A Probabilistic Day-ahead Scheduling with Considering Wind Power Curtailment

Large-scale wind power integration aggravates the uncertainty and complexity of the day-ahead scheduling (DAS) problem. Based on the uncertainty of prediction errors, an overall system prediction error integrating with all uncertain prediction errors is put forward to reduce the number of uncertain variables into one, which effectively simplifies the DAS model. By considering the different prediction error distributions of wind output and its influence on the reserve capacity, the DAS model is further refined. What's more, wind power curtailment (WPC) as a decision variable is introduced, which makes the uncertain wind power becoming partially controlled. Thus, based on the chance constraint programming (CCP), a probabilistic DAS model with considering WPC is established in this paper. Results show that the WPC and the overall prediction error can effectively enhance the economy and calculation of the system scheduling.