Journal of Modern Power Systems and Clean Energy

ISSN 2196-5625 CN 32-1884/TK

Availability estimation of wind power forecasting and optimization of day-ahead unit commitment
Author:
Affiliation:

1 Shenyang University of Technology, Shenyang 110870, China 2 State Grid Liaoning Electric Power Research Institute Customer Service Center, Shenyang 110004, China 3 State Grid East Inner Mongolia Electric Power Co, Ltd., Huhhot 010020, China 4 Economic and Technological Research Institute of State Grid East Inner Mongolia Electric Power Co. Ltd, Huhhot 010020, China 5 Aalborg University, Aalborg, Denmark

Fund Project:

This work was supported by the National Key Research and Development Program of China (No. 2017YFB0902100).

  • Article
  • |
  • Figures
  • |
  • Metrics
  • |
  • Reference
  • |
  • Related
  • |
  • Cited by
  • |
  • Materials
    Abstract:

    Due to the uncertainty of the accuracy of wind power forecasting, wind turbines cannot be accurately equated with dispatchable units in the preparation of a day-ahead dispatching plan for power grid. A robust optimization model for the uncertainty of wind power forecasting with a given confidence level is established. Based on the forecasting value of wind power and the divergence function of forecasting error, a robust evaluation method for the availability of wind power forecasting during given load peaks is established. A simulation example is established based on a power system in Northeast China and an IEEE 39-node model. The availability estimation parameters are used to calculate the equivalent value of wind power of the conventional unit to participate in the day-ahead dispatching plan. The simulation results show that the model can effectively handle the challenge of uncertainty of wind power forecasting, and enhance the consumption of wind power for the power system.

    Reference
    Related
    Cited by
Get Citation
Share
Article Metrics
  • Abstract:
  • PDF:
  • HTML:
  • Cited by:
History
  • Received:
  • Revised:
  • Adopted:
  • Online: November 27,2019
  • Published: