Energy management and control for direct current microgrid with composite energy storage system using combined cuckoo search algorithm and neural network

This paper describes a novel energy management strategy (EMS) based on a combined cuckoo search algorithm and neural network (CCSNN) for the control of a DC microgrid (DCMG) with composite energy storage system (CESS). The presented control technique intends to enhance the power-sharing between batteries and supercapacitors (SCs) in order to handle the demand-generation discrepancy, preserve state-of-charge (SOC) inside predetermined parameters, and manage DC bus voltage (DBV). Furthermore, the efficacy of the suggested technique for enactment in terms of voltage overshoot and settling time was compared to conventional control strategy-based findings. The results are validated by experimental studies employing a hardware-in-loop (HIL) configuration on an FPGA-based real-time simulator. • A novel EMS centred on a CCSNN is proposed for control of DCMGs using HESS. • To maintain battery and supercapacitor SOC and eliminate disturbance at the threshold of SOC. • The Proposed energy management strategy successfully preserves the DC bus voltage contained by the acceptable ±5% kind. • Effective power-sharing amongst HESS for different events.