• DocumentCode
    684256
  • Title

    Control strategy for a hybrid energy storage system to mitigate wind power fluctuations

  • Author

    Zhang, Tianzhu ; Bao, Z.J. ; Chen, Gang ; Yang, Qingxiong ; Yan, W.J.

  • Author_Institution
    Coll. of Electr. Eng., Zhejiang Univ., Hangzhou, China
  • fYear
    2013
  • fDate
    19-21 Oct. 2013
  • Firstpage
    27
  • Lastpage
    32
  • Abstract
    With the intermittency and uncertainty of wind power, two-time-scale maximal power fluctuation restrictions (MPFRs) are set by electrical company for the combined power of the wind farm and the hybrid energy storage system (HESS): the maximal fluctuation of the combined power in any 1- and 30-min time window must be kept within γ1min% and γ30-min% of the wind farm rated power, respectively. Under this circumstance, an approach of improved first-order low-pass filter (IFLF) is proposed, which could adjust the time constant in any second if necessary when the fluctuation during the latest two seconds is exceeding a certain valve. Then a strategy to manage the power sharing between the battery and the super-capacitor at every second is described, which is based on Particle Swarm Optimization (PSO) algorithm and consider the constraints for the state of charge (SOC) of battery and the change times between battery charging and discharging. Simulation results demonstrate that the proposed control strategy is effective both in mitigating the wind power fluctuation to satisfy the MPFRs with lower HESS capacity and prolonging the lifetime of battery.
  • Keywords
    particle swarm optimisation; secondary cells; supercapacitors; wind power plants; HESS; IFLF; MPFR; PSO algorithm; battery SOC; battery state of charge; control strategy; first-order low-pass filter; hybrid energy storage system; maximal power fluctuation restrictions; particle swarm optimization; power sharing; supercapacitor; wind power fluctuation mitigation; Batteries; Companies; Fluctuations; Smoothing methods; System-on-chip;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Advanced Computational Intelligence (ICACI), 2013 Sixth International Conference on
  • Conference_Location
    Hangzhou
  • Print_ISBN
    978-1-4673-6341-9
  • Type

    conf

  • DOI
    10.1109/ICACI.2013.6748469
  • Filename
    6748469