Adaptive energy management strategy for a DC microgrid using intelligent Controller

This paper presents the enhanced operation of a standalone DC microgrid (DCMG) consisting of PV, Fuel cell (FC), battery and supercapacitor (SC). Intermittent variations in PV power and loads are a common problem in standalone systems. Therefore, a novel energy management and control strategy based on hybrid fuzzy and proportional-integral controllers (H-FPI) is proposed to effectively distribute power between the battery and the SC. FLCs are designed based on Mamdani fuzzy inference system (FIS) to generate a duty cycle to control bidirectional DC-DC controllers (BDCs) associated with battery and SC. The proposed work utilizes a battery and SC as a hybrid energy storage system (HESS), while the reference current for controlling the charge through them is generated via a filtration-based technique. This optimization enhances battery longevity by diverting high-frequency and uncompensated power components toward the SC. Digital simulations substantiate the effectiveness of this energy management and control strategy. The proposed controller demonstrates its efficacy across all considered scenarios, with maximum undershoot and overshoot limited to 3.79% and 3.65%, respectively, well within the acceptable range prescribed by IEEE std. 519-1992 (+/- 5%). Furthermore, the results are compared with conventional controller and robustness of H-FPI controller is tested against the benchmarks such as settling time, overshoot, and undershoot in DC bus voltage$({V_{DC}})$(VDC).