In power systems, the sub-problems associated with economical operation can be reduced through effective optimization techniques. However, in a deregulated electricity market environment, system operation becomes highly complex. Congestion management has emerged as a significant challenge in ensuring a reliable and continuous power supply to consumers. This paper focuses on alleviating transmission line congestion through optimal generation rescheduling while minimizing the associated rescheduling cost. An Adaptive Bacterial Foraging Optimization Algorithm integrated with the Nelder–Mead method is proposed to optimize congestion cost effectively. The performance of the proposed method is compared with other optimization techniques, including Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and the conventional Bacterial Foraging Algorithm. Numerical simulations are carried out on a six-generator unit system using the IEEE 30-bus system test system. The results demonstrate the effectiveness of the proposed hybrid approach. Furthermore, the technique can be extended for continuous simulation iterations, enhancing its applicability in real-time and practical evolutionary optimization problems in power system operations.
Divya Lakshmi Raghunathan (Sun,) studied this question.