Research on Economic Optimization of Microgrid Cluster Based on Chaos Sparrow Search Algorithm

Area of Science:

• Electrical Engineering
• Optimization Algorithms
• Renewable Energy Systems

Background:

• Retail power market reforms necessitate economic optimization of microgrid clusters.
• Microgrid clusters involve complex energy transactions and component costs.

Purpose of the Study:

• To establish an optimal dispatch model for microgrid clusters.
• To develop an improved optimization algorithm for enhanced economic performance.

Main Methods:

• Developed an optimal dispatch model considering battery degradation, load compensation, and inter-microgrid/grid transaction costs.
• Proposed a Chaos Sparrow Search Algorithm (CSSA) incorporating Bernoulli chaotic mapping, dynamic adaptive weighting, Cauchy mutation, and reverse learning.
• Validated the CSSA's performance against standard algorithms using test functions.

Main Results:

• The proposed CSSA demonstrated superior convergence speed, accuracy, and global optimization capabilities compared to basic sparrow search, particle swarm, chaotic particle swarm, and genetic algorithms.
• The optimal dispatch model, utilizing the CSSA, achieved a nearly 20% increase in microgrid cluster economic benefits.

Conclusions:

• The enhanced Chaos Sparrow Search Algorithm effectively addresses the limitations of basic sparrow search algorithms.
• The integrated optimal dispatch model and CSSA significantly improve the economic efficiency of microgrid clusters.