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

Area of Science:

• Energy Management
• Optimization Algorithms
• Microgrid Systems

Background:

• The digital economy drives intelligent, data-driven energy management.
• Environmental, Social, and Governance (ESG) factors are crucial for corporate competitiveness.
• Microgrid scheduling is vital for enterprise carbon reduction and low-carbon development.

Purpose of the Study:

• To address limitations in traditional microgrid economic dispatch algorithms, such as low efficiency, scalability, and flexibility.
• To propose a novel Multi-Strategy Improved Sand Cat Swarm Optimization (MISCSO) algorithm for enhanced microgrid economic scheduling.

Main Methods:

• Developed a distribution-optimized initialization method with adaptive diversity guidance for superior initial population quality.
• Introduced an elite-centered global random movement strategy to balance elite guidance and global exploration.
• Implemented an adaptive elastic boundary mapping mechanism to manage boundary violations and enhance global search capability.

Main Results:

• MISCSO demonstrated superior optimization accuracy, convergence speed, and robustness compared to 11 state-of-the-art algorithms on the IEEE CEC2017 benchmark set.
• Statistical analyses confirmed significant performance improvements.
• The algorithm achieved outstanding optimization results in microgrid economic scheduling applications.

Conclusions:

• The MISCSO algorithm offers a significant advancement for intelligent microgrid economic scheduling.
• It effectively balances solution quality, convergence, and constraint handling.
• MISCSO provides a robust and efficient solution for enterprises pursuing carbon reduction and sustainable development goals.