An Economic Scheduling Management Method for Microgrids Using Multi-Strategy Improved Sand Cat Swarm Optimization

TL;DR

This paper introduces a new optimization algorithm to improve the economic scheduling of microgrids, enhancing efficiency and flexibility.

Contribution

The novel MISCSO algorithm combines multiple strategies to improve optimization performance for microgrid scheduling.

Findings

MISCSO outperforms 11 state-of-the-art algorithms in optimization accuracy and convergence.

The algorithm effectively handles boundary violations and maintains population diversity.

MISCSO achieves excellent results in microgrid economic scheduling applications.

Abstract

With the rise of the digital economy, energy management has become increasingly intelligent and data-driven. Environmental, Social, and Governance (ESG) considerations have emerged as a key driver of corporate competitiveness, while microgrid scheduling serves as an essential pathway for enterprises to achieve carbon reduction, attract green investment, and meet low-carbon development goals. However, traditional microgrid economic dispatch algorithms often suffer from low optimization efficiency, limited scalability, and poor flexibility. To address these challenges, this paper proposes a multi-strategy improved sand cat swarm optimization (MISCSO) algorithm for the economic scheduling of microgrids. First, a distribution-optimized initialization method based on adaptive diversity guidance is developed to enhance the quality of the initial population. This approach improves algorithmic performance by generating individuals in high-potential regions to ensure solution quality while maintaining population diversity through the inclusion of individuals from low-potential regions. Subsequently, an elite-centered global random movement strategy is introduced to balance elite guidance and global exploration, thereby improving both convergence speed and optimization accuracy. In addition, an adaptive elastic boundary mapping mechanism is proposed to effectively handle boundary violations, striking a balance between boundary constraints and global search capability. To evaluate the effectiveness of MISCSO, it is compared with 11 state-of-the-art algorithms using the IEEE CEC2017 benchmark set, and statistical analyses are conducted to assess performance differences. Experimental results demonstrate that MISCSO achieves superior optimization accuracy, convergence performance, and robustness. Finally, the applicability of MISCSO is verified through its implementation in microgrid economic scheduling, where it achieves outstanding optimization results.

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