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  • https://doi.org/10.17485/ijst/v18i11.3709Copy DOI Icon

Intelligent Power Dispatch Optimization: A Genetic Algorithm Approach with Unified Power Flow Controller

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Abstract

Objective: The goal of this project is to provide an advanced optimization approach for Dynamic Power Dispatch (DPD) by combining a Unified Power Flow Controller (UPFC) with a Genetic Algorithm (GA). The main objective is to improve power flow management and grid performance in complicated power networks. Methodology: The suggested method takes advantage of the capabilities of the UPFC, a versatile component of the Flexible AC Transmission System (FACTS), and combines them with the optimization power of GA. The UPFC provides close oversight over power flow, voltage ranges, and phase angles, while GA, known for its ability to solve multi-objective and nonlinear problems, improves the UPFC’s installation and operational settings. The effectiveness of this hybrid technique is proven by simulations using the IEEE 14-bus test system. Findings: Simulation results demonstrate that the integration of GA with UPFC yields significant improvements in power flow stability, reduces operational costs and transmission losses, and enhances voltage profiles compared to conventional DPD techniques. Furthermore, the approach increases grid flexibility and stability, addressing the evolving demands of modern power networks. Novelty: This study introduces a unique combination of UPFC and GA to address the challenges in DPD. The approach merges the real-time control capabilities of UPFC with the evolutionary optimization strengths of GA, presenting a scalable and adaptive solution for power dispatch optimization. Applications: The proposed technique is especially useful for improving the efficiency and reliability of current power grids, including those that use renewable energy sources. Its use in smart grid scenarios can help accelerate the transition to more sustainable and resilient energy systems. Keywords: Dynamic power dispatch; Unified Power Flow Controller; Genetic Algorithm; power system optimization; FACTS devices; and an IEEE 14bus testing system

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