An intelligent control strategy for energy storage systems in solar power generation based on long-short-term power prediction

This study proposes a control strategy for an energy storage system (ESS) based on the irradiance prediction. The energy output of photovoltaic (PV) systems is intermittent, which causes the power grid unstability and un reliability. It posts a great challenge to electric power industries. The development of the strategy is divided into two parts. First, a solar irradiance prediction model is proposed based on the Feed-forward Neural Networks (FNN), which uses the historical irradiance and satellite cloud images as the model inputs. The characteristic parameter are selected by the Principal Component Analysis (PCA). In order to improve the accuracy of prediction model, a hybrid method has also been proposed, which combines long-short-term prediction and can be used to predict the power generation of PV systems. Second, a control strategy for ESS has been developed, which considers the state of charge (SOC) of ESS, the microgrid (MG) power, and the change of the predicted power generation. The target of the control strategy is to reduce the grid power profile fluctuations which is interfered by the intermittent renewable energy generation, and thereby the strategy can improve the efficiency of the utilization of generated power, while reducing the operating costs and the energy loss caused by frequent power transmission in advance.