LAUSR

Abstract Due to the deterioration of non-renewable energy resources, the operation of wind-solar-hydro hybrid systems has become a prominent research topic. However, owing to the uncertainties in such a hybrid system, such as those in wind speed, solar radiation intensity, and power load, it is difficult for a dispatcher to develop the power generation plan of the day following the planning day (referred hereafter as the next day). The purpose of this study is to consider the uncertainties in a short-term optimal operation model and obtain for each state variable, the probability density function of the operation process of the next day. The latter can provide a dispatcher with a large amount of reliable decision reference information. In this study, simulation-estimation method is proposed to characterize the uncertainty and estimate the probability density function of the operation process. First, three short-term optimal operation models are established to provide flexible options to dispatchers. Second, a stochastic simulation method based on probabilistic forecasting is proposed to generate simulation scenarios for the next day. Third, each simulation scenario is input into the constructed optimal operation model for obtaining the solution. Fourth, the kernel density estimation method is used to estimate the probability density function of the operation process of the next day. Finally, the constructed optimal operation model and proposed simulation-estimation method are applied to the wind-solar-hydro Experimental Base of the Yalong river basin in China. In this case, the differences between the three operation models in three typical seasons are compared. Based on the verification of the prediction mean scenario and observation scenario, the experimental results show that the proposed model and method of this study are practical and effective.