Performance of Snow Ablation Optimization for Solving Optimum Allocation of Generator Units

The snow ablation optimization (SAO) is a new metaheuristic motivated by the melting and sublimation properties of snow. In this work, the economic load dispatch (ELD) problem, one of the key components of a power system, is solved using the SAO. There is one kind of ELD, that is focused on minimizing fuel usage costs. Assessing the reliability of the SAO, its performance is compared against some techniques. For the same case study, these techniques include the grey wolf optimization (GWO), the tunicate swarm algorithm (TSA), the monarch butterfly optimization (MBO), and the rime-ice algorithm (RIME). There are six cases used in this work: the first two cases are 6 generators at two loads 700 MW and 1000 MW for the ELD problem. The second two cases are 10 generators at two loads 1000 MW and 2000 MW for the ELD problem. The third two cases are 20 generators at two loads 2000 MW and 3000 MW for the ELD problem. The methods were assessed across 30 different runs using metrics for the maximum, mean, minimum objective function, and standard deviation. The primary component of ELD issues is the power mismatch element. This factor’s optimal value must approach zero. The optimal power mismatch values of 3.336E-13 and 1.57E-10 are obtained using the SAO method for six generator units at demand loads of 1000 MW and 700 MW, respectively. The optimal power mismatch values of 6.83E-6 and 1.65E-7 are obtained by the SAO method for ten generator units at demand loads of 1000 and 2000 MW, respectively. The optimal power mismatch values of 1.82E-4 and 7.91E-5 are obtained using the SAO method for 20 generator units at demand loads of 2000 and 3000 MW, respectively. The results produced for the six ELD case studies show that the SAO surpasses all competing algorithms, proving its superiority.