Strategy optimization of distributed battery energy storage systems for improving dynamic performances of VRB in active distribution networks

This paper proposed an improved genetic algorithm-based operational strategy for vanadium redox flow battery (VRB) energy storage systems (ESSs) in active distribution networks for improving the dynamic performances of batteries. Firstly, the accurate model of VRB considering the influences of external factors, such as temperature, electrolyte flow rate, ion exchange membrane, catalyst, polarization, self-discharge, and leakage current are constructed. By the test of the accurate model, the dynamic performances Phi of VRB consisting of efficiency eta, self-discharge rate lambda, utilization rate psi(u), maximum discharge depth D-oD, and cycle life kappa are reasonably proposed. And then, the mathematical framework for the operational strategy optimization of ESSs was developed considering both the dynamic performances Phi and the external benefits of VRB ESSs. Finally, case studies based on a modified IEEE 123 Node Test Feeder verified the safe and reasonable operational states of battery ESSs with higher efficiencies, utilization rate, cycle life and lower self-discharge rate, and maximum discharge depth. The dynamic performances of battery ESSs are enhanced by about 32.4%.

Automated summary

This paper proposes an improved genetic algorithm-based operational strategy for vanadium redox flow battery energy storage systems in active distribution networks. By considering various external factors, an accurate model is constructed and used to optimize the operational strategy of the battery system. Case studies show that the proposed strategy significantly improves the dynamic performances of the battery system, including efficiency, utilization rate, cycle life, self-discharge rate, and maximum discharge depth.