Research on Microgrid Optimal Dispatching Based on a Multi-Strategy Optimization of Slime Mould Algorithm

Area of Science:

• Electrical Engineering
• Computer Science
• Optimization Algorithms

Background:

• Addressing energy shortages and environmental pollution necessitates power grid optimization.
• Distributed power grids offer better control than centralized ones for renewable energy.
• Optimal dispatching of microgrids presents significant challenges due to complexity and variability.

Purpose of the Study:

• To propose an advanced optimization algorithm for microgrid optimal dispatch.
• To address limitations of traditional swarm intelligence algorithms like slow convergence and local optima.
• To develop a robust model for microgrid scheduling considering diverse energy sources and operational modes.

Main Methods:

• A multi-strategy fusion slime mould algorithm (MFSMA) was developed, incorporating reverse learning and adaptive parameters.
• The salp swarm algorithm's search mode was integrated to accelerate convergence.
• The MFSMA was benchmarked against other algorithms and applied to a 24-hour microgrid scheduling problem.

Main Results:

• MFSMA demonstrated superior performance in function optimization compared to traditional algorithms.
• Simulations confirmed MFSMA's effectiveness in solving the microgrid optimal scheduling problem.
• The algorithm successfully enhanced energy utilization efficiency and reduced total network costs and environmental pollution.

Conclusions:

• The proposed MFSMA is a highly effective tool for microgrid optimal dispatch.
• The developed microgrid dispatch model comprehensively considers costs and operational modes (grid-tied and island).
• MFSMA offers a promising solution for improving the economic and environmental performance of microgrids.