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During the last decade, meaningful advancements have occurred in global electricity grids due to the wide integration of renewable energy resources (RES). Meanwhile, these sources play an essential role in total generation cost reduction, power loss cutting, and reduction of environmental hazards related to traditional power plants. Still, however, the optimal planning and operation of the power system in the presence RES is considered a master challenge due to the their stochastic natural and intermittency. One of the most complex and motivating issues in a power system is optimal power flow (OPF), a constrained optimization problem characterized by non-linearity and non-convexity. From these specifications, researchers competed in the past decades to find optimal solutions to stochastic OPF problems while keeping system stability. To tackle this challenge, an effective optimization algorithm which mimics on the foraging behavior of dwarf mongooses&#x2019; in the nature is introduced. The introduced objective function is the total charge of the system, which includes a reserve charge for overestimation and a penalty cost for underestimating two types of PV energy&#x2013;solar and wind energy. To show the robustness and efficacy of the recommended optimizer, case studies on the customized IEEE 30-bus system and a realistic power system Adrar power system DZA 26-bus (isolated grid) are undertaken. Numerical findings show that the proposed DMOA beats all previous published-results and performs better over a variety of objective functions while finding high-quality optimally viable solutions. Furthermore, the one-way analysis of variance (ANOVA) test, a statistical approach, was employed to evaluate the superiority of the proposed algorithm and to highlight a certain level of confidence to our study.

Optimal Power Flow Analysis with Renewable Energy Resource Uncertainty Using Dwarf Mongoose Optimizer: Case of ADRAR Isolated Electrical Network