Optimal Generation Scheduling of Hybrid Systems using Manta Ray Foraging Optimizer

Load dispatch is an indispensable part of power system operation. Economic Dispatch (ED) and Combined Economic Emission Dispatch (CEED) are the standard complex contained benchmark used for accessing the potential of new optimization algorithms. The complexity of optimization problems has increased with time due to several factors like the type of generating units (dispatchable or non-dispatchable), their mathematical modelling (probabilistic or deterministic), internal characteristics (convex or non-convex), forecasted load demand and others. Metaheuristic optimization techniques have been able to handle these constraints appreciably due to their stochastic attributes. This paper attempts to explore the performance of a novel bio-inspired algorithm named as Manta Ray Foraging Optimizer (MRFO) for ED of a microgrid (MG) and CEED problems for a hybrid thermal solar PV system respectively. Both system considered for analysis have different scalability in terms of power demand. The performance of MRFO is compared with classical metaheuristic as well as new generation metaheuristic algorithms. Box plot analysis is also performed to compare the results statistically. Finally, it can be concluded that the performance of MRFO has outperformed the performance of other algorithms and has a future scope to entertain more practical and real-life engineering problems.