Certain investigation on efficient management of congestion in transmission lines using evolutionary algorithms

Abstract

Transmission line congestion has developed into a significant issue in a deregulated environment of the power system, in contrast to the conventional unregulated power system. Transmission line congestion can be defined as an inability to dispatch all the desired transactions at a time in the competitive electricity market due to the limited source of transmission line capacity. In transmission lines, congestion may occur due to various reasons, for instance, an outage of transmission lines, sudden increase and decrease in demand, generator shutdowns, etc. Congestion should be eliminated as soon as possible, or it may perhaps, create a burden in some other equipment and lead to tripping of the regular operating lines. Nevertheless, in some critical cases, the entire system may shutdown. Therefore, accurate estimation and mitigation techniques are essential to avoid total system blackout. Rescheduling of generator output powers is the most renowned practice for congestion issues. The primary objective of this research work is to minimize the transmission line congestion with minimum generation rescheduling cost in the network. To resolve the transmission line congestion problem efficiently the application of Real-coded Genetic Algorithm (RCGA), Magnetotactic Bacteria Moment Migration Optimization Algorithm (MBMMOA), and Satin Bowerbird Optimization Algorithms (SBBOA) have been proposed in the thesis. The obtained results were compared with the Differential Evolution, Particle Swarm Optimization, Hybrid Particle Swarm, and Evolutionary Programming algorithms. The review of the literature presents the above-mentioned techniques as tried and tested solutions for the given problem. The results indicate that the proposed solutions are faster and better for CM in transmission lines newline