A Novel Control Strategy of Energy Storage System Considering Prediction Errors of Photovoltaic Power

Abstract

The photovoltaic (PV) energy, as clean and renewable energy, has become increasingly important. With energy storage system, the PV power can become schedulable and the use efficiency of PV power can be greatly improved. The tracking output is one of the running modes for energy storage to adjust the PV power generation. However, the research on the tracking output do not consider the fluctuation of PV power generation, the upper and lower limits of prediction errors, or only consider tracking the power curve. In this paper, a novel control method is proposed based on the super short-term prediction PV power and the prediction errors distribution law to obtain the charge and discharge strategy for energy storage ahead. In this control method, the objective function includes three parts, respectively the D-value between the planned output and actual output of PV power and energy storage system, the change of the state of charge (SOC) with different weight, The constraint condition considers the power demand, flexibility requirement and storage costs comprehensive. Then the prediction errors distribution law can be obtained by analyzing prediction errors of the PV power, the proper errors correction parameters can be selected according to the law to get a more accurate predication range and make the energy storage scheduling strategy more flexible. The particle swarm optimization (PSO) algorithm is used to get the optimal charge and discharge power strategy in advance. With the data collected from typical PV power station as an example, the simulation results verify the feasibility and flexibility of the proposed strategy, it also provides effective reference scheme for day-ahead control of battery energy storage systems