Abstract: In order to improve the peak shaving performance of battery energy storage plants,an extended short-term electric load forecasting and dynamic optimization strategy for peak shaving is proposed. The objective function of energy storage peak shaving is determined. It is pointed out that the key factors affecting peak shaving performance are electric load forecasting accuracy and optimal control of energy storage battery. By improving the shape similarity method,the results of short-term electric load forecasting can be expanded,the accuracy of electric load forecasting can be improved,and the peak-shaving and valley-filling limits of energy storage can be determined. On this basis,real-time planning of energy storage output is carried out according to peak shaving objective,and the energy change curve of energy storage plan is obtained. At the same time,the peak shaving and valley filling limit of energy storage is corrected in real time by accumulative error of energy storage generated in actual peak shaving process,so as to further improve the peak shaving performance of energy storage. The results show that the proposed method improves the accuracy of electric load forecasting,satisfies the peak shaving demand of power grid and improves the effect of energy storage and peak shaving.