Entropy weight and grey relational analysis based virtual capacitance collaborative control for multiple energy storage units

Abstract

Flexible virtual capacitance control (FVCC) for converter can improve the stability and dynamics of DC bus voltage, but among multiple converters, essential inertia distribution is capable of optimizing the performance. To improve the rationality of inertia distribution, a virtual capacitance collaborative control (VCCC) strategy for multiple energy storage units (ESUs) is proposed. Firstly, the system required total inertia is adjusted according to each individual battery's rated capacity and state-of-charge (SOC). Then comes the establishment of an evaluation mechanism, which takes the accounts of battery SOC margin, converter power margin and battery power regulation speed. The entropy weight and grey relational analysis are used to calculate each ESU's distribution coefficient and then adjust their virtual capacitance in real-time. The results show that the adjustment and distribution of the total inertia are beneficial to controlling the SOC of the batteries within the safe range. The ESU with the smallest converter power margin or the slowest battery power regulation speed supports least virtual inertia, which can reduce the problems such as overload protection action or auxiliary power limitation, so as to improve the cooperative control effects. Finally, the effectiveness of the proposed control strategy is verified by hardware-in-the-loop (HIL) simulation experiments.