Hierarchical Data-Driven and PSO-Based Energy Management of Hybrid Energy Storage Systems in DC Microgrid

In the era of renewable dominated grids and integration of dynamic load such as EV charging stations has increased the operational challenges in multifolds particularly in DC microgrids (DC MG). Traditional battery dominated grid’s energy management strategies (EMS) are often not capable of handling fast transients due to limitations of battery electrochemistry. To overcome this limitation, an hierarchical hybrid energy management strategy is proposed that uses the combination of data driven and metaheuristic algorithms. The designed optimization framework consists of particle swarm optimization (PSO) and neural network (NN) implemented in central controller of 4 bus ringmain DC MG. A efficient decoupling of fast and slow storage dynamics is performed, where supercapacitor (SC) is optimized using NN and battery is optimized using PSO. This selective optimization reduces the computational overhead on the PSO making it more feasible for realtime implementation. The designed hybrid PSO-Neural EMS framework is initially designed on MATLAB and further validated on realtime hardware setup. Robustness of the control scheme is verified with various case studies such as, renewable intermittency, dynamic loading and partial shading scenarios. An effective optimization of SC in both transient and heavy load scenarios is observed. LabVIEW interfacing is used for MODBUS based interaction with PV emulators and DC-DC converters.