The Transactions of The Korean Institute of Electrical Engineers (전기학회논문지)

The Korean Institute of Electrical Engineers (대한전기학회)
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An Adaptive Control of Smart Appliances with Peak Shaving Considering EV Penetration

전기자동차 침투율을 고려한 피크 부하 저감용 스마트 기기의 적응적 제어

  • Haider, Zunaib Maqsood (College of Information and Communication Engineering, Sungkyunkwan University) ;
  • Malik, Farhan H. (Dept. of Electrical Engineering, Aalto University) ;
  • Rafique, M. Kashif (College of Information and Communication Engineering, Sungkyunkwan University) ;
  • Lee, Soon-Jeong (College of Information and Communication Engineering, Sungkyunkwan University) ;
  • Kim, Jun-Hyeok (College of Information and Communication Engineering, Sungkyunkwan University) ;
  • Mehmood, Khawaja Khalid (College of Information and Communication Engineering, Sungkyunkwan University) ;
  • Khan, Saad Ullah (College of Information and Communication Engineering, Sungkyunkwan University) ;
  • Kim, Chul-Hwan (College of Information and Communication Engineering, Sungkyunkwan University)
  • Received : 2016.03.24
  • Accepted : 2016.04.19
  • Published : 2016.05.01
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Abstract

Electric utilities may face new threats with increase in electric vehicles (EVs) in the personal automobile market. The peak demand will increase which may stress the distribution network equipment. The focus of this paper is on an adaptive control of smart household appliances by using an intelligent load management system (ILMS). The main objectives are to accomplish consumer needs and prevent overloading of power grid. The stress from the network is released by limiting the peak demand of a house when it exceeds a certain point. In the proposed strategy, for each smart appliance, the customers will set its order/rank according to their own preferences and then system will control the household loads intelligently for consumer reliability. The load order can be changed at any time by the customer. The difference between the set and actual value for each load's specific parameter will help the utility to estimate the acceptance of this intelligent load management system by the customers.

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References

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