A Hybrid GSF–CHMBO Framework for Cost Effective Congestion Management in Power Systems

The advent of competitive power markets in recent decades has facilitated a substantial rise in transactions between producers and consumers to meet the rapidly growing load demand. Power transactions in modern electricity markets are carried out through bilateral or multilateral agreements, either directly between market participants or through common trading platforms. These transactions require the transmission network to transfer electricity from generating stations to consumers, and excessive power transfers may lead to congestion when line capacity limits are exceeded. Such congestion affects the secure and economical execution of scheduled transactions and may increase operating costs. Therefore, the Independent System Operator (ISO) plays an important role in maintaining reliable system operation by managing congestion and ensuring that all network security constraints are satisfied. This study aims to reduce transmission congestion by controlling overloaded line flows, maintaining operational limits, improving system stability, and minimizing transmission losses. Generator Sensitivity Factor (GSF) is used to identify the generating units that significantly influence congested transmission lines and require suitable rescheduling. Two enhanced Monarch Butterfly Optimization approaches, namely Improved MBO with a Linear Weighting factor (IMBO-LW) and Chaos-Embedded MBO (CHMBO), are employed to determine optimal generator rescheduling strategies. The proposed methods simultaneously minimize rescheduling cost and changes in generator power output. Their performance is evaluated using standard benchmark functions and further validated on a modified IEEE 30-bus test system, demonstrating their effectiveness in achieving secure, reliable, and economically efficient congestion management.