Optimal adaptive fuzzy management strategy for fuel cell-based DC microgrid

Abstract

This study presents an optimal adaptive intelligent energy management strategy designed for a DC microgrid based on a fuel cell system (FC), photovoltaic array (PV), and battery bank. The main objective is to enhance the system's power saving. For this reason, an optimal adaptive FC-based energy management strategy (EMS) is designed. The optimizer updates the fuzzy membership functions according to the provided energy by the fuel cell and the main grid. The used optimizer is based on meta-heuristic optimization strategies such as particle swarm optimization (PSO); salp swarm algorithm (SSA); Archimedes optimization algorithm (AOA); marine predator algorithm (MPA); artificial ecosystem-based optimization (AEO); equilibrium optimizer (EO); political optimizer (PO) and tunicate swarm algorithm (TSA). Each one of the studied optimizers provides its proper performance depending on its optimization mechanism. To check the performance of both conventional and optimal adaptive FC based EMS, a comparative study was carried out utilizing Matlab Simulink. The obtained results prove the political optimizer's superiority (PO) over the other optimizers, where the obtained power saving using PO achieved 7.7% with a tracking efficiency of 99.571%. Statistical analysis using the Analysis of variance (ANOVA) test will be performed to approve the performance of the proposed energy management strategy.