Capacity Allocation in Distributed Wind Power Generation Hybrid Energy Storage Systems

Abstract

The inherent variability and uncertainty of distributed wind power generation exert profound impact on the stability and equilibrium of power storage systems. In response to this challenge, we present a pioneering methodology for the allocation of capacities in the integration of wind power storage. Firstly, we introduce a meticulously designed uncertainty modeling technique aimed at optimizing wind power forecasting deviations, thus augmenting the controllability of distributed wind power variations. Subsequently, we establish a cutting-edge real-time dynamic optimization model for state of charge, which effectively mitigates the fluctuations associated with grid-connected wind power. Moreover, we employ a state-of-the-art distributed robust optimization algorithm to enhance the stability of the distributed wind power storage system. Through comprehensive simulation testing, our findings unequivocally demonstrate the efficacy of our approach in preserving a harmonious balance between wind power load and output demand, thereby assuring the unwavering operation of the entire system. Notably, our approach attains an exceptional capacity allocation efficiency of 91% in the rigorous wind power grid-smoothing test, outperforming comparable methodologies. Lastly, we proffer essential recommendations pertaining to attenuation optimization at the effective capacity level of the batteries, effectively safeguarding the long-term stability of the energy storage system.