摘要
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 dayahead 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 dayahead 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.
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 dayahead 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 dayahead 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.
基金
supported by the National Key Research and Development Program of China(No.2017YFB0902100).