@article{che-che-shi-zha-sun-hua-18-aa-windpow,
  author = {Cheng, Weijie and Cheng, Renli and Shi, Jun and Zhang, Cong and Sun, Gaoxing and Hua, Dong},
  title = {Interval Power Flow Analysis Considering Interval Output of Wind Farms through Affine Arithmetic and Optimizing-Scenarios Method},
  journal = {Energies},
  volume = {11},
  year = 2018,
  number = {11},
  pages = {article 3176},
  issn = {1996-1073},
  doi = {10.3390/en11113176},
  abstract = {Wind power belongs to sustainable and clean energy sources which play a vital role of reducing environment pollution and addressing energy crisis. However, wind power outputs are quite difficult to predict because they are derived from wind speeds, which vary irregularly and greatly all the time. The uncertainty of wind power causes variation of the variables of power grids, which threatens the power grids' operating security. Therefore, it is significant to provide the accurate ranges of power grids' variables, which can be used by the operators to guarantee the power grid's operating security. To achieve this goal, the present paper puts forward the interval power flow with wind farms model, where the generation power outputs of wind farms are expressed by intervals and three types of control modes are considered for imitating the operation features of wind farms. To solve the proposed model, the affine arithmetic-based method and optimizing-scenarios method are modified and employed, where three types of constraints of wind control modes are considered in their solution process. The former expresses the interval variables as affine arithmetic forms, and constructs optimization models to contract the affine arithmetic forms to obtain the accurate intervals of power flow variables. The latter regards active power outputs of the wind farms as variables, which vary in their corresponding intervals, and accordingly builds the minimum and maximum programming models for estimating the intervals of the power flow variables. The proposed methods are applied to two case studies, where the acquired results are compared with those acquired by the Monte Carlo simulation, which is a traditional method for handling interval uncertainty. The simulation results validate the advantages, effectiveness and the applicability of the two methods.}
}