Research on Economic Optimal Dispatching of Microgrid Based on an Improved Bacteria Foraging Optimization

Area of Science:

• Optimization Algorithms
• Renewable Energy Systems
• Microgrid Management

Background:

• Traditional Bacterial Foraging Optimization (BFO) suffers from slow convergence, poor precision, and low efficiency.
• Microgrid economic dispatch is crucial for optimizing energy distribution and reducing operational costs.
• Uncertainty in renewable energy sources (wind and solar) poses challenges for microgrid stability and economic dispatch.

Purpose of the Study:

• To propose an improved Bacterial Foraging Optimization (BFO) algorithm for economically optimal microgrid dispatch.
• To enhance the convergence speed, precision, and global search performance of the BFO algorithm.
• To develop a multi-objective microgrid dynamic economic dispatch model that accounts for renewable energy uncertainties.

Main Methods:

• Implemented an enhanced BFO algorithm with self-adaptive step size, particle swarm velocity, crisscross algorithm, and sine-cosine algorithm.
• Developed a multi-objective microgrid dynamic economic dispatch model.
• Utilized the Monte Carlo method to address the uncertainty of wind and solar power generation.

Main Results:

• The enhanced BFO algorithm demonstrated superior performance in benchmark function tests.
• The proposed microgrid economic dispatch model effectively reduced operating costs and environmental pollution.
• Achieved a 3.79% reduction in economic cost compared to Particle Swarm Optimization (PSO) and a 5.23% reduction compared to traditional BFO.

Conclusions:

• The improved BFO algorithm offers a robust and efficient solution for microgrid economic dispatch.
• The developed model successfully mitigates the economic impact of renewable energy intermittency.
• This approach enhances microgrid economic benefits and promotes environmental sustainability.