• DocumentCode
    2423726
  • Title

    On the impact of SmartGrid metering infrastructure on load forecasting

  • Author

    Alizadeh, Mahnoosh ; Scaglione, Anna ; Wang, Zhifang

  • Author_Institution
    Sch. of Electr. & Comput. Eng., Univ. of California, Davis, CA, USA
  • fYear
    2010
  • fDate
    Sept. 29 2010-Oct. 1 2010
  • Firstpage
    1628
  • Lastpage
    1636
  • Abstract
    Accurate real-time load forecasting is essential for the reliable and efficient operation of a power system. Assuming that the number of electrical vehicles will increase substantially in the near future, the load profiles of the system will become too volatile and unpredictable for the current forecasting techniques. We propose to utilize the accurate reporting of the emerging Advanced Metering Infrastructure (AMI) to track the incoming PHEV load requests and their statistics. We propose a model for the PHEV loads statistics and an optimization of the generation dispatch that uses the full statistical information. This model offers an example of the potential impact of the smart metering infrastructure currently being deployed.
  • Keywords
    hybrid electric vehicles; load forecasting; power generation dispatch; power meters; smart power grids; PHEV load requests; PHEV loads statistics; SmartGrid metering infrastructure; advanced metering infrastructure; generation dispatch; load forecasting; power system reliability; smart metering infrastructure; Biological system modeling; Data models; Load modeling; Mathematical model; Predictive models; Random variables; Time series analysis;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Communication, Control, and Computing (Allerton), 2010 48th Annual Allerton Conference on
  • Conference_Location
    Allerton, IL
  • Print_ISBN
    978-1-4244-8215-3
  • Type

    conf

  • DOI
    10.1109/ALLERTON.2010.5707109
  • Filename
    5707109