Showing 2 results for Total Power Loss
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.
Nguyen Nhat Tung,
Volume 21, Issue 3 (8-2025)
Abstract
This paper presents an effective approach for determining optimal integration of renewable energy distributed generator (RE-DGs) of solar farms (SFs) and wind farms (WFs) in IEEE 69-node power distribution network (PDN) with target of minimizing (1) the single objective function of total active power loss and (2) multi-objective function including a) total active power loss, b) total reactive power loss, c) the voltage deviation and d) imported energy from the main power gird. Intelligent and adaptive meta-heuristic optimization algorithm called bonobo optimizer (BO) is introduced to address optimization problem considering the changing four seasons of winter, spring, summer and autumn from both generation and consumption. The obtained results from BO show its outstanding performance in determining the suitable installation of SFs and WFs compared with many published methods and implemented methods for two cases of single and multi-objective functions.
