Application of the forecast error on unit commitment with renewable power integration

In view of the uncertainty nature, integrating large-scale renewable generation aggravates the complexity of the system scheduling due to the uncertainty existing in both supply and demand sides. To reduce the quantity of uncertain variables, the uncertain variables are divided into two parts: the certain forecast and uncertain forecast error, and a total system forecast error integrating with all uncertain forecast errors is proposed in this paper. Moreover, considering the different forecast error levels of output forecasting of renewable generation under distinct weather conditions, a refined unit commitment model based on the total forecast error is established. The chance constraint programming is introduced to handle the randomness of forecast error, and the particle swarm optimization based on Monte-Carlo simulation is used to solve the model. Simulation results indicate the significant reduction on the computing time and operation cost by using of the proposed method.