Bald eagle search optimizer-based energy management strategy for microgrid with renewable sources and electric vehicles

The energy crises and environmental problem can be solved by using plug-in hybrid electric vehicles (PHEVs), the integration of large number of PHEVs with high control capabilities and storage can improve the distribution network flexibility. However, the optimal management of these vehicles in the presence of renewable energy resources (RESs) represents a big challenge that must be adopted, this can be accomplished in the form of microgrid (MG). Therefore, this paper proposes a new energy management strategy (EMS) incorporated bald eagle search (BES) optimizer for MG with RESs and PHEVs to regulate the generation of each unit. The considered devices installed in the MG are wind turbine (WT), photovoltaic (PV), micro turbine (MT), fuel cell (FC), storage battery, PHEVs, and grid. The problem is formulated as an optimization problem that aims at minimizing the total operating cost and mitigating the environmental pollutant emission. Two scenarios related to the operation of RESs are considered in addition to three charging modes of EVs which are uncoordinated, coordinated, and smart. The proposed BES-EMS is validated via conducting comparison to literature works of Fuzzy self-adaptive particle swarm optimizer (FSAPSO) and gravitational search and pattern search (GSA-PS) algorithm in addition to new programmed optimizers of Runge Kutta optimization (RUN), mountain gazelle optimizer (MGO), chef-based optimization algorithm (CBOA), beluga whale optimization (BWO), and dandelion optimizer (DO). Moreover, statistical tests of Friedman rank, ANOVA table, Wilcoxon rank, and Kruskal Wallis are conducted to assess the proposed BES-EMS. In scenario (1), the proposed BES outperformed all optimizers achieving the best operating cost and emission of 111.2518 euroct and 182.0741 kg respectively, the cost is saved by 57.66 % compared to GSA-PS while the emission is mitigated by 56.86 % compared to FSAPSO. In scenario (2), the proposed BES-EMS mitigated the cost and emission by 70.68 % and 51.78 % rather than GSA-PS and FSAPSO respectively. Moreover, in scenrio (3) the proposed approache saved the cost by 61.49 % and 67.29 % compared to GSA-PS during smart charging of PHEVs in normal and rated operations of RESs respectively. Furthermore, according to the Friedman rank test the proposed BES-EMS achieved the first rank with p-value of 2.1. The fetched results proved the robustness and competence of the proposed BES as an efficient energy management strategy for MG.