Investigation of congestion Management techniques in modern Power system

Abstract

Due to the monopoly power structure in a conventional power system, both power supply and demand increase dramatically. For this reason, the necessity of deregulation has a powerful role in international and national power markets. Despite the fact that there are numerous technical issues in a deregulated power market, congestion management plays a major role in power system security, and thus our proposed work focuses primarily on this area. Based on the cost of relief action congestion management is categorized as, Cost-free and Non-cost-free methods. The cost-free method includes FACTS devices, Transformer tap changing and line outages while non-cost-free involves generation rescheduling and load curtailment. In the field of congestion management, soft computing approaches such as fuzzy logic, evolutionary computing, swarm intelligence, artificial neural networks, machine learning algorithms and more recently deep learning algorithms are also used. newlineThis thesis primarily deals with the congestion management, which is performed using sensitive generator rescheduling technique by proposed Hybrid Nelder Mead - Grey Wolf Optimizer (HNM-GWO) by optimal rescheduling of active power generation of the selected generators based on their sensitivity. The results of the tests are then compared to various swarm intelligence techniques, and they are found to be very effective. The effectiveness of this work is applied to standard IEEE 30-bus power system and IEEE 118-bus system. newlineThe next part of thesis demonstrates the efficacy of FACTS devices in a deregulated electricity market for congestion management. Single and multiple type FACTS devices are used in this stage to manage congestion newline