• DocumentCode
    77421
  • Title

    Dynamic Reserve Zones for Day-Ahead Unit Commitment With Renewable Resources

  • Author

    Fengyu Wang ; Hedman, Kory W.

  • Author_Institution
    Sch. of Electr., Comput., & Energy Eng., Arizona State Univ., Tempe, AZ, USA
  • Volume
    30
  • Issue
    2
  • fYear
    2015
  • fDate
    Mar-15
  • Firstpage
    612
  • Lastpage
    620
  • Abstract
    As more non-dispatchable renewable resources are integrated into the grid, it will become increasingly difficult to predict the transfer capabilities and the network congestion. At the same time, renewable resources require operators to acquire more operating reserves. With today´s deterministic reserve requirements unable to ensure optimal reserve locations, improvements to reserve policies are needed to ensure reserve deliverability while maintaining a reliable system at least cost. This paper proposes a daily reserve zone determination procedure, which is able to reflect system operating conditions by utilizing probabilistic power flows. A statistical clustering algorithm is used to cluster buses together to produce the zones; the algorithm uses a centrality measurement, which is based on weighted power transfer distribution factors. The proposed method is validated by testing it on a modified IEEE 118-bus system for multiple days; the proposed method is compared against existing reserve zone partitioning procedures. While the proposed reserve zone determination method is a heuristic, it is shown to be effective and it is a computationally tractable method. The proposed method can be used on its own and can be used along with stochastic programming techniques that implicitly determine reserves.
  • Keywords
    power generation dispatch; power generation scheduling; power system measurement; renewable energy sources; stochastic programming; centrality measurement; daily reserve zone determination procedure; day ahead unit commitment; modified IEEE 118-bus system; probabilistic power flow; renewable resources; statistical clustering; stochastic programming; weighted power transfer distribution factors; Generators; Power transmission lines; Reliability; Uncertainty; Wind forecasting; Wind power generation; Integer programming; power generation dispatch; power system economics; power system reliability; reserve requirements; unit commitment;
  • fLanguage
    English
  • Journal_Title
    Power Systems, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    0885-8950
  • Type

    jour

  • DOI
    10.1109/TPWRS.2014.2328605
  • Filename
    6847237