Hybrid manta ray foraging and sine cosine algorithm for managing power transmission congestion influenced by wind energy.

This research work proposes a hybrid Manta ray Forging Optimization- Sine Cosine Algorithm (MRFO-SCA) for Congestion Management (CM) that addresses the power system transmission line congestion cost challenges with the integration of Wind Energy System (WES). The proposed method focuses on two key objectives: first, identifying the most influential bus within the power system using the Bus Sensitivity Factor (BSF) to optimally place a wind power source, thereby impacting the power flow in overloaded lines. Second, MRFO-SCA has been developed for optimal power rescheduling of the generators to alleviate congestion while minimizing the congestion cost. The hybrid MRFO-SCA has been formulated by integrating SCA into the MRFO that enhances the exploration and exploitation phases in MRFO leading to the rapid discovery of the global optima. MRFO-SCA has been verified on benchmark functions that have delivered appreciable results. The effectiveness of the proposed approach has been assessed and validated using the IEEE-30 bus system. Simulation results indicate that incorporating WES with MRFO-SCA has led to a reduction in congestion costs by 18.45%, 15.68%, 10.34%, 9.72%, 5.46%, and 1.57% as compared to several recent optimization techniques. A comparative evaluation demonstrates that MRFO-SCA outperforms other methods in terms of congestion cost reduction, system loss minimization, bus voltage improvement, faster convergence, and reduced computational time, making it a more efficient and accurate solution for CM.