• DocumentCode
    1763987
  • Title

    Time-Varying Stochastic Assessment of Conservation Voltage Reduction Based on Load Modeling

  • Author

    Zhaoyu Wang ; Jianhui Wang

  • Author_Institution
    Sch. of Electr. & Comput. Eng., Georgia Inst. of Technol., Atlanta, GA, USA
  • Volume
    29
  • Issue
    5
  • fYear
    2014
  • fDate
    Sept. 2014
  • Firstpage
    2321
  • Lastpage
    2328
  • Abstract
    This paper presents a time-varying stochastic technique to assess conservation voltage reduction (CVR) effects based on load modeling. A time-varying exponential load model is developed to represent voltage dependences of loads. The recursive least square (RLS) method is applied to identify model parameters in a recursive way. CVR factors can be calculated using the identified model parameters. The time-varying stochastic model for CVR effects can then be constructed by the Kolmogorov-Smirnov (K-S) test. The proposed CVR assessment method is applied to one-year measurement data from a utility company. The calculated CVR factors are verified by a Euclidian distance-based comparison method. Stochastic models of CVR effects in each time window are constructed. Compared with previous efforts on assessing CVR effects, the proposed method does not require control groups or assumptions of linear relationships between the load and its impact factors. The probabilistic nature of CVR effects is also fully considered.
  • Keywords
    least mean squares methods; load distribution; recursive estimation; stochastic processes; voltage control; CVR assessment method; Euclidian distance-based comparison method; K-S test; Kolmogorov-Smirnov test; RLS method; conservation voltage reduction; load modeling; model parameter identification; recursive least square method; time-varying exponential load model; time-varying stochastic model assessment; Energy consumption; Indexes; Load modeling; Stochastic processes; Substations; Voltage control; Voltage measurement; Conservation voltage reduction; Kolmogorov-Smirnov (K-S) test; load model identification; recursive least square; stochastic modeling;
  • fLanguage
    English
  • Journal_Title
    Power Systems, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    0885-8950
  • Type

    jour

  • DOI
    10.1109/TPWRS.2014.2304641
  • Filename
    6739151