An efficient honey badger algorithm for scheduling the microgrid energy management

Nowadays, it is essential for modern grids to operate at optimal scheduling, this helps in reducing the energy costs, mitigating the pollutant emissions, and making better use of renewable energy resources (RESs) such as photovoltaic (PV) and wind turbine (WT). Therefore, this paper proposes an energy management scheme for microgrid (MG) using recent metaheuristic honey badger algorithm (HBA) to enhance its operation via identifying the optimal scheduling of the installed generation units. HBA has the ability to solve complex optimization problem and avoid stuck in local optima due to balance between the exploration and exploitation phases. The constructed MG composes PV, WT, microturbine (MT), fuel cell (FC), and battery storage system. Operation of PV and WT at their normal generations, operation of WT at its rated power, and operation of PV and WT at their maximum limits are three cases analyzed in this work. Two objective functions are considered which are mitigating the operating cost and minimizing the pollutant emission. The proposed HBA is evaluated via conducting comparison to some reported approaches like Fuzzy self-adaptive particle swarm optimizer (FSAPSO), sparrow search algorithm (SSA), and gravitational search and pattern search algorithm (GSA-PS). Moreover, other programmed approaches of aquila optimizer (AO), Tasmanian devil optimizer (TDO), artificial rabbits optimizer (ARO), coronavirus herd immunity optimizer (CHIO), manta-ray foraging optimizer (MRFO), and dynamic arithmetic optimization approach (DAOA) are implemented and compared to the proposed algorithm. The fetched results proved the robustness and preference of HBA in achieving the best operation of MG in all studied operating conditions. (c) 2023 Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).