Dynamic energy management method for power systems with large-scale storage batteries and photovoltaic cells

A dynamic energy management method for power systems with large-scale storage batteries and PV cells is developed. With the proposed method, storage batteries charge and discharge the appropriate amount of energy at the appropriate time in order to gain economic benefits, including the reduction in electricity bills. Within the method, the power demand and power generation are predicted based on the past data, and the prediction is dynamically modified by feeding back the prediction errors. The charging/discharging plan of the storage battery is also modified based on the prediction errors. The case studies are conducted for an imaginary community, which show that CO₂ emissions are reduced and that peak-cut is achieved, proving the effectiveness of the proposed method. The case studies also infer that the optimal battery management algorithm is dependent not only on the pattern of power demand but also the sizes of storage batteries and PV cells.