Energy Management Strategy and Capacity Planning of an Autonomous Microgrid: A Comparative Study of Metaheuristic Optimization Searching Techniques

Electricity generation using renewable energy-based microgrid (REM) is a prerequisite to achieve one of the cardinal objectives of sustainable development goals. Nonetheless, the optimum design and sizing of the REM is challenging. This is because the REM needs to supply the fluctuating demand considering the sporadic behaviour of the renewable energy sources (RES). This paper, therefore, proposes a nature-inspired metaheuristic optimization searching technique (MOST) to optimize the components of an autonomous microgrid integrating a diesel generator {\left(D_{\text{GEN}}\right)}, battery bank, photovoltaic and wind turbine. In this regard, a cycle-charging energy management scheme (CEMS) control is proposed and implemented using a rule-based algorithm. The proposed CEMS provide a power delivery sequence for the different components of the microgrid. Subsequently, the CEMS is optimized using the metaheuristic optimization searching techniques (MOSTs). To benchmark, the paper compares the success of six different MOSTs. The simulation is performed for the climatic conditions of Yobe State, in northern Nigeria using MATLAB software. The comparative results show that the grasshopper optimization algorithm is found to yield a better result because it gives the least fitness function relative to other studied MOSTs. Remarkably, it outperforms the grey wolf optimizer, the ant lion optimizer, and the particle swarm optimization by ∼ 3.0 percent, ∼ 5.8 percent, and ∼ 3.6 percent (equivalent to a cost savings of 8332.38, 4219.87, and 5144.64 from the target microgrid project). Results also indicate that the proposed CEMS adopted for the microgrid control strategy has led to the implementation of a clean and affordable energy system, as it's significantly minimized CO2 (by 92.3%), fuel consumption (by 92.4%), compared fossil fuel-based {D_{\text{GEN}}}.