Energy storage system optimization based on a multi-time scale decomposition-coordination algorithm for wind-ESS systems

Abstract

Wind power is one of the most important renewable energy sources to build a sustainable power system. Energy storage technologies provide an effective control method for the operation of power systems with high penetration wind power. In existing energy storage system (ESS) optimization methods for wind-ESS systems, different ESS devices are deployed for several typical scenarios separately, including wind output power fluctuation smoothing, power imbalances mitigating, and peak load shaving. But the operational coupling of different ESS devices among these scenarios is usually omitted or simplified improperly. In this paper, the wavelet analysis algorithm was used to obtain three decomposed components of the wind output power with different time scales, and then the decomposition-coordination ESS optimization method was used to solve the constructed corresponding three submodels for different ESS application scenarios. Several coupling variables are designed to coordinate these sub-optimization models with different time scales. In the meantime, the emerging battery ESS has also been discussed in our optimization process based on the battery cost and the net earnings of the whole system to enhance the effectiveness of our proposed optimization method. Compared with traditional ESS deployment methods, our proposed method can reduce the total ESS equivalent investment annual cost by 20% in the selected regional power system in China. Our proposed multi-time scale decomposition-coordination algorithm is suitable for the optimal ESS devices deployment because the coordinated charge–discharge operation of different ESS devices can be inherited taken into consideration for short-term power fluctuation smoothing, power balancing during several hours, and peak load shaving in awhole day.