A Novel Adaptive Restarting Genetic Algorithm for Transmission Congestion Alleviation in the Deregulated Power Market

Abstract

A key concern in a deregulated power market (DPM) scenario is congestion on electricity transmission lines, which is one of the most pressing problems. The independent system operator (ISO) manages congestion to ensure the power system framework operates reliably and securely. Considering the context of the restructured power system’s inherent uncertainties, congestion management (CM) is critical in the operation and security of DPM. CM aims to ease congestion on the transmission lines while meeting all system constraints at the lowest possible congestion cost. This study employs a novel adaptive restarting genetic algorithm (ARGA) for rescheduling the generation to alleviate transmission congestion in the energy market. This work uses the proposed ARGA CM technique to reduce congestion costs by rescheduling generators’ active power output most cost-effectively. The participation of generation units in the CM is made possible by considering the generator sensitivity factors (GSF). IEEE 57-bus and IEEE 118-bus test systems are employed to evaluate the suggested methodology’s accuracy. The acquired results are contrasted using several contemporary optimization approaches to validate the suggested technique’s validity. The proposed algorithm was tested in Python, and power flow analysis was done using the PANDAPOWER tool.