed renewables and reduce carbon emission caused by the traditional power generation. Meanwhile, flexible charging mechanism of EVs is employed to provide preferred charging options for moving EVs and flatten the load profile simultaneously. The simulation results revealed that the proposed power scheduling mechanism not only achieves emission reduction and balances power load and supply effectively, but also fits each individual EV user's preference.
Keywords
power scheduling; electric vehicle charging; emission reduction; soft computing; Internet of Energy;
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