Random Fuzzy Optimization Model for Short-Term Hydropower Scheduling Considering Uncertainty of Power Load

Abstract

This paper proposes a random fuzzy optimization (RFO) model for short-term hydropower scheduling, in order to avoid frequent unit switches owing to uncertainty of the power load. The cascade hydropower stations of Qing River in China are firstly introduced for research, and the power load error of the cascade is described. Defined as a random fuzzy variable, the error is analyzed and its distribution is derived to signify the uncertainty. Then random fuzzy programming is integrated into short-term hydropower scheduling to develop the RFO model, with more attention payed to unit spare capacity, while satisfying the demand of power grids. Meanwhile, an applicable solution searching strategy is proposed to solve the model. Simulation results of random fuzzy optimization of the cascade prove that the strategy is practical and the developed model is effective to avoid frequent unit switches. The method also can provide available references for other cascade dispatching center and hydropower stations with the same plight.