Advanced AI approaches for the modeling and optimization of microgrid energy systems

Area of Science:

• Electrical Engineering
• Computer Science
• Renewable Energy Systems

Background:

• Microgrids are crucial for reliable power supply, especially in residential complexes.
• Reducing operational costs and CO2 emissions in microgrids is a key challenge.
• Integrating renewable energy sources like solar and wind requires sophisticated control strategies.

Purpose of the Study:

• To investigate the use of Artificial Intelligence (AI) techniques for cost and CO2 emission reduction in microgrid design and control.
• To optimize the energy mix of solar, wind, battery storage, and load profiles for a 100-unit residential complex.
• To enhance microgrid resilience and responsiveness to power supply and demand fluctuations.

Main Methods:

• Employing three AI techniques: Genetic Algorithm (GA), Artificial Bee Colony (ABC), and Ant Colony Optimization (ACO).
• Optimizing the composition of energy sources, including solar, wind, and battery storage.
• Analyzing load profiles to ensure a stable power supply.

Main Results:

• Achieved a minimum electricity cost of 0.037 USD/kWh, a 67% reduction from the reference cost.
• Maximized the utilization of renewable energy sources, minimizing waste.
• Demonstrated guaranteed power supply and improved microgrid responsiveness.

Conclusions:

• AI techniques offer a revolutionary approach to controlling and optimizing microgrids.
• The study validates the potential of AI for creating cost-effective and clean energy systems.
• AI-driven microgrid management significantly reduces energy costs and environmental impact.