Mathematical Modeling and Analysis of Improved Grey Wolf Optimization Algorithm-Based Multi-Objective Power Flow Optimization for IEEE-118 Bus Test System

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Sunil Kumar Gupta


This research article thoroughly examines the implementation of an enhanced Grey Wolf Optimization (GWO) method for Multi-Objective Power Flow Optimization (MO-PFO) in the IEEE-118 bus test system. Given the growing complexity and uncertainty in modern power systems, power flow optimization presents a crucial challenge. The suggested enhanced GWO algorithm improves the performance of the traditional GWO for MO-PFO issues while addressing its inherent flaws.

The goal of this study is to obtain optimal solutions concurrently for multiple objectives, including the reduction of power losses, voltage variations, and the enhancement of system stability. In the Related Works section, I review the research on power flow optimization in the literature, which also showcases the improvements achieved by applying various optimization techniques and discusses the benefits and drawbacks of traditional optimization methods and their effectiveness in dealing with MO-PFO issues.

The improved GWO algorithm, designed specifically for MO-PFO, is described in detail in the Methodology section. I explain the adjustments made to the regular GWO algorithm and introduce a cutting-edge method for handling multiple objectives. In the IEEE-118 bus test system, I present the mathematical model of the MO-PFO problem and develop fitness functions for each objective.

Furthermore, I describe the implementation of the enhanced GWO algorithm, including parameter settings and termination criteria. The proposed algorithm demonstrates its capability to effectively handle the multi-objective nature of power flow optimization, thereby paving the way for future power system operations that are more dependable and sustainableTop of Form


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