This paper demonstrates the enhanced performance of a novel optimization-based energy management strategy (EMS) for hybrid energy system (fuel cell/photovoltaic/battery/super-capacitor). A thorough comparison between several conventional and optimization-based EMSs is performed to define the robust approach. The energy management approaches are adapted to satisfy a highly volatile load demand by optimal power sharing from hybrid system components. The performance standards considered in this study are hydrogen fuel consumption, system efficiency, and components stress avoidance for increased lifetime. The simulation is performed in two scenarios; the first scenario assumes that photovoltaic power is available and the system is in charge of supplying the aforementioned load, the second scenario assumes the same conditions, but with a loss of photovoltaic power due to complete shading. The proposed EMS is designed based on recently presented metaheuristic algorithms, namely; coot bird optimizer (CBO) and improved grey wolf optimizer (IGWO). These algorithms are chosen upon recommendations for their reliability in handling similar engineering problems and fewer tunable parameters. The results of the proposed CBO-EMS surpassed the other competitors in the scale of hydrogen fuel saving, with values of 13.65 g for the first scenario and 17.51 g for the second scenario. The scale of system efficiency confirms the proposed strategy's reliability, as it scored 92.65% and 89.65% for the two scenarios, respectively. In addition to the computational speed scale, the CBO-EMS achieved the lowest elapsed time of 0.0162 s, outperforming the other competitors.
Ali et al. (Wed,) studied this question.