Optimization of short-term energy management for enhancing economic and environmental performance in microgrid operations

Plug-in hybrid electric vehicles (PHEVs) are playing an increasingly important role in modern transportation systems, as a result of the ecological crisis and the energy crisis. The enormous potential of the storage capacity of the large fleet of PHEVs, together with the flexibility they can offer power systems through effective control, cannot be ignored. Meanwhile, renewable energy sources (RESs) present significant challenges for energy management scheduling. This article analyzes a microgrid comprising several RESs, such as photovoltaics, fuel cells, wind turbines, microturbines, a battery as energy storage, and PHEVs. Monte Carlo simulation is used to address uncertainties in model development. To account for possible inaccuracies in PHEV charging predictions, electrical load consumption, hourly energy price fluctuations, and RES power output, the study uses a 24-h simulation. Furthermore, a nickel metal hydride battery is used in the microgrid to evaluate the operational impact of different storage systems. The optimization aims to minimize the total cost of the network, including the cost of load supply, the cost of PHEV charging demands, and the cost of power losses. The complexity of the problem requires a novel optimization technique, in this case, the modified manta ray foraging optimization algorithm, which provides a comprehensive global search of the entire search space. Numerical results reveal that the modified manta ray foraging optimization technique demonstrates favorable convergence properties and lower generation costs compared to the classical manta ray foraging optimization method and other recent optimization algorithms.