Optimization of Power System Flexibility through AI-Driven

Version 2.0.0 (1.21 MB) by saad
Utilizing the IEEE 14-bus system, we integrate transformer-based AI models for predictive load management and simulate on MATPOWER
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Updated 4 Jul 2024

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Utilizing the IEEE 14-bus system as a testbed, we integrate transformer-based AI models for predictive load man- agement and simulate various scenarios to assess the impact on system performance. Key metrics such as total power losses, voltage stability, and the Fast Voltage Stability Index (FVSI) are
analyzed to validate the effectiveness of the proposed methods. Results demonstrate significant improvements in power system stability, efficiency, and reliability, underscoring the potential of AI in optimizing modern power grids. Our approach shows a reduction in total power losses from 506.07 MW in the baseline scenario to 284.75 MW in the AI-driven scenario, with statistically significant improvements in voltage stability and system resilience.

Cite As

saad (2024). Optimization of Power System Flexibility through AI-Driven (https://www.mathworks.com/matlabcentral/fileexchange/169141-optimization-of-power-system-flexibility-through-ai-driven), MATLAB Central File Exchange. Retrieved .

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Created with R2024a
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Version Published Release Notes
2.0.0

Title changed.

1.0.0