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Robust Estimation Method of Wind Power with Simultaneous Optimization of Allowable Interval and Expected Generation Cost
Author:
Affiliation:

1.School of Electric Power, South China University of Technology, Guangzhou 510640, China;2.Electric Power Dispatching and Control Center of Guangdong Power Grid Co., Ltd., Guangzhou 510600, China

Abstract:

Existing robust interval dispatching models for wind power often aim to minimize the generation cost of base state and maximize the accommodation of wind power, which are difficult to consider the optimization of generation cost in the actual dispatching process. To solve the above problem, this paper proposes a method to simultaneously optimize allowable interval of wind power and expected generation cost of units. Based on the three-point estimation method of Nataf inverse transformation, the proposed method guarantees the allowable interval maximization of wind power, and takes the expected value of generation cost coping with the wind power fluctuations as the economic objective to reflect the minimization of actual generation cost. In order to improve the capacity of wind power accommodation, the proposed method takes the wind power commitment factor of the unit as a variable and relaxes the introduced nonlinear term. For the non-convexity introduced by the expected power generation cost as the objective, a convexity-concave process is adopted for iterative processing to ensure the accuracy of the method. Finally, the Monte Carlo simulation on modified IEEE 118-bus system is carried out by using the corrected model to verify the validity of the proposed method.

Keywords:

Foundation:

This work is supported by National Natural Science Foundation of China (No. 51777078) and China Southern Power Grid Company Limited (No. GDKJXM20180576).

Get Citation
[1]WANG Wenrui, QU Kaiping, YU Tao, et al. Robust Estimation Method of Wind Power with Simultaneous Optimization of Allowable Interval and Expected Generation Cost[J]. Automation of Electric Power Systems,2020,44(8):48-56. DOI:10.7500/AEPS20190601002
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History
  • Received:June 01,2019
  • Revised:August 08,2019
  • Adopted:
  • Online: April 23,2020
  • Published: