In this paper, we propose a prediction error model of wind power generation considering inter-temporal correlation and examine the impacts of the error on the evaluation of generation system schedules. The increasing installed capacity of renewable energy sources (RESs) raises the risk of supply shortage and surplus in operating power systems. Many researchers have developed a method based on Monte Carlo simulation for generation system scheduling with a large-scale integration of RESs. We propose an autoregressive model for the prediction error of wind power generation to be used for Monte Carlo methods. In addition, we simulated operation of a power system including unit commitment of thermal power plants, and analyzed the impacts of inter-temporal correlation in the prediction error on generation system scheduling. Fuel cost, unsupplied energy and surplus energy were evaluated with the prediction error. The results show the importance of considering inter-temporal correlation in the prediction error of wind power generation on generation system scheduling.
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