This article introduces a new attempt of utilizing the sunflower optimization (SFO) algorithm in solving the problem of optimal power flow (OPF) in the field of power systems. The principle target is to optimize the generating units' fuel cost under the system constraints. At initial stage, the objective function is solved to find the optimal siting of Distributed Generation (DG) units within the system under study. Then, different scenarios are performed to solve the OPF problem including and excluding DG units. The generators' real output power defines the exploration field for the OPF problem. The SFO algorithm is used to minimize the fitness function and yields the best solutions of the problem. More than one electric grid is tested to check the validity of the proposed algorithm such as the IEEE 14-bus, and 30-bus networks. Simulations included different scenarios are implemented in these two networks. To obtain a realistic result, real daily load curve is considered in this study. The results of simulations are investigated and analyzed. Results confirm the flexibility, validation, and applicability of the introduced SFO-based OPF methodology when compared with the genetic algorithm.

This paper presents a novel endeavor to use the Heap optimization algorithm (HOA) to solve the problem of optimal power flow (OPF) in the electricity networks. The key objective is to optimize the cost of fuel of the conventional generators under the system limitations. Various scenarios are studied in a later stage considering the addition of the PV panel and/or wind farm with changing load curves during a typical day. The active output power of the generators is selected to be the OPF problem search space. The HOA is employed to get the best solution of the fitness function and provides the corresponding best solutions. The modeling of the heap-based optimizer (HBO) depends on three levels: the relation between the subordinates and the boss, the relation between the same level employees, and the contribution of the employee oneself. The validity of the proposed algorithm is tested for a variety of electric grids, the IEEE 30-bus, IEEE 57-bus and 118 bus networks. These networks are simulated under various scenarios. Real load curves, in this study, are considered to achieve a practical outcome. The simulation outcomes are evaluated and tested. The results indicate that the implemented HOA-based OPF methodology is flexible and applicable compared with that achieved by using the genetic algorithm.

The appropriate planning of electric power systems has a significant effect on the economic situation of countries. For the protection and reliability of the power system, the optimal reactive power dispatch (ORPD) problem is an essential issue. The ORPD is a non-linear, non-convex, and continuous or non-continuous optimization problem. Therefore, introducing a reliable optimizer is a challenging task to solve this optimization problem. This study proposes a robust and flexible optimization algorithm with the minimum adjustable parameters named Improved Marine Predators Algorithm and Particle Swarm Optimization (IMPAPSO) algorithm, for dealing with the non-linearity of ORPD. The IMPAPSO is evaluated using various test cases, including IEEE 30 bus, IEEE 57 bus, and IEEE 118 bus systems. An effectiveness of the proposed optimization algorithm was verified through a rigorous comparative study with other optimization methods. There was a noticeable enhancement in the electric power networks behavior when using the IMPAPSO method. Moreover, the IMPAPSO high convergence speed was an observed feature in a comparison with its peers.