A Risk-Controllable Day-Ahead Transmission Schedule of Surplus Wind Power with Uncertainty in Sending Grids

Abstract: Transmitting the surplus wind power (SWP) to load centers is necessary for grids which would reduce the wind curtailment. The reliability and sharpness of the existing estimation models of SWP are not enough to support the optimization of the transmission schedule. Besides, the existing optimization models for wind power scheduling are insufficient in controlling the high risk of SWP transmission. To address the above problems, this paper proposes a random fuzzy model (RFM) for SWP estimation and a risk-controllable optimization (RCSO) model for the SWP transmission schedule. In the RFM, a conditional probability of SWP is utilized to represent the actual probability distribution. The impacts of wind power consumption that cause the misalignment of existing models are considered in RFM by a fuzzy set. In RCSO, the economic and security risks of SWP transmission are quantified by the conditional value at risk (CVaR) theory. Transmission risks are controlled through automatic adjustment of the risk factor. An equivalent transformation method of the RCSO model is derived which can solve the nonlinear stochastic optimization problem with multi-objective. A case based on the historical data in Northeast China is studied to verify the effectiveness of the proposed models.