Power flow management in hybrid AC/DC microgrids using the Artificial Bee Colony metaheuristic algorithm: A comparative study

This paper presents an advanced power flow management strategy for a hybrid AC/DC microgrid integrating renewable energy sources such as photovoltaic systems and wind turbines, alongside an energy storage system and a gas turbine as a polluting source. The microgrid is interconnected with the main grid, enabling bidirectional energy exchanges to efficiently balance supply and demand. The main technical innovation is the development of an economic dispatch method based on the Artificial Bee Colony metaheuristic algorithm, which simultaneously optimizes both active and reactive power flows using discrete-time control techniques. The approach features a unified objective function that combines cost minimization and reduction of greenhouse gas emissions, enabling coordinated decision making for improved economic efficiency and environmental sustainability. A comparative analysis with the Particle Swarm Optimization algorithm and a conventional rule-based approach demonstrates the superior performance of Artificial Bee Colony in several key aspects. Specifically, Artificial Bee Colony achieves a 43.1% reduction in C O 2 emissions compared to the conventional approach and 33.9% compared to Particle Swarm Optimization. Additionally, it lowers operational costs by 27.54% relative to the conventional method, outperforming Particle Swarm Optimization, which achieves a 14.83% reduction. Furthermore, Artificial Bee Colony increases the utilization of the Energy Storage System, reaching 463.05 kVA, compared to 284.07 kVA with Particle Swarm Optimization and 153.85 kVA with the conventional approach. The algorithm also reduces dependency on the main grid by 29. 30%, surpassing particle swarm optimization (12. 29%) and improves the integration of renewable energy sources by 12. 94%, compared to 5. 40% for particle swarm optimization. The simulation was conducted using MATLAB® 2024a, leveraging 24-hour ahead meteorological forecasts and load profiles to ensure optimal and adaptive operation under dynamic conditions. These results highlight the effectiveness of Artificial Bee Colony in addressing the challenges of hybrid microgrid management, making it a robust and scalable solution for modern energy systems.