• DocumentCode
    48930
  • Title

    Optimum Sizing of Distributed Generation and Storage Capacity in Smart Households

  • Author

    Kahrobaee, Salman ; Asgarpoor, Sohrab ; Wei Qiao

  • Author_Institution
    Dept. of Electr. Eng., Univ. of Nebraska-Lincoln, Lincoln, NE, USA
  • Volume
    4
  • Issue
    4
  • fYear
    2013
  • fDate
    Dec. 2013
  • Firstpage
    1791
  • Lastpage
    1801
  • Abstract
    In the near future, a smart grid will accommodate customers who are prepared to invest in generation-battery systems and employ energy management systems in order to cut down on their electricity bills. The main objective of this paper is to determine the optimum capacity of a customer´s distributed-generation system (such as a wind turbine) and battery within the framework of a smart grid. The proposed approach involves developing an electricity management system based on stochastic variables, such as wind speed, electricity rates, and load. Then, a hybrid stochastic method based on Monte Carlo simulation and particle swarm optimization is proposed to determine the optimum size of the wind generation-battery system. Several sensitivity analyses demonstrate the proper performance of the proposed method in different conditions.
  • Keywords
    Monte Carlo methods; building management systems; demand side management; distributed power generation; home automation; particle swarm optimisation; secondary cells; smart power grids; wind turbines; Monte Carlo simulation; distributed-generation system; electricity bill; electricity management system; energy management system; hybrid stochastic method; particle swarm optimization; smart grid; smart household; stochastic variable; storage capacity; wind generation-battery system; Batteries; Distributed power generation; Electricity; Load modeling; Smart homes; Wind speed; Wind turbines; Capacity planning; Monte Carlo simulation; distributed generation; energy storage; load management; particle swarm optimization (PSO); smart homes;
  • fLanguage
    English
  • Journal_Title
    Smart Grid, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    1949-3053
  • Type

    jour

  • DOI
    10.1109/TSG.2013.2278783
  • Filename
    6630120