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Optimizing power system operations: integrating FACTS and ESS through advanced algorithms

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Abstract

This research delves into the critical realm of unit commitment in electric power systems, aiming to optimize the operation of electronic equipment, flexible AC transmission system (FACTS) devices, and energy storage systems (ESS). The synergistic integration of these components holds immense potential for enhancing system performance by reducing transmission losses, enhancing stability, regulating voltage, and modulating energy density. The study employs an advanced honeybee colony-based algorithm, elevating the efficiency of local and global searches to navigate the complex interplay between FACTS and ESS. Four distinct scenarios are meticulously analyzed: (i) the base case without storage or FACT devices; (ii) the inclusion of only FACTS; (iii) the presence of solely ESS; (iv) the combination of both FACTS and ESS devices. Through comprehensive benefits analysis and economic evaluation, our findings underscore the transformative impact of integrating FACTS and ESS on power system operations. The results not only showcase the considerable advantages in terms of system stability and efficiency but also provide valuable insights into the economic viability of such integrated solutions. This study paves the way for advanced strategies in unit commitment, offering a holistic perspective on the intricate dynamics of electronic equipment, FACTS, and energy storage in contemporary power systems.

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Funding

Basic Research Support Program for Excellent Young Teachers in Heilongjiang Province (YQJH2023210).

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Contributions

Methodology, software, validation, and formal analysis were performed by Lijunyi Zhao; validation, methodology, and writing—original draft preparation were presented by Na Zhang; conceptualization, supervision, and project administration were provided by Donghui Wei.

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Correspondence to Donghui Wei.

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Zhao, L., Wei, D. & Zhang, N. Optimizing power system operations: integrating FACTS and ESS through advanced algorithms. Electr Eng (2024). https://doi.org/10.1007/s00202-024-02428-2

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