A Method of Operating State Estimation of Pumped Storage Power Station Based on Load Peak-Valley-Normal Prediction

Abstract

In order to make the power system absorb more renewable energy and improve the flexibility of power grid operation, one of the effective methods is to configure pumped storage power station in power system. In order to reduce the frequent start-up and shutdown of pumped storage power station, “A Method of Operating State Estimation of Pumped Storage Power Station Based on Load Peak-Valley-Normal Prediction” is proposed in this paper. This method is based on FCM clustering algorithm classifies peak-valley-normal loads at one time of power grid, evaluates the operation state of pumped storage power station on this basis, and finally applies it to the operation cost optimization model of wind-solar-fire-storage system to evaluate the renewable energy absorption capacity and economic operation cost. The simulation results prove that this method can reduce the start-up and shutdown times of pumped storage power station and is of positive significance for maintaining long-term and stable operation of pumped storage power station.