Artificial intelligence of optimal real power dispatch with constraints of lines overloading

Abstract

In the literature on optimal power flow (OPF), it has been shown that the suggested ways offer a higher degree of satisfaction in optimizing overall production costs while fulfilling power flow equations, system security, and equipment operational constraints. Despite this, the overloaded of the transmission lines are taken as a performance index but not a primary constraint. This article presents an improved approach to artificial intelligence algorithms of optimal real power dispatch with the security of lines; the main difference concerning our point seen relies on the additional penalization of the choices, which does not respect this constraint. The problem is implemented in the IEEE 14-bus system with "5" generator units. The results of the simulations of the metaheuristic algorithms without/with constraint (overloaded lines) were compared. Furthermore, this article suggests hybridizing ant colony optimization (ACO) and genetic algorithm (GA) as a means to enhance the optimization performance of these algorithms. This hybridization involves using ACO to generate a set of initial solutions, which are then refined using GA. The compound results obtained by the ant system-genetic algorithm hybrid (H-ASGA) for the problem of overloaded lines validated its potential.