Multi-objective economic environmental energy management microgrid using hybrid energy storage implementing and developed Manta Ray Foraging Optimization Algorithm

Different renewable energy sources, active loads, electric vehicles, and storage devices make an independent microgrid (MG). Energy management of MG is an important issue. In this paper, efficient energy management (EM) of a microgrid using a novel method based on a new meta heuristic algorithm is proposed. Proposed MGs include renewable energy resources (wind turbine, fuel cell, and solar cell), plug-in hybrid electric vehicles (PHEVs), and liquid air energy storage (LAES) combined with high-temperature thermal energy storage (HTES). In order to carefully investigate and improve the management method, uncertainty parameters such as wind speed, solar radiation, load demand, and energy price is modeled using a probabilistic approach based on the point estimate method (2m + 1) and the developed manta ray foraging optimization (DMRFO) algorithm for posture forecasting with an uncertainty-weighted measurement error of feature objective is used to solve the EM algorithm. The multi-objective optimization problem presents an improved energy management method considering cost minimization and pollution as objective functions. Afterward, the function of the proposed algorithm is compared with that of other methods and, then, the superiority of the proposed method is confirmed. Finally, it is proved that by using the proposed model, total cost and emission is reduced about 3.5% and 21.33%, respectively.