Optimal Multi-dimension Operation in Power Systems by an Improved Artificial Hummingbird Optimizer

Optimal power flow (OPF) is a restricted optimization problem that requires optimizing control variable settings to reach the best collection of operating, technical, and secure restrictions for electric power systems. This paper proposes an enhanced artificial hummingbird optimizer (EAHO) with a linear control mechanism (LCM) and diverse territorial foraging strategies (TFSs) for handling the OPF problem. In the newly suggested solution, the LCM is integrated to enhance both global and local search capabilities. In addition to that, diverse TFSs are provided to support the exploration phase through different search directions. The ECHO incorporates the features of the AHO, LCM, and TFS in order to minimize the total cost of fuel (TCF), the entire transmission losses (ETLs), and the volume of environmental emissions (VEEs). The proposed method was scrutinized on IEEE, 30-, 57-, and 118-bus test grids. The results of the simulation indicate competition between the proposed schema and the state-of-the-art in terms of convergence rate and quality of the solution. The proposed schema achieves the minimum TCF $799.0878/hr, $41,678.25/hr, and $129,790.25/hr for the IEEE 30-, 57 and 118-bus grids, respectively, and the minimum ETLs 2.8571 and 9.872 and VEEs 0.204 and 1.0389 ton/hr for the IEEE 30 and 57-bus grids, respectively. Furthermore, a test was conducted to authenticate the statistical efficacy of the EAHO-inspired scheme. The EAHO presents a robust and straightforward solution for the OPF problem under diverse goal functions.