Title page for etd-0624114-092234


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URN etd-0624114-092234
Author Guo-fong Hsu
Author's Email Address No Public.
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Department Communications Engineering
Year 2013
Semester 2
Degree Master
Type of Document
Language zh-TW.Big5 Chinese
Title Study on Electric Vehicle Charging Management by Network Flow Control Strategy
Date of Defense 2014-07-14
Page Count 59
Keyword
  • plug-in hybrid electric vehicle
  • charging
  • AIMD
  • decentralized algorithm
  • load management
  • Abstract Because of the rising awareness of environmental protection, the worldwide governments have proposed policies for enhancing the penetration of the plug-in electric vehicles (PEVs) to reduce the carbon emissions. PEVs are expected to replace the fuel vehicles in the future. PEVs can be charged at home as users finish their daily job. However, the residential power load plus the PEVs charging load would go beyond the limitation of power grid. In this thesis, we apply the concept of additive increase and multiplicative decrease (AIMD) to develop three load management approaches in decentralized ways: First, we modulate the available power according to the varying electricity price. Second, we adjust the charging rate by using the congestion prices. Third, we introduce weight factor to decide charging priority for the load management. The weight factor of the PEV is function of the remaining charging time and the stopping time of the PEV. In order to get high priority in charging, users may submit the less stopping time against their consciences. For fairness purpose, we combine the PEV weight factor with a pricing policy so that those users who submit the false report cannot get any benefit.
    Advisory Committee
  • Tsang-Yi Wang - chair
  • Pang-An Ting - co-chair
  • Jung-Chieh Chen - co-chair
  • Wan-Jen Huang - co-chair
  • Chao-Kai Wen - advisor
  • Files
  • etd-0624114-092234.pdf
  • Indicate in-campus at 5 year and off-campus access at 5 year.
    Date of Submission 2014-07-24

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