N. Tabrizi, E. Babaei, M. Mehdinejad,
Volume 12, Issue 1 (3-2016)
Abstract
Reactive power plays an important role in supporting real power transmission, maintaining system voltages within proper limits and overall system reliability. In this paper, the production cost of reactive power, cost of the system transmission loss, investment cost of capacitor banks and absolute value of total voltage deviation (TVD) are included into the objective function of the power flow problem. Then, by using particle swarm optimization algorithm (PSO), the problem is solved. The proposed PSO algorithm is implemented on standard IEEE 14-bus and IEEE 57-bus test systems and with using fuzzy satisfying method the optimal solutions are determined. The fuzzy goals are quantified by defining their corresponding membership functions and the decision maker is then asked to specify the desirable membership values. The obtained results show that solving this problem by using the proposed method gives much better results than all the other algorithms.
O. Herbadji, L. Slimani, T. Bouktir,
Volume 15, Issue 1 (3-2019)
Abstract
In this study, a multiobjective optimization is applied to Optimal Power Flow Problem (OPF). To effectively achieve this goal, a Multiobjective Ant Lion algorithm (MOALO) is proposed to find the Pareto optimal front for the multiobjective OPF. The aim of this work is to reach good solutions of Active and Reactive OPF problem by optimizing 4-conflicting objective functions simultaneously. Here are generation cost, environmental pollution emission, active power losses, and voltage deviation. The performance of the proposed MOALO algorithm has been tested on various electrical power systems with different sizes such as IEEE 30-bus, IEEE 57-bus, IEEE 118-bus, IEEE 300-bus systems and on practical Algerian DZ114-bus system. The results of the tests proved the versatility of the algorithm when applied to large systems. The effectiveness of the proposed method has been confirmed by comparing the results obtained with those obtained by other algorithms given in the literature for the same test systems.
Nguyen Cong Chinh,
Volume 20, Issue 3 (9-2024)
Abstract
This paper presents an intelligent meta-heuristic algorithm, named improved equilibrium optimizer (IEO), for addressing the optimization problem of multi-objective simultaneous integration of distributed generators at unity and optimal power factor in a distribution system. The main objective of this research is to consider the multi-objective function for minimizing total power loss, improving voltage deviation, and reducing integrated system operating costs with strict technical constraints. An improved equilibrium optimizer is an enhanced version of the equilibrium optimizer that can provide better performance, stability, and convergence characteristics than the original algorithm. For evaluating the effectiveness of the suggested method, the IEEE 69-bus radial distribution system is chosen as a test system, and simulation results from this method are also compared fairly with many previously existing methods for the same targets and constraints. Thanks to its ability to intelligently expand the search space and avoid local traps, the suggested method has become a robust stochastic optimization method in tackling complex optimization tasks.
